samife: A Grammar of Data Manipulation

Description Usage Arguments Details Value Useful functions Scoped selection and renaming Tidy data See Also Examples

Choose or rename variables from a tbl. select() keeps only the variables you mention; rename() keeps all variables.

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select(.data, ...)

rename(.data, ...)

.data

A tbl. All main verbs are S3 generics and provide methods for tbl_df(), dtplyr::tbl_dt() and dbplyr::tbl_dbi().

...

One or more unquoted expressions separated by commas. You can treat variable names like they are positions, so you can use expressions like x:y to select ranges of variables.

Positive values select variables; negative values drop variables. If the first expression is negative, select() will automatically start with all variables.

Use named arguments, e.g. new_name = old_name, to rename selected variables.

The arguments in ... are automatically quoted and evaluated in a context where column names represent column positions. They also support unquoting and splicing. See vignette("programming") for an introduction to these concepts.

See select helpers for more details and examples about tidyselect helpers such as starts_with(), everything(), ...

These functions work by column index, not value; thus, an expression like select(data.frame(x = 1:5, y = 10), z = x+1) does not create a variable with values 2:6. (In the current implementation, the expression z = x+1 wouldn't do anything useful.) To calculate using column values, see mutate()/transmute().

An object of the same class as .data.

As well as using existing functions like : and c(), there are a number of special functions that only work inside select():

starts_with(), ends_with(), contains()
matches()
num_range()
one_of()
everything()

To drop variables, use -.

Note that except for :, - and c(), all complex expressions are evaluated outside the data frame context. This is to prevent accidental matching of data frame variables when you refer to variables from the calling context.

The three scoped variants of select() (select_all(), select_if() and select_at()) and the three variants of rename() (rename_all(), rename_if(), rename_at()) make it easy to apply a renaming function to a selection of variables.

When applied to a data frame, row names are silently dropped. To preserve, convert to an explicit variable with tibble::rownames_to_column().

Other single table verbs: arrange, filter, mutate, slice, summarise

iris <- as_tibble(iris) # so it prints a little nicer
select(iris, starts_with("Petal"))
select(iris, ends_with("Width"))

# Move Species variable to the front
select(iris, Species, everything())

# Move Sepal.Length variable to back
# first select all variables except Sepal.Length, then re select Sepal.Length
select(iris, -Sepal.Length, Sepal.Length)

df <- as.data.frame(matrix(runif(100), nrow = 10))
df <- tbl_df(df[c(3, 4, 7, 1, 9, 8, 5, 2, 6, 10)])
select(df, V4:V6)
select(df, num_range("V", 4:6))

# Drop variables with -
select(iris, -starts_with("Petal"))


# The .data pronoun is available:
select(mtcars, .data$cyl)
select(mtcars, .data$mpg : .data$disp)

# However it isn't available within calls since those are evaluated
# outside of the data context. This would fail if run:
# select(mtcars, identical(.data$cyl))


# Renaming -----------------------------------------
# * select() keeps only the variables you specify
select(iris, petal_length = Petal.Length)

# * rename() keeps all variables
rename(iris, petal_length = Petal.Length)

# * select() can rename variables in a group
select(iris, obs = starts_with('S'))

# Unquoting ----------------------------------------

# Like all dplyr verbs, select() supports unquoting of symbols:
vars <- list(
  var1 = sym("cyl"),
  var2 = sym("am")
)
select(mtcars, !!!vars)

# For convenience it also supports strings and character
# vectors. This is unlike other verbs where strings would be
# ambiguous.
vars <- c(var1 = "cyl", var2 ="am")
select(mtcars, !!vars)
rename(mtcars, !!vars)