Data operations in R can be done in so many ways that it’s confusing for newbies. This note denotes simple operations using dplyr (and other packages from tidyverse). Let us do this using standard datasets available within R.
Load libraries
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(tidyr))
Column wise operations
Let us do some basic statistics.
Find maximum value in each column
iris %>%
summarise(across(where(is.numeric), max))
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 7.9 4.4 6.9 2.5
Find maximum value in each group
iris %>%
group_by(Species) %>%
summarise(across(where(is.numeric), max))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 3 x 5
## Species Sepal.Length Sepal.Width Petal.Length Petal.Width
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 setosa 5.8 4.4 1.9 0.6
## 2 versicolor 7 3.4 5.1 1.8
## 3 virginica 7.9 3.8 6.9 2.5
Find minimum and maximum value within each column
iris %>%
summarise(across(where(is.numeric), list(min = min, max = max)))
## Sepal.Length_min Sepal.Length_max Sepal.Width_min Sepal.Width_max
## 1 4.3 7.9 2 4.4
## Petal.Length_min Petal.Length_max Petal.Width_min Petal.Width_max
## 1 1 6.9 0.1 2.5
Now as you see, the table has grown horizontally with two functions (max and min). Imagine having 5 functions and multiple groups. Let us format this to a better table, with 3 functions (min, max, mean).
iris %>%
summarise(across(where(is.numeric), list(Min=min, Max=max, Mean=mean))) %>%
pivot_longer(cols = where(is.numeric),
names_to = c(".value", "Stat"),
names_pattern = "(\\w+\\W\\w+)_(\\w+)")
## # A tibble: 3 x 5
## Stat Sepal.Length Sepal.Width Petal.Length Petal.Width
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Min 4.3 2 1 0.1
## 2 Max 7.9 4.4 6.9 2.5
## 3 Mean 5.84 3.06 3.76 1.20
There is easier way to do this outside dplyr.
suppressPackageStartupMessages(library(Rfast))
data.frame(apply(iris[, -5], 2, colMinsMaxs), row.names = c("Min", "Max"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min 4.3 2.0 1.0 0.1
## Max 7.9 4.4 6.9 2.5
with Rfast package, finding min and maximum values in a vector might be easier, but not extensible. For eg, let us say you want mean, median and sd. You cannot get all of them in the same function. However, dplyr code allows to include as many functions as you need and format as per your needs.
Find minimum and maximum value within each column, for each plant species:
Now let us find minimum and maximum in each group (Species)
iris %>%
group_by(Species) %>%
summarise(across(where(is.numeric), list(min = min, max = max)))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 3 x 9
## Species Sepal.Length_min Sepal.Length_max Sepal.Width_min Sepal.Width_max
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 setosa 4.3 5.8 2.3 4.4
## 2 versic… 4.9 7 2 3.4
## 3 virgin… 4.9 7.9 2.2 3.8
## # … with 4 more variables: Petal.Length_min <dbl>, Petal.Length_max <dbl>,
## # Petal.Width_min <dbl>, Petal.Width_max <dbl>
Let us format this table to a meaningful table as below:
iris %>%
group_by(Species) %>%
summarise(across(where(is.numeric), list( min=min,max=max, mean=mean))) %>%
pivot_longer(cols = where(is.numeric),
names_to = c(".value", "Stat"),
names_pattern = "(\\w+\\W\\w+)_(\\w+)")
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 9 x 6
## Species Stat Sepal.Length Sepal.Width Petal.Length Petal.Width
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 setosa min 4.3 2.3 1 0.1
## 2 setosa max 5.8 4.4 1.9 0.6
## 3 setosa mean 5.01 3.43 1.46 0.246
## 4 versicolor min 4.9 2 3 1
## 5 versicolor max 7 3.4 5.1 1.8
## 6 versicolor mean 5.94 2.77 4.26 1.33
## 7 virginica min 4.9 2.2 4.5 1.4
## 8 virginica max 7.9 3.8 6.9 2.5
## 9 virginica mean 6.59 2.97 5.55 2.03
We can further beautify this table with kable package.