tidytable

In tidytable, you can use the .by argument in functions like summarize(), mutate(), and filter() as a shorthand for the group_by() %>% ... %>% ungroup() workflow. This reduces typing and avoids leaving the data frame in a grouped state.

• Pass a single column: .by = z
• Pass multiple columns: .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))

You can install the released version of tidytable from CRAN or the development version from GitHub.

From CRAN:

install.packages("tidytable")

From GitHub (using pak):

# install.packages("pak")
pak::pak("markfairbanks/tidytable")

tidytable replicates tidyverse syntax (like dplyr and tidyr) but uses data.table as the high-performance engine in the background. You can replace existing dplyr code by loading the library.

Commonly used functions include select(), filter(), arrange(), and mutate().

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)

tidytable supports tidyselect helpers, allowing you to select or drop columns using standard tidyverse patterns like everything(), starts_with(), ends_with(), any_of(), and where().

These helpers also work within the .by argument for grouping.

# Standard selection
df %>% select(a, starts_with("b"))

# Selection within .by
df %>% summarize(avg_z = mean(z), .by = where(is.character))

df <- data.table(
  a = 1:3,
  b1 = 4:6,
  b2 = 7:9,
  c = c("a", "a", "b")
)

df %>%
  select(a, starts_with("b"))

tidytable is compatible with tidy evaluation. When writing custom functions, you can use the embracing shortcut {{ }} or enquo() with !! to handle column names. The .data and .env pronouns are also supported.

# Using {{ }}
add_one <- function(data, add_col) {
  data %>%
    mutate(new_col = {{ add_col }} + 1)
}

# Using .data and .env
df %>%
  mutate(new_col = .data$x + .env$var)

add_one <- function(data, add_col) {
  data %>%
    mutate(new_col = {{ add_col }} + 1)
}

df %>%
  add_one(x)

The dt() helper allows you to use standard data.table syntax (like DT[i, j, by]) within a tidytable pipe chain. This is useful for leveraging specific data.table features while maintaining a readable pipeline.

df %>%
  dt(, .(x, y, z)) %>%           # Select columns
  dt(x < 4 & y > 1) %>%          # Filter rows
  dt(order(x, y)) %>%            # Arrange
  dt(, double_x := x * 2) %>%     # Mutate (in-place style)
  dt(, .(avg_x = mean(x)), by = z) # Summarize by group

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)