mutate: Add/modify/delete columns

In tidytable: Tidy Interface to 'data.table'

Add/modify/delete columns

Description

With mutate() you can do 3 things:

• Add new columns

• Modify existing columns

• Delete columns

Usage

mutate(
  .df,
  ...,
  .by = NULL,
  .keep = c("all", "used", "unused", "none"),
  .before = NULL,
  .after = NULL
)

Arguments

.df	A data.frame or data.table
...	Columns to add/modify
.by	Columns to group by
.keep	experimental: This is an experimental argument that allows you to control which columns from .df are retained in the output: "all", the default, retains all variables. "used" keeps any variables used to make new variables; it's useful for checking your work as it displays inputs and outputs side-by-side. "unused" keeps only existing variables not used to make new variables. "none", only keeps grouping keys (like transmute()).
.before, .after	Optionally indicate where new columns should be placed. Defaults to the right side of the data frame.

Examples

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

df %>%
  mutate(double_a = a * 2,
         a_plus_b = a + b)

df %>%
  mutate(double_a = a * 2,
         avg_a = mean(a),
         .by = c)

df %>%
  mutate(double_a = a * 2, .keep = "used")

df %>%
  mutate(double_a = a * 2, .after = a)

tidytable documentation built on Sept. 11, 2024, 8:05 p.m.