data_extract() (or its alias extract()) is similar to $. It extracts
either a single column or element from an object (e.g., a data frame, list),
or multiple columns resp. elements.
Usage
data_extract(data, select, ...)
# S3 method for class 'data.frame'
data_extract(
data,
select,
name = NULL,
extract = "all",
as_data_frame = FALSE,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)Arguments
- data
The object to subset. Methods are currently available for data frames and data frame extensions (e.g., tibbles).
- select
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g.,
column_name),a string with the variable name (e.g.,
"column_name"), a character vector of variable names (e.g.,c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via:(e.g.,c("col1:col3", "col5")),a formula with variable names (e.g.,
~column_1 + column_2),a vector of positive integers, giving the positions counting from the left (e.g.
1orc(1, 3, 5)),a vector of negative integers, giving the positions counting from the right (e.g.,
-1or-1:-3),one of the following select-helpers:
starts_with(),ends_with(),contains(), a range using:orregex("").starts_with(),ends_with(), andcontains()accept several patterns, e.gstarts_with("Sep", "Petal").or a function testing for logical conditions, e.g.
is.numeric()(oris.numeric), or any user-defined function that selects the variables for which the function returnsTRUE(like:foo <- function(x) mean(x) > 3),ranges specified via literal variable names, select-helpers (except
regex()) and (user-defined) functions can be negated, i.e. return non-matching elements, when prefixed with a-, e.g.-ends_with(""),-is.numericor-(Sepal.Width:Petal.Length). Note: Negation means that matches are excluded, and thus, theexcludeargument can be used alternatively. For instance,select=-ends_with("Length")(with-) is equivalent toexclude=ends_with("Length")(no-). In case negation should not work as expected, use theexcludeargument instead.
If
NULL, selects all columns. Patterns that found no matches are silently ignored, e.g.extract_column_names(iris, select = c("Species", "Test"))will just return"Species".- ...
For use by future methods.
- name
An optional argument that specifies the column to be used as names for the vector elements after extraction. Must be specified either as literal variable name (e.g.,
column_name) or as string ("column_name").namewill be ignored when a data frame is returned.- extract
String, indicating which element will be extracted when
selectmatches multiple variables. Can be"all"(the default) to return all matched variables,"first"or"last"to return the first or last match, or"odd"and"even"to return all odd-numbered or even-numbered matches. Note that"first"or"last"return a vector (unlessas_data_frame = TRUE), while"all"can return a vector (if only one match was found) or a data frame (for more than one match). Type safe return values are only possible whenextractis"first"or"last"(will always return a vector) or whenas_data_frame = TRUE(always returns a data frame).- as_data_frame
Logical, if
TRUE, will always return a data frame, even if only one variable was matched. IfFALSE, either returns a vector or a data frame. Seeextractfor details.- ignore_case
Logical, if
TRUEand when one of the select-helpers or a regular expression is used inselect, ignores lower/upper case in the search pattern when matching against variable names.- regex
Logical, if
TRUE, the search pattern fromselectwill be treated as regular expression. Whenregex = TRUE, select must be a character string (or a variable containing a character string) and is not allowed to be one of the supported select-helpers or a character vector of length > 1.regex = TRUEis comparable to using one of the two select-helpers,select = contains("")orselect = regex(""), however, since the select-helpers may not work when called from inside other functions (see 'Details'), this argument may be used as workaround.- verbose
Toggle warnings.
Value
A vector (or a data frame) containing the extracted element, or
NULL if no matching variable was found.
Details
data_extract() can be used to select multiple variables or pull a
single variable from a data frame. Thus, the return value is by default not
type safe - data_extract() either returns a vector or a data frame.
Extracting single variables (vectors)
When select is the name of a single column, or when select only matches
one column, a vector is returned. A single variable is also returned when
extract is either "first or "last". Setting as_data_frame to TRUE
overrides this behaviour and always returns a data frame.
Extracting a data frame of variables
When select is a character vector containing more than one column name (or
a numeric vector with more than one valid column indices), or when select
uses one of the supported select-helpers that match multiple columns, a
data frame is returned. Setting as_data_frame to TRUE always returns
a data frame.
Examples
# single variable
data_extract(mtcars, cyl, name = gear)
#> 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4
#> 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
data_extract(mtcars, "cyl", name = gear)
#> 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4
#> 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
data_extract(mtcars, -1, name = gear)
#> cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 4 121.0 109 4.11 2.780 18.60 1 1 4 2
data_extract(mtcars, cyl, name = 0)
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 6 6 4 6
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 8 6 8 4
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 4 6 6 8
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 8 8 8 8
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 8 4 4 4
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 4 8 8 8
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 8 4 4 4
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 8 6 8 4
data_extract(mtcars, cyl, name = "row.names")
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 6 6 4 6
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 8 6 8 4
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 4 6 6 8
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 8 8 8 8
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 8 4 4 4
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 4 8 8 8
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 8 4 4 4
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 8 6 8 4
# selecting multiple variables
head(data_extract(iris, starts_with("Sepal")))
#> Sepal.Length Sepal.Width
#> 1 5.1 3.5
#> 2 4.9 3.0
#> 3 4.7 3.2
#> 4 4.6 3.1
#> 5 5.0 3.6
#> 6 5.4 3.9
head(data_extract(iris, ends_with("Width")))
#> Sepal.Width Petal.Width
#> 1 3.5 0.2
#> 2 3.0 0.2
#> 3 3.2 0.2
#> 4 3.1 0.2
#> 5 3.6 0.2
#> 6 3.9 0.4
head(data_extract(iris, 2:4))
#> Sepal.Width Petal.Length Petal.Width
#> 1 3.5 1.4 0.2
#> 2 3.0 1.4 0.2
#> 3 3.2 1.3 0.2
#> 4 3.1 1.5 0.2
#> 5 3.6 1.4 0.2
#> 6 3.9 1.7 0.4
# select first of multiple variables
data_extract(iris, starts_with("Sepal"), extract = "first")
#> [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
#> [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
#> [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
#> [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
#> [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
#> [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
#> [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
#> [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
#> [145] 6.7 6.7 6.3 6.5 6.2 5.9
# select first of multiple variables, return as data frame
head(data_extract(iris, starts_with("Sepal"), extract = "first", as_data_frame = TRUE))
#> Sepal.Length
#> 1 5.1
#> 2 4.9
#> 3 4.7
#> 4 4.6
#> 5 5.0
#> 6 5.4