keys-manipulate: Manipulate keys

In keyholder: Store Data About Rows

Manipulate keys

Description

Functions to manipulate keys.

Usage

remove_keys(.tbl, ..., .unkey = FALSE)

restore_keys(.tbl, ..., .remove = FALSE, .unkey = FALSE)

pull_key(.tbl, var)

rename_keys(.tbl, ...)

Arguments

.tbl	Reference data frame.
...	Variables to be used for operations defined in similar fashion as in dplyr::select().
.unkey	Whether to unkey() .tbl in case there are no keys left.
.remove	Whether to remove keys after restoring.
var	Parameter for dplyr::pull().

Details

remove_keys() removes keys defined with ....

restore_keys() transfers keys defined with ... into .tbl and removes them from keys if .remove == TRUE. If .tbl is grouped the following happens:

• If restored keys don't contain grouping variables then groups don't change;

• If restored keys contain grouping variables then result will be regrouped based on restored values. In other words restoring keys beats 'not-modifying' grouping variables rule. It is made according to the ideology of keys: they contain information about rows and by restoring you want it to be available.

pull_key() extracts one specified column from keys with dplyr::pull().

rename_keys() renames columns in keys using dplyr::rename().

See Also

Get keys, Set keys

Scoped functions

Examples

df <- mtcars %>% dplyr::as_tibble() %>%
  key_by(vs, am, .exclude = TRUE)
df %>% remove_keys(vs)

df %>% remove_keys(dplyr::everything())

df %>% remove_keys(dplyr::everything(), .unkey = TRUE)


df %>% restore_keys(vs)

df %>% restore_keys(vs, .remove = TRUE)


df %>% restore_keys(dplyr::everything(), .remove = TRUE)

df %>% restore_keys(dplyr::everything(), .remove = TRUE, .unkey = TRUE)


# Restoring on grouped data frame
df_grouped <- df %>% dplyr::mutate(vs = 1) %>% dplyr::group_by(vs)
df_grouped %>% restore_keys(dplyr::everything())

# Pulling
df %>% pull_key(vs)

# Renaming
df %>% rename_keys(Vs = vs)

keyholder documentation built on March 31, 2023, 5:37 p.m.