crsh

# Embedding data into RMarkdown documents

<details>
<summary>Show data</summary>

```{r}
cars <- structure(list(speed = c(4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 
12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 16, 
16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 
22, 23, 24, 24, 24, 24, 25), dist = c(2, 10, 4, 22, 16, 10, 18, 

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# Function definition
add_event <- function(start, end = NULL, label = NULL, line = 0.5, las = 1, col = scales::alpha("steelblue", 0.5)) {
  if(is.null(end)) {
    end <- paste(start, "23:59:59")
    start <- paste(start, "00:00:00")
  }
  
  start <- as.POSIXct(start)
  end <- as.POSIXct(end)
  

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label_test <- cars
attr(label_test, "variable.labels") <- c("Speed (km/h)", "Distance traveled (km)")
View(label_test)
str(label_test) # data.frame attribute

# Alternative using Hmisc (does not show up in RStudio Viewer)
label_test <- cars

Hmisc::label(label_test$speed) <- "Speed (km/h)"
Hmisc::label(label_test$dist) <- "Distance traveled (km)"

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n_bf <- 50

# Simulate data
library("BayesFactor")
previous_data <- rnorm(2, mean = 0, sd = 3)

uninformative_bf <- function(x, previous_data = NULL, mu = 0) {
  bf <- as.vector(ttestBF(c(previous_data, x), mu = mu))
  (1 - bf)^2
}

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batch_read <- function(path, pattern, recursive = FALSE, read_fun, ...) {
  data.files <- list.files(path, pattern = pattern, recursive = recursive)
  data <- lapply(paste0(path, data.files), read_fun, ...)
  data <- do.call("rbind", data)
  data
}

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batch_read_github <- function(url, pattern, read_fun, ...) {
  if(!require("rvest")) stop("Please install the 'rvest' package.")
  if(!require("RCurl")) stop("Please install the 'RCurl' package.")
  
  # Fetch file names
  github_page <- read_html(url)
  file_nodes <- html_nodes(github_page, ".content .css-truncate-target .js-navigation-open")
  file_names <- html_text(file_nodes)
  file_url <- html_attr(file_nodes, "href")[grep(pattern, file_names)]
  

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# x       data.frame. Aggregated data for the factorial design, e.g., from aggregate(measure ~ subject * factor1 * factor2, data, mean).
# measure Character. Name of the measure.
# formula Formula. A formula specifying the factor (combination) for which to calculate the correlation, e.g., `subject ~ factor1` for a 
#                   main effect or `subject ~ factor1 + factor2` (yes, it needs to be a "+") for an interaction. Note that G*Power can be
#                   used to perform power analyses for up to two repeated measures factors as long as one of them has only two levels.
#                   To do so, enter the larger number of factor levels into the field "Number of measurements" and multiply the effect
#                   size f by √p, where p is the number of levels of the other factor). If both factor have more than two levels G*Power
#                   will underestimate the required sample size! (See http://goo.gl/RgciM4 [German] for details)
# type    Character. Specifies the estimation app

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#' Plot network of package dependencies
#'
#' @param pkg package description, can be path or package name. See \code{\link[devtools]{as.package}} for
#'    more information.
#'
#' @details The resulting plot visualizes the network of package dependencies. If you are trying to cut down
#'    on package dependencies look for big red dots that represent a lot of upstream but few downstream
#'    dependencies.
#' @import ggplot2
#' @export

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library("dplyr")
library("tidyr")
library("likert")
library("papaja")

dat <- data.frame(
  Year = factor(seq(2009, 2017, 2))
  , Democrats = c(17, 11, 12, 7, 6)
  , Mixed = c(24, 17, 14, 20, 19)
  , Republicans = c(9, 22, 24, 23, 25)

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install_load_all <- function(x) {
  .install_load_all <- function(x) {
    if(!require(x, character.only = TRUE)) {
      install.packages(x, repos = "http://cran.us.r-project.org")
      library(x, character.only = TRUE)
    }
  }
  
  invisible(sapply(x, .install_load_all))
}