extract_numeric()to point to
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).
table4b to make their connection more clear. The
value variables in
table2 have been renamed to
getS3method(envir = )(#205, @krlmlr).
separate_rows()separates observations with multiple delimited values into separate rows (#69, @aaronwolen).
complete() preserves grouping created by dplyr (#168).
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).
spread() gains a
sep argument. If not-null, this will name columns as “key
NULL missing values will be converted to
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
.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).
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
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).
full_seq(x, period) creates the full sequence of values from
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).
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).
fill() fills in missing values in a column with the last non-missing value (#4).
replace_na() makes it easy to replace missing values with something meaningful for your data.
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).
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
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.
unnest() method for lists has been removed.
extract_numeric() preserves negative signs (#20).
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”.
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).