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library(presenter)
library(dplyr)Transpose a tibble of summary statistics in tidy format. Convenient
function for transposing the output of dplyr”s group_by and
summarize operation.
Transpose a 1 row numerical summary:
wide format
iris %>%
summarize(across(where(is.numeric), mean), .groups = "drop") -> sumr0
sumr0
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 5.843333 3.057333 3.758 1.199333long format
sumr0 %>%
pivot_summary()
#> # A tibble: 4 × 2
#> column V1
#> <chr> <dbl>
#> 1 Sepal.Length 5.84
#> 2 Sepal.Width 3.06
#> 3 Petal.Length 3.76
#> 4 Petal.Width 1.20A grouped summary can be transposed by providing the name of the group column.
wide format
iris %>%
group_by(Species) %>%
summarize(across(where(is.numeric), mean), .groups = "drop") -> sumr1
sumr1
#> # A tibble: 3 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.01 3.43 1.46 0.246
#> 2 versicolor 5.94 2.77 4.26 1.33
#> 3 virginica 6.59 2.97 5.55 2.03long format
sumr1 %>%
pivot_summary(Species)
#> # A tibble: 4 × 4
#> column setosa versicolor virginica
#> <chr> <dbl> <dbl> <dbl>
#> 1 Sepal.Length 5.01 5.94 6.59
#> 2 Sepal.Width 3.43 2.77 2.97
#> 3 Petal.Length 1.46 4.26 5.55
#> 4 Petal.Width 0.246 1.33 2.03Supports transposing numerical summaries with multiple groups using tidyselect.
long format
iris %>%
mutate(Species1 = sample(Species)) %>%
group_by(Species, Species1) %>%
summarize(across(where(is.numeric), mean), .groups = "drop") -> sumr2
sumr2
#> # A tibble: 9 × 6
#> Species Species1 Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa setosa 5.13 3.61 1.46 0.269
#> 2 setosa versicolor 4.99 3.34 1.46 0.217
#> 3 setosa virginica 4.93 3.38 1.47 0.258
#> 4 versicolor setosa 5.92 2.78 4.34 1.32
#> 5 versicolor versicolor 5.94 2.78 4.23 1.35
#> 6 versicolor virginica 5.95 2.75 4.2 1.3
#> 7 virginica setosa 6.73 3.11 5.73 2.08
#> 8 virginica versicolor 6.62 2.92 5.44 1.95
#> 9 virginica virginica 6.42 2.87 5.44 2.02Group names are concatenated and pivoted.
wide format
sumr2 %>%
pivot_summary(matches("Spec"))
#> # A tibble: 4 × 10
#> column setos…¹ setos…² setos…³ versi…⁴ versi…⁵ versi…⁶ virgi…⁷ virgi…⁸ virgi…⁹
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Sepal… 5.13 4.99 4.93 5.92 5.94 5.95 6.73 6.62 6.42
#> 2 Sepal… 3.61 3.34 3.38 2.78 2.78 2.75 3.11 2.92 2.87
#> 3 Petal… 1.46 1.46 1.47 4.34 4.23 4.2 5.73 5.44 5.44
#> 4 Petal… 0.269 0.217 0.258 1.32 1.35 1.3 2.08 1.95 2.02
#> # … with abbreviated variable names ¹setosa_setosa, ²setosa_versicolor,
#> # ³setosa_virginica, ⁴versicolor_setosa, ⁵versicolor_versicolor,
#> # ⁶versicolor_virginica, ⁷virginica_setosa, ⁸virginica_versicolor,
#> # ⁹virginica_virginicaThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.