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.
recode_cat_scale(
data,
...,
assign = NULL,
other = NA,
overwrite = FALSE,
name = NULL
)A tibble or a tdcmm model.
Variables to recode.
A named vector where names are the old values and values are the new values to be assigned.
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 the assign vector.
Logical. If TRUE, it overwrites the original variable(s).
You cannot specify both 'name' and 'overwrite' parameters simultaneously.
The name of the new variable(s). If not specified, this is the same
name as the provided variable(s) but suffixed with _rec.
A tdcmm model or a tibble.
Other scaling:
categorize_scale(),
center_scale(),
dummify_scale(),
minmax_scale(),
reverse_scale(),
setna_scale(),
z_scale()
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