
tidytable is a data frame manipulation library for users
who need data.table
speed but prefer tidyverse-like syntax.
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")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 9You 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().
.by = z.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.
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.
.bytidyselect 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 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() helperThe 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 3For those interested in performance, speed comparisons can be found here.
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.