Column names are sometimes opaque. And sometimes there are hundreds of
them. The goal of dfdetective is to scour data frames when you have
only a vague idea what you’re looking for. The functions are mostly thin
wrappers around combinations of lapply()...unique()...grep().
You can install the released version of dfdetective from GitHub via:
remotes::install_github("daranzolin/dfdetective")library(dfdetective)
library(fivethirtyeight)
library(dplyr)
find_unique_vals(antiquities_act, "chaco")
#> # A tibble: 3 x 3
#> col_name unique_values n_unique_values
#> <chr> <chr> <int>
#> 1 current_na… Devils Tower National Monument, El Morro Nat… 151
#> 2 original_n… "NA, Petrified Forest National Monument, Cha… 67
#> 3 action Established, Enlarged, Deleted, Diminished, … 62
agencies <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-01-15/agencies.csv")
find_unique_vals(agencies, "^B$")
#> # A tibble: 1 x 3
#> col_name unique_values n_unique_values
#> <chr> <chr> <int>
#> 1 class D, C, B 3This is especially useful when searching for binary variables.
find_n_unique_vals(comic_characters, 2:4) # You can pass a numeric vector to the n_unique argument
#> # A tibble: 3 x 3
#> col_name unique_values n_unique_values
#> <chr> <chr> <int>
#> 1 publisher Marvel, DC 2
#> 2 align Good Characters, NA, Bad Characters, Reformed … 4
#> 3 alive Living Characters, Deceased Characters, NA 3get_unique_vals(san_andreas, contains("worry")) #you can pass classic dplyr select helpers, e.g. starts_with(), contains(), etc.
#> # A tibble: 2 x 3
#> col_name unique_values n_unique_values
#> <chr> <chr> <int>
#> 1 worry_gene… Not at all worried, Somewhat worried, Not so… 5
#> 2 worry_bigo… Not so worried, Very worried, Somewhat worri… 5