Clean variable labels

This function standardises the labels of all characters of factors in a data.frame. It uses the standardisation implemented by epitrix::clean_labels() in the epitrix package. See ?epitrix::clean_labels for more information.

clean_variable_labels(x, classes = NULL, ...)

Arguments

x	a data.frame
classes	a vector of class definitions for each of the columns. If this is not provided, the classes will be read from the columns themselves. Practically, this is used in clean_data() to mark columns as protected.
...	further arguments passed to epitrix::clean_labels(); the most important is sep, which refers to the separator used between words, and defaults to the underscore _.

Value

A data.frame with standardised labels for characters and factors.

Examples

## make toy data
toy_data <- clean_variable_names(messy_data())

## clean variable labels, store in new object, show results
clean_data <- clean_variable_labels(toy_data)
clean_data
#>        id date_of_onset  discharge gender epi_case_definition       messy_dates
#> 1  nitoug    2018-01-05 15_01_2018   male           suspected that_s_24_12_1989
#> 2  zrubxe    2018-01-07 17_01_2018   male            probable              male
#> 3  tcdawa    2018-01-07 17_01_2018 female            probable        2018_10_18
#> 4  uvzwsh    2018-01-02 12_01_2018   male            probable        2018_10_17
#> 5  wbqqij    2018-01-04 14_01_2018 female           confirmed              <NA>
#> 6  cknjtx    2018-01-02 12_01_2018 female            probable        2018_10_17
#> 7  hemwka    2018-01-10 20_01_2018   male          not_a_case              <NA>
#> 8  rnihsr    2018-01-02 12_01_2018 female          not_a_case        2018_10_18
#> 9  tesaao    2018-01-11 21_01_2018 female          not_a_case        2018_10_19
#> 10 sytcqa    2018-01-03 13_01_2018   male            probable        2018_10_18
#> 11 xtvmnz    2018-01-09 19_01_2018 female            probable              <NA>
#> 12 bagtwl    2018-01-11 21_01_2018 female          not_a_case        2018_10_19
#> 13 cqwsng    2018-01-11 21_01_2018 female          not_a_case        2018_10_19
#> 14 wgoncb    2018-01-02 12_01_2018 female           confirmed that_s_24_12_1989
#> 15 valtgd    2018-01-05 15_01_2018   male           suspected        24_12_1989
#> 16 knpgqn    2018-01-10 20_01_2018 female           suspected        24_12_1989
#> 17 kvjkyb    2018-01-02 12_01_2018 female          not_a_case              male
#> 18 yiqkas    2018-01-03 13_01_2018 female          not_a_case        2018_10_18
#> 19 cpjyeh    2018-01-04 14_01_2018   male          not_a_case        2018_10_18
#> 20 cmnpla    2018-01-03 13_01_2018   male            probable        24_12_1989
#>         lat      lon
#> 1  14.64411 46.70684
#> 2  14.40728 48.75475
#> 3  12.68457 47.73267
#> 4  12.64639 48.64208
#> 5  15.84507 47.58267
#> 6  10.36658 46.78294
#> 7  12.87732 50.13236
#> 8  13.19119 46.91052
#> 9  11.91412 48.11649
#> 10 13.99691 49.26118
#> 11 12.91624 49.04062
#> 12 13.99174 47.03056
#> 13 14.80186 48.39073
#> 14 16.23876 48.60770
#> 15 13.75793 47.00237
#> 16 12.66217 49.19711
#> 17 11.91513 46.22732
#> 18 10.09231 48.78484
#> 19 13.36070 47.56266
#> 20 13.99578 47.64826