collapse (version 1.1.0)

A3-select-replace-vars: Quick Select and Replace Data Frame Columns

Description

Efficiently select and replace (or add) a subset of columns from (to) a data frame. This can be done by data type, or using column names, indices, logical vectors, functions or regular expressions.

The performance is generally faster than `[`. It is also secure w.r.t. redefinitions of `[.data.frame` or `[<-.data.frame` for other classes (i.e. data.table's, tibbles etc.) and prevents the loss of attributes, but does not offer a lot of security in terms of performing all kinds of costly checks on the data.frame's or when lists of unequal-length columns are offered as replacements.

Usage

## Select and replace columns by data type
num_vars(x, return = c("data","names","indices","named_indices"))
      nv(x, return = c("data","names","indices","named_indices")) # Short for num_vars
num_vars(x) <- value
      nv(x) <- value                                              # Short for num_vars<-
cat_vars(x, return = c("data","names","indices","named_indices"))
cat_vars(x) <- value
char_vars(x, return = c("data","names","indices","named_indices"))
char_vars(x) <- value
fact_vars(x, return = c("data","names","indices","named_indices"))
fact_vars(x) <- value
logi_vars(x, return = c("data","names","indices","named_indices"))
logi_vars(x) <- value
Date_vars(x, return = c("data","names","indices","named_indices"))
Date_vars(x) <- value
## Select and replace columns by names, indices, logical vectors,
## regular expressions or using other functions to identify columns
get_vars(x, vars, return = c("data","names","indices","named_indices"),
         regex = FALSE, ...)
      gv(x, vars, return = c("data","names","indices","named_indices"),
         regex = FALSE, ...)                                      # Short for get_vars
get_vars(x, vars, regex = FALSE, ...) <- value
      gv(x, vars, regex = FALSE, ...) <- value                    # Short for get_vars<-
## Add columns at any position within a data.frame
add_vars(x, ..., pos = "end")
      av(x, ..., pos = "end")               # Short for add_vars
add_vars(x, pos = "end") <- value
      av(x, pos = "end") <- value             # Short for add_vars<-

Arguments

x

a data.frame.

value

a data.frame or list of columns whose dimensions exactly match those of the extracted subset of x. If only 1 variable is in the subset of x, value can also be an atomic vector or matrix, provided that NROW(value) == nrow(x).

vars

a vector of column names, indices (can be negative), a suitable logical vector, a vector of regular expressions matching column names if regex = TRUE, or a function returning TRUE or FALSE when applied to the columns of x.

return

an integer or string specifying what to return. The options are:

1. "data"

2. "names"

3. "indices"

4. "named_indices"

Note: replacement functions only replace data, not column names or indices (ordering). However column names are replaced together with the data.

regex

logical. TRUE will do regular expression search on the column names of x using a (vector of) regular expression(s) passed to vars.

pos

the position where columns are added in the data.frame. "end" (default) will add columns at the end (right) of the data.frame, "front" will add columns in front (left). Alternatively one can pass a vector of positions (matching length(value) if value is a list). In that case the other columns will be shifted around the new ones while maintaining their order.

...

for get_vars: further arguments passed to grep, if regex = TRUE. For add_vars: Same as value. A single argument passed may also be a vector or matrix, multiple arguments must each be a list (they are combined using c(...)).

See Also

Collapse Overview

Examples

# NOT RUN {
## Wold Development Data
head(num_vars(wlddev))                                     # Select numeric variables
head(get_vars(wlddev, is.numeric))                         # Same thing
head(cat_vars(wlddev))                                     # Select categorical (non-numeric) vars
head(get_vars(wlddev, is.categorical))                     # Same thing

num_vars(wlddev) <- num_vars(wlddev)                       # Replace Numeric Variables by themselves
get_vars(wlddev,is.numeric) <- get_vars(wlddev,is.numeric) # Same thing

head(get_vars(wlddev, 9:12))                               # Select columns 9 through 12, 2x faster
head(get_vars(wlddev, -(9:12)))                            # All except columns 9 through 12
head(get_vars(wlddev, c("PCGDP","LIFEEX","GINI","ODA")))   # Select using column names
head(get_vars(wlddev, "[[:upper:]]", regex = TRUE))        # Same thing: match upper-case var. names

get_vars(wlddev, 9:12) <- get_vars(wlddev, 9:12)           # 6x faster wlddev[9:12] <- wlddev[9:12]
add_vars(wlddev) <- STD(gv(wlddev,9:12), wlddev$iso3c)     # Add Standardized columns 9 through 12
head(wlddev)                                               # gv and av are shortcuts

get_vars(wlddev, 13:16) <- NULL                            # Efficient Deleting added columns again
av(wlddev, "front") <- STD(gv(wlddev,9:12), wlddev$iso3c)  # Again adding in Front
head(wlddev)
get_vars(wlddev, 1:4) <- NULL                              # Deleting
av(wlddev,c(10,12,14,16)) <- W(wlddev,~iso3c, cols = 9:12, # Adding next to original variables
                               keep.by = FALSE)
head(wlddev)
get_vars(wlddev, c(10,12,14,16)) <- NULL                   # Deleting

# }