r-data-recipes

A family cookbook of data Recipes.

Row totals
- Calculate row totals using rowwise(), do(), and case_when()
Row-wise string matching
- Use across() or c_across() inside case_when()
Partial string matching
- %gin%: A reimagination of %in% using grepl() for partial string matching
Keep distinct categories
- Keep distinct instances of a category using if_else() and distinct()
Reinstall packages after a major R update
- Reinstall packages from your previous library after a major R update.
Unnest all list-cols into columns
- Unnest all list-cols in a data frame into columns for each unique element.
Apply complex logic across multiple columns
- Use across() with case_when() to apply the same logic to multiple columns.
Find all matching variables in all data frames
- Find all matching variables in all data frames in the global environment.

Row totals

Calculate row totals using rowwise(), do(), and case_when()

# setup
library(tidyverse)
df <- data_frame(
  id = c(1,2,3,4),
  v1 = c(NA,1,1,NA),
  v2 = c(NA,1,NA,NA),
  v3 = c(NA,1,1,NA)
)
# mutate with if_else doesn't work
a <- df %>% mutate(v_sum =
    if_else(
        !(is.na(v1) & is.na(v2) & is.na(v3)),
        sum(v1, v2, v3, na.rm = TRUE), 
        if_else(
            is.na(v1) & is.na(v2) & is.na(v3),
            99,
            NA_real_)
    ))
# mutate with case_when doesn't work
b <- df %>% mutate(v_sum =
    case_when(
        !(is.na(v1) & is.na(v2) & is.na(v3)) ~ sum(v1, v2, v3, na.rm = TRUE),
        is.na(v1) & is.na(v2) & is.na(v3) ~ 99,
        TRUE ~ NA_real_)
)
# rowwise and do with case_when works, but requires binding the result back to the original data frame
c <- df %>% rowwise %>% do(v_sum =
case_when(
        !(is.na(.$v1) & is.na(.$v2) & is.na(.$v3)) ~ sum(.$v1, .$v2, .$v3, na.rm = TRUE),
        is.na(.$v1) & is.na(.$v2) & is.na(.$v3) ~ 99,
        TRUE ~ NA_real_)
) %>% unlist %>% as_tibble %>% bind_cols(df, .)

Row-wise string matching

Use across() or c_across() inside case_when().

# 1. setup ----
library(dplyr, warn.conflicts = FALSE)
library(magrittr)
library(stringr)
library(palmerpenguins)
# 2. data ----
set.seed(42L)
penguins %<>%
  mutate(
    island_two = sample(island),
    island_three = sample(island)
  )
penguins %>% select(species, contains("island"))
#> # A tibble: 344 × 4
#>    species island    island_two island_three
#>    <fct>   <fct>     <fct>      <fct>       
#>  1 Adelie  Torgersen Dream      Dream       
#>  2 Adelie  Torgersen Dream      Biscoe      
#>  3 Adelie  Torgersen Biscoe     Biscoe      
#>  4 Adelie  Torgersen Torgersen  Biscoe      
#>  5 Adelie  Torgersen Biscoe     Torgersen   
#>  6 Adelie  Torgersen Dream      Biscoe      
#>  7 Adelie  Torgersen Torgersen  Biscoe      
#>  8 Adelie  Torgersen Dream      Biscoe      
#>  9 Adelie  Torgersen Torgersen  Torgersen   
#> 10 Adelie  Torgersen Dream      Biscoe      
#> # … with 334 more rows
# 3. case_when then across ----
penguins_same_string <-
  penguins %>% 
  rowwise() %>%
  mutate(
    like_islands = case_when(
      any(across(contains("island")) == "Dream") ~ 1L,
      TRUE ~ 0L
    )
  )
penguins_same_string %>% select(species, contains("island"))
#> # A tibble: 344 × 5
#> # Rowwise: 
#>    species island    island_two island_three like_islands
#>    <fct>   <fct>     <fct>      <fct>               <int>
#>  1 Adelie  Torgersen Dream      Dream                   1
#>  2 Adelie  Torgersen Dream      Biscoe                  1
#>  3 Adelie  Torgersen Biscoe     Biscoe                  0
#>  4 Adelie  Torgersen Torgersen  Biscoe                  0
#>  5 Adelie  Torgersen Biscoe     Torgersen               0
#>  6 Adelie  Torgersen Dream      Biscoe                  1
#>  7 Adelie  Torgersen Torgersen  Biscoe                  0
#>  8 Adelie  Torgersen Dream      Biscoe                  1
#>  9 Adelie  Torgersen Torgersen  Torgersen               0
#> 10 Adelie  Torgersen Dream      Biscoe                  1
#> # … with 334 more rows
# 4. case_when then c_across ----
penguins_same_partial_string <-
  penguins %>% 
  rowwise() %>%
  mutate(
    like_islands = case_when(
      any(str_detect(c_across(contains("island")), "Dre")) ~ 1L,
      TRUE ~ 0L
    )
  )
penguins_same_partial_string %>% select(species, contains("island"))
#> # A tibble: 344 × 5
#> # Rowwise: 
#>    species island    island_two island_three like_islands
#>    <fct>   <fct>     <fct>      <fct>               <int>
#>  1 Adelie  Torgersen Dream      Dream                   1
#>  2 Adelie  Torgersen Dream      Biscoe                  1
#>  3 Adelie  Torgersen Biscoe     Biscoe                  0
#>  4 Adelie  Torgersen Torgersen  Biscoe                  0
#>  5 Adelie  Torgersen Biscoe     Torgersen               0
#>  6 Adelie  Torgersen Dream      Biscoe                  1
#>  7 Adelie  Torgersen Torgersen  Biscoe                  0
#>  8 Adelie  Torgersen Dream      Biscoe                  1
#>  9 Adelie  Torgersen Torgersen  Torgersen               0
#> 10 Adelie  Torgersen Dream      Biscoe                  1
#> # … with 334 more rows

^{Created on 2022-04-15 by the reprex package (v2.0.1)}

Partial string matching

%gin%: A reimagination of %in% using grepl() for partial string matching

# define %gin%
"%gin%" <- function(pattern, x) grepl(pattern, x)
# %in% evaluates to FALSE because it looks for full string matches
"a" %in% "apple"
# %gin% evaluates to TRUE
"a" %gin% "apple"

Keep distinct categories

Keep distinct instances of a category using if_else() and distinct()

# setup
library(tidyverse)
df <- data_frame(
  category = c("% proficient", "% proficient", "% proficient", "n proficient", "n proficient", "n proficient"),
  race = c("YES", "NO", "NO", "YES", "NO", "NO"),
  gender = c("NO", "NO", "YES", "NO", "NO", "NO"),
  frpl = c("NO", "NO", "NO", "NO", "YES", "NO"),
  race.x.gender = c("NO", "NO", "NO", "NO", "NO", "NO")
)
# answer 1
df %>%
  group_by(category) %>%
  mutate(
    race = TRUE & "YES" %in% race,
    gender = TRUE & "YES" %in% gender,
    frpl = TRUE & "YES" %in% frpl,
    race.x.gender = TRUE & "YES" %in% race.x.gender
  ) %>%
  distinct(.keep_all = TRUE)
# answer 2
df %>%
  group_by(category) %>%
  mutate(
    race = if_else("YES" %in% race, "YES", "NO"),
    gender = if_else("YES" %in% gender, "YES", "NO"),
    frpl = if_else("YES" %in% frpl, "YES", "NO"),
    race.x.gender = if_else("YES" %in% race.x.gender, "YES", "NO")
  ) %>%
  distinct(.keep_all = TRUE)

Reinstall packages after a major R update

Reinstall packages from your previous library after a major R update. This will work even if upgrading from R 3.x to 4.x. Note that RStudio may prompt you to restart R repeatedly; to keep the script going keep pressing "No" when this happens.

# setup
if (!require(tidyverse)) install.packages("tidyverse")
if (!require(fs)) install.packages("fs")
library(tidyverse)
library(fs)
# get all installed R versions
if (Sys.info()[["sysname"]] == "Darwin") { 
  r_dir <- tibble::tibble(path = fs::dir_ls(fs::path_dir(fs::path_dir(fs::path_dir(.libPaths()[[1]])))))
}
if (Sys.info()[["sysname"]] %in% c("Linux", "Windows")) {
  r_dir <- tibble::tibble(path = fs::dir_ls(fs::path_dir(.libPaths()[[1]])))
}
# cue music
r_dir <- r_dir %>%
  # drop current R version
  dplyr::filter(!(stringr::str_detect(path, "Current"))) %>%
  # extract the current and penultimate R versions as strings
  dplyr::rowwise() %>%
  dplyr::mutate(version = as.numeric(stringr::str_extract(path, "[0-9]\\.[0-9]"))) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(new_r = dplyr::nth(version, -1L), old_r = dplyr::nth(version, -2L)) %>%
  dplyr::mutate_at(vars("new_r", "old_r"), ~as.character(formatC(.x, digits = 1L, format = "f"))) %>%
  dplyr::filter(version == old_r)
# get new and old R library paths
new_libpath <- .libPaths()
old_libpath <- stringr::str_replace(new_libpath, r_dir$new_r, r_dir$old_r)
# get list of old installed R packages
pkg_list <- as.list(list.files(old_libpath))
# define install_all() function
install_all <- function(x) {
  print(x)
  install.packages(x, quiet = TRUE)
}
# install all R packages in pkg_list
purrr::quietly(purrr::walk(pkg_list, install_all))

Unnest all list-cols into columns

Unnest all list-cols in a data frame into columns for each unique element.

# setup
suppressPackageStartupMessages(library(dplyr))
library(purrr)
library(stats)
library(tibble)
library(tidyr)
# create `a`, a tbl with 1 list-col, note "four" is not lined up with "five" in row 3
a <- tibble::tribble(
  ~v1,     ~v2,
  "one",   c("four", "five", "six"),
  "two",   NA_character_,
  "three", "five"
)
# create `b`, an even more complicated tbl with 2 list-cols, separated by an atomic v3
b <- tibble::tribble(
  ~v1,     ~v2,                      ~v3,    ~v4,
  "one",   c("four", "five", "six"), "four", c("four", "five", "six"),
  "two",   NA_character_,            "five", "four",
  "three", "five",                   "six",  "six"
)
# implement `unnest_wide()`
unnest_wide <- function(.data) {
  stopifnot(is.data.frame(.data))
  .data <- tibble::rowid_to_column(.data)
  lst_index <- purrr::map_int(.data, is.list)
  lst_cols <- names(lst_index)[lst_index == 1L]
  lst_vals <- paste0(lst_cols, ".")
  unique_vals <- vector("list", length(lst_cols))
  tmp <- vector("list", length(lst_cols))
  for (i in seq_along(lst_cols)) {
    unique_vals[[i]] <- stats::na.omit(unique(unlist(.data[[lst_cols[i]]])))
    tmp[[i]] <- dplyr::select(.data, rowid, lst_cols[i])
    tmp[[i]] <- dplyr::mutate(tmp[[i]], !!lst_vals[i] := .data[[lst_cols[i]]])
    tmp[[i]] <- tidyr::unnest(tmp[[i]])
    tmp[[i]] <- dplyr::mutate(tmp[[i]], !!lst_cols[i] := match(tmp[[i]][[lst_cols[i]]], unique_vals[[i]]))
    tmp[[i]] <- tidyr::spread(tmp[[i]], !!lst_cols[i], !!lst_vals[i], convert = TRUE, sep = "_")
    tmp[[i]] <- dplyr::select_if(tmp[[i]], !grepl(paste0(lst_cols[i], "_NA"), colnames(tmp[[i]])))
    .data <- dplyr::select(.data, -(!!lst_cols[i]))
    .data <- dplyr::left_join(.data, tmp[[i]], by = "rowid")
  }
  .data <- dplyr::select(.data, -rowid)
  return(.data)
}
# run on `a` and `b`
(a_wide <- unnest_wide(a))
#> # A tibble: 3 x 4
#>   v1    v2_1  v2_2  v2_3 
#>   <chr> <chr> <chr> <chr>
#> 1 one   four  five  six  
#> 2 two   <NA>  <NA>  <NA> 
#> 3 three <NA>  five  <NA>
(b_wide <- unnest_wide(b))
#> # A tibble: 3 x 8
#>   v1    v3    v2_1  v2_2  v2_3  v4_1  v4_2  v4_3 
#>   <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 one   four  four  five  six   four  five  six  
#> 2 two   five  <NA>  <NA>  <NA>  four  <NA>  <NA> 
#> 3 three six   <NA>  five  <NA>  <NA>  <NA>  six

^{Created on 2018-07-25 by the reprex package (v0.2.0).}

Apply complex logic across multiple columns

Use across() with case_when() to apply the same logic to multiple columns.

library(dplyr, warn.conflicts = FALSE)
library(magrittr)
library(palmerpenguins)
penguins %<>%
  mutate(
    bill_length_quartile = ntile(bill_length_mm, 4L),
    bill_depth_quartile = ntile(bill_depth_mm, 4L)
  ) %>%
  mutate(
    across(
      .cols = contains("quartile"),
      .fns = ~ case_when(
        .x == 4L ~ 1L,
        !is.na(.x) ~ 0L,
        TRUE ~ NA_integer_
      ),
      .names = "top_{.col}"
    )
  )
penguins %>%
  group_by(species) %>%
  filter(top_bill_length_quartile == 1L) %>%
  summarize(n_in_top_bill_length_quartile = n())
#> # A tibble: 2 x 2
#>   species   n_in_top_bill_length_quartile
#>   <fct>                             <int>
#> 1 Chinstrap                            40
#> 2 Gentoo                               45
penguins %>%
  group_by(species) %>%
  filter(top_bill_depth_quartile == 1L) %>%
  summarize(n_in_top_bill_depth_quartile = n())
#> # A tibble: 2 x 2
#>   species   n_in_top_bill_depth_quartile
#>   <fct>                            <int>
#> 1 Adelie                              54
#> 2 Chinstrap                           31

^{Created on 2021-06-02 by the reprex package (v2.0.0)}

Find all matching variables in all data frames

Find all matching variables in all data frames in the global environment.

# setup
suppressPackageStartupMessages(library(tidyverse))
library(cli)
# define `find_var()`
find_var <- function(x, env = globalenv()) {
  obj_idx <- ls(envir = env)[-which(ls(envir = env) == "find_var")]
  vars <- NULL
  for (i in seq_along(obj_idx)) {
    if (is.data.frame(get(obj_idx[i]))) {
      var <- grep(x, names(get(obj_idx[i])), value = TRUE)
      if (length(var) > 0L) {
        vars <- c(vars, str_glue("{obj_idx[i]}${var}"))
      }
    }
  }
  if (is.null(vars)) {
    cli_alert_danger("Found no variables.")
  }
  if (length(vars) > 0L) {
    cli_alert_success(pluralize("Found {length(vars)} variable{?s}: {vars}"))
  }
}
# no matching variables
find_var("cyl")
#> x Found no variables.
# one matching variable
a <- mtcars
find_var("cyl")
#> ✓ Found 1 variable: a$cyl
# several matching variables
b <- mtcars %>% mutate(cyl2 = cyl, cyl3 = cyl, cyl4 = cyl)
find_var("cyl")
#> ✓ Found 5 variables: a$cyl, b$cyl, b$cyl2, b$cyl3, and b$cyl4

^{Created on 2021-11-04 by the reprex package (v2.0.1)}

ivelasq/r-data-recipes