sjmisc (version 2.8.7)

add_columns: Add or replace data frame columns

Description

add_columns() combines two or more data frames, but unlike cbind or dplyr::bind_cols(), this function binds data as last columns of a data frame (i.e., behind columns specified in ...). This can be useful in a "pipe"-workflow, where a data frame returned by a previous function should be appended at the end of another data frame that is processed in add_colums().

replace_columns() replaces all columns in data with identically named columns in ..., and adds remaining (non-duplicated) columns from ... to data.

add_id() simply adds an ID-column to the data frame, with values from 1 to nrow(data), respectively for grouped data frames, values from 1 to group size. See 'Examples'.

Usage

add_columns(data, ..., replace = TRUE)
replace_columns(data, ..., add.unique = TRUE)
add_id(data, var = "ID")

Arguments

data

A data frame. For add_columns(), will be bound after data frames specified in .... For replace_columns(), duplicated columns in data will be replaced by columns in ....

...

More data frames to combine, resp. more data frames with columns that should replace columns in data.

replace

Logical, if TRUE (default), columns in ... with identical names in data will replace the columns in data. The order of columns after replacing is preserved.

add.unique

Logical, if TRUE (default), remaining columns in ... that did not replace any column in data, are appended as new columns to data.

var

Name of new the ID-variable.

Value

For add_columns(), a data frame, where columns of data are appended after columns of .... For replace_columns(), a data frame where columns in data will be replaced by identically named columns in ..., and remaining columns from ... will be appended to data (if add.unique = TRUE). For add_id(), a new column with ID numbers. This column is always the first column in the returned data frame.

Examples

# NOT RUN {
data(efc)
d1 <- efc[, 1:3]
d2 <- efc[, 4:6]

library(dplyr)
head(bind_cols(d1, d2))
add_columns(d1, d2) %>% head()

d1 <- efc[, 1:3]
d2 <- efc[, 2:6]

add_columns(d1, d2, replace = TRUE) %>% head()
add_columns(d1, d2, replace = FALSE) %>% head()

# use case: we take the original data frame, select specific
# variables and do some transformations or recodings
# (standardization in this example) and add the new, transformed
# variables *to the end* of the original data frame
efc %>%
  select(e17age, c160age) %>%
  std() %>%
  add_columns(efc) %>%
  head()

# new variables with same name will overwrite old variables
# in "efc". order of columns is not changed.
efc %>%
  select(e16sex, e42dep) %>%
  to_factor() %>%
  add_columns(efc) %>%
  head()

# keep both old and new variables, automatically
# rename variables with identical name
efc %>%
  select(e16sex, e42dep) %>%
  to_factor() %>%
  add_columns(efc, replace = FALSE) %>%
  head()

# create sample data frames
d1 <- efc[, 1:10]
d2 <- efc[, 2:3]
d3 <- efc[, 7:8]
d4 <- efc[, 10:12]

# show original
head(d1)

library(sjlabelled)
# slightly change variables, to see effect
d2 <- as_label(d2)
d3 <- as_label(d3)

# replace duplicated columns, append remaining
replace_columns(d1, d2, d3, d4) %>% head()

# replace duplicated columns, omit remaining
replace_columns(d1, d2, d3, d4, add.unique = FALSE) %>% head()

# add ID to dataset
library(dplyr)
data(mtcars)
add_id(mtcars)

mtcars %>%
  group_by(gear) %>%
  add_id() %>%
  arrange(gear, ID) %>%
  print(n = 100)
# }