Reverses a continuous scale into a new variable. A 5-1 scale thus turns into a 1-5 scale. Missing values are retained. For a given continuous variable the lower and upper end of the scale should be provided. If they are not provided, the function assumes the scale's minimum and maximum value to represent these lower/upper ends (and issues a warning about this fact). This default behavior is prone to errors, however, because a scale may not include its actual lower and upper ends which might in turn affect correct reversing. Hence, it is strongly suggested to manually set the lower and upper bounds of the original continuous scale.
Usage
reverse_scale(
data,
...,
lower_end = NULL,
upper_end = NULL,
name = NULL,
overwrite = FALSE
)Arguments
- data
- ...
Numeric variables to be reverse scaled. If none are provided, all numeric columns will be scaled.
- lower_end
Lower end of provided continuous scale (default is to use minimum value of current values, which might not be the actual lower end of the scale).
- upper_end
Upper end of provided continuous scale (default is to use maximum value of current values, which might not be the actual upper end of the scale).
- name
Optional name for the new reversed variable when a single variable is provided. By default, the name will be the original variable name suffixed with
_rev.- overwrite
Logical. If
TRUE, it overwrites the original variable(s) with the reversed values. IfFALSE(default), a new variable(s) is created.
Value
A tdcmm model with the reversed variable(s).
See also
Other scaling:
categorize_scale(),
center_scale(),
dummify_scale(),
minmax_scale(),
recode_cat_scale(),
setna_scale(),
z_scale()
Examples
WoJ %>% reverse_scale(autonomy_emphasis, lower_end = 0, upper_end = 1)
#> # 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_rev <dbl>
WoJ %>% reverse_scale(autonomy_emphasis, name = "my_reversed_variable",
lower_end = 0, upper_end = 1)
#> # 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_reversed_variable <dbl>
WoJ %>% reverse_scale(overwrite = TRUE)
#> NOTE: No variables provided. All numeric columns will be reversed.
#> Warning: Lower and/or upper end missing. Based on the minimum and maximum values observed in the data, the original scale (autonomy_selection) is assumed to range from 1 to 5. To prevent this warning, please provide the lower_end and upper_end values as arguments when calling the function.
#> # 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 1 2
#> 2 Germany Nati… Full-time Permanent 3 2
#> 3 Switzerl… Regi… Full-time Permanent 2 2
#> 4 Switzerl… Local Part-time Permanent 2 1
#> 5 Austria Nati… Part-time Permanent 2 2
#> 6 Switzerl… Local Freelancer NA 2 2
#> 7 Germany Local Full-time Permanent 2 2
#> 8 Denmark Nati… Full-time Permanent 3 3
#> 9 Switzerl… Local Full-time Permanent 1 1
#> 10 Denmark Nati… Full-time Permanent 4 2
#> # 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 %>%
reverse_scale(autonomy_emphasis, lower_end = 0, upper_end = 1) %>%
tab_frequencies(autonomy_emphasis, autonomy_emphasis_rev)
#> # A tibble: 6 × 6
#> autonomy_emphasis autonomy_emphasis_rev n percent cum_n cum_percent
#> * <dbl> <dbl> <int> <dbl> <int> <dbl>
#> 1 1 0 10 0.00833 10 0.00833
#> 2 2 NA 36 0.03 46 0.0383
#> 3 3 NA 165 0.138 211 0.176
#> 4 4 NA 626 0.522 837 0.698
#> 5 5 NA 358 0.298 1195 0.996
#> 6 NA NA 5 0.00417 1200 1