Span keys and values across the columns

Pivot one or more values across the columns by one or more keys

stretch(
    data,
    key,
    value,
    sep = "_"
)

Arguments

data

A data.frame.

key

A vector of quoted/unquoted columns, positions, and/or tidyselect::select_helpers whose values will become the column name(s).

value

A vector of quoted/unquoted columns, positions, and/or tidyselect::select_helpers whose values will be spread across the columns.

sep

String to separate keys/values by in the resulting column names. Defaults to "_". Only used when there are more than one keys/values.

Details

In the case of multiple value's, the labels are always appended to the end of the resulting columns.

Value

A tibble::tibble

Author

Alex Zajichek

Examples

#Make a summary table
set.seed(123)
data <- 
  heart_disease %>%
  dplyr::group_by(
    Sex,
    BloodSugar,
    HeartDisease
  ) %>%
  dplyr::summarise(
    Mean = mean(Age),
    SD = sd(Age),
    .groups = "drop"
  ) %>%
  dplyr::mutate(
    Random =
      rbinom(nrow(.), size = 1, prob = .5) %>%
      factor
  )

data %>%
  stretch(
    key = c(BloodSugar, HeartDisease),
    value = c(Mean, SD, Random)
  )
#> # A tibble: 2 × 13
#>   Sex    FALSE…¹ FALSE…² FALSE…³ FALSE…⁴ FALSE…⁵ FALSE…⁶ TRUE_…⁷ TRUE_…⁸ TRUE_…⁹
#>   <fct>    <dbl>   <dbl> <fct>     <dbl>   <dbl> <fct>     <dbl>   <dbl> <fct>  
#> 1 Female    54.0   10.4  0          59.7    3.61 1          60.3    6.65 0      
#> 2 Male      50.2    8.82 1          55.8    8.56 0          54.7    6.76 1      
#> # … with 3 more variables: TRUE_Yes_Mean <dbl>, TRUE_Yes_SD <dbl>,
#> #   TRUE_Yes_Random <fct>, and abbreviated variable names ¹​FALSE_No_Mean,
#> #   ²​FALSE_No_SD, ³​FALSE_No_Random, ⁴​FALSE_Yes_Mean, ⁵​FALSE_Yes_SD,
#> #   ⁶​FALSE_Yes_Random, ⁷​TRUE_No_Mean, ⁸​TRUE_No_SD, ⁹​TRUE_No_Random

data %>%
  stretch(
    key = where(is.factor),
    value = where(is.numeric)
  )
#> # A tibble: 2 × 11
#>   BloodSugar Female_No…¹ Femal…² Femal…³ Femal…⁴ Male_…⁵ Male_…⁶ Male_…⁷ Male_…⁸
#>   <lgl>            <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1 FALSE             54.0   10.4     59.7    3.61    50.2    8.82    55.8    8.56
#> 2 TRUE              60.3    6.65    57.2    7.81    54.7    6.76    NA     NA   
#> # … with 2 more variables: Male_Yes_1_Mean <dbl>, Male_Yes_1_SD <dbl>, and
#> #   abbreviated variable names ¹​Female_No_0_Mean, ²​Female_No_0_SD,
#> #   ³​Female_Yes_1_Mean, ⁴​Female_Yes_1_SD, ⁵​Male_No_1_Mean, ⁶​Male_No_1_SD,
#> #   ⁷​Male_Yes_0_Mean, ⁸​Male_Yes_0_SD

data %>%
  stretch(
    key = c(where(is.factor), where(is.logical)),
    value = where(is.numeric)
  )
#> # A tibble: 1 × 16
#>   Female_No_0_…¹ Femal…² Femal…³ Femal…⁴ Femal…⁵ Femal…⁶ Femal…⁷ Femal…⁸ Male_…⁹
#>            <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1           54.0    10.4    60.3    6.65    59.7    3.61    57.2    7.81    50.2
#> # … with 7 more variables: Male_No_1_FALSE_SD <dbl>, Male_No_1_TRUE_Mean <dbl>,
#> #   Male_No_1_TRUE_SD <dbl>, Male_Yes_0_FALSE_Mean <dbl>,
#> #   Male_Yes_0_FALSE_SD <dbl>, Male_Yes_1_TRUE_Mean <dbl>,
#> #   Male_Yes_1_TRUE_SD <dbl>, and abbreviated variable names
#> #   ¹​Female_No_0_FALSE_Mean, ²​Female_No_0_FALSE_SD, ³​Female_No_0_TRUE_Mean,
#> #   ⁴​Female_No_0_TRUE_SD, ⁵​Female_Yes_1_FALSE_Mean, ⁶​Female_Yes_1_FALSE_SD,
#> #   ⁷​Female_Yes_1_TRUE_Mean, ⁸​Female_Yes_1_TRUE_SD, ⁹​Male_No_1_FALSE_Mean