I have a series of data sets and a dictionary to bring these together. But I'm struggling to figure out how to automate this.
Suppose this data and dictionary (actual one is much longer, thus I want to automate):
mtcarsA <- mtcars[1:5,] %>% rename(mpgA = mpg, cyl_A = cyl) %>% as_tibble()
mtcarsB <- mtcars[6:10,] %>% rename(mpg_B = mpg, B_cyl = cyl) %>% as_tibble()
dic <- tibble(true_name = c("mpg_true", "cyl_true"),
nameA = c("mpgA", "cyl_A"),
nameB = c("mpg_B", "B_cyl")
)
I want these datasets (from years A and B) appended to one another, and then to have the names changed or coalesced to the 'true_name' values.
I can bring the data sets together into mtcars_all, and then I tried recoding the column names with the dictionary as follows
mtcars_all <- bind_rows((mtcarsA, mtcarsB)
recode_colname <- function(df, tn=dic$true_name, fname){
colnames(df) <- dplyr::recode(colnames(df),
!!!setNames(as.character(tn), fname))
return(df)
}
mtcars_all <- mtcars_all %>%
recode_colname(fname=dic$nameA) %>%
recode_colname(fname=dic$nameB)
But then I get duplicate columns. Of course I could coalesce each of these duplicate columns by name, but there will be many of these in my real case, so I want to automate 'coalesce all columns with duplicate names'.
I'm giving the entire problem here because perhaps someone also has a better solution for 'using a data dictionary'.
You can create a named vector to replace column names.
library(tidyverse)
pmap(dic, ~setNames(..1, paste0(c(..2, ..3), collapse = '|'))) %>%
flatten_chr() -> val
val
# mpgA|mpg_B cyl_A|B_cyl
# "mpg_true" "cyl_true"
And apply it on list of dataframes and combine them.
list(mtcarsA,mtcarsB) %>%
map_df(function(x) x %>% rename_with(~str_replace_all(.x, val)))
# mpg_true cyl_true disp hp drat wt qsec vs am gear carb
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
# 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
# 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
# 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
# 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
# 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
# 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
# 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
# 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4