tidytable 
tidytable is a data frame manipulation library for users who need
data.table
speed
but prefer tidyverse-like syntax.
Installation
Install the released version from CRAN with:
install.packages("tidytable")Or install the development version from GitHub with:
# install.packages("pak")
pak::pak("markfairbanks/tidytable")General syntax
tidytable replicates tidyverse syntax but uses data.table in the
background. In general you can simply use library(tidytable) to
replace your existing dplyr and tidyr code with data.table backed
equivalents.
A full list of implemented functions can be found here.
library(tidytable)
df <- data.table(x = 1:3, y = 4:6, z = c("a", "a", "b"))
df %>%
select(x, y, z) %>%
filter(x < 4, y > 1) %>%
arrange(x, y) %>%
mutate(double_x = x * 2,
x_plus_y = x + y)
#> # A tidytable: 3 × 5
#> x y z double_x x_plus_y
#> <int> <int> <chr> <dbl> <int>
#> 1 1 4 a 2 5
#> 2 2 5 a 4 7
#> 3 3 6 b 6 9Applying functions by group
You can use the normal tidyverse group_by()/ungroup() workflow, or
you can use .by syntax to reduce typing. Using .by in a function is
shorthand for df %>% group_by() %>% some_function() %>% ungroup().
- A single column can be passed with
.by = z - Multiple columns can be passed with
.by = c(y, z)
df <- data.table(x = c("a", "a", "b"), y = c("a", "a", "b"), z = 1:3)
df %>%
summarize(avg_z = mean(z),
.by = c(x, y))
#> # A tidytable: 2 × 3
#> x y avg_z
#> <chr> <chr> <dbl>
#> 1 a a 1.5
#> 2 b b 3All functions that can operate by group have a .by argument built in.
(mutate(), filter(), summarize(), etc.)
The above syntax is equivalent to:
df %>%
group_by(x, y) %>%
summarize(avg_z = mean(z)) %>%
ungroup()
#> # A tidytable: 2 × 3
#> x y avg_z
#> <chr> <chr> <dbl>
#> 1 a a 1.5
#> 2 b b 3Both options are available for users, so you can use the syntax that you prefer.
tidyselect support
tidytable allows you to select/drop columns just like you would in the
tidyverse by utilizing the tidyselect
package in the background.
Normal selection can be mixed with all tidyselect helpers:
everything(), starts_with(), ends_with(), any_of(), where(),
etc.
df <- data.table(
a = 1:3,
b1 = 4:6,
b2 = 7:9,
c = c("a", "a", "b")
)
df %>%
select(a, starts_with("b"))
#> # A tidytable: 3 × 3
#> a b1 b2
#> <int> <int> <int>
#> 1 1 4 7
#> 2 2 5 8
#> 3 3 6 9A full overview of selection options can be found here.
Using tidyselect in .by
tidyselect helpers also work when using .by:
df <- data.table(x = c("a", "a", "b"), y = c("a", "a", "b"), z = 1:3)
df %>%
summarize(avg_z = mean(z),
.by = where(is.character))
#> # A tidytable: 2 × 3
#> x y avg_z
#> <chr> <chr> <dbl>
#> 1 a a 1.5
#> 2 b b 3Tidy evaluation compatibility
Tidy evaluation can be used to write custom functions with tidytable
functions. The embracing shortcut {{ }} works, or you can use
enquo() with !! if you prefer:
df <- data.table(x = c(1, 1, 1), y = 4:6, z = c("a", "a", "b"))
add_one <- function(data, add_col) {
data %>%
mutate(new_col = {{ add_col }} + 1)
}
df %>%
add_one(x)
#> # A tidytable: 3 × 4
#> x y z new_col
#> <dbl> <int> <chr> <dbl>
#> 1 1 4 a 2
#> 2 1 5 a 2
#> 3 1 6 b 2The .data and .env pronouns also work within tidytable functions:
var <- 10
df %>%
mutate(new_col = .data$x + .env$var)
#> # A tidytable: 3 × 4
#> x y z new_col
#> <dbl> <int> <chr> <dbl>
#> 1 1 4 a 11
#> 2 1 5 a 11
#> 3 1 6 b 11A full overview of tidy evaluation can be found here.
dt() helper
The dt() function makes regular data.table syntax pipeable, so you
can easily mix tidytable syntax with data.table syntax:
df <- data.table(x = 1:3, y = 4:6, z = c("a", "a", "b"))
df %>%
dt(, .(x, y, z)) %>%
dt(x < 4 & y > 1) %>%
dt(order(x, y)) %>%
dt(, double_x := x * 2) %>%
dt(, .(avg_x = mean(x)), by = z)
#> # A tidytable: 2 × 2
#> z avg_x
#> <chr> <dbl>
#> 1 a 1.5
#> 2 b 3Speed Comparisons
For those interested in performance, speed comparisons can be found here.
Acknowledgements
tidytable is only possible because of the great contributions to R by
the data.table and tidyverse teams. data.table is used as the main
data frame engine in the background, while tidyverse packages like
rlang, vctrs, and tidyselect are heavily relied upon to give users
an experience similar to dplyr and tidyr.