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@andrigerber
Last active October 7, 2021 14:03
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Codebook generator
manual instruction: https://www.youtube.com/watch?v=gMRwUzuGCQU&ab_channel=PsychProf
automated online version: https://opencpu.psych.bio.uni-goettingen.de/ocpu/library/codebook/www/
Cran site: https://cran.r-project.org/web/packages/codebook/index.html
Package study: https://journals.sagepub.com/doi/full/10.1177/2515245919838783
formr()package: https://formr.org/documentation#api
---
title: "Codebook"
output:
html_document:
toc: true
toc_depth: 4
toc_float: true
code_folding: 'hide'
self_contained: true
pdf_document:
toc: yes
toc_depth: 4
latex_engine: xelatex
---
```{r setup}
knitr::opts_chunk$set(
warning = TRUE, # show warnings during codebook generation
message = TRUE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)
```
We collected the following data.
```{r codebook}
# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
only_labelled = TRUE, # only labelled values are autodetected as
# missing
negative_values_are_missing = FALSE, # negative values are missing values
ninety_nine_problems = TRUE, # 99/999 are missing values, if they
# are more than 5 MAD from the median
)
# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
codebook_data <- detect_scales(codebook_data)
# Does your dataset have a name that is not reflected in the file name?
# Uncomment the line below and change the name
# metadata(codebook_data)$name <- "My Awesome Dataset"
codebook(codebook_data)
```
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