revdep/library/docxtools/old/dplyr/doc/programming.R

In graphdr/docxtools: Tools for R Markdown to Docx Documents

## ---- echo = FALSE, message = FALSE-------------------------------------------
knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)
set.seed(1014)

## ----setup, message = FALSE---------------------------------------------------
library(dplyr)

## ---- results = FALSE---------------------------------------------------------
starwars[starwars$homeworld == "Naboo" & starwars$species == "Human", ,]

## ---- results = FALSE---------------------------------------------------------
starwars %>% filter(homeworld == "Naboo", species == "Human")

## -----------------------------------------------------------------------------
df <- data.frame(x = runif(3), y = runif(3))
df$x

## ---- results = FALSE---------------------------------------------------------
var_summary <- function(data, var) {
  data %>%
    summarise(n = n(), min = min({{ var }}), max = max({{ var }}))
}
mtcars %>% 
  group_by(cyl) %>% 
  var_summary(mpg)

## ---- results = FALSE---------------------------------------------------------
for (var in names(mtcars)) {
  mtcars %>% count(.data[[var]]) %>% print()
}

## ---- results = FALSE---------------------------------------------------------
summarise_mean <- function(data, vars) {
  data %>% summarise(n = n(), across({{ vars }}, mean))
}
mtcars %>% 
  group_by(cyl) %>% 
  summarise_mean(where(is.numeric))

## ---- results = FALSE---------------------------------------------------------
vars <- c("mpg", "vs")
mtcars %>% select(all_of(vars))
mtcars %>% select(!all_of(vars))

## -----------------------------------------------------------------------------
mutate_y <- function(data) {
  mutate(data, y = a + x)
}

## -----------------------------------------------------------------------------
my_summary_function <- function(data) {
  data %>% 
    filter(x > 0) %>% 
    group_by(grp) %>% 
    summarise(y = mean(y), n = n())
}

## -----------------------------------------------------------------------------
#' @importFrom rlang .data
my_summary_function <- function(data) {
  data %>% 
    filter(.data$x > 0) %>% 
    group_by(.data$grp) %>% 
    summarise(y = mean(.data$y), n = n())
}

## -----------------------------------------------------------------------------
my_summarise <- function(data, group_var) {
  data %>%
    group_by({{ group_var }}) %>%
    summarise(mean = mean(mass))
}

## -----------------------------------------------------------------------------
my_summarise2 <- function(data, expr) {
  data %>% summarise(
    mean = mean({{ expr }}),
    sum = sum({{ expr }}),
    n = n()
  )
}

## -----------------------------------------------------------------------------
my_summarise3 <- function(data, mean_var, sd_var) {
  data %>% 
    summarise(mean = mean({{ mean_var }}), sd = sd({{ sd_var }}))
}

## -----------------------------------------------------------------------------
my_summarise4 <- function(data, expr) {
  data %>% summarise(
    "mean_{{expr}}" := mean({{ expr }}),
    "sum_{{expr}}" := sum({{ expr }}),
    "n_{{expr}}" := n()
  )
}
my_summarise5 <- function(data, mean_var, sd_var) {
  data %>% 
    summarise(
      "mean_{{mean_var}}" := mean({{ mean_var }}), 
      "sd_{{sd_var}}" := sd({{ sd_var }})
    )
}

## -----------------------------------------------------------------------------
my_summarise <- function(.data, ...) {
  .data %>%
    group_by(...) %>%
    summarise(mass = mean(mass, na.rm = TRUE), height = mean(height, na.rm = TRUE))
}

starwars %>% my_summarise(homeworld)
starwars %>% my_summarise(sex, gender)

## -----------------------------------------------------------------------------
my_summarise <- function(data, summary_vars) {
  data %>%
    summarise(across({{ summary_vars }}, ~ mean(., na.rm = TRUE)))
}
starwars %>% 
  group_by(species) %>% 
  my_summarise(c(mass, height))

## -----------------------------------------------------------------------------
my_summarise <- function(data, group_var, summarise_var) {
  data %>%
    group_by(across({{ group_var }})) %>% 
    summarise(across({{ summarise_var }}, mean))
}

## -----------------------------------------------------------------------------
my_summarise <- function(data, group_var, summarise_var) {
  data %>%
    group_by(across({{ group_var }})) %>% 
    summarise(across({{ summarise_var }}, mean, .names = "mean_{.col}"))
}

## ---- results = FALSE---------------------------------------------------------
for (var in names(mtcars)) {
  mtcars %>% count(.data[[var]]) %>% print()
}

## ---- results = FALSE---------------------------------------------------------
mtcars %>% 
  names() %>% 
  purrr::map(~ count(mtcars, .data[[.x]]))

## ---- eval = FALSE------------------------------------------------------------
#  library(shiny)
#  ui <- fluidPage(
#    selectInput("var", "Variable", choices = names(diamonds)),
#    tableOutput("output")
#  )
#  server <- function(input, output, session) {
#    data <- reactive(filter(diamonds, .data[[input$var]] > 0))
#    output$output <- renderTable(head(data()))
#  }