dish.RdSplit up columns into groups and apply a function to combinations of those columns with control over whether each group is entered as a single data.frame or individual vector's.
dish(
data,
f,
left,
right,
each_left = TRUE,
each_right = TRUE,
...
)A data.frame.
A function that takes a vector and/or data.frame in the first two arguments.
A vector of quoted/unquoted columns, positions, and/or tidyselect::select_helpers to be evaluated in the first argument of f.
A vector of quoted/unquoted columns, positions, and/or tidyselect::select_helpers to be evaluated in the second argument of f.
Should each left variable be indivdually evaluated in f? Defaults to TRUE. If FALSE, left columns are entered into f as a single data.frame.
Should each right variable be individually evaluated in f? Defaults to TRUE. If FALSE, right columns are entered into f as a single data.frame.
Additional arguments to be passed to f.
A list
#All variables on both sides
heart_disease %>%
dplyr::select(
where(is.numeric)
) %>%
dish(
f = cor
)
#> $Age
#> $Age$Age
#> [1] 1
#>
#> $Age$BP
#> [1] 0.2849459
#>
#> $Age$Cholesterol
#> [1] 0.2089503
#>
#> $Age$MaximumHR
#> [1] -0.3938058
#>
#>
#> $BP
#> $BP$Age
#> [1] 0.2849459
#>
#> $BP$BP
#> [1] 1
#>
#> $BP$Cholesterol
#> [1] 0.1301201
#>
#> $BP$MaximumHR
#> [1] -0.04535088
#>
#>
#> $Cholesterol
#> $Cholesterol$Age
#> [1] 0.2089503
#>
#> $Cholesterol$BP
#> [1] 0.1301201
#>
#> $Cholesterol$Cholesterol
#> [1] 1
#>
#> $Cholesterol$MaximumHR
#> [1] -0.003431832
#>
#>
#> $MaximumHR
#> $MaximumHR$Age
#> [1] -0.3938058
#>
#> $MaximumHR$BP
#> [1] -0.04535088
#>
#> $MaximumHR$Cholesterol
#> [1] -0.003431832
#>
#> $MaximumHR$MaximumHR
#> [1] 1
#>
#>
#Simple regression of each numeric variable on each other variable
heart_disease %>%
dish(
f =
function(y, x) {
mod <- lm(y ~ x)
tibble::tibble(
Parameter = names(mod$coef),
Estimate = mod$coef
)
},
left = where(is.numeric)
) %>%
#Bind rows together
fasten(
into = c("Outcome", "Predictor")
)
#> # A tibble: 48 × 4
#> Outcome Predictor Parameter Estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Age Sex (Intercept) 55.7
#> 2 Age Sex xMale -1.89
#> 3 Age ChestPain (Intercept) 55.9
#> 4 Age ChestPain xAtypical angina -4.51
#> 5 Age ChestPain xNon-anginal pain -2.17
#> 6 Age ChestPain xAsymptomatic -0.147
#> 7 Age BloodSugar (Intercept) 54.0
#> 8 Age BloodSugar xTRUE 3.01
#> 9 Age ExerciseInducedAngina (Intercept) 53.9
#> 10 Age ExerciseInducedAngina xYes 1.76
#> # … with 38 more rows
#Multiple regression of each numeric variable on all others simultaneously
heart_disease %>%
dish(
f =
function(y, x) {
mod <- lm(y ~ ., data = x)
tibble::tibble(
Parameter = names(mod$coef),
Estimate = mod$coef
)
},
left = where(is.numeric),
each_right = FALSE
) %>%
#Bind rows together
fasten(
into = "Outcome"
)
#> # A tibble: 32 × 3
#> Outcome Parameter Estimate
#> <chr> <chr> <dbl>
#> 1 Age (Intercept) 56.8
#> 2 Age SexMale -3.53
#> 3 Age ChestPainAtypical angina -4.28
#> 4 Age ChestPainNon-anginal pain -2.51
#> 5 Age ChestPainAsymptomatic -2.08
#> 6 Age BloodSugarTRUE 2.90
#> 7 Age ExerciseInducedAnginaYes -0.294
#> 8 Age HeartDiseaseYes 4.59
#> 9 BP (Intercept) 141.
#> 10 BP SexMale -5.43
#> # … with 22 more rows