LaroplanOCR/codebook.Rmd at main · JMSLab/LaroplanOCR · GitHub

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---
title: "Codebook for LaroplanOCR"
date: "\n`r format(Sys.time(), '%d %B, %Y')`"
output:
  pdf_document:
    toc: true
    toc_depth: 2
---

```{r prepare readme, include = F}
remove(list = ls())
library(knitr)
library(dplyr)
library(stringr)
knitr::opts_chunk$set(echo = TRUE)

## Prepare README data

readme <- read.delim("README.md", header = F, sep = "\n",
                     col.names = "text", encoding = 'UTF-8')

readme <- readme %>%
  mutate(title    = 1*grepl("## |### ", text),
         subtitle = 1*grepl("#### ",    text),
         title_num =  cumsum(title))

titles <- readme %>%
  group_by(title_num) %>%
  summarise(title_name = first(text)) %>%
  ungroup() %>%
  mutate(title_name = str_trim(gsub("#", "", title_name)))

readme <- readme %>%
  left_join(titles, by = "title_num") %>%
  filter(title == 0)

```


```{r prepare codebook, include = F}
counts_files <- list.files(path = "analysis/output", pattern = "*_counts.csv",
                           full.names = T)
dfs <- lapply(counts_files, read.csv)

df.all <- data.frame()
for (i in 1:length(dfs)) {
  yyyy <- substr(basename(counts_files[i]), 4, 7)
  if (yyyy == "1994") {
    yyyy <- substr(basename(counts_files[i]), 4, 8)
  }

  df <- dfs[[i]] %>%
    mutate(year = yyyy)

  df.all <- bind_rows(df.all, df)
}

laroplaner <- read.csv("laroplaner.csv",
                       encoding = 'UTF-8') %>%
  select(year, lgr_name)
```


## Overview
```{r overview, include = F}
readme <- readme %>%
  mutate(text = gsub("[`./analysis/output/`](analysis/output)",
                     "[`./datasets/`](datasets)", text, fixed = T))

overview_lines <- readme %>% filter(title_name == "LaroplanOCR")
overview_text  <- paste0(overview_lines$text, collapse = "\n")
```


`r overview_text`

We include the following Läroplaner in the data:

```{r, echo = F}
kable(laroplaner,
      col.names = c("Year", "Title"),
      align     = c('c', 'l'))
```

The files were obtained from the [Gothenburg University Publications Electronic Archive](https://gupea.ub.gu.se/).

The full code that processes the raw pdf files into the datasets available in this package can be found in [`github.com/JMSLab/LaroplanOCR`](https://github.com/JMSLab/LaroplanOCR).


## Using the Datasets
```{r datasets, include = F}
datasets_lines <- readme %>% filter(title_name == "Using the Datasets")
datasets_lines <- datasets_lines %>%
  mutate(text = ifelse(grepl(":", text),       paste(text, "\n"), text)) %>%
  mutate(text = ifelse(grepl("lgrYYYY", text), paste(text, "\n"), text))

datasets_text  <- paste0(datasets_lines$text, collapse = "\n")
```


`r datasets_text`

## Counts
```{r codebook, include = F}
vars <- names(dfs[[1]])
for (i in 1:length(vars)) vars[i] <- paste0('`', vars[i], '`')
vars <- paste0(vars, collapse = ", ")

sumstats <- df.all %>%
  group_by(year) %>%
  summarise(unique_words = n(),
            mean_count   = round(mean(n), 2)) %>%
  mutate(unique_words = format(unique_words, big.mark = ","))
```

The "counts" files include two variables: `r vars`.

Some summary statistics of these variables:
```{r, echo = F}
kable(sumstats,
      col.names = c("Year", "Unique word", "Mean n"),
      align     = rep('c', 3))
```


## Citations
```{r citations, include = F}
citations_lines <- readme %>% filter(title_name == "Citations")
citations_text  <- paste0(citations_lines$text, collapse = "\n")
```

`r citations_text`