Recode one or more categorical variables into new categories
Source:R/scaling.R
recode_cat_scale.RdThis function transforms one or more categorical variables into new categories based on specified mapping. For unmatched cases not specified in the mapping, a default value can be assigned. Missing values are retained.
Arguments
- data
A tibble or a tdcmm model.
- ...
Variables to recode.
- assign
A named vector where names are the old values and values are the new values to be assigned.
- other
The value for unmatched cases. By default, it is
NA. This parameter is used to assign a value to cases that do not match any of the keys in theassignvector.- overwrite
Logical. If
TRUE, it overwrites the original variable(s). You cannot specify both 'name' and 'overwrite' parameters simultaneously.- name
The name of the new variable(s). If not specified, this is the same name as the provided variable(s) but suffixed with
_rec.
Value
A tdcmm model or a tibble.
See also
Other scaling:
categorize_scale(),
center_scale(),
dummify_scale(),
minmax_scale(),
reverse_scale(),
setna_scale(),
z_scale()
Examples
WoJ %>%
recode_cat_scale(country,
assign = c("Germany" = 1, "Switzerland" = 2), overwrite = TRUE)
#> The following unassigned values were found in country : Austria, Denmark, UK . They were recoded to the 'other' value ( NA ).
#> # A tibble: 1,200 × 15
#> country reach employment temp_contract autonomy_selection autonomy_emphasis
#> * <fct> <fct> <chr> <fct> <dbl> <dbl>
#> 1 1 Nation… Full-time Permanent 5 4
#> 2 1 Nation… Full-time Permanent 3 4
#> 3 2 Region… Full-time Permanent 4 4
#> 4 2 Local Part-time Permanent 4 5
#> 5 NA Nation… Part-time Permanent 4 4
#> 6 2 Local Freelancer NA 4 4
#> 7 1 Local Full-time Permanent 4 4
#> 8 NA Nation… Full-time Permanent 3 3
#> 9 2 Local Full-time Permanent 5 5
#> 10 NA Nation… Full-time Permanent 2 4
#> # ℹ 1,190 more rows
#> # ℹ 9 more variables: ethics_1 <dbl>, ethics_2 <dbl>, ethics_3 <dbl>,
#> # ethics_4 <dbl>, work_experience <dbl>, trust_parliament <dbl>,
#> # trust_government <dbl>, trust_parties <dbl>, trust_politicians <dbl>
WoJ %>%
recode_cat_scale(country,
assign = c("Germany" = "german", "Switzerland" = "swiss"), other = "other",
overwrite = TRUE)
#> The following unassigned values were found in country : Austria, Denmark, UK . They were recoded to the 'other' value ( other ).
#> # A tibble: 1,200 × 15
#> country reach employment temp_contract autonomy_selection autonomy_emphasis
#> * <fct> <fct> <chr> <fct> <dbl> <dbl>
#> 1 german Nation… Full-time Permanent 5 4
#> 2 german Nation… Full-time Permanent 3 4
#> 3 swiss Region… Full-time Permanent 4 4
#> 4 swiss Local Part-time Permanent 4 5
#> 5 other Nation… Part-time Permanent 4 4
#> 6 swiss Local Freelancer NA 4 4
#> 7 german Local Full-time Permanent 4 4
#> 8 other Nation… Full-time Permanent 3 3
#> 9 swiss Local Full-time Permanent 5 5
#> 10 other Nation… Full-time Permanent 2 4
#> # ℹ 1,190 more rows
#> # ℹ 9 more variables: ethics_1 <dbl>, ethics_2 <dbl>, ethics_3 <dbl>,
#> # ethics_4 <dbl>, work_experience <dbl>, trust_parliament <dbl>,
#> # trust_government <dbl>, trust_parties <dbl>, trust_politicians <dbl>
WoJ %>%
recode_cat_scale(ethics_1, ethics_2,
assign = c(`1` = 5, `2` = 4, `3` = 3, `4` = 2, `5` = 1), other = 6, overwrite = TRUE)
#> # A tibble: 1,200 × 15
#> country reach employment temp_contract autonomy_selection autonomy_emphasis
#> * <fct> <fct> <chr> <fct> <dbl> <dbl>
#> 1 Germany Nati… Full-time Permanent 5 4
#> 2 Germany Nati… Full-time Permanent 3 4
#> 3 Switzerl… Regi… Full-time Permanent 4 4
#> 4 Switzerl… Local Part-time Permanent 4 5
#> 5 Austria Nati… Part-time Permanent 4 4
#> 6 Switzerl… Local Freelancer NA 4 4
#> 7 Germany Local Full-time Permanent 4 4
#> 8 Denmark Nati… Full-time Permanent 3 3
#> 9 Switzerl… Local Full-time Permanent 5 5
#> 10 Denmark Nati… Full-time Permanent 2 4
#> # ℹ 1,190 more rows
#> # ℹ 9 more variables: ethics_1 <fct>, ethics_2 <fct>, ethics_3 <dbl>,
#> # ethics_4 <dbl>, work_experience <dbl>, trust_parliament <dbl>,
#> # trust_government <dbl>, trust_parties <dbl>, trust_politicians <dbl>
WoJ %>%
recode_cat_scale(ethics_1, ethics_2,
assign = c(`1` = "very low", `2` = "low", `3` = "medium", `4` = "high", `5` = "very high"),
overwrite = TRUE)
#> # A tibble: 1,200 × 15
#> country reach employment temp_contract autonomy_selection autonomy_emphasis
#> * <fct> <fct> <chr> <fct> <dbl> <dbl>
#> 1 Germany Nati… Full-time Permanent 5 4
#> 2 Germany Nati… Full-time Permanent 3 4
#> 3 Switzerl… Regi… Full-time Permanent 4 4
#> 4 Switzerl… Local Part-time Permanent 4 5
#> 5 Austria Nati… Part-time Permanent 4 4
#> 6 Switzerl… Local Freelancer NA 4 4
#> 7 Germany Local Full-time Permanent 4 4
#> 8 Denmark Nati… Full-time Permanent 3 3
#> 9 Switzerl… Local Full-time Permanent 5 5
#> 10 Denmark Nati… Full-time Permanent 2 4
#> # ℹ 1,190 more rows
#> # ℹ 9 more variables: ethics_1 <fct>, ethics_2 <fct>, ethics_3 <dbl>,
#> # ethics_4 <dbl>, work_experience <dbl>, trust_parliament <dbl>,
#> # trust_government <dbl>, trust_parties <dbl>, trust_politicians <dbl>
WoJ %>%
dplyr::select(temp_contract) %>% recode_cat_scale(temp_contract,
assign = c(`Permanent` = "P", `Temporary` = "T"), other = "O")
#> # A tibble: 1,200 × 2
#> temp_contract temp_contract_rec
#> * <fct> <fct>
#> 1 Permanent P
#> 2 Permanent P
#> 3 Permanent P
#> 4 Permanent P
#> 5 Permanent P
#> 6 NA NA
#> 7 Permanent P
#> 8 Permanent P
#> 9 Permanent P
#> 10 Permanent P
#> # ℹ 1,190 more rows