This function z-standardizes the specified numeric columns or all numeric columns if none are specified. A z-standardized scale centers at a mean of 0.0 and has a standard deviation of 1.0, making it comparable to other z-standardized distributions.
z_scale(data, ..., name = NULL, overwrite = FALSE)Numeric variables to be z-standardized. If none are provided, all numeric columns will be z-standardized.
Optional name for the new z-standardized variable when a single variable is provided. By default, the name will be the original variable name suffixed with _z.
Logical. If TRUE, it overwrites the original variable(s) with the z-standardized values. If FALSE (default), a new variable(s) is created.
A tdcmm model with the z-standardized variable(s).
Other scaling:
categorize_scale(),
center_scale(),
dummify_scale(),
minmax_scale(),
recode_cat_scale(),
reverse_scale(),
setna_scale()
WoJ %>% z_scale(autonomy_emphasis)
#> # A tibble: 1,200 × 16
#> 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 10 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>,
#> # autonomy_emphasis_z <dbl>
WoJ %>% z_scale(autonomy_emphasis, name = "my_zstdized_variable")
#> # A tibble: 1,200 × 16
#> 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 10 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>,
#> # my_zstdized_variable <dbl>
WoJ %>%
z_scale(autonomy_emphasis) %>%
tab_frequencies(autonomy_emphasis, autonomy_emphasis_z)
#> # A tibble: 6 × 6
#> autonomy_emphasis autonomy_emphasis_z n percent cum_n cum_percent
#> * <dbl> <dbl> <int> <dbl> <int> <dbl>
#> 1 1 -3.88 10 0.00833 10 0.00833
#> 2 2 -2.62 36 0.03 46 0.0383
#> 3 3 -1.36 165 0.138 211 0.176
#> 4 4 -0.0961 626 0.522 837 0.698
#> 5 5 1.17 358 0.298 1195 0.996
#> 6 NA NA 5 0.00417 1200 1