header.R

library(tinytex)
library(huxtable)
library(dplyr)
library(janitor)
library(rvest)
library(magrittr)
library(tidyr)
library(ggplot2)
library(knitr)

set.seed(1963)
blueshade <- "#3d6da9" # match blue colour for plots to jamovi logo colour 

#### knitr options -----------
knitr::opts_chunk$set(
          fig.align = "left",
          out.width =  if (knitr::is_latex_output()) "100%" else "80%"
          )
if (knitr::is_html_output()) options(huxtable.knitr_output_format = 'html')
options(knitr.table.format = function() {
  if (knitr::is_latex_output()) 'latex'
  if (knitr::is_html_output()) 'html' else pandoc
})

##### read tables from html files 
lsj_chapter_tables <- list()
lsj_chapter_tables[[1]] <- read_html("data_and_tables/1. Why do we learn statistics.html") %>% html_table() 
lsj_chapter_tables[[2]] <- read_html("data_and_tables/2. A brief introduction to research design.html") %>% html_table() 
lsj_chapter_tables[[3]] <- read_html("data_and_tables/3. Getting started with jamovi.html") %>% html_table() 
lsj_chapter_tables[[4]] <- read_html("data_and_tables/4. Descriptive statistics.html") %>% html_table() 
lsj_chapter_tables[[5]] <- read_html("data_and_tables/5. Drawing graphs.html") %>% html_table() 
lsj_chapter_tables[[6]] <- read_html("data_and_tables/6. Pragmatic matters.html") %>% html_table() 
lsj_chapter_tables[[7]] <- read_html("data_and_tables/7. Introduction to probability.html") %>% html_table() 
lsj_chapter_tables[[8]] <- read_html("data_and_tables/8. Estimating unknown quantities from a sample.html") %>% html_table() 
lsj_chapter_tables[[9]] <- read_html("data_and_tables/9. Hypothesis testing.html") %>% html_table() 
lsj_chapter_tables[[10]] <- read_html("data_and_tables/10. Categorical data analysis.html") %>% html_table() 
lsj_chapter_tables[[11]] <- read_html("data_and_tables/11. Comparing two means.html") %>% html_table() 
lsj_chapter_tables[[12]] <- read_html("data_and_tables/12. Correlation and linear regression.html") %>% html_table() 
lsj_chapter_tables[[13]] <- read_html("data_and_tables/13. Comparing several means (one-way ANOVA).html") %>% html_table() 
lsj_chapter_tables[[14]] <- read_html("data_and_tables/14. Factorial ANOVA.html") %>% html_table() 
lsj_chapter_tables[[15]] <- read_html("data_and_tables/15. Factor Analysis.html") %>% html_table() 
lsj_chapter_tables[[16]] <- read_html("data_and_tables/16. Bayesian statistics.html") %>% html_table() 


#lsj_chapter_tables      
saveRDS(lsj_chapter_tables, "data_and_tables/lsj_chapter_tables.rds")
##### huxtable defaults and function ----

# create huxtable
huxthattibble <- function(x) {
  { if (names(x[1]) == "X1") 
    ( hux(x) %>% 
          row_to_names(1) %>%
          set_header_rows(1, TRUE))
    else ( hux(x) %>% 
             set_contents(1, value = names(x)) ) 
   } %>%
  { if (knitr::is_latex_output()) 
    (set_escape_contents(., everywhere, value = FALSE)) 
    else (set_escape_contents(., everywhere, value = TRUE))
  } %>% 
  set_align(everywhere, everywhere, "center") %>% 
  set_top_padding(everywhere, everywhere, 2 ) %>%
  set_bottom_padding(everywhere, everywhere, 2 ) %>%
  set_left_padding(everywhere, everywhere, 12 ) %>%
  set_right_padding(everywhere, everywhere, 12 ) %>%
  set_width(0.9) %>% 
  set_caption_pos("bottomleft") %>% 
  theme_article() #%>% 
  #set_background_color(odds, everywhere, "grey95")
}

# Recursively apply function to all data frames in a nested list
dfrapply <- function(object, f, ...) {
  if (inherits(object, "data.frame")) {
    return(f(object, ...))
  }
  if (inherits(object, "list")) {
    return(lapply(object, function(x) dfrapply(x, f, ...)))
  }
  stop("List element must be either a data frame or another list")
}

#lsj_chapter_tables <- readRDS("lsj_chapter_tables.rds")
huxtabs <- dfrapply(lsj_chapter_tables, huxthattibble)
saveRDS(huxtabs, "data_and_tables/lsj_chapter_huxtabs.rds")
#huxtabs <- readRDS("data_and_tables/lsj_chapter_huxtabs.rds")