Learn R Programming

collapse (version 1.7.5)

fselect-get_vars-add_vars: Fast Select, Replace or Add 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 expressions, column names, indices, logical vectors, selector functions or regular expressions matching column names.

Usage

## Select and replace variables, analgous to dplyr::select but significantly faster
fselect(x, …, return = "data")
fselect(x, …) <- value
slt(x, …, return = "data")   # Shortcut for fselect
slt(x, …) <- value           # Shortcut for fselect<-

## Select and replace columns by names, indices, logical vectors, ## regular expressions or using functions to identify columns

get_vars(x, vars, return = "data", regex = FALSE, …) gv(x, vars, return = "data", …) # Shortcut for get_vars gvr(x, vars, return = "data", …) # Shortcut for get_vars(\dots, regex = TRUE)

get_vars(x, vars, regex = FALSE, …) <- value gv(x, vars, …) <- value # Shortcut for get_vars<- gvr(x, vars, …) <- value # Shortcut for get_vars<-(\dots, regex = TRUE)

## Add columns at any position within a data.frame

add_vars(x, …, pos = "end") add_vars(x, pos = "end") <- value av(x, …, pos = "end") # Shortcut for add_vars av(x, pos = "end") <- value # Shortcut for add_vars<-

## Select and replace columns by data type

num_vars(x, return = "data") num_vars(x) <- value nv(x, return = "data") # Shortcut for num_vars nv(x) <- value # Shortcut for num_vars<- cat_vars(x, return = "data") # Categorical variables, see is_categorical cat_vars(x) <- value char_vars(x, return = "data") char_vars(x) <- value fact_vars(x, return = "data") fact_vars(x) <- value logi_vars(x, return = "data") logi_vars(x) <- value date_vars(x, return = "data") # See is_date date_vars(x) <- value

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, or a vector of regular expressions matching column names (if regex = TRUE). It is also possible to pass a function returning TRUE or FALSE when applied to the columns of x.

return

an integer or string specifying what the selector function should return. The options are:

Int. String Description
1 "data" subset of data frame (default)
2 "names" column names
3 "indices" column indices
4 "named_indices" named column indices
5 "logical" logical selection vector
6 "named_logical" named logical vector

Note: replacement functions only replace data, However column names are replaced together with the data (if available).

regex

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

pos

the position where columns are added in the data frame. "end" (default) will append the data frame at the end (right) side. "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 fselect: column names and expressions e.g. fselect(mtcars, newname = mpg, hp, carb:vs). 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(…)).

Details

get_vars(<-) is around 2x faster than `[.data.frame` and 8x faster than `[<-.data.frame`, so the common operation data[cols] <- someFUN(data[cols]) can be made 10x more efficient (abstracting from computations performed by someFUN) using get_vars(data, cols) <- someFUN(get_vars(data, cols)) or the shorthand gv(data, cols) <- someFUN(gv(data, cols)).

Similarly type-wise operations like data[sapply(data, is.numeric)] or data[sapply(data, is.numeric)] <- value are facilitated and more efficient using num_vars(data) and num_vars(data) <- value or the shortcuts nv and nv<- etc.

fselect provides an efficient alternative to dplyr::select, allowing the selection of variables based on expressions evaluated within the data frame, see Examples. It is about 100x faster than dplyr::select but also more simple as it does not provide special methods for grouped tibbles.

Finally, add_vars(data1, data2, data3, …) is a lot faster than cbind(data1, data2, data3, …), and preserves the attributes of data1 (i.e. it is like adding columns to data1). The replacement function add_vars(data) <- someFUN(get_vars(data, cols)) efficiently appends data with computed columns. The pos argument allows adding columns at positions other than the end (right) of the data frame, see Examples.

All functions introduced here perform their operations class-independent. They all basically work like this: (1) save the attributes of x, (2) unclass x, (3) subset, replace or append x as a list, (4) modify the "names" component of the attributes of x accordingly and (5) efficiently attach the attributes again to the result from step (3). Thus they can freely be applied to data.table's, grouped tibbles, panel data frames and other classes and will return an object of exactly the same class and the same attributes.

See Also

fsubset, ftransform, Data Frame Manipulation, Collapse Overview

Examples

Run this code
# NOT RUN {
## Wold Development Data
head(fselect(wlddev, Country = country, Year = year, ODA)) # Fast dplyr-like selecting
head(fselect(wlddev, -country, -year, -PCGDP))
head(fselect(wlddev, country, year, PCGDP:ODA))
head(fselect(wlddev, -(PCGDP:ODA)))
fselect(wlddev, country, year, PCGDP:ODA) <- NULL          # Efficient deleting
head(wlddev)
rm(wlddev)

head(num_vars(wlddev))                                     # Select numeric variables
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
head(gvr(wlddev, "[[:upper:]]"))                           # Same thing

get_vars(wlddev, 9:12) <- get_vars(wlddev, 9:12)           # 9x 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, 14:17) <- 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

# }

Run the code above in your browser using DataLab