I'm working with large datasets where often it can be difficult to always know whether or not the numerical values should be handled as a continuous feature or if they represent a categorical value. Other times, R gets it wrong and assigns something to be a character when it is infact numerical.
I'm hoping to build a lookup table to map specific datatypes to specific columns - by name. Is there a way to do this with the purrr package or something similar?
For example:
mylookup_table =data.frame(column_names = c('mpg','vs','hp'), column_types = c('numeric','factor','character') )
#apply to mtcars for just these columns..
I presume a purrr guru could show how to do this more elegantly, but seeing as there aren't too many data types in R, this isn't too bad:
library(tidyverse)
mylookup_table <- data_frame(
column_names = c('mpg','vs','hp'),
column_types = c('numeric','factor','character'))
mylookup_chars <- mylookup_table[mylookup_table$column_types == "character", 1]
mylookup_nums <- mylookup_table[mylookup_table$column_types == "numeric", 1]
mylookup_factors <- mylookup_table[mylookup_table$column_types == "factor", 1]
mtcars %>%
purrr::map_at(mylookup_chars$column_names, as.character) %>%
purrr::map_at(mylookup_nums$column_names, as.numeric) %>%
purrr::map_at(mylookup_factors$column_names, as.factor) %>%
str()
List of 11
$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num [1:32] 160 160 108 258 360 ...
$ hp : chr [1:32] "110" "110" "93" "110" ...
$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
$ vs : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 1 2 2 2 ...
$ am : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
$ gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...