使用列表更改 data.table 列中的值

Question

I want to use a list as a simple dictionary to change values when the lookup to the list is not null. I'd expected this to work, but it doesn't;

assoc_values <- list("A" = "GROUP1", "C" = "GROUP1", "B" = "GROUP2", "D" = "GROUP3")
some_data_table[!is.null(assoc_values[[lookup_column]]), "mapped_col" := assoc_values[[lookup_column]]]

It fails with error msg Error: recursive indexing failed at level 2 . I've tried other approaches like ifelse(.is,null(assoc_values[[lookup_column]]). ...) but always fails.

below there is a reproducible example:

library(data.table)
assoc_values <- list("A" = "GROUP1", "C" = "GROUP1", "B" = "GROUP2", "D" = "GROUP2")
some_data_table <- data.table('col1' = seq(1, 10), 'lookup_column' = c('A', 'A', 'E', 'B', 'D', 'C', 'A', 'F', 'C', 'T'))
some_data_table[!is.null(assoc_values[[lookup_column]]), "mapped_col" := assoc_values[[lookup_column]]]

Answer 1

You don't really need a list here since a named vector would do.

library(data.table)
assoc_values <- c("A" = "GROUP1", "C" = "GROUP1", "B" = "GROUP2", "D" = "GROUP2")
some_data_table <- data.table(col1 = seq(1, 10), 
                              lookup_column = c('A', 'A', 'E', 'B', 'D',
                                                'C', 'A', 'F', 'C', 'T'))


some_data_table[, new_col := assoc_values[lookup_column]]
some_data_table[is.na(new_col), new_col := lookup_column]

some_data_table

#    col1 lookup_column new_col
# 1:    1             A  GROUP1
# 2:    2             A  GROUP1
# 3:    3             E       E
# 4:    4             B  GROUP2
# 5:    5             D  GROUP2
# 6:    6             C  GROUP1
# 7:    7             A  GROUP1
# 8:    8             F       F
# 9:    9             C  GROUP1
#10:   10             T       T

Answer 2

The error is to mistake '[' with '[[' , see this SO post . The solution is to use assoc_values[lookup_column] .

some_data_table[!is.null(assoc_values[lookup_column]), 
                "mapped_col" := assoc_values[lookup_column]]

some_data_table
#    col1 lookup_column mapped_col
# 1:    1             A     GROUP1
# 2:    2             A     GROUP1
# 3:    3             E           
# 4:    4             B     GROUP2
# 5:    5             D     GROUP2
# 6:    6             C     GROUP1
# 7:    7             A     GROUP1
# 8:    8             F           
# 9:    9             C     GROUP1
#10:   10             T