Lab 6: Constructing and processing multilevel data

Applied Bayesian Modeling (ICPSR Summer Program 2025)

Author

Affiliation

Johannes Karreth

Ursinus College

Getting started

The purpose of this tutorial is to demonstrate how to merge and process data from different levels into one data frame in R. To get to this point, the tutorial will show:

• how to use spreadsheets to prepare data for analysis
• how to clean raw data from a public source
• how to process data for analysis
• how to merge separate data sources into one
• how to collapse datasets
• how to create new variables, including time-series operators

Additional resources

To learn more about how to manage and manipulate data in R, I recommend:

• The Manipulating Data section of the companion website to Chang (2012), R Graphics Cookbook. This site provides annotated examples for almost any data management scenario you’ll likely encounter in your research.
• The Getting your data into shape section of the companion website to Chang (2021), R Graphics Cookbook, 2nd edition. This book is an excellent resource for almost any graphing problem & solution in R.

Working directory

As always, it is useful to set a project-specific working directory—especially if you work with many files. You can set the WD to the location of your R script using our previous approach:

setwd(dirname(rstudioapi::getActiveDocumentContext()$path))

Alternatively, use the here package to set your WD at the same location as your R script. Or, use an RStudio project to organize folders and files automatically.

Entering your own data into a spreadsheet

Since many of you work with data you collect yourself, you will often enter data directly into spreadsheets. I don’t have any specific recommendations for this, but I strongly encourage you to use the following workflow:

• enter raw data into spreadsheets, then save them as .csv (not .xls or .xlsx) files
• use these (modified) principles of tidy data when constructing your dataset:
  1. Each variable forms a column.
  2. Each observation forms a row.
  3. Use only variable names (not values) as column headers. Avoid spaces in variable/column names.
  4. Use common identifiers for observations and groups.
• conduct all and any processing (as you learn below) in R with a documented script
• This makes your data cleaning and processing reproducible for yourself and for others in the future, and helps you recall and justify choices you made at this step
• It will also prevent you from being unable to reconstruct what you did down the road
• Beware of Microsoft Excel changing the content of cells/columns based on formatting. Always verify that your data have correct values once you have re-opened them in Excel or another application. This is especially important for dates and times.

Remember, one of the goals of our course is to help you build a better relationship with your future self. Following this workflow will help with that.

Keep data management and analysis separate

I recommend keeping a separate script file for data management and data analysis. This will also help you maintain a reproducible workflow and keep your code manageable. For instance, in my projects, I typically have at least two R scripts in my project folder:

• project_datamgmt.R, which starts by importing the original source data and cleans and prepares it for analysis
• project_analysis.R, which conducts all analysis and creates tables and graphs

A very good model to follow is the Project TIER protocol, as illustrated on the first day of our course. You can take a look at the demo project. An R version, created by me, is available on my Github page.

Importing and exporting data

You will data in many different formats when doing research. For this purpose, the R package “rio” (with which you’re already familiar) is particularly useful. Its developer describes it as “a set of tools aims to simplify the process of importing/exporting data.” The package has two main functions, import() and export(). It allows you to import and export data from/to the following popular formats (and others that I don’t list here):

• Tab-separated data (.tsv)
• Comma-separated data (.csv)
• Saved R objects (.RData)
• Stata (.dta)
• SPSS and SPSS portable (.sav, .por)
• Excel (.xls)
• Excel (.xlsx)
• SAS and SAS XPORT
• Minitab (.mtp)
• OpenDocument Spreadsheet (.ods)
• Google Sheets
• Clipboard (default is .tsv)
• … and others!

For more information, see the package website: https://cloud.r-project.org/web/packages/rio/index.html.

Important note on package versions

Because data formats change frequently (e.g., with new versions of commercial software), dealing with data import and export requires special attention. Be sure to always use the most recent version of the “rio” package.

Example: importing an SPSS dataset

In this example, we import a dataset from the Afrobarometer project into R. The Afrobarometer is an African-led series of national public attitude surveys on democracy and governance in Africa, and you can find more information on it at http://www.afrobarometer.org/. The survey data are provided to scholars in SPSS format. SPSS is a statistical software package akin to Stata or R. At http://www.afrobarometer.org/data/merged-data, you can find a download link for the fourth round of the Afrobarometer. The file is called “merged_r4_data.sav”. Let’s use this link to read this dataset into R using the import() function from the “rio” package.

First, install the “rio” package if you didn’t do so earlier during our course You only have to do this once.

install.packages("rio")

Next, load the package.

library("rio")

Now you can import the Afrobarometer dataset in your R environment. I’ll call it ab. This will take a few seconds since the dataset is over 15MB big.

Note

Note: as always, R will search for the file in your working directory.

ab <- import(file = "merged_r4_data.sav")

Alternatively, you can also import the file directly from its source by using the URL from the Afrobarometer website. But if the file is removed from that site, the code below won’t work.

Before actually looking at the dataset itself, you should look at its dimensions.

dim(ab)

[1] 27713   294

You can also use the glimpse() function from the dplyr package (which is part of the “tidyverse”).

suppressMessages(library("tidyverse"))
glimpse(ab)

Rows: 27,713
Columns: 294
$ COUNTRY  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ RESPNO   <chr> "BEN0001", "BEN0002", "BEN0003", "BEN0004", "BEN0005", "BEN0006", "BEN0007", "BEN0008", "BEN0009", "BEN0010", "BEN0011", "BEN0012", "BEN0013", "BEN0014", "BEN0015", "BEN0016", "BEN0017", "BEN0018", "BEN0019", "BEN0020", "BEN0021", "BEN0022", "BEN0023", "BEN0024", "BEN0025", "BEN00…
$ URBRUR   <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1,…
$ BACKCHK  <dbl> 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2,…
$ REGION   <dbl> 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 1…
$ DISTRICT <chr> "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "COTONOU", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "BOPA", "…
$ EA_SVC_A <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ EA_SVC_B <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ EA_SVC_C <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ EA_SVC_D <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ EA_FAC_A <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ EA_FAC_B <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ EA_FAC_C <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ EA_FAC_D <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ EA_FAC_E <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ EA_SEC_A <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,…
$ EA_SEC_B <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ EA_ROAD  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,…
$ NOCALL_1 <dbl> 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 1, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997…
$ NOCALL_2 <dbl> 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 9…
$ NOCALL_3 <dbl> 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 9…
$ NOCALL_4 <dbl> 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 9…
$ NOCALL_5 <dbl> 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 9…
$ NOCALL_6 <dbl> 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 9…
$ NOCALL_7 <dbl> 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 9…
$ PREVINT  <dbl> 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1,…
$ THISINT  <dbl> 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2,…
$ ADULT_CT <dbl> 1, 1, 1, 1, 1, 4, 2, 3, 2, 1, 3, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 4, 4, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 3, 1, 3, 3, 1, 3, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 5, 2, 3, 1, 2, 2, 3, 1, 2, 1, 2, 1, 2, 4, 1, 2, 3, 1, 2, 1, 3, 1, 1, 5, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1,…
$ CALLS    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ DATEINTR <date> 2008-06-23, 2008-06-23, 2008-06-24, 2008-06-24, 2008-06-23, 2008-06-23, 2008-06-23, 2008-06-23, 2008-06-23, 2008-06-23, 2008-06-24, 2008-06-24, 2008-06-23, 2008-06-23, 2008-06-23, 2008-06-23, 2008-06-25, 2008-06-25, 2008-06-25, 2008-06-25, 2008-06-25, 2008-06-25, 2008-06-25, 2008…
$ STRTIME  <time> 18:30:00, 19:40:00, 18:30:00, 17:20:00, 17:33:00, 18:33:00, 18:36:00, 19:58:00, 16:29:00, 14:20:00, 14:30:00, 15:33:00, 15:35:00, 14:15:00, 14:14:00, 16:04:00, 11:32:00, 12:40:00, 12:37:00, 11:45:00, 12:40:00, 11:40:00, 12:36:00, 11:40:00, 17:58:00, 18:45:00, 18:35:00, 17:40:00, …
$ Q1       <dbl> 38, 46, 28, 30, 23, 24, 40, 50, 24, 36, 22, 31, 50, 19, 41, 29, 55, 22, 47, 30, 36, 30, 26, 38, 25, 49, 41, 27, 20, 19, 55, 52, 20, 29, 25, 28, 44, 48, 20, 25, 28, 32, 40, 48, 28, 25, 46, 30, 40, 52, 25, 25, 26, 30, 42, 28, 22, 26, 25, 36, 50, 36, 22, 36, 26, 24, 22, 40, 32, 27, 2…
$ Q2       <dbl> 0, 9, 0, 1, 0, 0, 0, 1, 0, -1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, -1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, -1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,…
$ Q3       <dbl> 100, 104, 101, 100, 100, 100, 109, 100, 101, 100, 100, 100, 100, 100, 100, 101, 101, 101, 101, 101, 101, 101, 101, 101, 100, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 106, 101, 101, 101, 101, 101, 101, 101, 1…
$ Q3OTHER  <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "…
$ Q4A      <dbl> 2, 2, 1, 4, 3, 1, 2, 1, 3, 2, 3, 3, 5, 2, 1, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 3, 3, 2, 1, 2, 1, 3, 2, 2, 2, 1, 2, 2, 4, 2, 2, 2, 4, 4, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 4, 2, 4, 3, 2, 1, 1, 4, 2, 4, 2, 4, 2, 4, 2, 2, 4, 2, 2, 2, 1, 2, 4, 5, 1, 4, 3,…
$ Q4B      <dbl> 2, 3, 3, 3, 2, 2, 2, 1, 3, 2, 3, 3, 3, 4, 1, 2, 2, 2, 2, 3, 3, 2, 2, 2, 4, 2, 3, 2, 1, 4, 2, 3, 3, 3, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 1, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 1, 2, 3, 2, 1, 3, 4,…
$ Q5       <dbl> 2, 2, 3, 4, 4, 2, 2, 2, 2, 2, 3, 3, -1, 3, 4, 4, 2, 2, 2, 3, -1, 3, 2, 2, 3, 2, 3, 2, 2, 4, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 2, 2, 3, 3, 5, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 4, 3, 2, 2, 3, 1, 3, 3, 2, 4, 4, 2, 2, 2, 2, 3, 4, 2, 2, 2, 1, 3, 3, 3, 2, 2, …
$ Q6A      <dbl> 2, 2, 2, 3, 2, 2, 2, 2, 4, 3, 4, 3, 3, 4, 1, 4, 2, 2, 3, 2, 2, 4, 4, 2, 4, 3, 3, 3, 1, 4, 4, 4, 3, 3, 2, 2, 2, 2, 2, 4, 3, 3, 4, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 3, 4, 3, 2, 2, 2, 4, 4, 2, 3, 3, 4, 4, 4, 2, 2, 2, 4, 2, 2, 2, 3, 3, 2, 4, 2, 1, 4, 4, 4, 2, 3, 9,…
$ Q6B      <dbl> 2, 3, 3, 3, 4, 3, 2, 2, 3, 3, 4, 3, 3, 3, 1, 4, 2, 2, 3, 3, 2, 4, 4, 2, 4, 2, 3, 3, 3, 2, 4, 4, 3, 3, 2, 2, 2, 2, 2, 4, 3, 3, 4, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2, 2, 2, 4, 2, 2, 2, 3, 4, 4, 2, 2, 1, 4, 2, 2, 3, 3, 4, 2, 3, 2, 2, 4, 4, 2, 2, 2, 3, 3, 2, 4, 2, 1, 3, 4, 4, 2, 2, 2,…
$ Q7A      <dbl> 9, 9, 4, 4, 4, 3, 2, 2, 4, 1, 3, 2, 4, 4, 4, 4, 4, 4, 9, 4, 4, 9, 4, 4, 2, 4, 9, 4, 4, 4, 4, 4, 9, 9, 3, 2, 4, 4, 9, 9, 9, 9, 4, 4, 9, 2, 4, 4, 2, 9, 9, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 9, 9, 3, 4, 4, 4, 4, 4, 4, 3, 9, 2, 4, 4, 4, 4, 4, 2, 4, 4, 4, 9, 4, 3, 9, 4, 9, 9, 9, 4, 1, 9, 9,…
$ Q7B      <dbl> 9, 9, 4, 4, 5, 4, 2, 2, 4, 9, 3, 2, 4, 4, 4, 4, 4, 4, 9, 4, 4, 9, 4, 4, 4, 4, 9, 4, 4, 3, 4, 4, 9, 9, 3, 2, 4, 4, 9, 9, 9, 9, 4, 4, 9, 2, 4, 4, 3, 9, 9, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 9, 9, 2, 4, 4, 4, 4, 3, 4, 9, 9, 2, 4, 4, 4, 4, 4, 2, 4, 4, 4, 9, 4, 3, 9, 4, 9, 9, 9, 3, 2, 9, 9,…
$ Q8A      <dbl> 2, 1, 0, 0, 0, 1, 4, 0, 2, 0, 0, 0, 1, 0, 2, 0, 1, 0, 4, 3, 0, 2, 1, 1, 3, 2, 2, 2, 4, 1, 0, 0, 2, 3, 3, 3, 2, 2, 2, 1, 3, 3, 3, 2, 3, 2, 1, 2, 2, 2, 1, 2, 2, 0, 3, 4, 0, 2, 2, 2, 2, 3, 2, 3, 0, 1, 3, 3, 2, 3, 2, 3, 2, 1, 0, 0, 1, 0, 3, 3, 0, 2, 3, 0, 3, 3, 3, 3, 3, 2, 1, 4, 3, 3,…
$ Q8B      <dbl> 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 0, 2, 3, 4, 3, 4, 1, 0, 2, 2, 2, 2, 1, 0, 3, 3, 0, 3, 2, 2, 0, 0, 2, 0, 0, 0, 0, 3, 0, 1, 2, 0, 1, 3, 3, 3, 2, 1, 3, 4, 3, 3,…
$ Q8C      <dbl> 1, 1, 0, 0, 0, 3, 2, 4, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 2, 0, 1, 2, 1, 3, 2, 1, 2, 1, 0, 0, 0, 2, 2, 3, 3, 2, 2, 1, 1, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 3, 4, 3, 0, 2, 2, 2, 2, 2, 1, 0, 0, 3, 2, 1, 3, 2, 2, 2, 1, 1, 3, 0, 0, 0, 3, 2, 2, 2, 0, 1, 2, 2, 4, 2, 2, 1, 2, 3, 3,…
$ Q8D      <dbl> 1, 1, 0, 0, 0, 1, 2, 4, 1, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 2, 0, 2, 0, 0, 1, 1, 0, 2, 3, 1, 0, 0, 0, 0, 0, 1, 2, 2, 3, 4, 4, 0, 2, 2, 0, 0, 1, 4, 3, 0, 0, 0, 0, 1, 0, 1, 1, 0, 2, 3, 0, 0, 0, 2, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 3,…
$ Q8E      <dbl> 4, 4, 0, 3, 2, 3, 3, 4, 2, 4, 0, 0, 1, 0, 4, 0, 3, 0, 4, 3, 0, 2, 3, 4, 3, 3, 2, 3, 4, 2, 0, 0, 3, 3, 4, 3, 4, 4, 2, 3, 4, 4, 4, 2, 4, 4, 4, 0, 2, 3, 3, 1, 4, 4, 4, 4, 4, 0, 4, 4, 3, 4, 3, 0, 4, 4, 4, 3, 3, 4, 3, 4, 4, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4,…
$ Q9A      <dbl> 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 2, 0, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 3, 3, 0, 0, 0, 0, 2, 0, 0,…
$ Q9B      <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 1, 3, 2, 2, 0, 0, 0, 0, 3, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 3, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 3, 0, 0, 0, 2, 0, 0,…
$ Q9C      <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0,…
$ Q10      <dbl> 4, 4, 3, 4, 4, 3, 3, 4, 1, 1, 4, 3, 4, 3, 2, 3, 3, 3, 4, 3, 3, 4, 3, 3, 2, 2, 3, 3, 3, 4, 3, 3, 4, 4, 4, 2, 3, 2, 4, 4, 3, 4, 4, 4, 3, 2, 3, 3, 3, 9, 1, 3, 3, 3, 2, 3, 3, -1, 3, 3, 1, 4, 3, 2, 3, 3, 3, 3, 4, 3, 1, 4, 5, 1, 4, 1, 2, 3, 2, 3, 3, 2, 4, 4, 4, 2, 2, 2, 4, 4, 3, 4, 2, 3…
$ Q11      <dbl> 1, 1, 3, 3, 3, 4, 2, 1, 1, 1, 3, 2, 2, 2, 4, 4, 3, 2, 1, 4, 2, 1, 3, 2, 3, 4, 1, 1, 3, 4, 2, 3, 1, 1, 3, 3, 2, 3, 1, 3, 1, 3, 4, 3, 3, 4, 3, 2, 1, 1, 4, 9, 2, 2, 4, 4, 2, 1, 3, 2, 1, 1, 4, 4, 2, 2, 3, 3, 2, 4, 4, 1, 4, 1, 1, 3, 2, 2, 3, 3, 2, 3, 3, 1, 4, 3, 3, 4, 1, 1, 3, 1, 4, 3,…
$ Q12A     <dbl> 4, 3, 2, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 4, 0, 3, 4, 3, 4, 3, 4, 4, 4, 3, 3, 3, 4, 2, 4, 3, 4, 4, 4, 1, 4, 3, 3, 4, 3, 4, 4, 4, 3, 4, 4, 4, 3, 3, 4, 3, 0, 4, 4, 3, 1, 3, 4, 4, 4, 4, 0, 3, 3, 2, 4, 3, 3, 4, 2, 4, 4, 3, 3, 4, 4, 3, 4, 4, 4, 0, 4, 0, 4, 3, 2, 3, 4, 4, 4, 4, 2, 4, 3,…
$ Q12B     <dbl> 4, 4, 4, 3, 4, 4, 4, 3, 3, 2, 3, 2, 1, 4, 1, 0, 9, 0, 0, 4, 0, 0, 0, 0, 3, 1, 0, 0, 1, 3, 1, 4, 0, 4, 0, 0, 2, 3, 0, 0, 0, 0, 4, 0, 3, 4, 2, 2, 0, 0, 0, 0, 0, 0, 1, 0, 3, 3, 4, 4, 0, 0, 3, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4, 3, 4, 3, 3, 0, 0, 0, 4, 3, 0, 0, 3, 0, 4, 4, 0, 3, 1,…
$ Q12C     <dbl> 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q13      <dbl> 3, 3, 2, 0, 1, 3, 2, 3, 2, 2, 2, 2, 1, 0, 1, 0, 2, 2, 3, 3, 2, 3, 2, 2, 2, 1, 3, 3, 0, 0, 2, 1, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 0, 3, 3, 2, 2, 2, 1, 3, 2, 2, 2, 2, 3, 3, 1, 1, 2, 2, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 3, 2, 2, 3, 3, -1, 3, 2, 3, 3, 3, 3, 3, 1, 2…
$ Q14      <dbl> 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 9, 1, 1, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 0, 1, 2, 2, 1, 1, 1, 1, 1,…
$ Q15A     <dbl> 4, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 4, 3, 4, 3, 4, 4, 3, 4, 4, 3, 4, 3, 3, 3, 4, 4, 4, 4, 4, 2, 3, 4, 4, 4, 4, 3, 2, 4, 4, 4, 3, 4, 4, 4, 3, 2, 3, 4, 4, 4, 4, 3, 3, 9, 4, 4, 4, 3, 3, 4, 4, 4, 4, 3, 2, 2, 3, 4, 3, 4, 4, 4, 4, 4, 3, 3, 2, 4, 4, 2, 2, 4, 4, 4, 3, 3, 4, 4, 3, 4, 3, 4, 4,…
$ Q15B     <dbl> 4, 4, 4, 4, 4, 3, 1, 4, 4, 4, 2, 4, 3, 4, 1, 3, 3, 3, 4, 4, 3, 4, 3, 3, 3, 4, 4, 4, 4, 4, 2, 3, 4, 4, 4, 4, 3, 3, 4, 4, 4, 3, 4, 4, 4, 4, 2, 3, 4, 1, 4, 4, 3, 3, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 3, 2, 3, 4, 4, 3, 4, 4, 4, 4, 4, 3, 3, 2, 4, 4, 2, 2, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 4, 4,…
$ Q15C     <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 3, 4, 4, 4, 3, 3, 4, 4, 3, 4, 3, 3, 4, 4, 4, 4, 4, 4, 2, 3, 4, 4, 4, 4, 3, 3, 4, 4, 4, 3, 4, 4, 4, 4, 2, 3, 4, 1, 4, 4, 3, 3, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 3, 1, 3, 4, 4, 3, 4, 4, 4, 4, 4, 3, 2, 2, 4, 4, 2, 2, 4, 4, 4, 4, 3, 4, 4, 3, 4, 3, 4, 4,…
$ Q16      <dbl> 4, 4, 3, 4, 2, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 3, 3, 3, 4, 4, 2, 4, 3, 3, 3, 3, 4, 4, 3, 4, 3, 3, 4, 4, 2, 2, 3, 3, 3, 2, 4, 4, 4, 3, 3, 1, 3, 2, 3, 4, 3, 1, 3, 1, 4, 4, 3, 4, 3, 3, 4, 4, 4, 4, 3, 3, 3, 2, 3, 2, 4, 4, 4, 2, 4, 1, 4, 3, 2, 3, 1, 3, 4, 1, 4, 4, 2, 2, 4, 4, 3, 3, 4, 3,…
$ Q17      <dbl> 1, 1, 3, 1, 3, 2, 4, 3, 2, 1, 3, 3, 4, 3, 4, 4, 2, 2, 1, 1, 2, 1, 2, 2, 4, 4, 1, 1, 3, 4, 3, 3, 1, 1, 1, 1, 3, 2, 4, 5, 1, 1, 4, 3, 2, 2, 2, 4, 1, 1, 3, 4, 3, 3, 2, 1, 2, 1, 3, 2, 1, 1, 4, 3, 3, 3, 3, 3, 4, 1, 1, 1, 4, 4, 1, 1, 1, 2, 5, 2, 2, 2, 1, 1, 4, 4, 2, 2, 1, 1, 3, 3, 4, 1,…
$ Q18      <dbl> 1, 4, 3, 4, 4, 1, 2, 4, 1, 4, 2, 3, 2, 1, 1, 1, 3, 2, 4, 4, 3, 4, 3, 2, 2, 2, 1, 3, 4, 4, 3, 2, 3, 4, 4, 4, 2, 3, 1, 3, 4, 4, 4, 2, 2, 5, 3, 2, 3, 4, 4, 1, 2, 3, 2, 1, 1, 4, 2, 3, 4, 1, 1, 1, 2, 2, 2, 2, 1, 4, 4, 1, 2, 1, 4, 1, 2, 3, 5, 2, 3, 3, 1, 4, 3, 1, 2, 2, 4, 4, 1, 2, 2, 3,…
$ Q19      <dbl> 4, 4, 3, 3, 4, 4, 4, 3, 4, 4, 3, 3, 3, 4, 4, 3, 3, 2, 4, 4, 3, 4, 3, 3, 9, 3, 4, 4, 3, 2, 2, 2, 3, 4, 3, 4, 3, 2, 2, 2, 4, 4, 3, 4, 2, 2, 3, 3, 3, 4, 3, 2, 3, 3, 9, 4, 3, 4, 3, 3, 4, 4, 3, 3, 2, 3, 3, 3, 2, 4, 4, 4, 3, 4, 4, 4, 3, 2, 3, 3, 3, 2, 4, 4, 3, 2, 3, 3, 4, 4, 4, 3, 4, 3,…
$ Q20      <dbl> 4, 4, 3, 3, 3, 4, 4, 3, 4, 4, 3, 2, 3, 4, 4, 1, 2, 3, 4, 4, 2, 4, 2, 3, 3, 3, 4, 4, 2, 3, 2, 3, 3, 4, 3, 4, 3, 3, 3, 3, 4, 4, 3, 4, 3, 3, 2, 2, 3, 4, 3, 3, 2, 3, 4, 4, 3, 4, 3, 3, 4, 4, 3, 3, 3, 2, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 3, 2, 3, 3, 4, 4, 4, 3, 4, 3,…
$ Q21      <dbl> 4, 4, 3, 3, 1, 4, 3, 4, 1, 4, 3, 2, 3, 4, 4, 4, 3, 2, 4, 4, 3, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 3, 4, 4, 3, 4, 3, 3, 3, 4, 3, 4, 2, 3, 3, 3, 4, 3, 3, 4, 3, 3, 4, 4, 4, 2, 3, 3, 3, 3, 3, 1, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 3, 4, 4, 3, 2, 3, 3, 4, 4, 4, 3, 4, 3,…
$ Q22A     <dbl> 1, 1, 1, 2, 2, 0, 1, 2, 0, 1, 1, 1, 2, 0, 1, 1, 0, 0, 1, 2, 1, 0, 1, 0, 2, 2, 0, 0, 0, 1, 0, 0, 1, 1, 1, 2, 1, 1, 2, 2, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 0, 0, 0, 3, 0, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 3,…
$ Q22B     <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 2, 0, 3, 0, 0, 0, 1, 0, 0, 0, 2, 0, 1, 0, 1, 1, 3, 1, 0, 1, 1, 0, 1, 1, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 2, 0, 0, 1, 3, 1, 0, 1, 3, 0, 2, 0, 1, 3, 0, 0, 0, 3, 0, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0, 1, 1, 3, 2, 3,…
$ Q23A     <dbl> 4, 4, 0, 1, 1, 2, 1, 1, 1, 4, 1, 1, 1, 1, 0, 1, 3, 3, 1, 4, 1, 4, 1, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 2, 1, 4, 3, 1, 4, 2, 1, 3, 3, 4, 1, 3, 3, 3, 0, 1, 4, 1, 1, 3, 3, 3, 0, 1, 3, 3, 3, 1, 3, 3, 1, 4, 1, 1, 4, 4, 3, 1, 3, 1, 4, 1, 3, 3, 1, 3, 3, 3, 1, 4, 1, 1, 1, 2, 1, 3, 4, 4, 4, 4,…
$ Q23B     <dbl> 3, 1, 0, 1, 0, 1, 1, 1, 1, 3, 1, 1, 1, 1, 0, 1, 3, 1, 1, 4, 1, 4, 1, 1, 3, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 3, 4, 1, 1, 4, 1, 1, 3, 3, 1, 0, 1, 4, 1, 1, 1, 3, 4, 3, 4, 1, 1, 3, 1, 3, 3, 1, 1, 1, 1, 4, 3, 3, 1, 3, 0, 4, 1, 3, 1, 1, 4, 1, 3, 1, 4, 3, 1, 1, 1, 1, 3, 4, 4, 1, 4,…
$ Q23C     <dbl> 1, 1, 0, 1, 0, 1, 0, 1, 1, 4, 9, 1, 0, 1, 0, 0, 0, 1, 1, 4, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 3, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 3, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 0, 3, 9, 0, 1, 2, 1, 0, 0, 1, 1, 1, 0, 1, 3, 1,…
$ Q23D     <dbl> 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 6, 4, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 5, 1, 1, 1, 1, 1…
$ Q24A     <dbl> 2, 3, 2, 2, 3, 3, 0, 0, 2, 2, 2, 2, 0, 3, 3, 9, 2, 2, 2, 2, 2, 2, 1, 2, 3, 1, 0, 2, 2, 2, 1, 2, 0, 2, 3, 3, 2, 2, 3, 3, 0, 2, 3, 3, 3, 3, 2, 2, 0, 0, 3, 2, 2, 2, 0, 3, 2, 2, 2, 2, 2, 9, 3, 3, 1, 2, 0, 3, 2, 3, 2, 0, 2, 3, 3, 0, 2, 1, 3, 3, 2, 1, 0, 3, 3, 1, 9, 3, 0, 3, 3, 3, 0, 3,…
$ Q24B     <dbl> 2, 0, 2, 2, 3, 3, 2, 0, 0, 0, 2, 2, 0, 2, 0, 9, 2, 2, 0, 1, 1, 2, 1, 2, 3, 1, 0, 2, 2, 2, 1, 2, 0, 2, 3, 3, 2, 2, 2, 1, 0, 0, 3, 3, 3, 3, 2, 2, 0, 0, 3, 1, 2, 2, 0, 3, 0, 0, 2, 2, 2, 9, 2, 2, 1, 2, 2, 2, 1, 3, 0, 0, 0, 3, 2, 0, 1, 1, 3, 3, 2, 2, 0, 1, 2, 0, 9, 3, 0, 0, 3, 2, 0, 3,…
$ Q25A     <dbl> 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 3, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 9, 2, 2, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 2, 0, 0, 0, 0, 3, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0,…
$ Q25B     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q25C     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q26A     <dbl> 7, 2, 7, 7, 7, 7, 7, 7, 7, 2, 7, 7, 7, 7, 7, 7, 7, 2, 7, 7, 2, 2, 7, 7, 7, 7, 7, 7, 7, 7, 1, 2, 7, 2, 7, 7, 7, 7, 2, 7, 7, 7, 2, 2, 7, 7, 2, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 2, 9, 2, 2, 7, 7, 7, 7, 7, 2, 2, 7, 7, 7, 2, 7, 7, 7, 7, 2, 2, 7, 7, 2, 7, 7, 7, 7, 7, 7, 1, 7, 7, 7,…
$ Q26B     <dbl> 7, 1, 7, 7, 7, 7, 7, 7, 7, 1, 7, 7, 7, 7, 7, 7, 7, 1, 7, 7, 1, 1, 7, 7, 7, 7, 7, 7, 7, 7, 2, 1, 7, 1, 7, 7, 7, 7, 2, 7, 7, 7, 1, 1, 7, 7, 1, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 9, 1, 1, 7, 7, 7, 7, 7, 1, 1, 7, 7, 7, 1, 7, 7, 7, 7, 1, 1, 7, 7, 1, 7, 7, 7, 7, 7, 7, 1, 7, 7, 7,…
$ Q27A     <dbl> 0, 0, 0, 0, 3, 0, 0, 0, 1, 3, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 1, 1, 0, 2, 0, 0, 0, 3, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 3, 2, 0, 3, 0, 0, 0, 0, 2, 2, 0, 0, 3, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 3, 0, 3, 0, 0, 0, 0, 2, 3, 3, 2,…
$ Q27B     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 3, 0, 3, 3, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 3, 2, 1, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0,…
$ Q27C     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q28A     <dbl> 7, 7, 7, 7, 2, 7, 7, 7, 1, 2, 1, 7, 7, 7, 1, 9, 7, 1, 7, 7, 7, 2, 7, 7, 1, 1, 7, 1, 7, 7, 7, 1, 1, 1, 7, 1, 7, 7, 7, 1, 7, 1, 2, 7, 7, 2, 7, 7, 7, 1, 1, 7, 7, 7, 1, 2, 7, 1, 7, 7, 1, 1, 1, 2, 7, 7, 2, 7, 2, 1, 7, 1, 7, 7, 7, 7, 7, 7, 7, 2, 1, 7, 7, 1, 7, 2, 7, 7, 7, 7, 1, 2, 1, 2,…
$ Q28B     <dbl> 7, 7, 7, 7, 1, 7, 7, 7, 1, 2, 2, 7, 7, 7, 2, 9, 7, 1, 7, 7, 7, 1, 7, 7, 2, 2, 7, 2, 7, 7, 7, 2, 2, 2, 7, 1, 7, 7, 7, 2, 7, 2, 1, 7, 7, 1, 7, 7, 7, 2, 2, 7, 7, 7, 2, 2, 7, 2, 7, 7, 2, 2, 2, 1, 7, 7, 1, 7, 1, 2, 7, 2, 7, 7, 7, 7, 7, 7, 7, 1, 2, 7, 7, 2, 7, 1, 7, 7, 7, 7, 1, 1, 1, 1,…
$ Q29A     <dbl> 4, 4, 2, 1, 1, 1, 2, 1, 1, 4, 2, 3, 2, 2, 1, 2, 3, 3, 4, 2, 3, 4, 3, 3, 2, 1, 4, 4, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 4, 4, 2, 4, 2, 9, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 4, 4, 3, 4, 2, 1, 1, 2, 2, 1, 2, 2, 2, 4, 4, 1, 4, 3, 2, 4, 1, 3, 4, 2, 1, 4, 2, 2, 2, 1, 2,…
$ Q29B     <dbl> 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 3, 1, 1, 2, 1, 3, 3, 1, 1, 3, 1, 3, 3, 4, 1, 5, 1, 4, 1, 3, 2, 1, 1, -1, 2, 2, 4, 1, 1, 1, 1, 1, 2, 4, 4, 4, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 4, 3, 4, 2, 9, 1, 1, 1, 1, 1, 1, 1, 2, 4, 4, 4, 3, 2, 1, 1, 4, 1, 9, 1, 1, 1, 1, 1, 1, 4…
$ Q29C     <dbl> 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 3, 2, 2, 1, 1, 3, 3, 1, 1, 3, 1, 2, 3, 2, 1, 5, 1, 1, 2, 3, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 4, 2, 9, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 4, 3, 4, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 2, 3, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1,…
$ Q30      <dbl> 3, 3, 3, 2, 3, 2, 2, 3, 3, 3, 2, 3, 2, 1, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 9, 1, 3, 3, 3, 3, 3, 3,…
$ Q31      <dbl> 1, 1, 3, 3, 2, 3, 4, 2, 1, 1, 2, 3, 1, 1, 1, 1, 3, 2, 1, 1, 3, 1, 2, 2, 3, 1, 1, 1, 3, 3, 3, 3, 1, 1, 2, 1, 3, 3, 1, 2, 1, 1, 2, 4, 2, 2, 3, 3, 2, 1, 1, 1, 3, 3, 2, 1, 2, 4, 3, 3, 1, 1, 1, 2, 3, 3, 3, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 2, 3, 3, 3, 1, 1, 3, 2, 2, 1, 1, 1, 4, 2, 1, 2,…
$ Q32      <dbl> 1, 4, 3, 3, 3, 1, 3, 4, 4, 1, 2, 3, 3, 1, 4, 4, 2, 3, 4, 1, 3, 4, 3, 2, 2, 4, 4, 4, 3, 4, 3, 2, 3, 1, 3, 4, 3, 3, 4, 1, 1, 4, 4, 4, 4, 3, 2, 3, 3, 4, 4, 1, 2, 2, 4, 4, 3, 4, 3, 3, 4, 4, 4, 1, 3, 3, 2, 3, 2, 4, 4, 4, 4, 1, 4, 4, 4, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 4, 4, 4, 3, 2,…
$ Q33      <dbl> 1, 1, 3, 1, 1, 3, 2, 2, 1, 1, 1, 3, 3, 1, 1, 4, 3, 3, 1, 1, 3, 1, 2, 2, 2, 2, 1, 1, 2, 2, 3, 2, 1, 1, 2, 1, 3, 3, 2, 2, 1, 1, 2, 4, 2, 2, 3, 2, 2, 4, 2, 3, 3, 3, 9, 1, 3, 2, 3, 2, 1, 1, 2, 2, 3, 3, 2, 2, 1, 3, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 3, 3, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1,…
$ Q34      <dbl> 4, 4, 3, 4, 4, 3, 3, 1, 1, 4, 3, 2, 3, 2, 1, 4, 2, 3, 1, 4, 3, 4, 3, 3, 3, 4, 4, 4, 3, 2, 3, 2, 3, 4, 4, 1, 2, 2, 2, 2, 1, 4, 2, 4, 4, 4, 2, 3, 3, 1, 2, 2, 3, 2, 4, 4, 4, 4, 2, 1, 4, 4, 2, 2, 3, 2, 3, 3, 2, 4, 3, 4, 4, 4, 4, 4, 3, 3, 2, 3, 2, 1, 1, 1, 2, 2, 3, 1, 4, 4, 1, 2, 3, 4,…
$ Q35      <dbl> 1, 1, 2, 1, 1, 3, 2, 1, 1, 1, 2, 2, 1, 1, 1, 4, 2, 2, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 3, 2, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 1, 1, 4, 2, 2, 3, 2, 2, 1, 2, 2, 3, 3, 9, 2, 2, 1, 3, 1, 1, 1, 1, 1, 2, 3, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 3, 2, 2, 2, 1, 1, 1, 2, 9, 2, 1, 1, 1, 1, 2, 5, 2,…
$ Q36      <dbl> 1, 1, 2, 3, 1, 2, 2, 2, 1, 1, 1, 3, 3, 1, 1, 1, 2, 3, 1, 1, 3, 1, 3, 3, 5, 2, 2, 1, 3, 3, 2, 3, 1, 1, 2, 4, 3, 3, 2, 9, 1, 1, 3, 1, 2, 1, 3, 3, 2, 1, 9, 9, 3, 3, 9, 2, 3, 5, 3, 4, 1, 1, 2, 9, 3, 3, 2, 2, 3, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 3, 3, 2, 1, 1, 1, 1, 2, 2, 2,…
$ Q37      <dbl> 4, 4, 2, 2, 4, 2, 3, 2, 4, 4, 3, 3, 3, 2, 4, 4, 3, 3, 4, 4, 3, 3, 3, 2, 2, 3, 3, 4, 2, 2, 3, 3, 3, 4, 3, 3, 2, 3, 2, 3, 4, 4, 2, 4, 3, 3, 2, 3, 3, 4, 3, 3, 3, 3, 9, 4, 3, 3, 3, 3, 4, 4, 3, 9, 3, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 4, 4, 4, 4, 3, 3, 3,…
$ Q38      <dbl> 1, 1, 3, 1, 2, 2, 2, 2, 1, 1, 2, 3, 1, 2, 1, 1, 3, 3, 1, 1, 2, 1, 3, 3, 2, 2, 1, 1, 2, 1, 2, 3, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 1, 1, 1, 1, 3, 3, 3, 1, 2, 1, 3, 3, 1, 1, 1, 1, 3, 3, 3, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 3, 1, 4, 3, 1, 1, 1, 1, 1, 2, 3, 1, 1, 1, 1, 1, 4,…
$ Q39      <dbl> 3, 4, 3, 3, 3, 3, 3, 2, 4, 1, 3, 3, 3, 3, 1, 4, 2, 2, 1, 4, 2, 1, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 9, 3, 2, 2, 3, 2, 9, 9, 3, 4, 2, 4, 2, 2, 3, 3, 9, 1, 9, 9, 2, 3, 2, 3, 2, 1, 3, 2, 1, 1, 9, 9, 3, 3, 2, 3, 2, 1, 4, 4, 2, 3, 2, 4, 1, 3, 3, 2, 3, 3, 3, 2, 2, 2, 2, 2, 1, 1, 4, 3, 2, 3,…
$ Q40A     <dbl> 4, 4, 4, 4, 4, 1, 3, 4, 1, 4, 4, 4, 4, 3, 2, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 3, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 4, 4, 1, 4, 4, 3, 2, 4, 1, 3, 4, 4, 4, 4, 4, 2, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4,…
$ Q40B     <dbl> 1, 3, 2, 1, 3, 3, -1, 3, 4, 2, 2, 1, 1, 4, 4, 2, 2, 2, 1, 4, 1, 2, 2, 2, 1, 1, 1, 1, 3, 3, 4, 1, 1, 2, 1, 3, 1, 2, 3, 1, 2, 3, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 3, 2, 1, 3, 2, 2, 2, 1, 3, 3, 2, 2, 4, 4, 2, 3, 4, 3, 3, 1, 3, 3, 4, 2, 3, 1, 2, 3, 2, 4, 3, 3, 1, 4, 1, 3, 3, 2, 1, 2…
$ Q41A1    <chr> "-1", "-1", "-1", "-1", "-1", "NAGO MATHURIN", "-1", "-1", "NAGO MATHURIN", "-1", "-1", "-1", "VIEYRA SOGLO ROSINE", "-1", "-1", "-1", "-1", "-1", "-1", "NAGO MATHURIN", "-1", "NAGO MATHURIN", "NAGO MATHURIN", "-1", "NAGO MATHURIN", "NAGO MATHURIN", "-1", "NAGO MATHURIN", "NAGO MA…
$ Q41A2    <dbl> 9, 1, 9, 9, 9, 2, 9, 9, 2, 1, 9, 9, 3, 1, 9, 9, 1, 9, 9, 3, 9, 3, 3, 9, 3, 3, 9, 3, 3, 2, 3, 9, 3, 3, 9, 3, 9, 2, 9, 9, 3, 3, 9, 2, 9, 9, 9, 9, 9, 3, 3, 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 9, 3, 2, 9, 2, 9, 9, 2, 2, 9, 9, 3, 2, 2, 2, 2, 9, 9, 9, 9, 2, 3, 2, 9, 9, 9, 2, 9, 3, 3, 2, 9, 9,…
$ Q41B1    <chr> "-1", "LAWANI SOULE MANA", "-1", "-1", "-1", "-1", "-1", "LAWANI SOULE MANA", "LAWANI SOULE MANA", "BIO-TCHANE", "LAWANI SOULE MANA", "-1", "LAWANI SOULE MANA", "-1", "3", "-1", "-1", "-1", "-1", "LAWANI SOULE MANA", "-1", "-1", "-1", "-1", "-1", "-1", "-1", "-1", "-1", "-1", "-1"…
$ Q41B2    <dbl> 9, 3, 9, 9, 1, 1, 9, 3, 3, 2, 3, 9, 3, 1, -1, 9, 9, 9, 9, 3, 9, 9, 9, 9, 9, 9, 9, 9, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 1, 9, 9, 1, 9, 1, 3, 3, 9, 9, 9, 9, 9, 9, 3, 9, 9, 9, 1, 9, 9, 1, 9, 9, 9…
$ Q42A     <dbl> 4, 4, 4, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 4, 3, 4, 4, 4, 4, 3, 3, 4, 4, 4, 3, 4, 4, 4, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 3, 3, 3, 2, 4, 4, 4, 4, 4, 3, 3, 2, 8, 3, 3, 4, 3, 4, 4, 2, 4, 3, 3, 3, 8, 3, 4, 2, 4, 8, 3, 3, 3, 3, 4, 3, 3, 4, 3, 1, 2, 3, 3, 3, 4, 3, 2, 8, 3, 3, 4, 3, 2, 3, 4,…
$ Q42B     <dbl> 4, 4, 4, 4, 4, 3, 4, 3, 4, 4, 4, 4, 3, 3, 3, 2, 3, 3, 4, 4, 3, 4, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 4, 3, 4, 4, 4, 4, 4, 4, 3, 3, 3, 4, 2, 8, 4, 4, 9, 4, 2, 2, 4, 3, 4, 4, 2, 8, 3, 3, 2, 3, 8, 3, 4, 4, 2, 3, 3, 4, 4, 4, 4, 3, 2, 3, 4, 4, 2, 3, 8, 4, 4, 4, 3, 2, 4, 3,…
$ Q42C     <dbl> 2, 2, 3, 3, 2, 1, 2, 3, 2, 2, 3, 3, 3, 3, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 4, 2, 2, 3, 2, 2, 2, 3, 3, 2, 2, 1, 4, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 3, 8, 2, 3, 9, 3, 2, 2, 3, 2, 2, 2, 1, 8, 2, 2, 2, 3, 8, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 3, 2, 2, 2, 2, 2, 8, 2, 2, 2, 1, 2, 1, 3,…
$ Q42D     <dbl> 1, 1, 2, 2, 3, 2, 2, 2, 1, 1, 2, 2, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 8, 1, 2, 9, 3, 2, 3, 2, 1, 1, 1, 1, 8, 1, 1, 1, 2, 8, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 2,…
$ Q43      <dbl> 4, 4, 2, 2, 2, 3, 2, 4, 4, 3, 2, 3, 3, 1, 2, 4, 3, 3, 3, 2, 3, 4, 3, 3, 2, 3, 4, 4, 4, 3, 2, 3, 3, 3, 3, 4, 3, 3, 4, 3, 3, 3, 4, 3, 4, 4, 3, 3, 3, 3, 1, 9, 3, 3, 3, 4, 4, 4, 2, 3, 4, 3, 3, 9, 3, 3, 2, 4, 9, 2, 4, 4, 4, 3, 3, 3, 4, 4, 1, 1, 3, 3, 4, 4, 3, 2, 3, 2, 4, 4, 4, 3, 3, 4,…
$ Q44A     <dbl> 4, 3, 2, 2, 4, 1, 4, 4, 4, 3, 2, 4, 2, 2, 1, 4, 3, 3, 4, 4, 4, 4, 4, 3, 2, 4, 4, 4, 4, 4, 2, 3, 2, 2, 4, 5, 2, 4, 1, 2, 2, 2, 4, 2, 5, 4, 4, 4, 9, 2, 5, 9, 4, 2, 4, 5, 2, 5, 4, 4, 2, 2, 2, 9, 4, 4, 2, 4, 4, 1, 2, 2, 5, 2, 2, 2, 4, 4, 5, 4, 4, 2, 2, 4, 4, 9, 4, 2, 2, 2, 1, 2, 3, 4,…
$ Q44B     <dbl> 5, 4, 2, 4, 2, 4, 4, 4, 5, 5, 2, 4, 2, 2, 4, 5, 3, 3, 5, 4, 4, 5, 4, 3, 4, 4, 5, 5, 4, 2, 2, 3, 1, 1, 4, 5, 2, 4, 1, 4, 5, 5, 2, 2, 4, 4, 4, 4, 5, 5, 5, 9, 4, 2, 4, 5, 4, 2, 4, 4, 5, 1, 2, 2, 4, 4, 4, 4, 4, 2, 5, 5, 5, 1, 5, 5, 4, 4, 5, 4, 4, 3, 5, 5, 4, 9, 4, 2, 5, 5, 2, 2, 3, 4,…
$ Q44C     <dbl> 5, 5, 2, 4, 4, 4, 4, 4, 5, 5, 2, 4, 2, 4, 4, 4, 3, 3, 5, 4, 4, 5, 4, 3, 4, 2, 5, 5, 4, 4, 2, 3, 1, 1, 4, 4, 2, 4, 1, 2, 5, 4, 1, 2, 2, 5, 4, 4, 9, 4, 1, 9, 4, 2, 4, 2, 2, 3, 4, 4, 5, 1, 1, 1, 4, 4, 4, 4, 1, 1, 4, 4, 2, 1, 5, 5, 4, 4, 1, 4, 4, 4, 4, 5, 1, 9, 4, 2, 4, 5, 5, 1, 2, 4,…
$ Q45A     <dbl> 0, 0, 1, 1, 3, 1, 2, 1, 1, 1, 1, 0, 2, 2, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 3, 1, 1, 2, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 9, 0, 1, 9, 1, 1, 0, 1, 3, 1, 1, 1, 0, 9, 1, 1, 0, 1, 1, 1, 1, 2, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,…
$ Q45B     <dbl> 0, 1, 1, 0, 2, 1, 1, 1, 0, 2, 1, 0, 1, 0, 1, 1, 0, 0, 9, 1, 0, 0, 0, 9, 1, 9, 9, 9, 2, 9, 0, 1, 0, 1, 1, 1, 1, 0, 9, 1, 9, 1, 9, 0, 0, 1, 0, 0, 9, 1, 1, 9, 0, 0, 2, 1, 1, 2, 0, 0, 1, 9, 9, 9, 0, 0, 3, 1, 9, 1, 0, 9, 9, 1, 1, 1, 0, 0, 0, 0, 0, 0, 9, 0, 9, 9, 9, 1, 9, 0, 0, 9, 9, 9,…
$ Q45C     <dbl> 0, 2, 1, 0, 2, 1, 1, 1, 0, 2, 1, 0, 1, 3, 2, 1, 0, 0, 0, 2, 0, 0, 0, 2, 1, 1, 1, 0, 3, 2, 0, 1, 0, 0, 1, 3, 2, 2, 1, 1, 0, 1, 1, 0, 0, 1, 2, 2, 9, 1, 1, 9, 2, 2, 0, 1, 1, 3, 2, 2, 0, 9, 1, 1, 2, 2, 2, 2, 1, 1, 1, 0, 1, 1, 2, 1, 2, 2, 0, 1, 2, 2, 0, 1, 1, 1, 2, 1, 0, 0, 0, 9, 9, 1,…
$ Q45D     <dbl> 0, 2, 1, 0, 2, 0, 2, 1, 0, 0, 1, 0, 1, 2, 3, 1, 0, 0, 0, 2, 0, 2, 0, 2, 1, 1, 1, 1, 3, 1, 0, 1, 0, 0, 1, 1, 2, 2, 1, 1, 0, 1, 1, 0, 0, 1, 2, 2, 9, 1, 1, 0, 2, 2, 0, 1, 1, 1, 2, 2, 0, 9, 9, 9, 2, 2, 1, 2, 1, 1, 2, 0, 9, 3, 2, 2, 2, 2, 1, 0, 2, 2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 9, 1,…
$ Q45E     <dbl> 0, 3, 1, 0, 2, 3, 2, 1, 0, 0, 1, 0, 2, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 2, 1, 1, 0, 0, 1, 2, 0, 1, 0, 0, 1, 1, 2, 2, 1, 1, 0, 1, 0, 0, 0, 1, 2, 2, 9, 0, 1, 0, 2, 2, 0, 1, 1, 3, 2, 2, 0, 9, 1, 1, 2, 2, 2, 2, 1, 0, 0, 0, 1, 3, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0, 2, 1, 2, 1, 0, 0, 0, 1, 2, 1,…
$ Q46      <dbl> 3, 3, 2, 2, 2, 1, 3, 3, 3, 2, 2, 2, 1, 3, 2, 2, 2, 2, 3, 3, 2, 3, 2, 3, 2, 1, 3, 3, 3, 3, 1, 2, 2, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 3, 2, 3, 2, 2, 2, 3, 3, 2, 1, 2, 1, 2, 3, 0, 1, 1, 3, 2, 1, 1, 2, 2, 3, 2, 3, 2, 3, 3, 1, 3, 3, 3, 2, 2, 1, 1, 2, 1, 3, 3, 2, 1, 2, 1, 2, 2, 3, 3, 1, 2,…
$ Q47      <dbl> 3, 1, 1, 2, 3, 1, 0, 2, 3, 1, 1, 1, 2, 3, 3, 3, 2, 2, 3, 3, 1, 3, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 3, 3, 0, 3, 2, 2, 3, 2, 3, 3, 3, 2, 2, 0, 2, 2, 9, 3, 2, 2, 3, 0, 3, 2, 0, 0, 2, 2, 3, 3, 3, 3, 2, 2, 3, 2, 0, 0, 3, 3, 3, 2, 3, 3, 0, 3, 3, 3, 2, 1, 3, 3, 3, 3, 2, 2, 3, 3, 3, 1, 2, 2,…
$ Q48A     <dbl> 0, 0, 1, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 3, 0, 0, 2, 2, 0, 0, 1, 0, 2, 2, 0, 0, 0, 0, 3, 2, 2, 1, 0, 0, 0, 0, 2, 2, 0, 1, 0, 0, 0, 0, 3, 0, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 1, 0, 2, 2, 0, 0, 0, 0, 2, 2, 1, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 3, 0, 0, 3, 2, 0, 0,…
$ Q48B     <dbl> 0, 0, 1, 1, 1, 2, 2, 1, 0, 1, 1, 1, 0, 3, 0, 3, 2, 2, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 2, 2, 0, 1, 0, 0, 0, 3, 2, 3, 2, 2, 0, 0, 0, 0, 2, 2, 0, 0, 1, 3, 2, 2, 0, 0, 9, 9, 2, 2, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 2, 2, 0, 0, 0, 9, 0, 3, 0, 0, 0, 0, 0, 0,…
$ Q49A     <dbl> 3, 1, 3, 1, 3, 1, 2, 3, 2, 0, 1, 0, 3, 3, 2, 1, 2, 3, 2, 2, 3, 2, 2, 2, 1, 3, 2, 2, 3, 0, 2, 2, 2, 0, 3, 1, 1, 2, 3, 0, 2, 1, 3, 1, 3, 3, 2, 0, 9, 0, 2, 9, 2, 1, 0, 3, 3, 1, 0, 2, 1, 1, 2, 3, 2, 0, 0, 1, 3, 2, 1, 2, 2, 1, 3, 1, 3, 3, 3, 3, 2, 2, 1, 3, 0, 0, 9, 3, 1, 2, 3, 2, 2, 2,…
$ Q49B     <dbl> 1, 1, 2, 1, 3, 2, 1, 2, 2, 2, 1, 0, 2, 3, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 3, 1, 2, 3, 1, 1, 2, 1, 2, 3, 3, 1, 1, 3, 0, 1, 1, 2, 1, 3, 3, 1, 1, 9, 0, 1, 9, 3, 1, 1, 2, 0, 0, 1, 1, 1, 1, 0, 2, 0, 2, 0, 1, 1, 1, 1, 2, 3, 3, 2, 1, 1, 3, 3, 3, 1, 2, 2, 3, 2, 1, 9, 0, 1, 2, 2, 1, 2, 3,…
$ Q49C     <dbl> 1, 1, 2, 0, 1, 2, 0, 1, 2, 1, 1, 0, 1, 1, 0, 1, 1, 2, 9, 2, 1, 2, 1, 1, 1, 3, 2, 2, 0, 0, 0, 2, 1, 2, 0, 0, 1, 1, 2, 2, 1, 1, 2, 1, 3, 3, 1, 1, 9, 9, 1, 9, 1, 1, 1, 1, 0, 0, 1, 2, 2, 1, 1, 9, 1, 1, 0, 2, 1, 0, 0, 2, 0, 1, 1, 1, 0, 0, 3, 2, 1, 2, 2, 2, 1, 9, 9, 3, 1, 2, 0, 1, 1, 3,…
$ Q49D     <dbl> 0, 1, 2, 0, 1, 0, 1, 1, 2, 1, 1, 0, 1, 1, 9, 1, 1, 3, 1, 1, 2, 2, 1, 1, 1, 3, 1, 1, 2, 2, 0, 1, 1, 2, 3, 3, 2, 1, 3, 2, 1, 1, 3, 3, 3, 3, 2, 1, 9, 0, 1, 1, 2, 1, 2, 1, 0, 2, 2, 1, 2, 1, 3, 2, 2, 1, 0, 2, 0, 0, 1, 2, 1, 1, 0, 1, 1, 3, 3, 3, 1, 0, 1, 3, 3, 0, 9, 3, 1, 2, 2, 2, 1, 2,…
$ Q49E     <dbl> 1, 0, 2, 0, 1, 2, 0, 2, 2, 2, 1, 0, 2, 1, 1, 1, 1, 3, 1, 1, 3, 2, 1, 1, 1, 3, 1, 1, 3, 2, 1, 1, 1, 0, 2, 1, 2, 1, 3, 1, 1, 1, 3, 1, 3, 3, 2, 1, 9, 0, 9, 9, 9, 1, 0, 2, 3, 0, 0, 1, 1, 1, 2, 2, 1, 1, 0, 2, 1, 2, 1, 2, 3, 1, 0, 1, 2, 3, 3, 2, 1, 0, 1, 2, 2, 1, 9, 0, 1, 1, 3, 1, 0, 2,…
$ Q49F     <dbl> 1, 0, 2, 1, 1, 1, 0, 1, 2, 2, 1, 0, 2, 0, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 3, 2, 1, 1, 2, 0, 0, 3, 2, 1, 2, 1, 1, 1, 2, 0, 3, 3, 2, 1, 9, 1, 2, 2, 1, 1, 2, 1, 0, 0, 2, 1, 0, 1, 1, 2, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 2, 0, 0, 2, 1, 1, 0, 3, 2, 1, 9, 0, 1, 2, 0, 1, 0, 2,…
$ Q49G     <dbl> 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 3, 2, 1, 3, 0, 1, 1, 2, 2, 3, 1, 2, 1, 2, 2, 2, 2, 2, 3, 3, 3, 2, 1, 9, 1, 9, 9, 1, 1, 3, 2, 3, 0, 1, 1, 1, 2, 0, 2, 1, 1, 0, 2, 2, 0, 2, 3, 1, 3, 1, 1, 1, 1, 3, 2, 1, 2, 2, 3, 1, 1, 9, 0, 2, 2, 3, 1, 2, 3,…
$ Q49H     <dbl> 0, 2, 2, 1, 3, 2, 1, 1, 2, 1, 1, 0, 1, 3, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 3, 2, 2, 3, 0, 1, 1, 2, 2, 3, 1, 2, 1, 2, 1, 2, 1, 2, 3, 3, 3, 2, 1, 9, 2, 9, 9, 1, 1, 3, 2, 0, 2, 1, 1, 1, 2, 0, 9, 1, 1, 0, 2, 2, 3, 1, 3, 1, 3, 1, 1, 0, 2, 3, 1, 1, 2, 1, 3, 1, 1, 9, 2, 1, 2, 3, 2, 9, 3,…
$ Q49I     <dbl> 1, 1, 2, 1, 1, 1, 0, 1, 2, 2, 1, 0, 1, 1, 1, 9, 1, 2, 1, 1, 2, 1, 1, 1, 1, 3, 1, 9, 3, 0, 1, 1, 2, 2, 3, 1, 2, 1, 3, 1, 0, 2, 2, 1, 3, 1, 2, 1, 9, 1, 1, 9, 1, 1, 3, 2, 3, 1, 1, 1, 2, 2, 3, 2, 1, 1, 0, 2, 1, 0, 0, 0, 3, 0, 1, 1, 0, 2, 3, 2, 1, 1, 0, 0, 2, 1, 9, 0, 0, 2, 3, 1, 3, 3,…
$ Q50A     <dbl> 9, 2, 2, 3, 3, 1, 1, 1, 3, 3, 1, 2, 1, 0, 1, 0, 2, 1, 2, 2, 1, 2, 1, 1, 9, 9, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 9, 1, 1, 1, 9, 2, 9, 9, 1, 1, 1, 1, 0, 1, 1, 1, 2, 3, 1, 9, 1, 1, 3, 9, 1, 1, 2, 2, 1, 2, 1, 3, 0, 1, 0, 2, 1, 1, 9, 2, 9, 1, 9, 9, 2, 2, 0, 2, 9, 1,…
$ Q50B     <dbl> 3, 1, 2, 2, 3, 1, 1, 1, 3, 2, 1, 2, 1, 2, 2, 0, 2, 1, 2, 2, 1, 1, 1, 1, 9, 9, 2, 2, 3, 1, 1, 1, 2, 2, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 9, 1, 1, 1, 9, 1, 9, 9, 1, 1, 1, 1, 2, 3, 1, 1, 2, 3, 9, 9, 2, 1, 3, 9, 9, 3, 2, 2, 1, 2, 1, 3, 1, 1, 0, 2, 1, 1, 9, 2, 9, 1, 9, 9, 9, 2, 1, 2, 9, 1,…
$ Q50C     <dbl> 2, 1, 2, 3, 3, 1, 2, 1, 2, 1, 1, 2, 1, 0, 2, 0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 9, 1, 1, 2, 1, 2, 1, 1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 9, 1, 1, 1, 9, 2, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 3, 9, 1, 3, 1, 1, 1, 2, 1, 3, 1, 1, 3, 2, 1, 1, 1, 2, 1, 1, 9, 9, 9, 1, 1, 2, 9, 1,…
$ Q50D     <dbl> 2, 2, 2, 2, 3, 1, 2, 1, 3, 3, 1, 2, 1, 1, 1, 0, 2, 1, 2, 2, 1, 2, 1, 1, 1, 9, 2, 2, 3, 1, 1, 1, 2, 2, 3, 2, 1, 1, 1, 1, 2, 2, 9, 2, 9, 1, 1, 1, 9, 2, 9, 9, 1, 1, 1, 1, 1, 3, 1, 1, 2, 2, 1, 1, 1, 1, 2, 9, 1, 3, 2, 2, 1, 2, 1, 3, 1, 1, 9, 2, 1, 1, 9, 2, 1, 1, 9, 9, 2, 2, 0, 2, 9, 1,…
$ Q50E     <dbl> 1, 1, 3, 2, 3, 2, 2, 1, 2, 1, 1, 2, 1, 2, 9, 0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 9, 1, 1, 3, 1, 1, 1, 1, 3, 1, 2, 1, 1, 1, 9, 1, -1, 1, 2, 9, 1, 1, 1, 9, 1, 9, 9, 1, 1, 1, 1, 1, 3, 1, 1, 2, 1, 1, 9, 1, 1, 2, 9, 1, 3, 1, 1, 1, 2, 1, 3, 0, 1, 1, 2, 1, 1, 1, 2, 1, 1, 9, 9, 9, 1, 1, 2, 1, 2…
$ Q50F     <dbl> 2, 2, 3, 2, 3, 2, 2, 1, 2, 2, 1, 2, 1, 0, 1, 0, 2, 2, 3, 2, 1, 2, 1, 1, 1, 9, 2, 2, 2, 2, 3, 3, 1, 3, 3, 3, 1, 1, 2, 2, 2, 2, 2, 2, 9, 2, 1, 1, 9, 2, 9, 1, 1, 1, 1, 1, 1, 3, 1, 1, 3, 3, 2, 9, 1, 1, 1, 9, 1, 3, 2, 2, 2, 2, 3, 3, 0, 1, 0, 2, 1, 1, 2, 2, 2, 1, 9, 9, 9, 2, 3, 2, 1, 3,…
$ Q50G     <dbl> 2, 2, 3, 2, 3, 2, 2, 1, 2, 3, 1, 2, 1, 0, 1, 0, 2, 2, 3, 2, 1, 2, 1, 1, 1, 9, 1, 2, 2, 1, 3, 3, 1, 1, 2, 1, 1, 1, 1, 9, 1, 1, 2, 2, 9, 1, 1, 2, 9, 1, 9, 9, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 9, 2, 1, 1, 1, 9, 1, 3, 1, 2, 2, 2, 1, 3, 0, 1, 3, 2, 1, 1, 2, 2, 9, 1, 9, 9, 9, 1, 3, 2, 1, 2,…
$ Q50H     <dbl> 0, 1, 2, 9, 3, 1, 3, 1, 1, 0, 1, 2, 1, 0, 2, 0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 9, 9, 1, 2, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 9, 9, 1, 0, 2, 9, 1, 1, 0, 9, 1, 9, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 9, 1, 1, 1, 9, 1, 1, 1, 1, 1, 2, 1, 3, 0, 1, 0, 2, 1, 1, 1, 0, 0, 1, 9, 9, 9, 0, 1, 2, 0, 1,…
$ Q51A     <dbl> 7, 0, 7, 7, 7, 7, 7, 0, 7, 2, 7, 2, 0, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 7, 7, 7, 7, 7, 7, 7, 0, 0, 2, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 3, 0, 7, 7, 7, 7, 7, 1, 7, 7, 0, 0, 7, 1, 7, 7, 7, 7, 7, 7, 7, 7, 3, 2, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7, 0, 0, 7, 7,…
$ Q51B     <dbl> 0, 0, 7, 7, 7, 7, 2, 0, 7, 0, 7, 2, 0, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7, 0, 0, 7, 7,…
$ Q51C     <dbl> 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 2, 0, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 3, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 0, 0, 7, 0, 7, 7, 0, 0, 7, 7,…
$ Q52      <dbl> 4, 4, 2, 4, 3, 4, 4, 3, 3, 4, 2, 1, 3, 3, 3, 3, 1, 2, 4, -1, 2, 4, 2, 2, 3, 1, 4, 4, 4, 4, 2, 2, 4, 4, 3, 3, 2, 2, 2, 2, 4, 4, 2, 2, 3, 3, 1, 2, 4, 4, 2, 2, 3, 3, 1, 3, 3, 2, 3, 3, 4, 4, 2, 2, 2, 2, 2, 3, 3, 2, 4, 4, 2, 3, 4, 4, 2, 3, 3, 3, 3, 2, 4, 4, 3, 3, 9, 2, 3, 4, 3, 2, 1, 3…
$ Q53A     <dbl> 2, 2, 4, 3, 4, 3, 1, 2, 2, 0, 3, 2, 2, 3, 2, 9, 2, 3, 2, 2, 3, 2, 2, 3, 3, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 2, 0, 1, 1, 3, 2, 3, 2, 3, 3, 1, 1, 1, 2, 3, 3, 2, 2, 3, 0, 2, 3, 2, 1, 4, 2, 3, 3, 3, 2, 1, 3, 2, 2, 1, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 1, 1, 3, 2, 3, 2, 2, 1, 2, 1,…
$ Q53B     <dbl> 2, 2, 4, 3, 0, 1, 1, 0, 0, 0, 3, 2, 0, 0, 2, 9, 3, 3, 2, 2, 3, 2, 2, 4, 2, 2, 2, 2, 2, 1, 2, 2, 1, 0, 2, 2, 3, 3, 0, 0, 0, 0, 0, 4, 1, 2, 3, 3, 0, 0, 0, 0, 3, 3, 3, 1, 0, 0, 3, 3, 0, 0, 3, 1, 3, 1, 0, 1, 0, 0, 0, 0, 1, 2, 0, 0, 2, 2, 0, 0, 3, 2, 0, 2, 0, 0, 2, 2, 0, 1, 0, 0, 1, 1,…
$ Q54A     <dbl> 0, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 2, 0, 1, 0, 1, 1, 2, 1, 0, 0, 0, 1, 0, 1, 2, 0, 1, 3, 2, 2, 2, 0, 0, 0, 0, 0, 1, 9, 3, 0, 2, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 2, 2, 0, 9, 1, 1, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 0, 0, 0, 2, 9, 2, 2, 2, 0, 1, 0, 0, 1, 1, 0, 1, 3, 0, 0, 1,…
$ Q54B     <dbl> 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 2, 0, 1, 1, 2, 1, 0, 0, 1, 1, 0, 1, 2, 0, 1, 3, 2, 2, 2, 0, 0, 1, 1, 0, 1, 2, 3, 0, 2, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 2, 2, 2, 9, 2, 1, 1, 1, 0, 9, 0, 0, 1, 0, 1, 1, 2, 0, 1, 2, 9, 3, 2, 0, 1, 2, 1, 9, 2, 1, 0, 2, 3, 1, 1, 2,…
$ Q54C     <dbl> 0, 0, 1, 1, 1, 0, 2, 3, 0, 3, 0, 1, 0, 1, 2, 0, 2, 1, 0, 0, 1, 0, 1, 1, 1, 2, 0, 0, 1, 0, 1, 2, 0, 1, 3, 0, 2, 2, 0, 0, 0, 2, 0, 1, 2, 3, 0, 2, 0, 2, 0, 0, 1, 2, 3, 2, 3, 1, 2, 2, 2, 9, 2, 1, 1, 1, 0, 2, 0, 1, 0, 0, 1, 1, 2, 0, 1, 2, 1, 3, 2, 1, 0, 0, 1, 2, 2, 1, 0, 2, 3, 2, 2, 2,…
$ Q55      <dbl> 4, 4, 3, 3, 4, 4, 3, 3, 4, 4, 4, 2, 4, 1, 4, 1, 3, 3, 4, 4, 2, 4, 3, 3, 3, 2, 4, 4, 3, 2, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 4, 4, 3, 4, 4, 4, 3, 3, 3, 4, 4, 4, 3, 3, 2, 4, 4, 4, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 4, 4, 4, 3, 4, 3, 4, 4, 4, 3, 4, 3,…
$ Q56PT1   <dbl> 4, 7, 1, 1, 8, 1, 1, 3, 17, 1, 1, 1, 1, 1, 1, 3, 3, 2, 7, 1, 1, 13, 3, 1, 22, 4, 1, 3, 1, 6, 11, 1, 16, 32, 16, 7, 1, 1, 20, 6, 16, 20, 23, 2, 8, 14, 14, 1, 16, 32, 16, 8, 1, 1, 17, 17, 13, 3, 1, 1, 1, 20, 13, 13, 16, 16, 16, 4, 23, 3, 24, 1, 6, 7, 14, 3, 24, 1, 4, 17, 3, 14, 1, 2…
$ Q56PT2   <dbl> 1, 1, 9, 3, 16, 14, 3, 13, 1, 3, 7, 14, 13, 4, 23, 1, 8, 8, 19, 7, 10, 7, 7, 32, 4, 7, 3, 7, 15, 7, 20, 7, 32, 13, 17, 16, 7, 7, 13, 13, 13, 7, 16, 13, 4, 20, 11, 16, 32, 3, 17, 16, 16, 7, 6, 16, 14, 13, 7, 7, 16, 1, 8, 16, 11, 11, 14, 1, 6, 20, 3, 16, 13, 14, 8, 24, 8, 7, 16, 20,…
$ Q56PT3   <dbl> 32, 13, 16, 10, 14, 13, 7, 7, 13, 15, 14, 20, 23, 24, 20, 1, 16, 16, 1, 20, 19, 1, 14, 14, 1, 2, 32, 16, 16, 20, 32, 16, 13, 16, 7, 17, 16, 16, 16, 16, 1, 14, 13, 6, 13, 16, 20, 11, 1, 16, 13, 17, 17, 17, 16, 13, 6, 6, 20, 20, 13, 14, 6, 17, 14, 16, 17, 996, 20, 14, 13, 17, 4, 8, …
$ Q57A     <dbl> 3, 3, 3, 3, 3, 3, 1, 3, 3, 1, 3, 2, 3, 3, 2, 1, 3, 3, 2, 2, 3, 2, 3, 2, 9, 2, 9, 3, 2, 3, 2, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 3, 2, 1, 3, 4, 4, 2, 2, 2, 2, 4, 3, 3, 3, 3, 3, 3, 4, 3, 2, 3, 2, 3, 2, 3, 3, 2, 2, 3, 3, 2, 3, 4, 1, 3, 4, 2, 3, 3, 3, 3, 3,…
$ Q57B     <dbl> 2, 2, 3, 3, 3, 3, 3, 3, 2, 1, 2, 1, 3, 4, 2, 2, 3, 3, 2, 1, 3, 2, 3, 2, 3, 3, 2, 2, 1, 3, 3, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 4, 1, 3, 3, 3, 3, 9, 2, 2, 2, 3, 3, 1, 1, 1, 3, 2, 2, 1, 2, 3, 3, 3, 3, 3, 2, 2, 1, 3, 1, 2, 1, 1, 2, 1, 3, 2, 2, 3, 3, 2, 3, 3, 2, 2, 1, 2, 3, 3, 3, 2, 3,…
$ Q57C     <dbl> 2, 2, 2, 1, 4, 1, 3, 3, 2, 1, 1, 1, 2, 3, 2, 3, 3, 3, 3, 2, 3, 2, 3, 2, 3, 2, 2, 1, 1, 3, 3, 3, 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 3, 3, 3, 2, 3, 9, 2, 2, 2, 2, 1, 1, 2, 1, 3, 3, 2, 2, 9, 2, 9, 3, 1, 3, 1, 2, 3, 3, 2, 2, 3, 2, 2, 1, 3, 1, 2, 3, 1, 9, 3, 3, 2, 2, 2, 2, 3, 3, 2, 2, 3,…
$ Q57D     <dbl> 2, 2, 3, 1, 3, 1, 3, 3, 2, 1, 1, 1, 3, 4, 1, 4, 3, 1, 2, 2, 3, 1, 2, 3, 3, 2, 2, 1, 2, 3, 3, 3, 2, 2, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 3, 1, 1, 9, 2, 1, 1, 1, 3, 1, 1, 1, 3, 1, 1, 1, -1, 3, 2, 3, 1, 2, 1, 2, 1, 2, 2, 2, 3, 1, 2, 1, 3, 1, 1, 3, 1, 2, 2, 3, 1, 1, 1, 2, 2, 3, 2, 3, 1…
$ Q57E     <dbl> 1, 1, 3, 1, 3, 1, 3, 3, 1, 1, 1, 1, 2, 3, 1, 4, 3, 1, 1, 1, 2, 1, 2, 3, 3, 2, 1, 1, 4, 2, 3, 3, 1, 1, 2, 2, 3, 2, 2, 2, 1, 1, 2, 3, 3, 2, 2, 3, 9, 1, 1, 1, 3, 3, 1, 3, 2, 3, 1, 1, 1, -1, 2, 2, 3, 2, 3, 3, 9, 1, 2, 1, 2, 1, 1, 1, 2, 4, 2, 2, 3, 3, 1, 3, 2, 1, 3, 2, 1, 2, 3, 2, 1, 3…
$ Q57F     <dbl> 3, 3, 2, 2, 3, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 2, 3, 1, 2, 3, 2, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 1, 2, 2, 2, 3, 9, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 9, 3, 3, 1, 3, 3, 4, 2, 2, 3, 1, 3, 3, 2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 3, 2, 3, 3, 4, 1, 3, 2, 3, 3, 3, 2, 3, 3,…
$ Q57G     <dbl> 3, 3, 2, 3, 3, 1, 3, 4, 3, 2, 1, 3, 3, 3, 3, 4, 3, 2, 4, 2, 2, 3, 2, 3, 3, 3, 4, 3, 2, 3, 2, 3, 2, 3, 2, 2, 2, 2, 2, 9, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 1, 3, 4, 4, 3, 2, 2, 2, 3, 3, 2, 2, 3, 3, 2, 1, 3, 3, 2, 2, 3, 3, 2, 1, 3, 2, 3, 3, 3, 3, 3, 1, 3, 2, 3, 3, 3, 2, 2, 2,…
$ Q57H     <dbl> 3, 3, 2, 2, 3, 1, 3, 3, 3, 3, 2, 3, 3, 3, 9, 3, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 4, 3, 4, 3, 2, 2, 3, 3, 2, 3, 2, 3, 2, 9, 3, 2, 3, 4, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 1, 3, 1, 4, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 3, 2, 2, 3, 3, 2, 2, 3,…
$ Q57I     <dbl> 3, 3, 2, 2, 3, 3, 3, 4, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 9, 3, 3, 4, 3, 3, 2, 2, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 1, 3, 3, 1, 3, 1, 2, 3, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 2, 3, 2, 2, 2, 3, 2, 3, 3, 3, 3, 3, 2, 2, 3,…
$ Q57J     <dbl> 3, 3, 2, 2, 3, 1, 3, 4, 3, 2, 2, 3, 3, 4, 3, 3, 2, 3, 3, 2, 3, 2, 3, 4, 3, 2, 3, 3, 4, 2, 3, 3, 2, 3, 2, 2, 2, 3, 2, 2, 2, 1, 2, 1, 3, 3, 2, 2, 9, 3, 2, 2, 3, 3, 1, 3, 1, 3, 3, 3, 2, 9, 2, 2, 2, 2, 3, 3, 3, 1, 2, 2, 2, 4, 2, 9, 2, 2, 2, 2, 3, 3, 3, 2, 3, 2, 3, 1, 2, 3, 3, 2, 2, 3,…
$ Q57K     <dbl> 3, 2, 2, 2, 3, 1, 3, 4, 2, 1, 2, 1, 3, 4, 9, 3, 2, 3, 2, 2, 3, 3, 3, 3, 3, 9, 2, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 2, 2, 1, 1, 3, 4, 3, 3, 2, 2, 9, 2, 9, 9, 3, 3, 1, 3, 2, 3, 3, 3, 1, 2, 9, 9, 3, 2, 3, 3, 3, 3, 2, 2, 3, 4, 1, 1, 3, 3, 2, 2, 3, 3, 2, 3, 3, 9, 3, 4, 2, 2, 3, 2, 3, 3,…
$ Q57L     <dbl> 3, 3, 2, 2, 3, 1, 3, 4, 3, 1, 2, 2, 3, 4, 3, 3, 3, 3, 4, 3, 3, 3, 2, 3, 2, 3, 4, 3, 4, 2, 2, 2, 3, 4, 2, 4, 2, 3, 2, 2, 4, 4, 3, 4, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 1, 3, 3, 3, 3, 2, 3, 4, 2, 9, 3, 2, 3, 3, 2, 1, 4, 4, 2, 4, 4, 3, 3, 2, 3, 2, 3, 3, 4, 4, 3, 2, 3, 4, 3, 3, 3, 2, 3, 3,…
$ Q57M     <dbl> 2, 3, 2, 2, 3, 1, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 1, 2, 3, 2, 2, 3, 3, 4, 9, 3, 4, 2, 3, 3, 2, 1, 2, 2, 2, 3, 3, 3, 2, 2, 3, 4, 3, 3, 3, 2, 2, 2, 2, 2, 3, 3, 1, 3, 1, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 3, 3, 2, 3, 2, 2, 2, 1, 2, 2, 3, 2, 2, 2,…
$ Q57N     <dbl> 2, 3, 2, 2, 4, 1, 2, 3, 2, 2, 2, 2, 2, 3, 2, 3, 2, 3, 1, 2, 3, 2, 2, 3, 3, 2, 2, 2, 4, 2, 3, 3, 2, 2, 2, 2, 2, 3, 1, 1, 2, 2, 3, 2, 3, 2, 3, 2, 2, 2, 1, 2, 3, 3, 1, 3, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 3, 3, 2, 1, 2,…
$ Q57O     <dbl> 2, 2, 2, 2, 3, 3, 2, 3, 3, 2, 2, 2, 2, 3, 1, 3, 3, 3, 1, 2, 3, 2, 2, 3, 3, 2, 2, 2, 4, 3, 3, 3, 2, 2, 2, 4, 2, 3, 2, 2, 2, 2, 3, 3, 3, 2, 3, 2, 9, 2, 2, 2, 3, 3, 1, 3, 2, 3, 3, 2, 9, 9, 2, 2, 3, 2, 3, 3, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 3, 3, 2, 2, 3, 2, 3, 3, 2, 2, 3, 3, 9, 3,…
$ Q57P     <dbl> 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 1, 3, 9, 3, 3, 3, 2, 3, 2, 9, 3, 2, 2, 3, 3, 1, 3, 4, 4, 3, 2, 3, 9, 2, 2, 3, 2, 3, 3, 2, 1, 3, 3, 2, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 9, 3,…
$ Q58A     <dbl> 2, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 2, 2, 4, 4, 1, 4, 4, 4, 1, 4, 4, 1, 4, 1, 2, 4, 4, 4, 1, 1, 4, 4, 2, 4, 1, 1, 2, 2, 4, 4, 4, 4, 2, 2, 1, 1, 4, 4, 2, 2, 1, 1, 1, 2, 1, 4, 1, 1, 4, 4, 2, 1, 1, 1, 2, 2, 4, 4, 4, 4, 2, 4, 4, 4, 1, 1, 2, 2, 1, 1, 4, 4, 2, 2, 2, 2, 4, 4, 2, 2, 2, 1,…
$ Q58B     <dbl> 1, 1, 1, 9, 1, 2, 4, 2, 1, 1, 4, 4, 1, 2, 1, 2, 1, 4, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2,…
$ Q58C     <dbl> 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 9,…
$ Q58D     <dbl> 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 2, 1, 3, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 9, 2, 1, 1, 9, 1, 1, 1, 3, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2,…
$ Q58E     <dbl> 4, 2, 2, 3, 3, 2, 2, 2, 2, 2, 4, 3, 1, 2, 3, 2, 4, 4, 2, 2, 3, 2, 4, 1, 1, 1, 4, 2, 4, 2, 1, 1, 2, 4, 2, 1, 3, 3, 2, 2, 4, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 4, 4, 1, 2, 2, 3, 3, 3, 2, 2, 2, 2, 4, 4, 2, 2, 2, 4, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2,…
$ Q58F     <dbl> 4, 4, 2, 1, 2, 1, 4, 2, 4, 4, 4, 3, 2, 2, 2, 2, 4, 4, 4, 4, 3, 4, 4, 4, 4, 1, 4, 2, 3, 2, 1, 1, 4, 4, 1, 1, 3, 3, 2, 2, 4, 4, 2, 2, 2, 2, 3, 3, 9, 4, 2, 2, 4, 4, 1, 2, 2, 3, 1, 3, 4, 4, 2, 2, 4, 4, 2, 2, 2, 4, 4, 4, 2, 2, 4, 4, 1, 1, 3, 2, 1, 1, 4, 2, 3, 2, 2, 2, 1, 4, 2, 2, 1, 2,…
$ Q58G     <dbl> 1, 1, 2, 1, 1, 1, 4, 1, 1, 1, 4, 2, 1, 2, 1, 2, 4, 4, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 9, 2, 2, 3, 9, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 9, 2, 1, 4, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1,…
$ Q58H     <dbl> 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 4, 2, 1, 1, 1, 4, 4, 4, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 9, 2, 2, 3, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 4, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,…
$ Q59A     <dbl> 2, 3, 2, 3, 2, 1, 9, 3, 2, 2, 2, 2, 2, 4, 1, 3, 2, 2, 1, 1, 2, 1, 2, 2, 3, 2, 2, 1, 1, 1, 2, 3, 2, 2, 3, 2, 2, 2, 1, 1, 2, 2, 1, 4, 3, 3, 2, 3, 2, 1, 3, 1, 2, 2, 2, 3, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 3, 1, 2, 1, 2, 1, 2, 3, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2,…
$ Q59B     <dbl> 3, 3, 2, 3, 2, 1, 9, 3, 2, 2, 2, 3, 2, 2, 3, 4, 2, 2, 1, 1, 2, 1, 3, 2, 3, 2, 2, 1, 1, 1, 2, 2, 2, 2, 3, 3, 2, 2, 1, 1, 2, 2, 1, 4, 3, 3, 2, 3, 2, 2, 3, 1, 2, 3, 2, 3, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 3, 1, 1, 1, 2, 3, 2, 2, 1, 2, 1, 2, 3, 2, 2, 2, 2, 9, 1, 2, 1, 2, 2, 2, 1, 1, 2,…
$ Q59C     <dbl> 2, 9, 2, 2, 2, 1, 3, 3, 3, 3, 2, 2, 2, 2, 9, 4, 2, 2, 3, 2, 2, 3, 2, 3, 3, 9, 3, 3, 1, 1, 1, 2, 3, 3, 3, 3, 2, 2, 1, 1, 2, 2, 1, 3, 3, 2, 2, 3, 9, 2, 3, 1, 2, 3, 2, 3, 1, 3, 2, 2, 3, 9, 1, 1, 2, 2, 2, 3, 1, 1, 1, 2, 3, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 3, 1, 1, 2, 2,…
$ Q59D     <dbl> 3, 3, 2, 2, 2, 1, 2, 3, 3, 2, 2, 3, 2, 3, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 1, 1, 2, 2, 3, 3, 3, 3, 2, 2, 1, 1, 2, 2, 1, 3, 3, 2, 2, 3, 9, 3, 3, 1, 2, 3, 2, 3, 1, 3, 2, 2, 3, 2, 1, 1, 2, 2, 2, 3, 1, 1, 1, 2, 3, 3, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 1, 1, 2, 1, 2, 2, 1, 1, 1, 3,…
$ Q59E     <dbl> 2, 2, 2, 3, 2, 2, 3, 3, 2, 4, 2, 3, 2, 4, 3, 3, 2, 3, 1, 3, 2, 1, 2, 2, 3, 2, 1, 1, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 1, 1, 2, 2, 1, 3, 3, 3, 2, 3, 9, 9, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 9, 9, 2, 2, 2, 2, 3, 3, 1, 1, 9, 2, 3, 3, 9, 2, 2, 1, 2, 3, 2, 2, 2, 9, 2, 9, 3, 1, 2, 2, 9, 1, 9, 3,…
$ Q59F     <dbl> 2, 2, 2, 3, 2, 1, 3, 4, 2, 4, 2, 3, 2, 4, 3, 3, 2, 3, 2, 3, 2, 2, 2, 2, 3, 9, 1, 9, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 1, 1, 2, 9, 1, 3, 3, 3, 2, 3, 9, 9, 3, 2, 2, 3, 2, 3, 2, 3, 2, 3, 9, 9, 2, 2, 2, 2, 2, 3, 1, 1, 9, 2, 3, 3, 9, 2, 2, 1, 2, 3, 2, 2, 2, 2, 9, 9, 3, 1, 2, 2, 9, 1, 9, 3,…
$ Q60A     <dbl> 9, 9, 2, 2, 2, 1, 3, 3, 2, 1, 1, 2, 2, 3, 1, 9, 2, 2, 3, 1, 2, 4, 2, 2, 9, 9, 4, 4, 1, 1, 2, 2, 4, 4, 3, 4, 2, 2, 1, 2, 4, 4, 1, 4, 9, 3, 2, 2, 4, 4, 2, 3, 2, 2, 2, 3, 1, 1, 2, 2, 4, 4, 1, 2, 2, 2, 2, 3, 1, 1, 4, 4, 3, 3, 4, 4, 2, 3, 3, 3, 2, 2, 4, 4, 4, 1, 9, 1, 4, 4, 1, 1, 2, 3,…
$ Q60B     <dbl> 9, 9, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 3, 1, 9, 2, 2, 3, 2, 3, 4, 2, 2, 9, 9, 4, 4, 1, 1, 2, 2, 4, 4, 3, 4, 2, 3, 1, 1, 4, 4, 1, 3, 9, 2, 2, 2, 4, 4, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 4, 4, 1, 2, 2, 2, 1, 2, 1, 1, 4, 4, 3, 2, 4, 4, 2, 2, 3, 3, 2, 3, 4, 4, 1, 1, 9, 1, 4, 4, 1, 1, 2, 2,…
$ Q60C     <dbl> 9, 9, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 9, 2, 2, 3, 2, 2, 3, 2, 2, 9, 2, 4, 4, 1, 2, 2, 3, 4, 4, 3, 4, 3, 3, 1, 1, 4, 4, 1, 3, 9, 3, 2, 2, 4, 4, 1, 1, 2, 2, 2, 3, 1, 1, 2, 2, 4, 4, 1, 2, 2, 2, 2, 3, 1, 1, 4, 4, 2, 3, 4, 4, 2, 3, 3, 3, 2, 3, 4, 4, 2, 1, 9, 1, 4, 4, 1, 1, 1, 3,…
$ Q60D     <dbl> 9, 9, 2, 2, 2, 1, 3, 2, 2, 1, 1, 2, 2, 1, 4, 9, 2, 2, 3, 2, 2, 4, 3, 2, 9, 9, 4, 4, 1, 2, 1, 3, 4, 4, 3, 4, 3, 2, 1, 2, 4, 4, 1, 4, 9, 3, 2, 2, 4, 4, 1, 1, 2, 2, 9, 3, 1, 1, 2, 2, 4, 4, 1, 2, 2, 2, 2, 3, 1, 1, 4, 4, 2, 3, 4, 4, 2, 3, 4, 3, 2, 3, 4, 4, 3, 2, 9, 1, 4, 4, 1, 1, 2, 3,…
$ Q60E     <dbl> 9, 9, 2, 2, 2, 2, 3, 2, 2, 2, 1, 2, 2, 3, 9, 9, 2, 2, 3, 2, 2, 3, 3, 2, 9, 9, 4, 3, 1, 2, 1, 3, 4, 4, 3, 4, 2, 2, 1, 1, 4, 4, 1, 3, 9, 3, 2, 2, 4, 4, 1, 1, 2, 2, 9, 3, 1, 1, 2, 2, 4, 4, 1, 2, 2, 2, 2, 3, 1, 1, 4, 4, 3, 3, 4, 4, 2, 3, 3, 3, 2, 3, 4, 4, 2, 2, 9, 1, 4, 4, 1, 1, 9, 3,…
$ Q60F     <dbl> 9, 9, 2, 2, 2, 1, 3, 3, 3, 3, 1, 2, 2, 3, 3, 9, 2, 2, 3, 2, 2, 3, 3, 2, 9, 2, 4, 3, 1, 2, 1, 3, 4, 4, 3, 4, 2, 2, 1, 1, 4, 4, 1, 3, 9, 3, 2, 2, 4, 4, 1, 1, 2, 2, 2, 3, 1, 1, 2, 2, 4, 4, 1, 2, 2, 2, 2, 3, 1, 2, 4, 4, 3, 3, 4, 4, 2, 1, 3, 3, 2, 3, 4, 4, 2, 2, 9, 1, 4, 4, 1, 1, 1, 2,…
$ Q61      <dbl> 1, 9, 3, 3, 4, 4, 2, 1, 4, 1, 3, 2, 2, 4, 1, 3, 3, 3, 4, 3, 2, 4, 3, 3, 2, 9, 4, 4, 3, 2, 2, 3, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 3, 4, 9, 3, 3, 3, 9, 4, 4, 3, 3, 3, 1, 3, 3, 3, 3, 2, 4, 4, 4, 3, 3, 3, 2, 2, 3, 4, 4, 4, 3, 3, 4, 4, 3, 3, 2, 3, 3, 3, 4, 4, 1, 2, 9, 4, 4, 4, 2, 2, 1, 3,…
$ Q62A1    <dbl> 0, 9, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,…
$ Q62A     <dbl> 7, 0, 7, 7, 2, 7, 7, 7, 7, 7, 0, 7, 2, 7, 7, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 2, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 1, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 3, 3, 7, 7,…
$ Q62B     <dbl> 7, 0, 7, 7, 2, 7, 7, 7, 7, 7, 0, 7, 1, 7, 7, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 2, 7, 7,…
$ Q62C     <dbl> 7, 0, 7, 7, 2, 7, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 2, 7, 7,…
$ Q62D     <dbl> 7, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7,…
$ Q62E     <dbl> 7, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 1, 7, 7, 7, 7, 7, 7, 7, 7, 1, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7,…
$ Q62F     <dbl> 7, 0, 7, 7, 0, 7, 7, 7, 7, 7, 0, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 7, 7,…
$ Q63A     <dbl> 9, 9, 3, 3, 3, 2, 9, 9, 9, 9, 3, 3, 2, 1, 9, 9, 3, 3, 9, 3, 3, 9, 3, 3, 9, 3, 9, 9, 1, 9, 3, 3, 9, 9, 2, 2, 3, 2, 9, 9, 9, 9, 9, 3, 9, 3, 3, 3, 9, 9, 4, 9, 3, 3, 9, 3, 1, 1, 3, 3, 9, 9, 9, 9, 2, 3, 2, 3, 9, 9, 1, 9, 9, 2, 9, 9, 3, 3, 3, 3, 3, 3, 9, 3, 9, 9, 9, 3, 9, 9, 1, 9, 9, 3,…
$ Q63B     <dbl> 9, 9, 3, 3, 3, 4, 9, 9, 9, 9, 3, 3, 2, 4, 9, 9, 3, 3, 9, 9, 3, 9, 3, 3, 9, 9, 9, 9, 1, 9, 3, 3, 9, 9, 3, 2, 2, 2, 9, 9, 9, 9, 3, 3, 9, 4, 3, 3, 9, 9, 3, 9, 3, 4, 9, 3, 1, 1, 3, 3, 9, 9, 9, 9, 2, 3, 1, 3, 2, 9, 9, 9, 2, 3, 9, 9, 3, 3, 3, 3, 3, 3, 9, 9, 9, 2, 9, 3, 9, 9, 9, 2, 1, 3,…
$ Q63C     <dbl> 9, 9, 3, 3, 3, 2, 9, 9, 9, 9, 3, 3, 2, 2, 9, 9, 3, 3, 9, 9, 3, 9, 3, 3, 9, 9, 9, 9, 1, 9, 3, 3, 9, 9, 3, 2, 1, 2, 9, 9, 9, 9, 3, 2, 9, 3, 3, 3, 9, 9, 3, 9, 3, 4, 9, 3, 1, 1, 3, 3, 9, 9, 3, 9, 3, 3, 2, 3, 9, 9, 9, 9, 2, 2, 9, 9, 3, 3, 3, 4, 3, 3, 9, 9, 9, 2, 9, 9, 9, 9, 9, 2, 1, 3,…
$ Q63D     <dbl> 9, 9, 3, 3, 3, 1, 9, 9, 9, 9, 3, 3, 2, 1, 9, 9, 3, 3, 9, 9, 3, 9, 3, 3, 9, 9, 9, 9, 1, 9, 3, 3, 9, 9, 3, 2, 1, 2, 9, 9, 9, 9, 3, 4, 9, 3, 3, 3, 9, 9, 3, 9, 3, 4, 9, 3, 1, 1, 3, 3, 9, 9, 3, 9, 3, 3, 2, 3, 9, 9, 9, 9, 9, 4, 9, 9, 3, 3, 3, 3, 3, 3, 9, 9, 9, 9, 9, 9, 9, 9, 9, 2, 2, 2,…
$ Q64A     <dbl> 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 9, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 9, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,…
$ Q64B     <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 9, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,…
$ Q64C     <dbl> 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 9, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,…
$ Q64D     <dbl> 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 9, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1,…
$ Q64E     <dbl> 1, 1, 1, 1, 0, 0, 1, 0, 9, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 9, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 9, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,…
$ Q65      <dbl> 9, 9, 3, 2, 4, 4, 4, 4, 9, 1, 2, 2, 2, 1, 2, 9, 3, 3, 9, 9, 2, 9, 4, 3, 2, 9, 9, 9, 2, 3, 3, 3, 1, 1, 3, 3, 3, 3, 4, 4, 1, 1, 4, 1, 3, 2, 9, 3, 9, 1, 2, 2, 3, 3, 3, 2, 4, 1, 3, 3, 1, 9, 3, 4, 3, 3, 2, 2, 2, 3, 1, 9, 3, 2, 2, 9, 3, 3, 1, 3, 3, 3, 9, 9, 3, 4, 3, 1, 9, 1, 1, 3, 1, 3,…
$ Q66      <dbl> 9, 9, 4, 4, 2, 2, 1, 2, 9, 1, 3, 2, 2, 2, 4, 9, 3, 2, 9, 9, 3, 9, 4, 4, 3, 3, 9, 9, 2, 4, 3, 4, 9, 3, 5, 5, 4, 3, 1, 4, 4, 9, 5, 3, 4, 4, 4, 4, 9, 3, 4, 2, 4, 4, 5, 4, 1, 5, 4, 3, 3, 9, 4, 4, 3, 4, 4, 3, 4, 2, 3, 9, 5, 1, 2, 9, 4, 4, 4, 3, 3, 3, 9, 9, 4, 5, 4, 3, 9, 9, 1, 5, 4, 4,…
$ Q67      <dbl> 9, 9, 0, 0, 0, 3, 0, 0, 9, 3, 0, 1, 0, 2, 3, 9, 0, 1, 9, 9, 1, 9, 1, 1, 0, 0, 9, 9, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 2, 0, 0, 1, 0, 1, 1, 0, 1, 9, 0, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 1, 1, 1, 2, 1, 0, 9, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 9, 9, 2, 0, 1, 1, 9, 9, 0, 1, 3, 1,…
$ Q68      <dbl> 9, 9, 3, 3, 3, 3, 3, 3, 9, 3, 3, 3, 2, 3, 1, 9, 3, 3, 9, 9, 3, 9, 3, 3, 3, 3, 9, 9, 3, 3, 3, 3, 9, 4, 3, 3, 3, 3, 4, 1, 4, 4, 4, 3, 3, 3, 4, 3, 9, 3, 1, 3, 3, 3, 3, 3, 4, 4, 2, 3, 3, 3, 4, 3, 3, 3, 3, 2, 3, 1, 4, 9, 3, 3, 4, 1, 3, 3, 3, 3, 3, 3, 9, 9, 2, 1, 3, 1, 9, 9, 1, 1, 2, 3,…
$ Q69      <dbl> 9, 9, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 9, 3, 3, 9, 9, 3, 9, 3, 3, 3, 2, 9, 9, 3, 4, 3, 3, 9, 2, 3, 1, 3, 3, 3, 4, 9, 9, 4, 4, 3, 2, 3, 3, 9, 1, 2, 2, 3, 3, 3, 4, 1, 1, 2, 2, 1, 1, 4, 4, 3, 3, 3, 3, 4, 4, 1, 9, 1, 3, 1, 5, 2, 2, 2, 3, 3, 3, 9, 9, 1, 1, 3, 3, 9, 9, 1, 4, 2, 2,…
$ Q70A     <dbl> 3, 9, 3, 3, 4, 2, 4, 4, 3, 3, 3, 2, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 9, 3, 4, 2, 3, 3, 3, 2, 1, 1, 2, 2, 4, 3, 2, 2, 4, 4, 2, 4, 3, 2, 9, 2, 2, 3, 3, 2, 1, 4, 4, 4, 2, 2, 2, 2, 3, 3, 3, 2, 3, 2, 4, 4, 3, 2, 4, 2, 3, 2, 3, 3, 4, 2, 3, 3, 2, 3, 4, 2, 3, 3, 2, 3, 4, 3, 3, 2,…
$ Q70B     <dbl> 9, 9, 3, 9, 3, 2, 3, 3, 3, 2, 3, 1, 3, 3, 9, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 4, 9, 3, 3, 3, 3, 3, 2, 3, 4, 4, 3, 3, 3, 3, 2, 2, 3, 3, 3, 4, 3, 2, 9, 3, 2, 3, 3, 2, 9, 4, 2, 1, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 3, 1, 3, 2, 2, 1, 3, 2, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3, 2, 3, 2, 2, 9, 2,…
$ Q70C     <dbl> 9, 9, 3, 2, 3, 3, 3, 2, 3, 2, 2, 3, 2, 3, 9, 9, 2, 4, 3, 2, 3, 2, 2, 3, 9, 3, 2, 2, 3, 3, 3, 2, 2, 3, 4, 4, 3, 3, 2, 3, 2, 3, 4, 3, 3, 3, 3, 2, 9, 9, 2, 3, 3, 2, 3, 4, 2, 2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 3, 2, 1, 1, 2, 3, 9, 3, 2, 3, 3, 1, 3, 3, 3, 2, 2, 3, 2, 3, 2, 2, 3, 4, 2, 2, 3,…
$ Q71      <dbl> 3, 3, 4, 3, 3, 1, 3, 4, 2, 3, 4, 1, 4, 3, 3, 3, 4, 3, 4, 3, 3, 4, 4, -1, 4, 4, 3, 3, 2, 3, 3, 2, 3, 3, 4, 3, 3, 3, 4, 3, 4, 3, 3, 4, 4, 4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 4, 4, 4, 2, 3, 3, 3, 3, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 1, 3, 3, 4, 4, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 1, 3, 4…
$ Q72A     <dbl> 2, 2, 2, 1, 1, 2, 2, 2, 2, 3, 1, 2, 2, 2, 9, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 9, 2, 1, 0, 2, 3, 2, 2, 2, 2, 9, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 9, 3, 1, 1, 9, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 9, 1, 2, 1, 1, 3, 1, 2, 2,…
$ Q72B     <dbl> 2, 2, 2, 1, 1, 1, 2, 2, 3, 3, 1, 2, 2, 2, 9, 3, 2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2, 2, 9, 2, 0, 0, 2, 2, 2, 2, 2, 2, 9, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 9, 9, 2, 2, 1, 1, 9, 0, 0, 0, 9, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0, 0, 1, 9, 1, 2, 0, 1, 9, 1, 2, 2,…
$ Q73A     <dbl> 3, 3, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 1, 0, 3, 3, 3, 0, 3, 3, 3, 3, 9, 0, 3, 3, 3, 0, 3, 3, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 0, 1, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 9, 0, 3, 3, 3, 3, 0, 1,…
$ Q73B     <dbl> 3, 3, 3, 3, 3, 1, 3, 3, 3, 9, 3, 3, 3, 0, 3, 9, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 0, 2, 3, 3, 3, 3, 9, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 0, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 9, 1, 3, 3, 3, 3, 3, 1,…
$ Q73C     <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 1, 9, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 9, 0, 3, 3, 3, 1, 3, 3, 3, 1, 3, 0, 3, 3, 3, 3, 1, 3, 3, 3, 2, 3, 0, 3, 3, 3, 3, 3, 3, 9, 3, 3, 0, 3, 3, 1, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 1, 3, 3, 3, 3, 3, 0,…
$ Q74      <dbl> 1, 9, 3, 2, 2, 2, 3, 2, 9, 9, 1, 2, 3, 4, 1, 9, 2, 3, 1, 1, 3, 1, 3, 2, 2, 1, 1, 1, 2, 2, -1, 3, 1, 1, 3, 3, 3, 3, 2, 1, 1, 1, 1, 1, 3, 2, 2, 3, 9, 1, 2, 2, 3, 3, 2, 3, 1, 1, 2, 3, 1, 1, 2, 2, 3, 3, 2, 3, 2, 4, 1, 1, 1, 2, 1, 1, 3, 3, 4, 2, 1, 3, 1, 1, 2, 2, 2, 2, 1, 1, 9, 2, 1, 2…
$ Q79      <dbl> 100, 104, 990, 100, 108, 101, 109, 100, 101, 104, 104, 101, 100, 100, 100, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 106, 101, 101, 101, 101, 101, 101, 101, 1…
$ Q79OTHER <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "…
$ Q80      <dbl> 3, 3, 7, 3, 3, 4, 3, 3, 3, 3, 4, 3, 3, 2, 9, 2, 3, 2, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 4, 4, 3, 2, 3, 3, 5, 4, 2, 4, 2, 4, 4, 3, 3, 2, 4, 2, 2, 3, 4, 3, 3, 4, 3, 4, 3, 4, 4, 3, 3, 3, 3, 3, 4, 4, 4, 2, 2, 4, 3, 4, 3, 3, 2, 4, 3, 3, 2, 4, 4, 4, 4, 2, 3, 3, 3, 4, 4, 4, 3, 3, 3, 5, 3, 3,…
$ Q81      <dbl> 9, 5, 7, 3, 3, 5, 4, 1, 3, 5, 3, 3, 3, 2, 9, 1, 3, 4, 5, 5, 3, 3, 3, 3, 4, 3, 5, 3, 5, 4, 3, 3, 4, 3, 4, 4, 4, 2, 2, 4, 3, 4, 3, 4, 4, 2, 4, 2, 5, 3, 4, 4, 3, 3, 3, 4, 5, 4, 3, 3, 4, 3, 2, 9, 4, 3, 2, 4, 9, 4, 4, 3, 4, 4, 4, 4, 5, 3, 2, 3, 4, 2, 5, 5, 4, 3, 4, 4, 5, 4, 3, 4, 4, 9,…
$ Q82      <dbl> 0, 0, 7, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 9, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 1, 0, 0, 0, 0, 3, 3, 1, 1, 0, 1, 0, 0, 0, 1, 3, 0, 1, 3, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 2, 2, 0, 0, 1, 1, 2, 1, 3, 2, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 3, 2, 9, 0, 0, 0, 9, 1, 0, 1, 0, 0, 0, 1, 2, 9,…
$ Q83      <dbl> 2, 4, 7, 5, 5, 5, 5, 5, 5, 4, 5, 2, 5, 5, 3, 5, 4, 5, 4, 4, 5, 3, 5, 5, 5, 3, 3, 4, 5, 5, 5, 5, 3, 3, 3, 3, 5, 5, 5, 1, 3, 3, 1, 3, 1, 5, 4, 2, 3, 3, 5, 1, 5, 5, 2, 5, 1, 5, 5, 4, 3, 3, 1, 1, 5, 4, 5, 4, 5, 5, 3, 3, 1, 3, 3, 3, 5, 5, 2, 1, 5, 5, 3, 3, 1, 1, 5, 5, 3, 3, 5, 1, 4, 5,…
$ Q84A     <dbl> 0, 0, 2, 2, 1, 2, 0, 0, 0, 0, 1, 0, 2, 1, 1, 1, 2, 1, 0, 2, 2, 1, 2, 3, 2, 3, 0, 0, 0, 1, 2, 2, 0, 0, 2, 3, 1, 1, 2, 2, 0, 1, 3, 1, 3, 3, 2, 0, 1, 0, 3, 2, 1, 2, 3, 3, 0, 2, 0, 1, 0, 0, 2, 2, 2, 2, 3, 2, 3, 2, 0, 0, 1, 1, 1, 1, 0, 3, 3, 3, 0, 0, 3, 3, 2, 2, 3, 3, 0, 1, 3, 1, 1, 3,…
$ Q84B     <dbl> 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 1, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 0, 1, 2, 1, 3, 0, 0, 3, 2, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1, 0, 3, 1, 0, 1, 0, 0, 1, 1, 1, 3, 3, 0, 2, 0, 1, 0, 0, 3, 2, 1, 1, 1, 1, 2, 3, 0, 0, 1, 1, 0, 1, 0, 2, 3, 3, 0, 0, 1, 2, 1, 1, 2, 2, 0, 0, 2, 1, 0, 1,…
$ Q84C     <dbl> 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 1, 0, 2, 1, 1, 1, 1, 1, 0, 0, 2, 0, 1, 2, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 2, 1, 0, 3, 0, 0, 0, 0, 0, 1, 1, 0, 2, 0, 0, 2, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 2, 3, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 2, 1, 0, 0,…
$ Q85      <dbl> 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,…
$ Q86      <dbl> 100, 997, 997, 100, 106, 997, 997, 100, 997, 997, 997, 100, 997, 997, 997, 997, 115, 100, 997, 997, 100, 100, 100, 997, 997, 100, 997, 100, 997, 997, 115, 100, 997, 997, 103, 103, 103, 103, 103, 997, 997, 103, 103, 997, 997, 100, 103, 103, 997, 997, 103, 103, 103, 103, 103, 997, 1…
$ Q87      <dbl> 0, 0, 4, 0, 0, 0, 3, 0, 2, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 5, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q88A     <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 0, 0, 0, 4, 0, 4, 0, 0, 3, 1, 0, 1, 4, 0, 4, 0, 0, 4, 0, 4, 0, 3, 4, 0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 0, 0, 4, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 4, 1, 0, 4, 3,…
$ Q88B     <dbl> 0, 0, 0, 0, 3, 1, 1, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q88C     <dbl> 0, 0, 0, 0, 3, 1, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q88D     <dbl> 9, 3, 1, 1, 3, 3, 3, 4, 2, 3, 2, 2, 1, 3, 3, 0, 1, 1, 3, 4, 1, 3, 1, 1, 2, 1, 3, 3, 4, 4, 1, 1, 4, 4, 4, 3, 1, 0, 3, 0, 4, 3, 3, 2, 3, 4, 1, 1, 3, 4, 2, 0, 1, 1, 3, 4, 2, 3, 1, 1, 4, 4, 4, 3, 1, 0, 3, 0, 2, 3, 3, 4, 0, 0, 4, 3, 1, 1, 4, 3, 1, 1, 3, 4, 1, 3, 0, 3, 3, 3, 1, 2, 2, 0,…
$ Q88E     <chr> "FON,FRANCAIS", "FRANCAIS,FON,YORUBA", "YORUBA,ADJA", "FON,FRANCAIS", "FON,ADJA,FRANCAIS", "FON,FRANCAIS,DENDI,ADJA", "ADJA,FON,FRANCAIS", "FON,FANCAIS", "FRANCAIS,ANGLAIS,FON,ADJA", "FRANCAIS,FON,YORUBA", "FON,FRANCAIS,ADJA", "ADJA,FON,FRANCAIS", "FON", "FRANCAIS", "FON,FRANCAIS"…
$ Q88F     <dbl> 2, 3, 2, 2, 3, 4, 3, 2, 4, 3, 3, 3, 1, 1, 2, 3, 2, 2, 2, 3, 1, 2, 1, 1, 2, 1, 2, 2, 3, 3, 2, 1, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 3, 1, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 3, 2, 1, 1, 3, 3, 3, 2, 1, 1, 1, 2, 2, 2, 3, 2, 1, 1, 2, 2, 3, 3, 1, 2, 2,…
$ Q89      <dbl> 4, 2, 4, 3, 4, 4, 5, 2, 4, 4, 5, 2, 0, 4, 4, 4, 2, 2, 0, 7, 0, 0, 0, 0, 4, 2, 0, 0, 4, 2, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2, 3, 4, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 4, 4, 0, 4, 4, 4, 4, 5, 0, 2, 0, 0, 2, 0, 5, 0, 0, 0, 4, 2, 2, 4, 0, 3, 0,…
$ Q90      <dbl> 2, 18, 18, 1, 2, 2, 2, 12, 13, 2, 1, 1, 2, 2, 2, 12, 25, 1, 2, 12, 25, 0, 13, 25, 12, 25, 0, 0, 25, 2, 25, 25, 12, 12, 25, 14, 25, 25, 25, 25, 2, 25, 25, 2, 13, 25, 25, 25, 18, 25, 13, 12, 25, 14, 25, 13, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 12, 25, 12, 12, 25, 25, 25, …
$ Q91      <dbl> 4, 1, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 4, 1, 3, 4, 4, -1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4…
$ Q92A     <dbl> 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,…
$ Q92B     <dbl> 1, 1, 1, 9, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,…
$ Q92C     <dbl> 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 9, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,…
$ Q93A     <dbl> 3, 3, 3, 3, 2, 2, 1, 3, 2, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 1, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 2, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3,…
$ Q93B     <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, -1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, -1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, -1,…
$ Q94      <dbl> 0, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 4, 0, 1, 1, 1, 5, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 2, 1, 1, 1, 5, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 4, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,…
$ Q95      <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,…
$ Q96      <dbl> 997, 997, 997, 997, 997, 997, 2, 997, 997, 997, 997, 998, 998, 997, 997, 997, 997, 997, 997, 1, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 1, 2, 997, 3, 997, 997, 2, 997, 997, 997, 5, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, 997, …
$ Q97      <dbl> 100, 100, 100, 100, 100, 102, 998, 100, 999, 100, 101, 100, 100, 100, 999, 100, 100, 100, 100, 999, 100, 100, 100, 100, 999, 100, 100, 100, 100, 998, 100, 100, 100, 106, 100, 103, 103, 103, 100, 100, 100, 103, 100, 100, 999, 100, 103, 103, 999, 103, 999, 103, 100, 103, 103, 100, 1…
$ Q98A     <dbl> 3, 3, 1, 2, 2, 3, 2, 9, -1, -1, -1, -1, -1, -1, -1, 9, 1, 2, 3, 2, 3, 3, 1, 1, 2, 9, 3, 3, 1, 9, 2, 1, 3, 3, 9, 2, 2, 1, 0, 9, 3, 3, 9, 3, 9, 1, 2, 2, 3, 3, 1, 9, 2, 2, 9, 9, 3, -1, -1, -1, -1, -1, -1, -1, 2, 1, 9, 9, 1, 3, 3, 3, 2, 3, 3, 3, 2, 2, 9, 3, 1, 1, 3, 3, 9, 9, 9, 9, 3, …
$ Q98B     <dbl> 3, 3, 1, 2, 2, 1, 2, 9, 3, 3, 1, 1, 1, 3, 9, 3, 1, 2, 3, 2, 3, 3, 1, 1, 9, 9, 3, 3, 3, 9, 2, 1, 3, 3, 9, 1, 2, 1, 9, 9, 3, 3, 9, 3, 9, 9, 2, 2, 3, 3, 9, 9, 2, 2, 9, 9, 3, 1, 2, 2, 3, 3, 9, 9, 2, 1, 9, 9, 1, 3, 3, 3, 2, 3, 3, 3, 2, 2, 9, 3, 1, 1, 3, 3, 9, 9, 9, 9, 3, 3, 0, 9, 3, 9,…
$ Q98C     <dbl> 3, 3, 1, 2, 2, 2, 2, 9, 3, 3, 2, 1, 1, 3, 9, 3, 2, 2, 3, 2, 3, 3, 2, 1, 1, 9, 3, 3, 3, 9, 2, 2, 3, 3, 9, 3, 2, 1, 1, 9, 3, 3, 9, 3, 9, 1, 2, 2, 3, 3, 9, 9, 2, 2, 9, 9, 3, 1, 2, 2, 3, 3, 9, 9, 2, 1, 9, 9, 9, 3, 3, 3, 2, 3, 3, 3, 2, 2, 9, 3, 1, 1, 3, 3, 9, 9, 9, 9, 3, 3, 3, 9, 3, 9,…
$ Q98D     <dbl> 3, 3, 1, 2, 2, 2, 2, 9, 3, 3, 2, 1, 1, 3, 9, 3, 2, 1, 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 3, 9, 2, 2, 3, 3, 9, 3, 2, 1, 9, 9, 3, 3, 9, 3, 9, 3, 2, 2, 3, 3, 2, 9, 2, 2, 3, 9, 3, 2, 2, 2, 3, 3, 9, 9, 2, 2, 9, 9, 1, 3, 3, 3, 9, 3, 3, 3, 2, 2, 9, 3, 1, 2, 3, 3, 9, 9, 9, 9, 3, 3, 3, 9, 3, 9,…
$ Q98E     <dbl> 3, 3, 1, 2, 2, 2, 2, 9, 3, 3, 1, 1, 1, 3, 9, 2, 2, 1, 3, 2, 3, 3, 2, 2, 2, 9, 3, 3, 3, 9, 2, 2, 3, 3, 9, 3, 2, 1, 9, 9, 3, 3, 9, 3, 9, 1, 2, 2, 3, 3, 1, 9, 2, 2, 3, 9, 1, 2, 2, 2, 3, 3, 9, 9, 2, 2, 9, 9, 9, 3, 3, 3, 1, 3, 3, 3, 2, 2, 9, 3, 1, 2, 3, 3, 9, 2, 9, 9, 3, 3, 3, 9, 3, 9,…
$ Q98F     <dbl> 3, 3, 1, 2, 2, 2, 2, 9, 3, 2, 2, 1, 1, 3, 2, 3, 2, 2, 3, 2, 3, 3, 2, 2, 1, 3, 3, 3, 3, 9, 1, 2, 3, 3, 9, 3, 2, 1, 3, 3, 3, 3, 9, 3, 9, 3, 2, 2, 3, 3, 3, 9, 2, 2, 3, 9, 3, 1, 2, 2, 3, 3, 9, 9, 2, 2, 9, 9, 1, 3, 3, 3, 1, 3, 3, 3, 2, 2, 9, 3, 1, 2, 3, 3, 9, 9, 9, 9, 3, 3, 3, 9, 3, 9,…
$ Q98G     <dbl> 3, 2, 1, 2, 2, 0, 2, 9, 3, 2, 2, 1, 1, 3, 1, 9, 2, 2, 3, 2, 3, 3, 2, 2, 1, 9, 3, 3, 3, 9, 1, 2, 3, 3, 9, 1, 2, 1, 9, 9, 3, 3, 9, 1, 9, 2, 2, 2, 3, 3, 2, 9, 2, 2, 9, 9, 3, 2, 1, 2, 3, 3, 9, 9, 2, 2, 9, 9, 9, 1, 3, 3, 1, 2, 3, 3, 2, 2, 9, 3, 1, 2, 3, 3, 9, 9, 9, 9, 3, 3, 1, 9, 3, 9,…
$ Q98H     <dbl> 3, 3, 1, 2, 2, 2, 2, 9, 3, 3, 2, 1, 1, 1, 9, 9, 2, 2, 3, 2, 3, 3, 2, 2, 2, 9, 3, 3, 3, 9, 1, 2, 3, 3, 9, 3, 2, 1, 9, 9, 3, 3, 9, 3, 9, 3, 2, 2, 3, 3, 2, 9, 2, 2, 9, 9, 3, 9, 1, 2, 3, 3, 9, 9, 2, 2, 9, 9, 9, 3, 3, 3, 1, 3, 3, 3, 2, 2, 9, 3, 1, 2, 3, 3, 9, 9, 9, 9, 3, 3, 3, 9, 3, 9,…
$ Q98I     <dbl> 3, 3, 1, 2, 2, 0, 2, 9, 3, 3, 2, 1, 1, 3, 9, 3, 2, 2, 3, 2, 3, 3, 2, 2, 2, 9, 3, 3, 3, 9, 2, 2, 3, 3, 9, 3, 2, 1, 9, 9, 3, 3, 9, 3, 9, 3, 2, 2, 3, 3, 2, 9, 2, 2, 9, 9, 3, 3, 1, 2, 3, 3, 9, 9, 2, 2, 9, 9, 9, 3, 3, 3, 2, 3, 3, 3, 2, 2, 9, 3, 1, 2, 3, 3, 9, 2, 9, 9, 3, 3, 3, 9, 3, 9,…
$ Q98J     <dbl> 3, 3, 1, 2, 2, 0, 2, 9, 3, 3, 2, 1, 1, 3, 9, 1, 2, 2, 3, 2, 3, 3, 2, 2, 3, 9, 3, 3, 3, 9, 2, 2, 3, 3, 9, 1, 2, 1, 3, 3, 3, 3, 9, 3, 9, 3, 2, 2, 3, 3, 2, 9, 2, 2, 9, 9, 3, 3, 1, 2, 3, 3, 9, 9, 2, 2, 9, 9, 2, 1, 3, 3, 2, 3, 3, 3, 2, 2, 9, 3, 1, 2, 3, 3, 9, 2, 9, 9, 3, 3, 3, 9, 3, 9,…
$ Q98J1    <dbl> 3, 3, 1, 2, 2, 0, 2, 9, 3, 3, 1, 1, 1, 3, 2, 3, 2, 2, 3, 2, 3, 3, 2, 2, 2, 9, 3, 3, 3, 9, 2, 2, 3, 3, 9, 1, 2, 1, 3, 9, 3, 3, 9, 3, 9, 3, 2, 2, 3, 3, 2, 9, 2, 2, 9, 9, 3, 2, 1, 2, 3, 3, 9, 9, 2, 2, 9, 9, 2, 1, 3, 3, 2, 3, 3, 3, 2, 2, 9, 3, 1, 2, 3, 3, 9, 2, 9, 9, 3, 3, 3, 9, 3, 9,…
$ Q98K     <dbl> 1, 1, 3, 9, 2, 3, 2, 9, 1, 1, 1, 3, 9, 3, 9, 9, 3, 3, 1, 1, 9, 9, 2, 3, 9, 9, 1, 1, 3, 9, 9, 9, 2, 1, 9, 2, 9, 3, 9, 9, 1, 1, 9, 1, 9, 9, 9, 3, 9, 1, 9, 9, 9, 9, 9, 9, 9, 2, 1, 3, 1, 9, 9, 9, 9, 9, 9, 9, 9, 1, 1, 1, 9, 1, 1, 1, 3, 3, 9, 9, 9, 3, 1, 1, 9, 9, 9, 9, 1, 1, 1, 9, 9, 9,…
$ Q99A     <dbl> 2, 2, 3, 2, 3, 3, 2, 9, 2, 4, 2, 2, 3, 2, 9, 4, 3, 3, 9, 5, 3, 9, 3, 3, 2, 9, 9, 9, 3, 9, 3, 2, 9, 9, 9, 2, 2, 3, 9, 2, 9, 9, 2, 1, 2, 2, 2, 2, 9, 9, 3, 9, 3, 3, 9, 2, 2, 3, 3, 3, 2, 9, 9, 9, 3, 4, 9, 9, 9, 2, 4, 9, 9, 3, 2, 9, 3, 2, 3, 3, 3, 3, 9, 3, 9, 9, 9, 9, 9, 2, 1, 3, 4, 2,…
$ Q99B     <dbl> 2, 1, 3, 2, 3, 3, 2, 9, 2, 4, 1, 2, 4, 2, 9, 1, 2, 3, 9, 5, 3, 9, 2, 3, 4, 9, 9, 9, 4, 3, 3, 2, 9, 9, 9, 2, 2, 2, 9, 9, 9, 9, 9, 1, 2, 2, 2, 2, 9, 9, 2, 9, 3, 3, 9, 2, 3, 3, 3, 3, 2, 9, 9, 9, 3, 4, 9, 9, 9, 1, 4, 9, 2, 3, 2, 9, 3, 3, 3, 3, 3, 3, 9, 3, 9, 9, 9, 9, 9, 2, 1, 3, 4, 2,…
$ Q99C     <dbl> 1, 2, 3, 2, 3, 2, 2, 9, 2, 3, 1, 2, 4, 2, 2, 2, 2, 3, 9, 4, 3, 9, 2, 3, 3, 9, 9, 9, 3, 2, 3, 2, 9, 9, 9, 4, 1, 1, 9, 2, 9, 9, 2, 1, 9, 4, 2, 2, 9, 9, 2, 9, 3, 3, 9, 3, 2, 4, 3, 3, 2, 9, 9, 9, 3, 4, 9, 9, 2, 1, 3, 9, 1, 3, 2, 9, 3, 3, 3, 3, 3, 3, 9, 3, 9, 2, 9, 9, 9, 2, 1, 2, 4, 2,…
$ Q100     <dbl> 19, 17, 1, 1, 20, 17, 1, 1, 19, 19, 17, 1, 17, 2, 19, 19, 1, 1, 1, 19, 1, 1, 1, 999, 7, 1, 19, 1, 1, 1, 1, 1, 19, 19, 1, 1, 1, 17, 1, 1, 1, 1, 1, 17, 1, 5, 999, 1, 19, 19, 1, 999, 1, 1, 1, 23, 5, 1, 1, 1, 19, 19, 1, 1, 1, 1, 1, 18, 1, 17, 19, 19, 17, 1, 19, 19, 23, 1, 999, 999, 1,…
$ ENDTIME  <time> 19:30:00, 20:40:00, 19:10:00, 18:17:00, 18:25:00, 19:35:00, 19:50:00, 20:40:00, 17:45:00, 15:47:00, 15:16:00, 16:29:00, 16:50:00, 15:18:00, 15:55:00, 18:07:00, 12:26:00, 13:41:00, 13:42:00, 12:25:00, 13:40:00, 12:25:00, 13:42:00, 12:25:00, 18:40:00, 19:36:00, 19:22:00, 18:25:00, …
$ LENGTH   <dbl> 60, 60, 40, 57, 52, 62, 74, 42, 76, 87, 46, 56, 75, 63, 101, 123, 54, 61, 65, 40, 60, 45, 66, 45, 42, 51, 47, 45, 48, 33, 54, 27, 48, 44, 52, 45, 51, 58, 38, 52, 52, 52, 44, 59, 60, 49, 46, 51, 74, 63, 56, 43, 54, 42, 38, 48, 63, 49, 47, 43, 80, 63, 52, 57, 47, 35, 44, 49, 65, 60,…
$ Q101     <dbl> 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2,…
$ Q102     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q103     <dbl> 100, 2, 2, 100, 2, 2, 2, 2, 2, 2, 2, 100, 100, 2, 2, 101, 101, 101, 101, 101, 101, 101, 101, 101, 2, 101, 101, 101, 2, 100, 101, 101, 101, 101, 101, 101, 101, 101, 100, 100, 101, 101, 101, 2, 101, 101, 101, 101, 100, 101, 100, 100, 101, 101, 101, 101, 101, 2, 101, 101, 101, 101, 1…
$ Q104     <dbl> 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 5, 1, 1, 1, 1, 4, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q105A    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q105B    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q105C    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q105D    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q105E    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Q106     <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 2, 1, 0, 0, 1, 1, 0, 1,…
$ Q107A    <chr> "000", "000", "000", "000", "000", "000", "000", "000", "0", "0", "0", "0", "0", "0", "0", "Q42", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "00", "000", "000", "000", "000", "000", "000", "000", "000", "0…
$ Q107B    <chr> "000", "000", "000", "000", "000", "000", "000", "000", "0", "0", "0", "000", "0", "0", "0", "Q50", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000",…
$ Q107C    <chr> "000", "000", "000", "000", "000", "000", "000", "000", "0", "0", "0", "000", "0", "0", "0", "Q08", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000", "000",…
$ Q108A    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q108B    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q108C    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q108D    <dbl> 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q108E    <dbl> 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q108F    <dbl> 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q110     <chr> "BEN10", "BEN10", "BEN12", "BEN12", "BEN11", "BEN11", "BEN09", "BEN09", "BEN10", "BEN10", "BEN12", "BEN12", "BEN11", "BEN11", "BEN09", "BEN09", "BEN12", "BEN12", "BEN10", "BEN10", "BEN11", "BEN11", "BEN09", "BEN09", "BEN09", "BEN09", "BEN10", "BEN10", "BEN11", "BEN11", "BEN12", "B…
$ Q111     <dbl> 34, 34, 30, 30, 26, 26, 26, 26, 34, 34, 30, 30, 26, 26, 26, 26, 30, 30, 34, 34, 26, 26, 26, 26, 26, 26, 34, 34, 26, 26, 30, 30, 34, 34, 26, 26, 30, 30, 26, 26, 34, 34, 26, 26, 26, 26, 30, 30, 34, 34, 26, 26, 30, 30, 26, 26, 26, 26, 30, 30, 34, 34, 26, 26, 30, 30, 26, 26, 26, 26, 3…
$ Q112     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Q113     <dbl> 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2,…
$ Q114     <dbl> 101, 101, 101, 101, 2, 2, 2, 2, 101, 101, 101, 101, 2, 2, 2, 2, 101, 101, 101, 101, 2, 2, 2, 2, 2, 2, 101, 101, 2, 2, 101, 101, 101, 101, 2, 2, 101, 101, 2, 2, 101, 101, 2, 2, 2, 2, 101, 101, 101, 101, 2, 2, 101, 101, 2, 2, 2, 2, 101, 101, 101, 101, 2, 2, 101, 101, 2, 2, 2, 2, 101…
$ Q115     <dbl> 7, 7, 8, 8, 8, 8, 7, 7, 7, 7, 8, 8, 8, 8, 7, 7, 8, 8, 7, 7, 8, 8, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 7, 7, 7, 7, 8, 8, 8, 8, 7, 7, 8, 8, 7, 7, 8, 8, 7, 7, 8, 8, 8, 8, 7, 7, 7, 7, 8, 8, 7, 7, 8, 8, 8, 8, 7, 7, 8, 8, 7, 7, 8, 8, 7, 7, 8, 8, 7, 7, 8, 8, 7, 7, 8, 8, 7, 7, 7, 7, 8, 8, 7, 7,…
$ Withinwt <dbl> 1.413, 1.413, 1.413, 1.413, 1.413, 1.413, 1.413, 1.413, 1.060, 1.060, 1.060, 1.060, 1.060, 1.060, 1.060, 1.060, 1.181, 1.181, 1.181, 1.181, 1.181, 1.181, 1.181, 1.181, 0.953, 0.953, 0.953, 0.953, 0.953, 0.953, 0.953, 0.953, 0.766, 0.766, 0.766, 0.766, 0.766, 0.766, 0.766, 0.766, 1…
$ Acrosswt <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Combinwt <dbl> 1.413, 1.413, 1.413, 1.413, 1.413, 1.413, 1.413, 1.413, 1.060, 1.060, 1.060, 1.060, 1.060, 1.060, 1.060, 1.060, 1.181, 1.181, 1.181, 1.181, 1.181, 1.181, 1.181, 1.181, 0.953, 0.953, 0.953, 0.953, 0.953, 0.953, 0.953, 0.953, 0.766, 0.766, 0.766, 0.766, 0.766, 0.766, 0.766, 0.766, 1…

This is a fairly large dataset. While it doesn’t matter for computing time, sometimes you way want to trim datasets down a bit. From the codebook at http://afrobarometer.org/sites/default/files/data/round-4/merged_r4_codebook3.pdf, I determine that I’ll be interested only in the following variables:

• COUNTRY
• URBRUR: Urban or Rural Primary Sampling Unit
• Q42A: In your opinion how much of a democracy is [Ghana/Kenya/etc.]? today?
• Q89: What is the highest level of education you have completed?
• Q101: Respondent’s gender
• Q1: Respondent’s age

So we use the select() function to create a new object, ab.small, that contains only these six variables.

ab.small <- select(ab, COUNTRY, URBRUR, Q42A, Q89, Q101, Q1)

Here, we can also introduce the “pipe” symbol |>. The pipe was recently added to base-R, but has been around for a while. Its main purpose is to make long, complex operations easier to read:

x |> mean() |> round() |> print()

can be read as: Start with x, then mean(), then round(), then print(). You will see the pipe used a lot in most tidyverse documentation. You can set a shortcut for the pipe under Tools \(\rightarrow\) Global Options \(\rightarrow\) Code.

ab.small <- ab.small |> 
            select(COUNTRY, URBRUR, Q42A, Q89, Q101, Q1)

Test yourself!

How would you create the same dataset, but using square brackets?

The resulting dataset now only has the six desired variables:

dim(ab.small)

[1] 27713     6

str(ab.small)

'data.frame':   27713 obs. of  6 variables:
 $ COUNTRY: num  1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "label")= chr "Country"
  ..- attr(*, "format.spss")= chr "F4.0"
  ..- attr(*, "labels")= Named num [1:20] 1 2 3 4 5 6 7 8 9 10 ...
  .. ..- attr(*, "names")= chr [1:20] "Benin" "Botswana" "Burkina Faso" "Cape Verde" ...
 $ URBRUR : num  1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "label")= chr "Urban or Rural Primary Sampling Unit"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:2] 1 2
  .. ..- attr(*, "names")= chr [1:2] "Urban" "Rural"
 $ Q42A   : num  4 4 4 3 2 3 2 3 3 3 ...
  ..- attr(*, "label")= chr "Q42a. Extent of democracy"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:8] -1 1 2 3 4 8 9 998
  .. ..- attr(*, "names")= chr [1:8] "Missing" "Not a democracy" "A democracy, with major problems" "A democracy, but with minor problems" ...
 $ Q89    : num  4 2 4 3 4 4 5 2 4 4 ...
  ..- attr(*, "label")= chr "Q89. Education of respondent"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:13] -1 0 1 2 3 4 5 6 7 8 ...
  .. ..- attr(*, "names")= chr [1:13] "Missing" "No formal schooling" "Informal schooling only" "Some primary schooling" ...
 $ Q101   : num  2 1 2 1 2 1 2 1 1 2 ...
  ..- attr(*, "label")= chr "Q101. Gender of respondent"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:3] -1 1 2
  .. ..- attr(*, "names")= chr [1:3] "Missing" "Male" "Female"
 $ Q1     : num  38 46 28 30 23 24 40 50 24 36 ...
  ..- attr(*, "label")= chr "Q1. Age"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:3] -1 998 999
  .. ..- attr(*, "names")= chr [1:3] "Missing" "Refused" "Don't know"

summary(ab.small)

    COUNTRY         URBRUR          Q42A            Q89             Q101           Q1       
 Min.   : 1.0   Min.   :1.00   Min.   :-1.00   Min.   :-1.00   Min.   :1.0   Min.   : -1.0  
 1st Qu.: 6.0   1st Qu.:1.00   1st Qu.: 2.00   1st Qu.: 2.00   1st Qu.:1.0   1st Qu.: 25.0  
 Median :12.0   Median :2.00   Median : 3.00   Median : 3.00   Median :2.0   Median : 33.0  
 Mean   :11.2   Mean   :1.62   Mean   : 3.45   Mean   : 3.27   Mean   :1.5   Mean   : 47.7  
 3rd Qu.:16.0   3rd Qu.:2.00   3rd Qu.: 4.00   3rd Qu.: 5.00   3rd Qu.:2.0   3rd Qu.: 45.0  
 Max.   :20.0   Max.   :2.00   Max.   : 9.00   Max.   :99.00   Max.   :2.0   Max.   :999.0  

Now that we’ve read the dataset into R, we can process it for further analysis - which we’ll do further below in this tutorial.

Sidenote: Dealing with value labels in SPSS files

Datasets in SPSS format often contain variables with value labels. You can see this above in the output following the str(ab.small) command. Value labels can be useful to help you quickly identify the meaning of different codes without revisiting the codebook, e.g. that with Q101, 1 stands for Male and 2 for female. In many situations, this makes your life easier.

There are a few ways to display and use labels instead of numbers in R. One of them is through the sjlabelled package. The vignettes (1, 2) offer more detail, but here is the gist:

library("sjlabelled")
table(ab.small$Q42A)

  -1    1    2    3    4    8    9 
  15 1875 7338 8249 7310 1202 1724 

table(as_label(ab.small$Q42A))

                             Missing                      Not a democracy     A democracy, with major problems A democracy, but with minor problems                     A full democracy Do not understand question/democracy                           Don't know                              Refused 
                                  15                                 1875                                 7338                                 8249                                 7310                                 1202                                 1724                                    0 

What do the numbers under the country names tell you?

Example: importing a Stata dataset

For this example, we use the European Social Survey, an academically driven cross-national survey that has been conducted every two years across Europe since 2001. You can find more information on the ESS at http://www.europeansocialsurvey.org/ (under Data and Documentation > Round 6) after you register on the site to access data and codebooks. Let’s download the Stata version of the 2012 round of the ESS, called “ESS6e02_1.dta”, and use the import() function again to read the dataset into R.

ess <- import(file = "ESS6e02_1.dta")

Before actually looking at the dataset itself, you should look at its dimensions.

dim(ess)

[1] 54673   626

This is again a fairly large dataset, so let’s trim it down a bit. From the variable list at http://www.europeansocialsurvey.org/docs/round6/survey/ESS6_appendix_a7_e02_1.pdf, I decide that I’ll be interested only in the following variables:

• cntry: Country
• trstlgl: Trust in the legal system, 0 means you do not trust an institution at all, and 10 means you have complete trust.
• lrscale: Placement on left right scale, where 0 means the left and 10 means the right
• fairelc: How important R thinks it is for democracy in general that national elections are free and fair
• yrbrn: Year of birth
• gndr: Gender
• hinctnta: Household’s total net income, all sources

Again, we use R’s indexing structure to create a new object, ess.small, that contains only these seven variables.

ess.small <- ess |> 
             select(cntry, trstlgl, lrscale, fairelc, yrbrn, gndr, hinctnta)
dim(ess.small)

[1] 54673     7

str(ess.small)

'data.frame':   54673 obs. of  7 variables:
 $ cntry   : chr  "AL" "AL" "AL" "AL" ...
  ..- attr(*, "label")= chr "Country"
  ..- attr(*, "format.stata")= chr "%2s"
 $ trstlgl : num  0 0 2 7 6 5 10 9 7 0 ...
  ..- attr(*, "label")= chr "Trust in the legal system"
  ..- attr(*, "format.stata")= chr "%10.0g"
  ..- attr(*, "labels")= Named num [1:14] 0 1 2 3 4 5 6 7 8 9 ...
  .. ..- attr(*, "names")= chr [1:14] "No trust at all" "1" "2" "3" ...
 $ lrscale : num  0 88 5 5 10 5 1 5 5 0 ...
  ..- attr(*, "label")= chr "Placement on left right scale"
  ..- attr(*, "format.stata")= chr "%10.0g"
  ..- attr(*, "labels")= Named num [1:14] 0 1 2 3 4 5 6 7 8 9 ...
  .. ..- attr(*, "names")= chr [1:14] "Left" "1" "2" "3" ...
 $ fairelc : num  10 10 10 88 10 10 10 10 10 10 ...
  ..- attr(*, "label")= chr "National elections are free and fair"
  ..- attr(*, "format.stata")= chr "%10.0g"
  ..- attr(*, "labels")= Named num [1:14] 0 1 2 3 4 5 6 7 8 9 ...
  .. ..- attr(*, "names")= chr [1:14] "Not at all important for democracy in general" "1" "2" "3" ...
 $ yrbrn   : num  1949 1983 1946 9999 1953 ...
  ..- attr(*, "label")= chr "Year of birth"
  ..- attr(*, "format.stata")= chr "%10.0g"
  ..- attr(*, "labels")= Named num [1:3] 7777 8888 9999
  .. ..- attr(*, "names")= chr [1:3] "Refusal" "Don't know" "No answer"
 $ gndr    : num  1 2 2 1 1 1 2 2 2 2 ...
  ..- attr(*, "label")= chr "Gender"
  ..- attr(*, "format.stata")= chr "%10.0g"
  ..- attr(*, "labels")= Named num [1:3] 1 2 9
  .. ..- attr(*, "names")= chr [1:3] "Male" "Female" "No answer"
 $ hinctnta: num  5 2 2 99 2 2 1 2 4 1 ...
  ..- attr(*, "label")= chr "Household's total net income, all sources"
  ..- attr(*, "format.stata")= chr "%10.0g"
  ..- attr(*, "labels")= Named num [1:13] 1 2 3 4 5 6 7 8 9 10 ...
  .. ..- attr(*, "names")= chr [1:13] "J - 1st decile" "R - 2nd decile" "C - 3rd decile" "M - 4th decile" ...

summary(ess.small)

    cntry              trstlgl         lrscale        fairelc         yrbrn           gndr         hinctnta   
 Length:54673       Min.   : 0.00   Min.   : 0.0   Min.   : 0.0   Min.   :1909   Min.   :1.00   Min.   : 1.0  
 Class :character   1st Qu.: 3.00   1st Qu.: 4.0   1st Qu.: 8.0   1st Qu.:1950   1st Qu.:1.00   1st Qu.: 3.0  
 Mode  :character   Median : 5.00   Median : 5.0   Median :10.0   Median :1964   Median :2.00   Median : 6.0  
                    Mean   : 7.05   Mean   :17.5   Mean   :10.9   Mean   :1981   Mean   :1.55   Mean   :20.1  
                    3rd Qu.: 7.00   3rd Qu.: 8.0   3rd Qu.:10.0   3rd Qu.:1980   3rd Qu.:2.00   3rd Qu.:10.0  
                    Max.   :99.00   Max.   :99.0   Max.   :99.0   Max.   :9999   Max.   :9.00   Max.   :99.0  

table(ess.small$hinctnta)

   1    2    3    4    5    6    7    8    9   10   77   88   99 
5063 5492 5011 4888 4538 4324 4147 3829 3262 3427 6211 4342  139 

Now that we’ve read the dataset into R, we can process it for further analysis. We won’t revisit the ESS data in this tutorial.

Sidenote: Dealing with value labels in Stata files

Datasets in Stata format often contain variables with value labels. You can see this above in the output following the str(ess.small) command. Value labels can be useful to help you quickly identify the meaning of different codes without revisiting the codebook, e.g. that with gndr, 1 stands for Male and 2 for female. In many situations, this makes your life easier.

Just like with SPSS above, the as_label() function is handy to print value labels in tables or graphs. Just wrap as_label() around your variable of interest where needed.

table(as_label(ess.small$hinctnta))

 J - 1st decile  R - 2nd decile  C - 3rd decile  M - 4th decile  F - 5th decile  S - 6th decile  K - 7th decile  P - 8th decile  D - 9th decile H - 10th decile         Refusal      Don't know       No answer 
           5063            5492            5011            4888            4538            4324            4147            3829            3262            3427            6211            4342             139 

Data cleaning

Before any analysis, you will often, if not always, need to process data that you obtained from elsewhere or that you collected yourself. In this section, we’ll go over some typical scenarios for this.

Often, you need to make sure that the variables have the correct numerical or character values. Different data sources often use different codes for missing values, for instance -99, -9999, ., or NA. Let’s work through this with the ab.small dataset. First, I check the structure of the dataset:

str(ab.small)

'data.frame':   27713 obs. of  6 variables:
 $ COUNTRY: num  1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "label")= chr "Country"
  ..- attr(*, "format.spss")= chr "F4.0"
  ..- attr(*, "labels")= Named num [1:20] 1 2 3 4 5 6 7 8 9 10 ...
  .. ..- attr(*, "names")= chr [1:20] "Benin" "Botswana" "Burkina Faso" "Cape Verde" ...
 $ URBRUR : num  1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "label")= chr "Urban or Rural Primary Sampling Unit"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:2] 1 2
  .. ..- attr(*, "names")= chr [1:2] "Urban" "Rural"
 $ Q42A   : num  4 4 4 3 2 3 2 3 3 3 ...
  ..- attr(*, "label")= chr "Q42a. Extent of democracy"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:8] -1 1 2 3 4 8 9 998
  .. ..- attr(*, "names")= chr [1:8] "Missing" "Not a democracy" "A democracy, with major problems" "A democracy, but with minor problems" ...
 $ Q89    : num  4 2 4 3 4 4 5 2 4 4 ...
  ..- attr(*, "label")= chr "Q89. Education of respondent"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:13] -1 0 1 2 3 4 5 6 7 8 ...
  .. ..- attr(*, "names")= chr [1:13] "Missing" "No formal schooling" "Informal schooling only" "Some primary schooling" ...
 $ Q101   : num  2 1 2 1 2 1 2 1 1 2 ...
  ..- attr(*, "label")= chr "Q101. Gender of respondent"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:3] -1 1 2
  .. ..- attr(*, "names")= chr [1:3] "Missing" "Male" "Female"
 $ Q1     : num  38 46 28 30 23 24 40 50 24 36 ...
  ..- attr(*, "label")= chr "Q1. Age"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:3] -1 998 999
  .. ..- attr(*, "names")= chr [1:3] "Missing" "Refused" "Don't know"

It looks like we’re dealing with all numeric variables here. This is good, but you want to make sure that codes for missing observations, or otherwise non-numeric values, are discarded appropriately. The most fail-safe way to do this is to convert the variable into a factor manually and to assign value labels as you find them in the codebook.

Screenshot from the codebook of the Afrobarometer Round 4 survey.

Here, I can already conclude that I only wish to keep the values of this variable that indicate a substantive response, as missing values or refusals to answer won’t be part of my statistical model. Therefore, I create a new variable perceivedDem that recodes all values to missing (NA in R) that are not 1, 2, 3, or 4.

ab.small$perceivedDem <- ifelse(ab.small$Q42A < 1 | ab.small$Q42A > 4, 
                                NA, 
                                ab.small$Q42A)
table(ab.small$perceivedDem)

   1    2    3    4 
1875 7338 8249 7310 

table(is.na(ab.small$perceivedDem))

FALSE  TRUE 
24772  2941 

The ifelse() function has three main arguments:

1. The condition: ab.small$Q42A < 1 | ab.small$Q42A > 4. Here, | stands for “or”.
2. The replacement if the condition is met. Here, NA means: replace with NA.
3. The replacement if the condition is not met. Here, ab.small$Q42A means replace with current values of ab.small$Q42A.

Next, I want to assign the relevant values to the four levels of the newly created variables perceivedDem, so we can create a second version of perceivedDem as a factor and assign these levels:

ab.small$perceivedDem_factor <- factor(ab.small$perceivedDem, 
                                       levels = c(1, 2, 3, 4),
                                       labels = c("Not a democracy", 
                                                  "A democracy, with major problems",
                                                  "A democracy, but with minor problems",
                                                  "A full democracy"))
table(ab.small$perceivedDem_factor)

                     Not a democracy     A democracy, with major problems A democracy, but with minor problems                     A full democracy 
                                1875                                 7338                                 8249                                 7310 

Lastly, let’s compare our new variable with the original one to make sure we didn’t mix up labels:

table(ab.small$perceivedDem_factor, ab.small$Q42A)

                                      
                                         -1    1    2    3    4    8    9
  Not a democracy                         0 1875    0    0    0    0    0
  A democracy, with major problems        0    0 7338    0    0    0    0
  A democracy, but with minor problems    0    0    0 8249    0    0    0
  A full democracy                        0    0    0    0 7310    0    0

Now let’s make the education variable an ordinal numerical variable. First, let’s have a look at the variable in its current form:

Screenshot from the codebook of the Afrobarometer Round 4 survey.

table(ab.small$Q89)

  -1    0    1    2    3    4    5    6    7    8    9   99 
  10 4365 1260 5111 3897 5950 4165 1674  649  506   92   34 

We should first recode “Missing”, “Don’t know”, and “Refused” to NA. Again, we create a new variable, education.

ab.small$education <- ifelse(ab.small$Q89 == 99 | 
                                  ab.small$Q89 == 998 | 
                                  ab.small$Q89 == -1,
                                  NA, ab.small$Q89)
table(ab.small$education)

   0    1    2    3    4    5    6    7    8    9 
4365 1260 5111 3897 5950 4165 1674  649  506   92 

We now have numeric values. If we wanted to put labels on the variable, we can use the factor function again. This time, instead of typing the value labels manually, I use the get_labels() function from the sjlabelled package, and pick only those labels corresponding to substantive answers to the question. Here, these are the labels after the first (“missing”) and before the last two (“don’t know” and “refused”).

get_labels(ab.small$Q89)

 [1] "Missing"                                       "No formal schooling"                           "Informal schooling only"                       "Some primary schooling"                        "Primary school completed"                      "Some secondary school/high school"            
 [7] "Secondary school completed/high school"        "Post-secondary qualifications, not university" "Some university"                               "University completed"                          "Post-graduate"                                 "Don't know"                                   
[13] "Refused"                                      

ab.small$education_factor <- factor(ab.small$education,
                                    levels = c(0:9),
                                    labels = get_labels(ab.small$Q89)[2:11])
table(ab.small$education_factor)

                          No formal schooling                       Informal schooling only                        Some primary schooling                      Primary school completed             Some secondary school/high school        Secondary school completed/high school 
                                         4365                                          1260                                          5111                                          3897                                          5950                                          4165 
Post-secondary qualifications, not university                               Some university                          University completed                                 Post-graduate 
                                         1674                                           649                                           506                                            92 

Again, a quick comparison with the original variable:

table(ab.small$education_factor, ab.small$Q89)

                                               
                                                  -1    0    1    2    3    4    5    6    7    8    9   99
  No formal schooling                              0 4365    0    0    0    0    0    0    0    0    0    0
  Informal schooling only                          0    0 1260    0    0    0    0    0    0    0    0    0
  Some primary schooling                           0    0    0 5111    0    0    0    0    0    0    0    0
  Primary school completed                         0    0    0    0 3897    0    0    0    0    0    0    0
  Some secondary school/high school                0    0    0    0    0 5950    0    0    0    0    0    0
  Secondary school completed/high school           0    0    0    0    0    0 4165    0    0    0    0    0
  Post-secondary qualifications, not university    0    0    0    0    0    0    0 1674    0    0    0    0
  Some university                                  0    0    0    0    0    0    0    0  649    0    0    0
  University completed                             0    0    0    0    0    0    0    0    0  506    0    0
  Post-graduate                                    0    0    0    0    0    0    0    0    0    0   92    0

Next, let’s check the Q101 variable for gender:

table(ab.small$Q101)

    1     2 
13837 13876 

I want this variable to be numerical so that males are 0 and females 1. Here, this is easy because males are currently 1 and females coded as 2.

ab.small$female <- ab.small$Q10 - 1
table(ab.small$female)

    0     1 
13837 13876 

ab.small$female_factor <- factor(ab.small$female, 
                                       levels = c(0, 1),
                                       labels = c("Male", "Female"))

Lastly, let’s check the age variable.

table(ab.small$Q1)

  -1   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48   49   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64   65   66   67   68   69   70   71   72   73   74   75   76 
   6  852  944 1066  778  948  908  886 1147  837  829 1012  789 1149  524  922  590  534  873  581  479  694  463  818  341  578  410  350  607  346  297  479  297  536  233  311  248  245  242  266  189  229  162  328  134  147  144  109  156  114   95  146   77  154   90   91   66   64   82   61 
  77   78   79   80   81   82   83   84   85   86   87   88   89   90   91   92   93   94   95   97   98   99  100  110  998  999 
  37   58   36   59   17   22   20   11   18    5   15    8    5    6    3    2    1    1    2    2    1    2    1    1    8  319 

We can see that the values 999, 998, and -1 don’t seem to correspond to realistic ages. A quick look at the codebook reveals the following:

Screenshot from the codebook of the Afrobarometer Round 4 survey.

These values correspond to “Don’t know”, “Refused”, and “Missing”. So let’s recode those again to NA as we did with the democracy variable above:

ab.small$age <- ifelse(ab.small$Q1 == 999 | 
                    ab.small$Q1 == 998 | 
                    ab.small$Q1 == -1,
                    NA, ab.small$Q1)
summary(ab.small$age)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   18.0    25.0    33.0    36.3    45.0   110.0     333 

hist(ab.small$age, main = "", xlab = "Age")

Now let’s look at our dataset:

str(ab.small)

'data.frame':   27713 obs. of  13 variables:
 $ COUNTRY            : num  1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "label")= chr "Country"
  ..- attr(*, "format.spss")= chr "F4.0"
  ..- attr(*, "labels")= Named num [1:20] 1 2 3 4 5 6 7 8 9 10 ...
  .. ..- attr(*, "names")= chr [1:20] "Benin" "Botswana" "Burkina Faso" "Cape Verde" ...
 $ URBRUR             : num  1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "label")= chr "Urban or Rural Primary Sampling Unit"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:2] 1 2
  .. ..- attr(*, "names")= chr [1:2] "Urban" "Rural"
 $ Q42A               : num  4 4 4 3 2 3 2 3 3 3 ...
  ..- attr(*, "label")= chr "Q42a. Extent of democracy"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:8] -1 1 2 3 4 8 9 998
  .. ..- attr(*, "names")= chr [1:8] "Missing" "Not a democracy" "A democracy, with major problems" "A democracy, but with minor problems" ...
 $ Q89                : num  4 2 4 3 4 4 5 2 4 4 ...
  ..- attr(*, "label")= chr "Q89. Education of respondent"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:13] -1 0 1 2 3 4 5 6 7 8 ...
  .. ..- attr(*, "names")= chr [1:13] "Missing" "No formal schooling" "Informal schooling only" "Some primary schooling" ...
 $ Q101               : num  2 1 2 1 2 1 2 1 1 2 ...
  ..- attr(*, "label")= chr "Q101. Gender of respondent"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:3] -1 1 2
  .. ..- attr(*, "names")= chr [1:3] "Missing" "Male" "Female"
 $ Q1                 : num  38 46 28 30 23 24 40 50 24 36 ...
  ..- attr(*, "label")= chr "Q1. Age"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:3] -1 998 999
  .. ..- attr(*, "names")= chr [1:3] "Missing" "Refused" "Don't know"
 $ perceivedDem       : num  4 4 4 3 2 3 2 3 3 3 ...
 $ perceivedDem_factor: Factor w/ 4 levels "Not a democracy",..: 4 4 4 3 2 3 2 3 3 3 ...
 $ education          : num  4 2 4 3 4 4 5 2 4 4 ...
 $ education_factor   : Factor w/ 10 levels "No formal schooling",..: 5 3 5 4 5 5 6 3 5 5 ...
 $ female             : num  1 0 1 0 1 0 1 0 0 1 ...
  ..- attr(*, "label")= chr "Q101. Gender of respondent"
  ..- attr(*, "format.spss")= chr "F3.0"
  ..- attr(*, "labels")= Named num [1:3] -1 1 2
  .. ..- attr(*, "names")= chr [1:3] "Missing" "Male" "Female"
 $ female_factor      : Factor w/ 2 levels "Male","Female": 2 1 2 1 2 1 2 1 1 2 ...
 $ age                : num  38 46 28 30 23 24 40 50 24 36 ...

For our further analyses, let’s keep only the variables we just created. For this, we create a new object, ab.work by keeping only the columns we need. I would generally recommend working with numeric variables rather than factors if you will use these variables in regression, but there are contexts where factors and their labels are useful. So for now, I’ll keep both the numeric and factor versions of perceivedDem and education in my working dataset.

ab.work <- ab.small |> 
          select(COUNTRY, URBRUR, perceivedDem_factor, female, age, 
                 education, education_factor)

Alternatively, we could also drop the columns we don’t need by putting a \(-\) sign in front of the variable. This might make more sense if you want to keep many and drop few variables:

ab.work <- ab.small |> 
           select(-Q42A, -Q89, -Q101, -Q1)

Finally, let’s summarize our dataset:

summary(ab.work)

    COUNTRY         URBRUR      perceivedDem                            perceivedDem_factor   education                                  education_factor     female      female_factor       age       
 Min.   : 1.0   Min.   :1.00   Min.   :1.00   Not a democracy                     :1875     Min.   :0.00   Some secondary school/high school     :5950    Min.   :0.000   Male  :13837   Min.   : 18.0  
 1st Qu.: 6.0   1st Qu.:1.00   1st Qu.:2.00   A democracy, with major problems    :7338     1st Qu.:2.00   Some primary schooling                :5111    1st Qu.:0.000   Female:13876   1st Qu.: 25.0  
 Median :12.0   Median :2.00   Median :3.00   A democracy, but with minor problems:8249     Median :3.00   No formal schooling                   :4365    Median :1.000                  Median : 33.0  
 Mean   :11.2   Mean   :1.62   Mean   :2.85   A full democracy                    :7310     Mean   :3.15   Secondary school completed/high school:4165    Mean   :0.501                  Mean   : 36.3  
 3rd Qu.:16.0   3rd Qu.:2.00   3rd Qu.:4.00   NA's                                :2941     3rd Qu.:5.00   Primary school completed              :3897    3rd Qu.:1.000                  3rd Qu.: 45.0  
 Max.   :20.0   Max.   :2.00   Max.   :4.00                                                 Max.   :9.00   (Other)                               :4181    Max.   :1.000                  Max.   :110.0  
                               NA's   :2941                                                 NA's   :44     NA's                                  :  44                                   NA's   :333    

Note

There are also other functions to recode variables, aiming to make the process more convenient than what you see here with the ifelse() function. Some of these include:

• car::recode(), like so: ab.small$age <- car::recode(ab.small$Q1, "c(999, 998, -1) = NA, else = ab.small$Q1")
• dplyr::recode(), like so: ab.small$female <- dplyr::recode(ab.small$Q10, "1" = 0, "2" = 1)

I’m prefixing each recode() function by the package name because the functions have the same names. Also: Do keep a close eye on variable types (numeric, factor, string, …) before writing your code to recode variables. The tidyverse package offers some more functionalities to recode factor variables that you might find useful.

Why all this extra effort around factors?

Factors can be useful in some contexts because they carry extra information (numeric values and value labels). But if the underlying numeric values are important for your calculations, you should know that the numeric values underlying factors always start with 1. So if you have a factor “female”, it will typically take the values 1 and 2 (and not 0 and 1, which you might expect). The code above shows you how to deal with this. If this is of interest to you, you can view more on where factors are more useful than numeric variables in this talk by Amelia McNamara.

In any event, be sure to always inspect your variables of interest to make sure you know whether you’re dealing with a numeric, factor, or character variable, and what labels are associated with which values, if any. Use the str() or class() functions for this.

class(ab.work$education_factor)

[1] "factor"

Data structure

In the article Tidy Data you learn that the default structure of dataset consists of rows and columns. However, datasets typically have more underlying structure than just rows and columns. For instance, the ab.small dataframe is properly structured so that each observation is one row and each variable is one column. But each observation comes from one of the 20 countries in the survey. You can therefore think as respondent i being also nested in country j.

Collapsing a dataset

This can become important when you want to summarize information across a group indicator j, such as countries in this case. What if you were interested in creating a country-level variable that measure the average perception of the country’s political system as democratic, based on the perceivedDem variable we created from the Afrobarometer. To do this, you need to “collapse” the dataset by country and create a new dataset with only country names and the average value of perceivedDem by country.

For this operation, we will begin to make use of the “dplyr” package in R. This package is a great tool for “data wrangling” and will become on of your standard tools. It is also part of the tidyverse set of packages. Take a look at the website for dplyr, where you can find more information on how this package works.

We’ll first create a new dataset named ab.country with only mean values of perceived democracy by country.

Test yourself!

If we collapse the ab.work dataset by countries, how many rows will the resulting dataset have?

ab.country <- ab.work |> 
              group_by(COUNTRY) |> 
              summarize(perceivedDem = mean(perceivedDem, na.rm = TRUE))

Note the use of na.rm = TRUE within the mean function. The perceivedDem variable has missing values, and R cannot calculate a mean on a vector with missing values unless na.rm is set to TRUE.

Test yourself!

What command/s would you use if you wanted to summarize this variable with the median instead of the mean? What if you wanted to calculate its range?

Also note that we don’t pass ab.work to the summarize() function, but we wrap it into group_by(). Here, we tell R to summarize ab.work by the grouping variable COUNTRY.

This is the dataset we just created:

ab.country

# A tibble: 20 × 2
   COUNTRY perceivedDem
     <dbl>        <dbl>
 1       1         3.19
 2       2         3.49
 3       3         2.85
 4       4         3.11
 5       5         3.44
 6       6         2.57
 7       7         2.51
 8       8         2.91
 9       9         2.80
10      10         2.82
11      11         2.95
12      12         2.97
13      13         3.15
14      14         2.43
15      15         2.57
16      16         2.82
17      17         3.23
18      18         2.74
19      19         2.69
20      20         2.06

Adding a summarized variable to a dataset

If you wanted to simply add the average perceivedDem variable to your original survey data without collapsing it, you can do this using the exact same language, but substitute mutate for summarize.

Note

This will be the typical step if you wish to create group-level means for further analysis, as we did in class today.*

ab.work <- ab.work |> 
           group_by(COUNTRY) |> 
           mutate(perceivedDemAvg = mean(perceivedDem, na.rm = TRUE))

If we look at the data, you will notice a new variable on the right:

head(ab.work)

# A tibble: 6 × 10
# Groups:   COUNTRY [1]
  COUNTRY URBRUR perceivedDem perceivedDem_factor                  education education_factor                  female female_factor   age perceivedDemAvg
    <dbl>  <dbl>        <dbl> <fct>                                    <dbl> <fct>                              <dbl> <fct>         <dbl>           <dbl>
1       1      1            4 A full democracy                             4 Some secondary school/high school      1 Female           38            3.19
2       1      1            4 A full democracy                             2 Some primary schooling                 0 Male             46            3.19
3       1      1            4 A full democracy                             4 Some secondary school/high school      1 Female           28            3.19
4       1      1            3 A democracy, but with minor problems         3 Primary school completed               0 Male             30            3.19
5       1      1            2 A democracy, with major problems             4 Some secondary school/high school      1 Female           23            3.19
6       1      1            3 A democracy, but with minor problems         4 Some secondary school/high school      0 Male             24            3.19

Centering by a group mean

To center an individual-level variable by a group mean, you similarly use mutate() to calculate the group mean first, and then subtract the group mean from the individual-level variable. Here, if I wanted to center the individual-level variable age by the group (country) mean of age, I would first generate age_country_mean and then subtract age_country_mean from each individual respondent’s age to create age_ctd. Then, I could standardize it by dividing by two country-specific standard deviations (age_country_sd) and generate age_ctd_sdt:

ab.work <- ab.work |> 
           group_by(COUNTRY) |> 
           mutate(age_country_mean = mean(age, na.rm = TRUE),
                  age_country_sd = sd(age, na.rm = TRUE))

ab.work$age_ctd <- ab.work$age - ab.work$age_country_mean
ab.work$age_ctd_std <- ab.work$age_ctd / (2 * ab.work$age_country_sd)

ab.work |> 
  select(age_ctd, age_ctd_std) |> 
  summary()

    COUNTRY        age_ctd        age_ctd_std    
 Min.   : 1.0   Min.   :-23.37   Min.   :-0.760  
 1st Qu.: 6.0   1st Qu.:-10.86   1st Qu.:-0.395  
 Median :12.0   Median : -3.21   Median :-0.111  
 Mean   :11.2   Mean   :  0.00   Mean   : 0.000  
 3rd Qu.:16.0   3rd Qu.:  8.44   3rd Qu.: 0.297  
 Max.   :20.0   Max.   : 70.98   Max.   : 2.397  
                NA's   :333      NA's   :333     

Time-series cross-sectional data

In cross-country survey data, you can think of respondents i nested in countries j. When you work with macro-economic or macro-political data from several countries over multiple time points, you can similarly think of countries i and years t. One row in your dataset is then a country-year it. In this context, you can use the same operations as above.

As an example, we’ll introduce a handy data compendium from the University of Gothenburg, Sweden. The Quality of Government project, online at http://qog.pol.gu.se/data/, has put together one master dataset containing dozens of political and economic indicators for a large number of countries over long time spans. If you work with cross-country data, this makes your life much easier.

First, have a look at the codebook at http://qog.pol.gu.se/data/datadownloads/qogstandarddata. This codebook is a great example of how to document a dataset. When you collect your own data and share it with others, you should aim for a similarly clear style of documentation.

Let’s now use the “QoG Standard Time-Series Data (version January 2020)”. I first downloaded the .csv version of “The QoG Time-Series Data (version January 2023)” from https://www.gu.se/en/quality-government/qog-data/data-downloads/standard-dataset (look for the link on the right) to my working directory, and then read it into R with the familiar import() function (you could use read.csv() as well). Note: this is a large file (93MB), so be patient when downloading it.

qog <- import("qog_std_ts_jan23.csv")
dim(qog)

[1] 15366  1953

This dataset is organized in the structure I described above: countries i and years t, with one row being one country-year observation. Countries are identified by names and a set of country codes.

As in the earlier examples, let’s assume we’re working on a project where we only need a limited set of indicators. In this case, let’s focus on measures for democracy and economic development. After consulting the codebook, we decide we want to work with the following variables:

• wdi_gdpcapcon2015: GDP per Capita (measured in 2015 US Dollar)
• vdem_polyarchy: V-Dem’s polyarchy index for democracy (see https://v-dem.net/data/the-v-dem-dataset/)

For country identifiers, we’ll keep the Correlates of War country codes ccodecow and the country names cname as well as a regional identifier ht_region; we’ll only look at the years 2000-2010 for now. This means we’ll be subsetting the dataset on two dimensions: we only need 5 columns, and we only need the rows for which the condition year >= 2000 & year <= 2010 apply. You will remember that R operates by rows first, and columns second, with a comma separating the two indices within hard brackets [ , ]. That way, we can use one line of code to create our desired subset, which we call qog.small:

qog.small <- qog[qog$year >= 2000 & qog$year <= 2010, 
                 c("cname", "ccodecow", "ht_region", "year", "wdi_gdpcapcon2015", "vdem_polyarchy")]
str(qog.small)

'data.frame':   2178 obs. of  6 variables:
 $ cname            : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ ccodecow         : int  700 700 700 700 700 700 700 700 700 700 ...
 $ ht_region        : int  8 8 8 8 8 8 8 8 8 8 ...
 $ year             : int  2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ...
 $ wdi_gdpcapcon2015: num  NA NA 360 363 354 ...
 $ vdem_polyarchy   : num  0.072 0.079 0.223 0.231 0.254 0.336 0.395 0.395 0.4 0.392 ...

Alternatively, you can use the tidyverse function filter() and select() to accomplish the same:

qog.small <- qog |> 
             filter(year >= 2000 & year <= 2010) |> 
             select(cname, ccodecow, ht_region, year, wdi_gdpcapcon2015, vdem_polyarchy)
str(qog.small)

'data.frame':   2178 obs. of  6 variables:
 $ cname            : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ ccodecow         : int  700 700 700 700 700 700 700 700 700 700 ...
 $ ht_region        : int  8 8 8 8 8 8 8 8 8 8 ...
 $ year             : int  2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ...
 $ wdi_gdpcapcon2015: num  NA NA 360 363 354 ...
 $ vdem_polyarchy   : num  0.072 0.079 0.223 0.231 0.254 0.336 0.395 0.395 0.4 0.392 ...

Transforming variables

You’ve already worked on transforming variables before, so let’s briefly reiterate one type of transformation and introduce a few new ones.

Logarithmic transformation

Have a quick look at the GDP per capita variable.

hist(qog.small$wdi_gdpcapcon2015, main = "", xlab = "GDP per capita", breaks = 20)

This variable has a strong right skew, and we may consider using the logarithmic transformation for it. For this, we create a new variable, gle_cgdpc_ln:

qog.small$wdi_gdpcapcon2015_ln <- log(qog.small$wdi_gdpcapcon2015)
hist(qog.small$wdi_gdpcapcon2015_ln, main = "", xlab = "GDP per capita (logged)")

Time-series operators

Time-series cross-sectional (TSCS) data are data with multiple observations over time (“time-series”) for multiple units (“cross-sectional”). You should be familiar with some basic data manipulation that applies specifically to time-series cross-sectional data. This includes the creation of time-series operators.

To start, let’s focus on one single time series though: we’ll only pick the United States from our QoG dataset. In the Correlates of War (COW) country code system, the United States is assigned the ID 2 (see http://www.jkarreth.net/countrycodes.html for a sortable list of all COW country codes).

usa <- filter(qog.small, ccodecow == 2)
usa

                            cname ccodecow ht_region year wdi_gdpcapcon2015 vdem_polyarchy wdi_gdpcapcon2015_ln
1  United States of America (the)        2         5 2000             48746          0.868                 10.8
2  United States of America (the)        2         5 2001             48727          0.829                 10.8
3  United States of America (the)        2         5 2002             49095          0.832                 10.8
4  United States of America (the)        2         5 2003             50036          0.866                 10.8
5  United States of America (the)        2         5 2004             51485          0.869                 10.8
6  United States of America (the)        2         5 2005             52790          0.867                 10.9
7  United States of America (the)        2         5 2006             53738          0.872                 10.9
8  United States of America (the)        2         5 2007             54300          0.898                 10.9
9  United States of America (the)        2         5 2008             53854          0.900                 10.9
10 United States of America (the)        2         5 2009             51996          0.907                 10.9
11 United States of America (the)        2         5 2010             52963          0.907                 10.9

Now, let’s say we want to create a variable that expresses the absolute change in GDP per capita from year to year. We can do this in two steps. First, we create a “lag” of GDP per capita, using the lag() function. This function takes two arguments. The first is the vector from which we are creating the lag, and the second, k, is the number of time units (here: years) by which we want to lag the original variable. Before we use the lag function, I sort the data by years to make sure that the lag is taken from the previous year. I do this using the arrange function, which is part of the “dplyr” package that you loaded above.

usa <- arrange(usa, year)
usa$wdi_gdpcapcon2015_lag <- dplyr::lag(usa$wdi_gdpcapcon2015, n = 1)
usa

                            cname ccodecow ht_region year wdi_gdpcapcon2015 vdem_polyarchy wdi_gdpcapcon2015_ln wdi_gdpcapcon2015_lag
1  United States of America (the)        2         5 2000             48746          0.868                 10.8                    NA
2  United States of America (the)        2         5 2001             48727          0.829                 10.8                 48746
3  United States of America (the)        2         5 2002             49095          0.832                 10.8                 48727
4  United States of America (the)        2         5 2003             50036          0.866                 10.8                 49095
5  United States of America (the)        2         5 2004             51485          0.869                 10.8                 50036
6  United States of America (the)        2         5 2005             52790          0.867                 10.9                 51485
7  United States of America (the)        2         5 2006             53738          0.872                 10.9                 52790
8  United States of America (the)        2         5 2007             54300          0.898                 10.9                 53738
9  United States of America (the)        2         5 2008             53854          0.900                 10.9                 54300
10 United States of America (the)        2         5 2009             51996          0.907                 10.9                 53854
11 United States of America (the)        2         5 2010             52963          0.907                 10.9                 51996

I can now create the “change in GDP per capita” variable by simply subtracting the previous from the current value:

usa$wdi_gdpcapcon2015_ch <- usa$wdi_gdpcapcon2015 - usa$wdi_gdpcapcon2015_lag
usa

                            cname ccodecow ht_region year wdi_gdpcapcon2015 vdem_polyarchy wdi_gdpcapcon2015_ln wdi_gdpcapcon2015_lag wdi_gdpcapcon2015_ch
1  United States of America (the)        2         5 2000             48746          0.868                 10.8                    NA                   NA
2  United States of America (the)        2         5 2001             48727          0.829                 10.8                 48746                -19.5
3  United States of America (the)        2         5 2002             49095          0.832                 10.8                 48727                368.8
4  United States of America (the)        2         5 2003             50036          0.866                 10.8                 49095                940.9
5  United States of America (the)        2         5 2004             51485          0.869                 10.8                 50036               1449.0
6  United States of America (the)        2         5 2005             52790          0.867                 10.9                 51485               1304.5
7  United States of America (the)        2         5 2006             53738          0.872                 10.9                 52790                948.4
8  United States of America (the)        2         5 2007             54300          0.898                 10.9                 53738                561.5
9  United States of America (the)        2         5 2008             53854          0.900                 10.9                 54300               -445.5
10 United States of America (the)        2         5 2009             51996          0.907                 10.9                 53854              -1858.0
11 United States of America (the)        2         5 2010             52963          0.907                 10.9                 51996                967.3

And now we can create a quick plot of percentage growth in GDP per capita, using another new variable, gle_cgdpc_growth, that we create as well:

usa$wdi_gdpcapcon2015_growth <- usa$wdi_gdpcapcon2015_ch / usa$wdi_gdpcapcon2015_lag
ggplot(data = usa, aes(x = year, y = wdi_gdpcapcon2015_growth)) + 
  geom_line() + 
  geom_hline(yintercept = 0, linetype = "dashed") + 
  xlab("") + ylab("Economic growth")

We can also create lags in time-series cross-sectional data as in the qog.small dataset, where we want lags for individual units. For this, we return to the mutate function from the “dplyr” package that you already used above. We also need to take into account the grouping structure and use the group_by function for that purpose.

First, I eliminate all observations that do not have COW country codes, assuming that these are duplicate observations. In your research, you should carefully check such cases by hand.

qog.small <- qog.small |> 
             filter(!is.na(qog.small$ccodecow))

Next, I sort the dataset by country codes and then years. I do this in order to have the data ready for creating lagged variables further below.

qog.small <- qog.small |> 
             arrange(ccodecow, year)

Test yourself!

Which country and year will be first (and last) in this sorted dataset?

Now, I use again the mutate() function to add a variable to this dataset without changing the structure of the dataset.

qog.small <- qog.small |> 
             group_by(ccodecow) |>  
             mutate(wdi_gdpcapcon2015_lag = dplyr::lag(wdi_gdpcapcon2015, n = 1))
tail(qog.small)

# A tibble: 6 × 8
# Groups:   ccodecow [1]
  cname ccodecow ht_region  year wdi_gdpcapcon2015 vdem_polyarchy wdi_gdpcapcon2015_ln wdi_gdpcapcon2015_lag
  <chr>    <int>     <int> <int>             <dbl>          <dbl>                <dbl>                 <dbl>
1 Samoa      990         9  2005             3723.             NA                 8.22                 3501.
2 Samoa      990         9  2006             3787.             NA                 8.24                 3723.
3 Samoa      990         9  2007             3784.             NA                 8.24                 3787.
4 Samoa      990         9  2008             3892.             NA                 8.27                 3784.
5 Samoa      990         9  2009             3843.             NA                 8.25                 3892.
6 Samoa      990         9  2010             4047.             NA                 8.31                 3843.

You can use all sorts of other variable creation commands like you did above for the usa time series.

Merging data

Often in your work, you will find yourself having to combine datasets from different sources. We’ll work through two quick examples returning to the Afrobarometer survey data from the beginning of this tutorial.

Merge two datasets with the same level of analysis

Let’s say we are interested in the relationship between a common measure for democracy, the Unified Democracy Score, and survey respondents’ perception of how democratic their country is. We already have both pieces of information. Recall that we created a country-level version of the Afrobarometer data that contains the average perception of democracy:

ab.country

# A tibble: 20 × 2
   COUNTRY perceivedDem
     <dbl>        <dbl>
 1       1         3.19
 2       2         3.49
 3       3         2.85
 4       4         3.11
 5       5         3.44
 6       6         2.57
 7       7         2.51
 8       8         2.91
 9       9         2.80
10      10         2.82
11      11         2.95
12      12         2.97
13      13         3.15
14      14         2.43
15      15         2.57
16      16         2.82
17      17         3.23
18      18         2.74
19      19         2.69
20      20         2.06

And, we have the qog.small dataset containing the V-Dem democracy score variable.

To bring the two together, we need two things: first, matching identifiers that help our software package assign the appropriate observations to each other. The QoG data contains COW country codes, but our Afrobarometer data only has country numbers. We need to create a character vector with country names first. To do this, we use the same practice as above: create a factor using the labels included in the vector.

Again, double-check the codebook to make sure all labels are applied correctly.

ab.country$country_factor <- factor(ab.country$COUNTRY,
                                    levels = unique(ab.country$COUNTRY),
                                    labels = get_labels(ab.country$COUNTRY))

Again, double-check the codebook to make sure all labels are applied correctly.

Screenshot from the codebook of the Afrobarometer Round 4 survey.

ab.country

# A tibble: 20 × 3
   COUNTRY perceivedDem country_factor
     <dbl>        <dbl> <fct>         
 1       1         3.19 Benin         
 2       2         3.49 Botswana      
 3       3         2.85 Burkina Faso  
 4       4         3.11 Cape Verde    
 5       5         3.44 Ghana         
 6       6         2.57 Kenya         
 7       7         2.51 Lesotho       
 8       8         2.91 Liberia       
 9       9         2.80 Madagascar    
10      10         2.82 Malawi        
11      11         2.95 Mali          
12      12         2.97 Mozambique    
13      13         3.15 Namibia       
14      14         2.43 Nigeria       
15      15         2.57 Senegal       
16      16         2.82 South Africa  
17      17         3.23 Tanzania      
18      18         2.74 Uganda        
19      19         2.69 Zambia        
20      20         2.06 Zimbabwe      

Now, we can use the countrycode() function from the package with the same name to add a variable ccodecow to our Afrobarometer data. Install the “countrycode” package once, and then load it every R session where you use it. Have a look at the help file for the countrycode() function before using it here.

library("countrycode")
ab.country$ccodecow <- countrycode(sourcevar = ab.country$country_factor, 
                                   origin = "country.name", 
                                   destination = "cown")
ab.country

# A tibble: 20 × 4
   COUNTRY perceivedDem country_factor ccodecow
     <dbl>        <dbl> <fct>             <dbl>
 1       1         3.19 Benin               434
 2       2         3.49 Botswana            571
 3       3         2.85 Burkina Faso        439
 4       4         3.11 Cape Verde          402
 5       5         3.44 Ghana               452
 6       6         2.57 Kenya               501
 7       7         2.51 Lesotho             570
 8       8         2.91 Liberia             450
 9       9         2.80 Madagascar          580
10      10         2.82 Malawi              553
11      11         2.95 Mali                432
12      12         2.97 Mozambique          541
13      13         3.15 Namibia             565
14      14         2.43 Nigeria             475
15      15         2.57 Senegal             433
16      16         2.82 South Africa        560
17      17         3.23 Tanzania            510
18      18         2.74 Uganda              500
19      19         2.69 Zambia              551
20      20         2.06 Zimbabwe            552

We can now prepare the QoG data for the merge. Recall that the Afrobarometer survey was conducted in 2008, so limiting ourselves to the QoG data from that year probably makes sense.

qog.2008 <- qog.small |> 
            filter(year == 2008)

Now we use the left_join() function to create a new object that contains both the ab.country and qog.2008 datasets. Because we are only interested in the observations for which we have survey data, we use left_join(), indicating to add observations to all observations on the “left” dataset in the function call. (Look up right_join() and inner_join, too.)

ab.qog <- left_join(x = ab.country, 
                    y = qog.2008, 
                    by = "ccodecow")
ab.qog

# A tibble: 20 × 11
   COUNTRY perceivedDem country_factor ccodecow cname                            ht_region  year wdi_gdpcapcon2015 vdem_polyarchy wdi_gdpcapcon2015_ln wdi_gdpcapcon2015_lag
     <dbl>        <dbl> <fct>             <dbl> <chr>                                <int> <int>             <dbl>          <dbl>                <dbl>                 <dbl>
 1       1         3.19 Benin               434 Benin                                    4  2008              977.          0.651                 6.88                  960.
 2       2         3.49 Botswana            571 Botswana                                 4  2008             5983.          0.72                  8.70                 5913.
 3       3         2.85 Burkina Faso        439 Burkina Faso                             4  2008              536.          0.567                 6.28                  521.
 4       4         3.11 Cape Verde          402 Cabo Verde                               4  2008             2901.          0.774                 7.97                 2750.
 5       5         3.44 Ghana               452 Ghana                                    4  2008             1278.          0.735                 7.15                 1201.
 6       6         2.57 Kenya               501 Kenya                                    4  2008             1274.          0.397                 7.15                 1310.
 7       7         2.51 Lesotho             570 Lesotho                                  4  2008              958.          0.563                 6.86                  913.
 8       8         2.91 Liberia             450 Liberia                                  4  2008              597.          0.645                 6.39                  580.
 9       9         2.80 Madagascar          580 Madagascar                               4  2008              501.          0.507                 6.22                  483.
10      10         2.82 Malawi              553 Malawi                                   4  2008              325.          0.449                 5.78                  310.
11      11         2.95 Mali                432 Mali                                     4  2008              685.          0.596                 6.53                  676.
12      12         2.97 Mozambique          541 Mozambique                               4  2008              457.          0.479                 6.12                  437.
13      13         3.15 Namibia             565 Namibia                                  4  2008             4053.          0.669                 8.31                 4002.
14      14         2.43 Nigeria             475 Nigeria                                  4  2008             2170.          0.417                 7.68                 2089.
15      15         2.57 Senegal             433 Senegal                                  4  2008             1156.          0.687                 7.05                 1144.
16      16         2.82 South Africa        560 South Africa                             4  2008             6075.          0.785                 8.71                 5954.
17      17         3.23 Tanzania            510 Tanzania, the United Republic of         4  2008              747.          0.474                 6.62                  726.
18      18         2.74 Uganda              500 Uganda                                   4  2008              723.          0.342                 6.58                  685.
19      19         2.69 Zambia              551 Zambia                                   4  2008             1067.          0.516                 6.97                 1026.
20      20         2.06 Zimbabwe            552 Zimbabwe                                 4  2008              825.          0.25                  6.72                 1010.

And now we can investigate the relationship between respondents’ average perception of democracy and the democracy scores, which are based on information about political institutions and political processes.

ggplot(data = ab.qog, aes(y = perceivedDem, x = vdem_polyarchy)) + 
  geom_point() + 
  ylab("Perceived Democracy (average response)") + 
  xlab("V-Dem polyarchy score")

Of course we can also estimate a bivariate regression model of this relationship:

mod <- lm(perceivedDem ~ vdem_polyarchy, data = ab.qog)
summary(mod)

Call:
lm(formula = perceivedDem ~ vdem_polyarchy, data = ab.qog)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.4806 -0.1328 -0.0047  0.1947  0.4992 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)       2.013      0.245    8.21  1.7e-07 ***
vdem_polyarchy    1.516      0.423    3.59   0.0021 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.274 on 18 degrees of freedom
Multiple R-squared:  0.417, Adjusted R-squared:  0.384 
F-statistic: 12.9 on 1 and 18 DF,  p-value: 0.00211

Merge two datasets with different levels of analysis

A more typical use case in the MLM setup is to merge data at level-1 with data at level-2, or a higher level. We had initially created a version of the Afrobarometer data that contains a number of variables of interest at the level of the survey respondent:

glimpse(ab.work)

Rows: 27,713
Columns: 14
Groups: COUNTRY [20]
$ COUNTRY             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ URBRUR              <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, …
$ perceivedDem        <dbl> 4, 4, 4, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 4, 3, 4, 4, 4, 4, 3, 3, 4, 4, 4, 3, 4, 4, 4, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 3, 3, 3, 2, 4, 4, 4, 4, 4, 3, 3, 2, NA, 3, 3, 4, 3, 4, 4, 2, 4, 3, 3, 3, NA, 3, 4, 2, 4, NA, 3, 3, 3, 3, 4, 3, 3, 4, 3, 1, 2, 3, 3, 3, 4, 3, 2, NA, 3, 3,…
$ perceivedDem_factor <fct> "A full democracy", "A full democracy", "A full democracy", "A democracy, but with minor problems", "A democracy, with major problems", "A democracy, but with minor problems", "A democracy, with major problems", "A democracy, but with minor problems", "A democracy, but …
$ education           <dbl> 4, 2, 4, 3, 4, 4, 5, 2, 4, 4, 5, 2, 0, 4, 4, 4, 2, 2, 0, 7, 0, 0, 0, 0, 4, 2, 0, 0, 4, 2, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2, 3, 4, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 4, 4, 0, 4, 4, 4, 4, 5, 0, 2, 0, 0, 2, 0, 5, 0, 0, 0, 4, 2, 2, …
$ education_factor    <fct> "Some secondary school/high school", "Some primary schooling", "Some secondary school/high school", "Primary school completed", "Some secondary school/high school", "Some secondary school/high school", "Secondary school completed/high school", "Some primary schooling", …
$ female              <dbl> 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, …
$ female_factor       <fct> Female, Male, Female, Male, Female, Male, Female, Male, Male, Female, Female, Male, Female, Male, Female, Male, Male, Female, Female, Male, Female, Female, Male, Female, Female, Male, Female, Male, Male, Female, Male, Female, Female, Male, Female, Male, Male, Female, Ma…
$ age                 <dbl> 38, 46, 28, 30, 23, 24, 40, 50, 24, 36, 22, 31, 50, 19, 41, 29, 55, 22, 47, 30, 36, 30, 26, 38, 25, 49, 41, 27, 20, 19, 55, 52, 20, 29, 25, 28, 44, 48, 20, 25, 28, 32, 40, 48, 28, 25, 46, 30, 40, 52, 25, 25, 26, 30, 42, 28, 22, 26, 25, 36, 50, 36, 22, 36, 26, 24, 22, 40…
$ perceivedDemAvg     <dbl> 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, …
$ age_country_mean    <dbl> 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, …
$ age_country_sd      <dbl> 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, …
$ age_ctd             <dbl> 2.613, 10.613, -7.387, -5.387, -12.387, -11.387, 4.613, 14.613, -11.387, 0.613, -13.387, -4.387, 14.613, -16.387, 5.613, -6.387, 19.613, -13.387, 11.613, -5.387, 0.613, -5.387, -9.387, 2.613, -10.387, 13.613, 5.613, -8.387, -15.387, -16.387, 19.613, 16.613, -15.387, -6.…
$ age_ctd_std         <dbl> 0.1019, 0.4137, -0.2880, -0.2100, -0.4829, -0.4439, 0.1798, 0.5697, -0.4439, 0.0239, -0.5219, -0.1710, 0.5697, -0.6388, 0.2188, -0.2490, 0.7646, -0.5219, 0.4527, -0.2100, 0.0239, -0.2100, -0.3659, 0.1019, -0.4049, 0.5307, 0.2188, -0.3269, -0.5998, -0.6388, 0.7646, 0.647…

And, we still have the qog.small dataset containing the Unified Democracy Scores variable.

To bring the two together, we again need two things: first, matching identifiers that help our software package assign the appropriate observations to each other. The QoG data contains COW country codes, but our Afrobarometer data only has country numbers. We need to create a character vector with country names first. To do this, we use the same practice as above: create a factor using the labels included in the vector.

Again, double-check the codebook to make sure all labels are applied correctly.

ab.work$country_factor <- factor(ab.work$COUNTRY,
                                    levels = unique(ab.work$COUNTRY),
                                    labels = get_labels(ab.work$COUNTRY))

Again, double-check the codebook to make sure all labels are applied correctly.

Screenshot from the codebook of the Afrobarometer Round 4 survey.

table(ab.work$country_factor)

       Benin     Botswana Burkina Faso   Cape Verde        Ghana        Kenya      Lesotho      Liberia   Madagascar       Malawi         Mali   Mozambique      Namibia      Nigeria      Senegal South Africa     Tanzania       Uganda       Zambia     Zimbabwe 
        1200         1200         1200         1264         1200         1104         1200         1200         1350         1200         1232         1200         1200         2324         1200         2400         1208         2431         1200         1200 

Now, we can use the countrycode() function from the package with the same name to add a variable ccodecow to our Afrobarometer data.

ab.work$ccodecow <- countrycode(ab.work$country_factor, origin = "country.name", 
                                   destination = "cown")
table(ab.work$country_factor, ab.work$ccodecow)

              
                402  432  433  434  439  450  452  475  500  501  510  541  551  552  553  560  565  570  571  580
  Benin           0    0    0 1200    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  Botswana        0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0 1200    0
  Burkina Faso    0    0    0    0 1200    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  Cape Verde   1264    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  Ghana           0    0    0    0    0    0 1200    0    0    0    0    0    0    0    0    0    0    0    0    0
  Kenya           0    0    0    0    0    0    0    0    0 1104    0    0    0    0    0    0    0    0    0    0
  Lesotho         0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0 1200    0    0
  Liberia         0    0    0    0    0 1200    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  Madagascar      0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0 1350
  Malawi          0    0    0    0    0    0    0    0    0    0    0    0    0    0 1200    0    0    0    0    0
  Mali            0 1232    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  Mozambique      0    0    0    0    0    0    0    0    0    0    0 1200    0    0    0    0    0    0    0    0
  Namibia         0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0 1200    0    0    0
  Nigeria         0    0    0    0    0    0    0 2324    0    0    0    0    0    0    0    0    0    0    0    0
  Senegal         0    0 1200    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  South Africa    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0 2400    0    0    0    0
  Tanzania        0    0    0    0    0    0    0    0    0    0 1208    0    0    0    0    0    0    0    0    0
  Uganda          0    0    0    0    0    0    0    0 2431    0    0    0    0    0    0    0    0    0    0    0
  Zambia          0    0    0    0    0    0    0    0    0    0    0    0 1200    0    0    0    0    0    0    0
  Zimbabwe        0    0    0    0    0    0    0    0    0    0    0    0    0 1200    0    0    0    0    0    0

We can now prepare the QoG data for the merge. Recall that the Afrobarometer survey was conducted in 2008, so limiting ourselves to the QoG data from that year still makes sense.

qog.2008 <- filter(qog.small, year == 2008)

Now we use the left_join() function to create a new object that contains both the ab.country and qog.2008 datasets. Because we are only interested in the observations for which we have survey data, we use left_join(), indicating to add observations to all observations on the “left” dataset in the function call. (Look up right_join() and inner_join, too.)

ab.survey.qog <- left_join(x = ab.work, y = qog.2008, 
                    by = "ccodecow")
glimpse(ab.survey.qog)

Rows: 27,713
Columns: 23
Groups: COUNTRY [20]
$ COUNTRY               <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ URBRUR                <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1…
$ perceivedDem          <dbl> 4, 4, 4, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 4, 3, 4, 4, 4, 4, 3, 3, 4, 4, 4, 3, 4, 4, 4, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 3, 3, 3, 2, 4, 4, 4, 4, 4, 3, 3, 2, NA, 3, 3, 4, 3, 4, 4, 2, 4, 3, 3, 3, NA, 3, 4, 2, 4, NA, 3, 3, 3, 3, 4, 3, 3, 4, 3, 1, 2, 3, 3, 3, 4, 3, 2, NA, 3, …
$ perceivedDem_factor   <fct> "A full democracy", "A full democracy", "A full democracy", "A democracy, but with minor problems", "A democracy, with major problems", "A democracy, but with minor problems", "A democracy, with major problems", "A democracy, but with minor problems", "A democracy, bu…
$ education             <dbl> 4, 2, 4, 3, 4, 4, 5, 2, 4, 4, 5, 2, 0, 4, 4, 4, 2, 2, 0, 7, 0, 0, 0, 0, 4, 2, 0, 0, 4, 2, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 2, 3, 4, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 4, 4, 0, 4, 4, 4, 4, 5, 0, 2, 0, 0, 2, 0, 5, 0, 0, 0, 4, 2, 2…
$ education_factor      <fct> "Some secondary school/high school", "Some primary schooling", "Some secondary school/high school", "Primary school completed", "Some secondary school/high school", "Some secondary school/high school", "Secondary school completed/high school", "Some primary schooling"…
$ female                <dbl> 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0…
$ female_factor         <fct> Female, Male, Female, Male, Female, Male, Female, Male, Male, Female, Female, Male, Female, Male, Female, Male, Male, Female, Female, Male, Female, Female, Male, Female, Female, Male, Female, Male, Male, Female, Male, Female, Female, Male, Female, Male, Male, Female, …
$ age                   <dbl> 38, 46, 28, 30, 23, 24, 40, 50, 24, 36, 22, 31, 50, 19, 41, 29, 55, 22, 47, 30, 36, 30, 26, 38, 25, 49, 41, 27, 20, 19, 55, 52, 20, 29, 25, 28, 44, 48, 20, 25, 28, 32, 40, 48, 28, 25, 46, 30, 40, 52, 25, 25, 26, 30, 42, 28, 22, 26, 25, 36, 50, 36, 22, 36, 26, 24, 22, …
$ perceivedDemAvg       <dbl> 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19, 3.19…
$ age_country_mean      <dbl> 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4, 35.4…
$ age_country_sd        <dbl> 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8…
$ age_ctd               <dbl> 2.613, 10.613, -7.387, -5.387, -12.387, -11.387, 4.613, 14.613, -11.387, 0.613, -13.387, -4.387, 14.613, -16.387, 5.613, -6.387, 19.613, -13.387, 11.613, -5.387, 0.613, -5.387, -9.387, 2.613, -10.387, 13.613, 5.613, -8.387, -15.387, -16.387, 19.613, 16.613, -15.387, -…
$ age_ctd_std           <dbl> 0.1019, 0.4137, -0.2880, -0.2100, -0.4829, -0.4439, 0.1798, 0.5697, -0.4439, 0.0239, -0.5219, -0.1710, 0.5697, -0.6388, 0.2188, -0.2490, 0.7646, -0.5219, 0.4527, -0.2100, 0.0239, -0.2100, -0.3659, 0.1019, -0.4049, 0.5307, 0.2188, -0.3269, -0.5998, -0.6388, 0.7646, 0.6…
$ country_factor        <fct> Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Benin, Be…
$ ccodecow              <dbl> 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434, 434…
$ cname                 <chr> "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin"…
$ ht_region             <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4…
$ year                  <int> 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008…
$ wdi_gdpcapcon2015     <dbl> 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977, 977…
$ vdem_polyarchy        <dbl> 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.651, 0.…
$ wdi_gdpcapcon2015_ln  <dbl> 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88, 6.88…
$ wdi_gdpcapcon2015_lag <dbl> 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960, 960…

And now we can investigate the relationship between respondents’ average perception of democracy and the democracy score, which is based on information about political institutions and political processes - but with a multilevel model, using the individual predictor of education, gender, and age.

library("lme4")
mod.lmer <- lmer(perceivedDem ~ education_factor + female + age_ctd_std + 
                               vdem_polyarchy + (1 | country_factor), 
                 data = ab.survey.qog)
summary(mod.lmer)

Linear mixed model fit by REML ['lmerMod']
Formula: perceivedDem ~ education_factor + female + age_ctd_std + vdem_polyarchy +      (1 | country_factor)
   Data: ab.survey.qog

REML criterion at convergence: 62870

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.0283 -0.7984  0.0817  0.8161  2.4629 

Random effects:
 Groups         Name        Variance Std.Dev.
 country_factor (Intercept) 0.0709   0.266   
 Residual                   0.7550   0.869   
Number of obs: 24531, groups:  country_factor, 20

Fixed effects:
                                                              Estimate Std. Error t value
(Intercept)                                                    2.14421    0.24023    8.93
education_factorInformal schooling only                       -0.06063    0.03190   -1.90
education_factorSome primary schooling                        -0.10337    0.02148   -4.81
education_factorPrimary school completed                      -0.10113    0.02326   -4.35
education_factorSome secondary school/high school             -0.15559    0.02143   -7.26
education_factorSecondary school completed/high school        -0.16998    0.02327   -7.31
education_factorPost-secondary qualifications, not university -0.24277    0.02840   -8.55
education_factorSome university                               -0.33460    0.03904   -8.57
education_factorUniversity completed                          -0.23867    0.04298   -5.55
education_factorPost-graduate                                 -0.34073    0.09336   -3.65
female                                                         0.02446    0.01132    2.16
age_ctd_std                                                    0.00412    0.01209    0.34
vdem_polyarchy                                                 1.48259    0.41309    3.59

Creating new variables through recoding

This tutorial concludes with two common ways of creating new variables: recoding a variable into a binary variable, and cutting a dichotomous variable into percentiles.

Dichotomizing variables

Sometimes you may be interested in condensing information into a binary yes/no indicator. For instance, instead of including an education measure with 10 categories (which we have in our survey data, as education and education_level), you may want to know whether someone finished high school or not. To do this, we return to using the ifelse() function. Let’s create a variable hs that is set to 1 for all survey respondents that finished high school and to 0 for all who did not. For this, we need to recall the levels of the education variable.

table(ab.work$education_factor, ab.work$education)

                                               
                                                   0    1    2    3    4    5    6    7    8    9
  No formal schooling                           4365    0    0    0    0    0    0    0    0    0
  Informal schooling only                          0 1260    0    0    0    0    0    0    0    0
  Some primary schooling                           0    0 5111    0    0    0    0    0    0    0
  Primary school completed                         0    0    0 3897    0    0    0    0    0    0
  Some secondary school/high school                0    0    0    0 5950    0    0    0    0    0
  Secondary school completed/high school           0    0    0    0    0 4165    0    0    0    0
  Post-secondary qualifications, not university    0    0    0    0    0    0 1674    0    0    0
  Some university                                  0    0    0    0    0    0    0  649    0    0
  University completed                             0    0    0    0    0    0    0    0  506    0
  Post-graduate                                    0    0    0    0    0    0    0    0    0   92

So, values of 5 and above were assigned to respondents who completed high school. We use this cutoff for our new binary variable hs.

ab.work$hs <- ifelse(ab.work$education >= 5, 1, 0)
table(ab.work$education_factor, ab.work$hs)

                                               
                                                   0    1
  No formal schooling                           4365    0
  Informal schooling only                       1260    0
  Some primary schooling                        5111    0
  Primary school completed                      3897    0
  Some secondary school/high school             5950    0
  Secondary school completed/high school           0 4165
  Post-secondary qualifications, not university    0 1674
  Some university                                  0  649
  University completed                             0  506
  Post-graduate                                    0   92

Test yourself!

What would you do if you wanted to create a “trichotomous” variable with values of 0, 1, and 2 for respondents whose education ended before high school (0), after high school (1), and who graduated from a post-secondary institution (2)?

Creating percentiles

For many variables, percentiles are often more useful to interpret than absolute values. Let’s take a slightly challenging example and say we want to create an indicator for groups of 10% of GDP per capita for countries. That is, the bottom 10% of countries receive a 0, the next 10% a 1, and so forth. To do this, we combine a couple of R functions:

• cut() divides the range of a continuous variable into intervals and codes the values according to which interval they fall.
• quantile() calculates percentiles (and requires the na.rm argument to be set to TRUE!)
• as.numeric() converts a factor into a numeric variable

We work with the qog.small data again.

deciles <- quantile(qog.small$wdi_gdpcapcon2015, probs = seq(0, 1, by = 0.1), na.rm = TRUE)
qog.small$wdi_gdpcapcon2015_dec <- cut(qog.small$wdi_gdpcapcon2015, 
                               breaks = deciles, include.lowest = TRUE)
table(qog.small$wdi_gdpcapcon2015_dec)

          [255,627]      (627,1.17e+03] (1.17e+03,1.89e+03] (1.89e+03,2.89e+03] (2.89e+03,4.12e+03] (4.12e+03,6.45e+03] (6.45e+03,1.01e+04] (1.01e+04,1.93e+04] (1.93e+04,3.98e+04] (3.98e+04,1.63e+05] 
                202                 202                 202                 202                 202                 202                 202                 202                 202                 202 

If we want to make this new variable a numeric variable, we can do the following:

qog.small$wdi_gdpcapcon2015_dec2 <- as.numeric(qog.small$wdi_gdpcapcon2015_dec)
table(qog.small$wdi_gdpcapcon2015_dec, qog.small$wdi_gdpcapcon2015_dec2)

                     
                        1   2   3   4   5   6   7   8   9  10
  [255,627]           202   0   0   0   0   0   0   0   0   0
  (627,1.17e+03]        0 202   0   0   0   0   0   0   0   0
  (1.17e+03,1.89e+03]   0   0 202   0   0   0   0   0   0   0
  (1.89e+03,2.89e+03]   0   0   0 202   0   0   0   0   0   0
  (2.89e+03,4.12e+03]   0   0   0   0 202   0   0   0   0   0
  (4.12e+03,6.45e+03]   0   0   0   0   0 202   0   0   0   0
  (6.45e+03,1.01e+04]   0   0   0   0   0   0 202   0   0   0
  (1.01e+04,1.93e+04]   0   0   0   0   0   0   0 202   0   0
  (1.93e+04,3.98e+04]   0   0   0   0   0   0   0   0 202   0
  (3.98e+04,1.63e+05]   0   0   0   0   0   0   0   0   0 202

Reshaping data

You can read about wide and long formats in the Tidy Data article. Sometimes you will encounter data in wide format, where different measurements of the same variable (e.g., measurements of income over several years) are entered in a spreadsheet as columns, not rows. This is the case, for instance, for economic indicators released by statistics offices.

R offers some easy options to convert these data into long format. We’ll briefly work along two examples.

Clean data

The first example that is provided by UCLA’s Stat Consulting Group at https://stats.oarc.ucla.edu/stata/modules/reshaping-data-wide-to-long/. First, we read in a dataset in wide format. The UCLA link I used to use for the data may be offline, so I’m providing the data for course participants on Canvas. Download the dataset, place it in the working directory for this lab, and proceed.

# dat.wide <- import(file = "https://stats.idre.ucla.edu/stat/stata/modules/faminc.dta")
dat.wide <- import(file = "faminc.dta")
dat.wide

  famid faminc96 faminc97 faminc98
1     3    75000    76000    77000
2     1    40000    40500    41000
3     2    45000    45400    45800

Now, we use the pivot_longer() function from the “tidyr” package that you loaded with the tidyverse package. Read the documentation for examples. Here, I wish to convert the dat.wide dataframe so that year is my new identifier (key), faminc is the name of the new variable measuring the value of interest, and famid is the identifier of units in the existing data.

dat.long <- dat.wide |> 
            pivot_longer(cols = -famid,
                         names_to = "year",
                         values_to = "faminc")
dat.long

# A tibble: 9 × 3
  famid year     faminc
  <dbl> <chr>     <dbl>
1     3 faminc96  75000
2     3 faminc97  76000
3     3 faminc98  77000
4     1 faminc96  40000
5     1 faminc97  40500
6     1 faminc98  41000
7     2 faminc96  45000
8     2 faminc97  45400
9     2 faminc98  45800

Next, I convert the year variable into a true numeric variable, removing the faminc prefix:

dat.long$year <- as.numeric(gsub(pattern = "faminc", 
                                 replacement = "19",
                                 x = dat.long$year))
arrange(dat.long, famid, year)

# A tibble: 9 × 3
  famid  year faminc
  <dbl> <dbl>  <dbl>
1     1  1996  40000
2     1  1997  40500
3     1  1998  41000
4     2  1996  45000
5     2  1997  45400
6     2  1998  45800
7     3  1996  75000
8     3  1997  76000
9     3  1998  77000

Messier data

We can also use these functions for “messier” datasets. For instance, let’s say we want to download some economic indicators from the U.S. Bureau of Economic Analysis. The website “U.S. Economy at a Glance” offers these data as an Excel spreadsheet (link on the top right: http://www.bea.gov/newsreleases/xls/glance.xlsx).

After downloading this spreadsheet, we get the following data:

The original spreadsheet “Overview of the Economy: Table” from the Bureau of Economic Analysis.

I clean these data up by hand in Excel so that they can be read into R:

The cleaned spreadsheet, saved as “commerce_202006.csv”.

com.wide <- import("commerce202006.csv")
com.wide

  Percent change at seasonally adjusted annual rate (unless otherwise noted) 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q3 2019Q4 2020Q1
1                                                    Gross domestic product*    2.5    3.5    2.9    1.1    3.1    2.0    2.1    2.1   -5.0
2                                                  Gross domestic purchases*    2.5    2.8    4.9    1.4    2.3    2.6    2.2    0.6   -6.1
3                                         Personal consumption expenditures*    1.7    4.0    3.5    1.4    1.1    4.6    3.2    1.8   -6.8
4                                           Nonresidential fixed investment*    8.8    7.9    2.1    4.8    4.4   -1.0   -2.3   -2.4   -7.9
5                                                    Residential investment*   -5.3   -3.7   -4.0   -4.7   -1.0   -3.0    4.6    6.5   18.5
6                                             Exports of goods and services*    0.8    5.8   -6.2    1.5    4.1   -5.7    1.0    2.1   -8.7
7                                             Imports of goods and services*    0.6    0.3    8.6    3.5   -1.5    0.0    1.8   -8.4  -15.5
8                  Government consumption expenditures and gross investment*    1.9    2.6    2.1   -0.4    2.9    4.8    1.7    2.5    0.8

com.long <- pivot_longer(com.wide, cols = -1, names_to = "quarter", values_to = "change")
names(com.long) <- c("indicator", "quarter", "change")
com.long <- com.long |> pivot_wider(names_from = "indicator", values_from = "change")
com.long

# A tibble: 9 × 9
  quarter `Gross domestic product*` `Gross domestic purchases*` `Personal consumption expenditures*` `Nonresidential fixed investment*` `Residential investment*` `Exports of goods and services*` `Imports of goods and services*` `Government consumption expenditures and gross investment*`
  <chr>                       <dbl>                       <dbl>                                <dbl>                              <dbl>                     <dbl>                            <dbl>                            <dbl>                                                       <dbl>
1 2018Q1                        2.5                         2.5                                  1.7                                8.8                      -5.3                              0.8                              0.6                                                         1.9
2 2018Q2                        3.5                         2.8                                  4                                  7.9                      -3.7                              5.8                              0.3                                                         2.6
3 2018Q3                        2.9                         4.9                                  3.5                                2.1                      -4                               -6.2                              8.6                                                         2.1
4 2018Q4                        1.1                         1.4                                  1.4                                4.8                      -4.7                              1.5                              3.5                                                        -0.4
5 2019Q1                        3.1                         2.3                                  1.1                                4.4                      -1                                4.1                             -1.5                                                         2.9
6 2019Q2                        2                           2.6                                  4.6                               -1                        -3                               -5.7                              0                                                           4.8
7 2019Q3                        2.1                         2.2                                  3.2                               -2.3                       4.6                              1                                1.8                                                         1.7
8 2019Q4                        2.1                         0.6                                  1.8                               -2.4                       6.5                              2.1                             -8.4                                                         2.5
9 2020Q1                       -5                          -6.1                                 -6.8                               -7.9                      18.5                             -8.7                            -15.5                                                         0.8

Now all I need to do is (1) change the variable names and (2) convert quarters into proper dates. For (1), I use the gsub() function, which is very helpful for the purpose of changing each element of a character vector:

names(com.long) <- gsub(pattern = "[/*]", 
                        replacement = "", 
                        x = names(com.long))
com.long

# A tibble: 9 × 9
  quarter `Gross domestic product` `Gross domestic purchases` `Personal consumption expenditures` `Nonresidential fixed investment` `Residential investment` `Exports of goods and services` `Imports of goods and services` `Government consumption expenditures and gross investment`
  <chr>                      <dbl>                      <dbl>                               <dbl>                             <dbl>                    <dbl>                           <dbl>                           <dbl>                                                      <dbl>
1 2018Q1                       2.5                        2.5                                 1.7                               8.8                     -5.3                             0.8                             0.6                                                        1.9
2 2018Q2                       3.5                        2.8                                 4                                 7.9                     -3.7                             5.8                             0.3                                                        2.6
3 2018Q3                       2.9                        4.9                                 3.5                               2.1                     -4                              -6.2                             8.6                                                        2.1
4 2018Q4                       1.1                        1.4                                 1.4                               4.8                     -4.7                             1.5                             3.5                                                       -0.4
5 2019Q1                       3.1                        2.3                                 1.1                               4.4                     -1                               4.1                            -1.5                                                        2.9
6 2019Q2                       2                          2.6                                 4.6                              -1                       -3                              -5.7                             0                                                          4.8
7 2019Q3                       2.1                        2.2                                 3.2                              -2.3                      4.6                             1                               1.8                                                        1.7
8 2019Q4                       2.1                        0.6                                 1.8                              -2.4                      6.5                             2.1                            -8.4                                                        2.5
9 2020Q1                      -5                         -6.1                                -6.8                              -7.9                     18.5                            -8.7                           -15.5                                                        0.8

And for (2), if I choose to do so, I use two functions from the zoo package to create a “date” variable capturing the last day of each quarter:

com.long$date <- zoo::as.Date(zoo::as.yearqtr(com.long$quarter, format = "%YQ%q"), frac = 1)
table(com.long$date)

2018-03-31 2018-06-30 2018-09-30 2018-12-31 2019-03-31 2019-06-30 2019-09-30 2019-12-31 2020-03-31 
         1          1          1          1          1          1          1          1          1 

Tables from PDF files

To import tables from PDF files into R, I recommend the “tabulizer” package. This package works well in my experience, but it may require some extra work to install and configure Java on your computer (and the rjava interface for R). This page has some suggestions for troubleshooting common issues during installation as well as some code examples.

Concluding thoughts

Data management and data processing will almost always eat up much of the overall time you spend on a quantitative research project, especially when you work with clustered data. Often, you might end up spending 80% of your time on data management before getting to the remaining 20% that it takes you to analyze your data. Developing good habits and an easy-to-use toolset will therefore go long ways to make you a more efficient (and effective) researcher.

Note

Here are some more tutorials on data cleaning and data processing if you find some time and want to practice your skills:

• dplyr package website
• dplyr package vignette
• The R for Data Science book