deprecated-se {tidyr} | R Documentation |
tidyr used to offer twin versions of each verb suffixed with an underscore. These versions had standard evaluation (SE) semantics: rather than taking arguments by code, like NSE verbs, they took arguments by value. Their purpose was to make it possible to program with tidyr. However, tidyr now uses tidy evaluation semantics. NSE verbs still capture their arguments, but you can now unquote parts of these arguments. This offers full programmability with NSE verbs. Thus, the underscored versions are now superfluous.
complete_(data, cols, fill = list(), ...) drop_na_(data, vars) expand_(data, dots, ...) crossing_(x) nesting_(x) extract_(data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ...) fill_(data, fill_cols, .direction = c("down", "up")) gather_(data, key_col, value_col, gather_cols, na.rm = FALSE, convert = FALSE, factor_key = FALSE) nest_(data, key_col, nest_cols = character()) separate_rows_(data, cols, sep = "[^[:alnum:].]+", convert = FALSE) separate_(data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ...) spread_(data, key_col, value_col, fill = NA, convert = FALSE, drop = TRUE, sep = NULL) unite_(data, col, from, sep = "_", remove = TRUE) unnest_(data, unnest_cols, .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL)
data |
A data frame |
fill |
A named list that for each variable supplies a single value to
use instead of |
... |
Specification of columns to expand. Columns can be atomic vectors or lists. To find all unique combinations of x, y and z, including those not
found in the data, supply each variable as a separate argument.
To find only the combinations that occur in the data, use nest:
You can combine the two forms. For example,
For factors, the full set of levels (not just those that appear in the
data) are used. For continuous variables, you may need to fill in values
that don't appear in the data: to do so use expressions like
Length-zero (empty) elements are automatically dropped. |
vars, cols, col |
Name of columns. |
x |
For |
into |
Names of new variables to create as character vector. |
regex |
a regular expression used to extract the desired values.
The should be one group (defined by |
remove |
If |
convert |
If |
fill_cols |
Character vector of column names. |
.direction |
Direction in which to fill missing values. Currently either "down" (the default) or "up". |
key_col, value_col |
Strings giving names of key and value columns to create. |
gather_cols |
Character vector giving column names to be gathered into pair of key-value columns. |
na.rm |
If |
factor_key |
If |
nest_cols |
Character vector of columns to nest. |
sep |
Separator delimiting collapsed values. |
extra |
If
|
drop |
If |
from |
Names of existing columns as character vector |
unnest_cols |
Name of columns that needs to be unnested. |
.drop |
Should additional list columns be dropped? By default,
|
.id |
Data frame identifier - if supplied, will create a new column
with name |
.sep |
If non- |
.preserve |
Optionally, list-columns to preserve in the output. These
will be duplicated in the same way as atomic vectors. This has
dplyr::select semantics so you can preserve multiple variables with
|
expand_cols |
Character vector of column names to be expanded. |
key_col |
Name of the column that will contain the nested data frames. |
key_col, value_col |
Strings giving names of key and value cols. |
Unquoting triggers immediate evaluation of its operand and inlines
the result within the captured expression. This result can be a
value or an expression to be evaluated later with the rest of the
argument. See vignette("programming", "dplyr")
for more information.