R/scaling.R
minmax_scale.RdGiven a specified minimum and maximum, this function translates each value into a new value within this specified range. The transformation maintains the relative distances between values, resulting in changes to the mean and standard deviations. However, if both the original scale and the transformed scale are z-standardized, they will be equal again, indicating that the relative positions and distributions of the values remain consistent.
minmax_scale(
data,
...,
change_to_min = 0,
change_to_max = 1,
name = NULL,
overwrite = FALSE
)Numeric variables to be min-max scaled. If none are provided, all numeric columns will be scaled.
The desired minimum value after scaling.
The desired maximum value after scaling.
Optional name for the new scaled variable when a single variable is provided. By default, the name will be the original variable name suffixed with the range. For example, "variable" becomes "variable_3to5". Negative values are prefixed with "neg" to avoid invalid columns names (e.g., -3 to 3 becomes "variable_neg3to5").
Logical. If TRUE, it overwrites the original variable(s)
with the scaled values. If FALSE (default), a new variable(s) is created.
A tdcmm model with the min-max scaled variable(s).
Other scaling:
categorize_scale(),
center_scale(),
dummify_scale(),
recode_cat_scale(),
reverse_scale(),
setna_scale(),
z_scale()
WoJ %>% minmax_scale(autonomy_emphasis, change_to_min = 0,
change_to_max = 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_0to1 <dbl>
WoJ %>% minmax_scale(autonomy_emphasis, name = "my_scaled_variable",
change_to_min = 0, change_to_max = 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_scaled_variable <dbl>
WoJ %>%
minmax_scale(autonomy_emphasis, change_to_min = 0, change_to_max = 1) %>%
tab_frequencies(autonomy_emphasis, autonomy_emphasis_0to1)
#> # A tibble: 6 × 6
#> autonomy_emphasis autonomy_emphasis_0to1 n percent cum_n cum_percent
#> * <dbl> <dbl> <int> <dbl> <int> <dbl>
#> 1 1 0 10 0.00833 10 0.00833
#> 2 2 0.25 36 0.03 46 0.0383
#> 3 3 0.5 165 0.138 211 0.176
#> 4 4 0.75 626 0.522 837 0.698
#> 5 5 1 358 0.298 1195 0.996
#> 6 NA NA 5 0.00417 1200 1