tomhopper

# Sort a grouping variable by a second numeric variable, in the presence of a faceting variable
# Ostensibly so that the grouping variable plots in ascending (or descending) order of the
# central tendency of the numeric variable.

library(tidyverse)

# Create a dataset
set.seed(10001)
my_df <- tibble(facet_var = rep(letters[1:4], each = 5*10),
                group_var = rep(rep(letters[13:17], each = 10), times = 4),

tomhopper

First-thing at the top of the Rmd file, after the YAML header, is the 
following css style block.

    <style>
    pre {
      overflow-x: auto;
    }
    pre code {
      word-wrap: normal;
      white-space: pre;

tomhopper

# Set up some test data
my_names <- letters[1:5]
my_names
# [1] "a" "b" "c" "d" "e"

# We want a list, like "a, b, c, d, e"

# Paste the names together
paste(my_names)
# [1] "a" "b" "c" "d" "e"

tomhopper

At the top of your R markdown, beneath the yaml title block, add the following `<script>` section

    ---

    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
      TeX: { 
        equationNumbers: { 
          autoNumber: "all",
          formatNumber: function (n) {return n}

tomhopper

The _captioner_ library makes this work.

    ```{r libraries}
    library(captioner)
    ```
 
Before captioning any tables, we have to set up the table captions and the numbering using `captioner::captioner()`. _captioner_ numbers tables in the order they appear in this code block, so _tab_curve_ will be table 1, and _tab_comp_ will be table 2, wherever they appear in the document.
 
    ```{r setup_captions}
    table_nums <- captioner(prefix = "Table")

tomhopper

#' @description Returns a list of all packages on CRAN
#' @param columns_list A character vector of field names to return from package DESCRIPTION files
#' @return A data frame containing all packages on CRAN
#' @details Function modified from StackOverflow answer at \url{https://stackoverflow.com/a/11561793}.
#' @importFrom magrittr %>%
#' @importFrom tibble as.tibble
#' @importFrom dplyr select_
getCRANPackages <- function(columns_list = c("Package", "Title", "Version", "Date", "Published", "URL")) {
  contrib.url(getOption("repos")["CRAN"], "source") 
  description <- sprintf("%s/web/packages/packages.rds", 

tomhopper

# Paste the data into a string:
data_string <- "     Filename         Fc 
Q5161811.04    20.36 
Q5161811.04    20.46 
Q5161811.04    24.17 
Q5161811.04    20.20 "

# Use a fileConnection to read the data into a data frame
the_data <- read.table(file = textConnection(object = data_string), 
                       header = TRUE, 

tomhopper

# Limit factor levels displayed in ggplot2 legend
# When using many factor levels, the legend can become too large to be useful
# We want to pair back the legend to something readable, with less detail.

# For our minimally reproducible example, we will create a series of 40 sinusoidal 
# curves that are slightly offset from each other, then plot each one in a different
# color. The resulting plot will have a somewhat rainbow look, but the legend will
# contain 40 labels; we want to reduce this to about 5 labels in the legend.

# Libraries used

tomhopper

## Demonstration of central limit theorem
## Based on code in an anonymous comment to the blog post at \url{https://sas-and-r.blogspot.com/2012/01/example-919-demonstrating-central-limit.html}

## Libraries ####
library(nortest)
library(dplyr)
library(ggplot2)

## Data used ####
# right-triangle distribution (peak at 0; minimum at 1)

tomhopper

## Access data provided by packages

## List all available data sets, plus ancillary information ####
the_data <- data()

## Extract just the packages as a data frame ####
the_data_df <- data.frame(the_data$results, stringsAsFactors = FALSE)

## Extract specific data sets ####
specific_data <- get(the_data_df$Item[1])