crossing() preserves factor levels (#410), now works with list-columns (#446, @SamanthaToet). (These also help expand() which is built on top of crossing())
nest() is compatible with dplyr 0.8.0.
spread() works when the id variable has names (#525).
unnest() preserves column being unnested when input is zero-length (#483), using list_of() attribute to correctly restore columns, where possible.
unnest() will run with named and unnamed list-columns of same length (@hlendway, #460).
separate() now accepts NA as a column name in the into argument to denote columns which are omitted from the result. (@markdly, #397).
Minor updates to ensure compatibility with dependencies.
unnest() weakens test of “atomicity” to restore previous behaviour when unnesting factors and dates (#407).There are no deliberate breaking changes in this release. However, a number of packages are failing with errors related to numbers of elements in columns, and row names. It is possible that these are accidental API changes or new bugs. If you see such an error in your package, I would sincerely appreciate a minimal reprex.
separate() now correctly uses -1 to refer to the far right position, instead of -2. If you depended on this behaviour, you’ll need to switch on packageVersion("tidyr") > "0.7.2"
Increased test coverage from 84% to 99%.
uncount() performs the inverse operation of dplyr::count() (#279)
complete(data) now returns data rather than throwing an error (#390). complete() with zero-length completions returns original input (#331).
crossing() preserves NAs (#364).
expand() with empty input gives empty data frame instead of NULL (#331).
expand(), crossing(), and complete() now complete empty factors instead of dropping them (#270, #285)
extract() has a better error message if regex does not contain the expected number of groups (#313).
drop_na() no longer drops columns (@jennybryan, #245), and works with list-cols (#280). Equivalent of NA in a list column is any empty (length 0) data structure.
nest() is now faster, especially when a long data frame is collapsed into a nested data frame with few rows.
nest() on a zero-row data frame works as expected (#320).
replace_na() no longer complains if you try and replace missing values in variables not present in the data (#356).
replace_na() now also works with vectors (#342, @flying-sheep), and can replace NULL in list-columns. It throws a better error message if you attempt to replace with something other than length 1.
separate() no longer checks that ... is empty, allowing methods to make use of it. This check was added in tidyr 0.4.0 (2016-02-02) to deprecate previous behaviour where ... was passed to strsplit().
separate() and extract() now insert columns in correct position when drop = TRUE (#394).
separate() now works correctly counts from RHS when using negative integer sep values (@markdly, #315).
separate() gets improved warning message when pieces aren’t as expected (#375).
separate_rows() supports list columns (#321), and works with empty tibbles.
spread() now consistently returns 0 row outputs for 0 row inputs (#269).
spread() now works when key column includes NA and drop is FALSE (#254).
spread() no longer returns tibbles with row names (#322).
spread(), separate(), extract() (#255), and gather() (#347) now replace existing variables rather than creating an invalid data frame with duplicated variable names (matching the semantics of mutate).
unite() now works (as documented) if you don’t supply any variables (#355).
unnest() gains preserve argument which allows you to preserve list columns without unnesting them (#328).
unnest() can unnested list-columns contains lists of lists (#278).
unnest(df) now works if df contains no list-cols (#344)
The SE variants gather_(), spread_() and nest_() now treat non-syntactic names in the same way as pre tidy eval versions of tidyr (#361).
Fix tidyr bug revealed by R-devel.
This is a hotfix release to account for some tidyselect changes in the unit tests.
Note that the upcoming version of tidyselect backtracks on some of the changes announced for 0.7.0. The special evaluation semantics for selection have been changed back to the old behaviour because the new rules were causing too much trouble and confusion. From now on data expressions (symbols and calls to : and c()) can refer to both registered variables and to objects from the context.
However the semantics for context expressions (any calls other than to : and c()) remain the same. Those expressions are evaluated in the context only and cannot refer to registered variables. If you’re writing functions and refer to contextual objects, it is still a good idea to avoid data expressions by following the advice of the 0.7.0 release notes.
This release includes important changes to tidyr internals. Tidyr now supports the new tidy evaluation framework for quoting (NSE) functions. It also uses the new tidyselect package as selecting backend.
If you see error messages about objects or functions not found, it is likely because the selecting functions are now stricter in their arguments An example of selecting function is gather() and its ... argument. This change makes the code more robust by disallowing ambiguous scoping. Consider the following code:
x <- 3
df <- tibble(w = 1, x = 2, y = 3)
gather(df, "variable", "value", 1:x)Does it select the first three columns (using the x defined in the global environment), or does it select the first two columns (using the column named x)?
To solve this ambiguity, we now make a strict distinction between data and context expressions. A data expression is either a bare name or an expression like x:y or c(x, y). In a data expression, you can only refer to columns from the data frame. Everything else is a context expression in which you can only refer to objects that you have defined with <-.
In practice this means that you can no longer refer to contextual objects like this:
mtcars %>% gather(var, value, 1:ncol(mtcars))
x <- 3
mtcars %>% gather(var, value, 1:x)
mtcars %>% gather(var, value, -(1:x))You now have to be explicit about where to find objects. To do so, you can use the quasiquotation operator !! which will evaluate its argument early and inline the result:
mtcars %>% gather(var, value, !! 1:ncol(mtcars))
mtcars %>% gather(var, value, !! 1:x)
mtcars %>% gather(var, value, !! -(1:x))An alternative is to turn your data expression into a context expression by using seq() or seq_len() instead of :. See the section on tidyselect for more information about these semantics.
Following the switch to tidy evaluation, you might see warnings about the “variable context not set”. This is most likely caused by supplyng helpers like everything() to underscored versions of tidyr verbs. Helpers should be always be evaluated lazily. To fix this, just quote the helper with a formula: drop_na(df, ~everything()).
The selecting functions are now stricter when you supply integer positions. If you see an error along the lines of
`-0.949999999999999`, `-0.940000000000001`, ... must resolve to
integer column positions, not a double vectorplease round the positions before supplying them to tidyr. Double vectors are fine as long as they are rounded.
tidyr is now a tidy evaluation grammar. See the programming vignette in dplyr for practical information about tidy evaluation.
The tidyr port is a bit special. While the philosophy of tidy evaluation is that R code should refer to real objects (from the data frame or from the context), we had to make some exceptions to this rule for tidyr. The reason is that several functions accept bare symbols to specify the names of new columns to create (gather() being a prime example). This is not tidy because the symbol do not represent any actual object. Our workaround is to capture these arguments using rlang::quo_name() (so they still support quasiquotation and you can unquote symbols or strings). This type of NSE is now discouraged in the tidyverse: symbols in R code should represent real objects.
Following the switch to tidy eval the underscored variants are softly deprecated. However they will remain around for some time and without warning for backward compatibility.
The selecting backend of dplyr has been extracted in a standalone package tidyselect which tidyr now uses for selecting variables. It is used for selecting multiple variables (in drop_na()) as well as single variables (the col argument of extract() and separate(), and the key and value arguments of spread()). This implies the following changes:
The arguments for selecting a single variable now support all features from dplyr::pull(). You can supply a name or a position, including negative positions.
Multiple variables are now selected a bit differently. We now make a strict distinction between data and context expressions. A data expression is either a bare name of an expression like x:y or c(x, y). In a data expression, you can only refer to columns from the data frame. Everything else is a context expression in which you can only refer to objects that you have defined with <-.
You can still refer to contextual objects in a data expression by being explicit. One way of being explicit is to unquote a variable from the environment with the tidy eval operator !!:
x <- 2
drop_na(df, 2) # Works fine
drop_na(df, x) # Object 'x' not found
drop_na(df, !! x) # Works as if you had supplied 2On the other hand, select helpers like start_with() are context expressions. It is therefore easy to refer to objects and they will never be ambiguous with data columns:
x <- "d"
drop_na(df, starts_with(x))While these special rules is in contrast to most dplyr and tidyr verbs (where both the data and the context are in scope) they make sense for selecting functions and should provide more robust and helpful semantics.
Register C functions
Added package docs
Patch tests to be compatible with dev dplyr.
Patch test to be compatible with dev tibble
Changed deprecation message of extract_numeric() to point to readr::parse_number() rather than readr::parse_numeric()
drop_na() removes observations which have NA in the given variables. If no variables are given, all variables are considered (#194, @janschulz).
extract_numeric() has been deprecated (#213).
Renamed table4 and table5 to table4a and table4b to make their connection more clear. The key and value variables in table2 have been renamed to type and count.
expand(), crossing(), and nesting() now silently drop zero-length inputs.
crossing_() and nesting_() are versions of crossing() and nesting() that take a list as input.
full_seq() works correctly for dates and date/times.
getS3method(envir = ) (#205, @krlmlr).separate_rows() separates observations with multiple delimited values into separate rows (#69, @aaronwolen).complete() preserves grouping created by dplyr (#168).
expand() (and hence complete()) preserves the ordered attribute of factors (#165).
full_seq() preserve attributes for dates and date/times (#156), and sequences no longer need to start at 0.
gather() can now gather together list columns (#175), and gather_.data.frame(na.rm = TRUE) now only removes missing values if they’re actually present (#173).
nest() returns correct output if every variable is nested (#186).
separate() fills from right-to-left (not left-to-right!) when fill = “left” (#170, @dgrtwo).
separate() and unite() now automatically drop removed variables from grouping (#159, #177).
spread() gains a sep argument. If not-null, this will name columns as “keyNULL missing values will be converted to <NA> (#68).
spread() works in the presence of list-columns (#199)
unnest() works with non-syntactic names (#190).
unnest() gains a sep argument. If non-null, this will rename the columns of nested data frames to include both the original column name, and the nested column name, separated by .sep (#184).
unnest() gains .id argument that works the same way as bind_rows(). This is useful if you have a named list of data frames or vectors (#125).
Moved in useful sample datasets from the DSR package.
Made compatible with both dplyr 0.4 and 0.5.
tidyr functions that create new columns are more aggresive about re-encoding the column names as UTF-8.
nest() where nested data was ending up in the wrong row (#158).nest() and unnest() have been overhauled to support a useful way of structuring data frames: the nested data frame. In a grouped data frame, you have one row per observation, and additional metadata define the groups. In a nested data frame, you have one row per group, and the individual observations are stored in a column that is a list of data frames. This is a useful structure when you have lists of other objects (like models) with one element per group.
nest() now produces a single list of data frames called “data” rather than a list column for each variable. Nesting variables are not included in nested data frames. It also works with grouped data frames made by dplyr::group_by(). You can override the default column name with .key.
unnest() gains a .drop argument which controls what happens to other list columns. By default, they’re kept if the output doesn’t require row duplication; otherwise they’re dropped.
unnest() now has mutate() semantics for ... - this allows you to unnest transformed columns more easily. (Previously it used select semantics).
expand() once again allows you to evaluate arbitrary expressions like full_seq(year). If you were previously using c() to created nested combinations, you’ll now need to use nesting() (#85, #121).
nesting() and crossing() allow you to create nested and crossed data frames from individual vectors. crossing() is similar to base::expand.grid()
full_seq(x, period) creates the full sequence of values from min(x) to max(x) every period values.
fill() fills in NULLs in list-columns.
fill() gains a direction argument so that it can fill either upwards or downwards (#114).
gather() now stores the key column as character, by default. To revert to the previous behaviour of using a factor (which allows you to preserve the ordering of the columns), use key_factor = TRUE (#96).
All tidyr verbs do the right thing for grouped data frames created by group_by() (#122, #129, #81).
seq_range() has been removed. It was never used or announced.
spread() once again creates columns of mixed type when convert = TRUE (#118, @jennybc). spread() with drop = FALSE handles zero-length factors (#56). spread()ing a data frame with only key and value columns creates a one row output (#41).
unite() now removes old columns before adding new (#89, @krlmlr).
separate() now warns if defunct … argument is used (#151, @krlmlr).
New complete() provides a wrapper around expand(), left_join() and replace_na() for a common task: completing a data frame with missing combinations of variables.
fill() fills in missing values in a column with the last non-missing value (#4).
New replace_na() makes it easy to replace missing values with something meaningful for your data.
nest() is the complement of unnest() (#3).
unnest() can now work with multiple list-columns at the same time. If you don’t supply any columns names, it will unlist all list-columns (#44). unnest() can also handle columns that are lists of data frames (#58).
tidyr no longer depends on reshape2. This should fix issues if you also try to load reshape (#88).
%>% is re-exported from magrittr.
expand() now supports nesting and crossing (see examples for details). This comes at the expense of creating new variables inline (#46).
expand_ does SE evaluation correctly so you can pass it a character vector of columns names (or list of formulas etc) (#70).
extract() is 10x faster because it now uses stringi instead of base R regular expressions. It also returns NA instead of throwing an error if the regular expression doesn’t match (#72).
extract() and separate() preserve character vectors when convert is TRUE (#99).
The internals of spread() have been rewritten, and now preserve all attributes of the input value column. This means that you can now spread date (#62) and factor (#35) inputs.
spread() gives a more informative error message if key or value don’t exist in the input data (#36).
separate() only displays the first 20 failures (#50). It has finer control over what happens if there are two few matches: you can fill with missing values on either the “left” or the “right” (#49). separate() no longer throws an error if the number of pieces aren’t as expected - instead it uses drops extra values and fills on the right and gives a warning.
If the input is NA separate() and extract() both return silently return NA outputs, rather than throwing an error. (#77)
Experimental unnest() method for lists has been removed.
Experimental expand() function (#21).
Experiment unnest() function for converting named lists into data frames. (#3, #22)
extract_numeric() preserves negative signs (#20).
gather() has better defaults if key and value are not supplied. If ... is ommitted, gather() selects all columns (#28). Performance is now comparable to reshape2::melt() (#18).
separate() gains extra argument which lets you control what happens to extra pieces. The default is to throw an “error”, but you can also “merge” or “drop”.
spread() gains drop argument, which allows you to preserve missing factor levels (#25). It converts factor value variables to character vectors, instead of embedding a matrix inside the data frame (#35).