A Grammar of Data Manipulation

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

dplyr is the next iteration of plyr, focussed on tools for working with data frames (hence the d in the name). It has three main goals:

  • Identify the most important data manipulation tools needed for data analysis and make them easy to use from R.

  • Provide blazing fast performance for in-memory data by writing key pieces in C++.

  • Use the same interface to work with data no matter where it's stored, whether in a data frame, a data table or database.

You can install:

  • the latest released version from CRAN with

  • the latest development version from github with

    if (packageVersion("devtools") < 1.6) {

You'll probably also want to install the data packages used in most examples: install.packages(c("nycflights13", "Lahman")).

If you encounter a clear bug, please file a minimal reproducible example on github. For questions and other discussion, please use the manipulatr mailing list.

To get started, read the notes below, then read the intro vignette: vignette("introduction", package = "dplyr"). To make the most of dplyr, I also recommend that you familiarise yourself with the principles of tidy data: this will help you get your data into a form that works well with dplyr, ggplot2 and R's many modelling functions.

If you need more, help I recommend the following (paid) resources:

  • dplyr on datacamp, by Garrett Grolemund. Learn the basics of dplyr at your own pace in this interactive online course.

  • Introduction to Data Science with R: How to Manipulate, Visualize, and Model Data with the R Language, by Garrett Grolemund. This O'Reilly video series will teach you the basics needed to be an effective analyst in R.

The key object in dplyr is a tbl, a representation of a tabular data structure. Currently dplyr supports:

You can create them as follows:

library(dplyr) # for functions
library(nycflights13) # for data
#>     year month   day dep_time dep_delay arr_time arr_delay carrier tailnum
#>    (int) (int) (int)    (int)     (dbl)    (int)     (dbl)   (chr)   (chr)
#> 1   2013     1     1      517         2      830        11      UA  N14228
#> 2   2013     1     1      533         4      850        20      UA  N24211
#> 3   2013     1     1      542         2      923        33      AA  N619AA
#> 4   2013     1     1      544        -1     1004       -18      B6  N804JB
#> 5   2013     1     1      554        -6      812       -25      DL  N668DN
#> 6   2013     1     1      554        -4      740        12      UA  N39463
#> 7   2013     1     1      555        -5      913        19      B6  N516JB
#> 8   2013     1     1      557        -3      709       -14      EV  N829AS
#> 9   2013     1     1      557        -3      838        -8      B6  N593JB
#> 10  2013     1     1      558        -2      753         8      AA  N3ALAA
#> ..   ...   ...   ...      ...       ...      ...       ...     ...     ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#>   (dbl), distance (dbl), hour (dbl), minute (dbl).
# Caches data in local SQLite db
flights_db1 <- tbl(nycflights13_sqlite(), "flights")
# Caches data in local postgres db
flights_db2 <- tbl(nycflights13_postgres(), "flights")

Each tbl also comes in a grouped variant which allows you to easily perform operations "by group":

carriers_df  <- flights %>% group_by(carrier)
carriers_db1 <- flights_db1 %>% group_by(carrier)
carriers_db2 <- flights_db2 %>% group_by(carrier)

dplyr implements the following verbs useful for data manipulation:

  • select(): focus on a subset of variables
  • filter(): focus on a subset of rows
  • mutate(): add new columns
  • summarise(): reduce each group to a smaller number of summary statistics
  • arrange(): re-order the rows

They all work as similarly as possible across the range of data sources. The main difference is performance:

system.time(carriers_df %>% summarise(delay = mean(arr_delay)))
#>    user  system elapsed 
#>   0.040   0.001   0.043
system.time(carriers_db1 %>% summarise(delay = mean(arr_delay)) %>% collect())
#>    user  system elapsed 
#>   0.348   0.302   1.280
system.time(carriers_db2 %>% summarise(delay = mean(arr_delay)) %>% collect())
#>    user  system elapsed 
#>   0.015   0.000   0.142

Data frame methods are much much faster than the plyr equivalent. The database methods are slower, but can work with data that don't fit in memory.

system.time(plyr::ddply(flights, "carrier", plyr::summarise,
  delay = mean(arr_delay, na.rm = TRUE)))
#>    user  system elapsed 
#>   0.104   0.029   0.134

As well as the specialised operations described above, dplyr also provides the generic do() function which applies any R function to each group of the data.

Let's take the batting database from the built-in Lahman database. We'll group it by year, and then fit a model to explore the relationship between their number of at bats and runs:

by_year <- lahman_df() %>% 
  tbl("Batting") %>%
by_year %>% 
  do(mod = lm(R ~ AB, data = .))
#> Source: local data frame [144 x 2]
#> Groups: <by row>
#>    yearID     mod
#>     (int)  (list)
#> 1    1871 <S3:lm>
#> 2    1872 <S3:lm>
#> 3    1873 <S3:lm>
#> 4    1874 <S3:lm>
#> 5    1875 <S3:lm>
#> 6    1876 <S3:lm>
#> 7    1877 <S3:lm>
#> 8    1878 <S3:lm>
#> 9    1879 <S3:lm>
#> 10   1880 <S3:lm>
#> ..    ...     ...

Note that if you are fitting lots of linear models, it's a good idea to use biglm because it creates model objects that are considerably smaller:

by_year %>% 
  do(mod = lm(R ~ AB, data = .)) %>%
  object.size() %>%
  print(unit = "MB")
#> 22.7 Mb
by_year %>% 
  do(mod = biglm::biglm(R ~ AB, data = .)) %>%
  object.size() %>%
  print(unit = "MB")
#> 0.8 Mb

As well as verbs that work on a single tbl, there are also a set of useful verbs that work with two tbls at a time: joins and set operations.

dplyr implements the four most useful joins from SQL:

  • inner_join(x, y): matching x + y
  • left_join(x, y): all x + matching y
  • semi_join(x, y): all x with match in y
  • anti_join(x, y): all x without match in y

And provides methods for:

  • intersect(x, y): all rows in both x and y
  • union(x, y): rows in either x or y
  • setdiff(x, y): rows in x, but not y

You'll need to be a little careful if you load both plyr and dplyr at the same time. I'd recommend loading plyr first, then dplyr, so that the faster dplyr functions come first in the search path. By and large, any function provided by both dplyr and plyr works in a similar way, although dplyr functions tend to be faster and more general.


dplyr 0.5.0

  • arrange() once again ignores grouping (#1206).

  • distinct() now only keeps the distinct variables. If you want to return all variables (using the first row for non-distinct values) use .keep_all = TRUE (#1110). For SQL sources, .keep_all = FALSE is implemented using GROUP BY, and .keep_all = TRUE raises an error (#1937, #1942, @krlmlr). (The default behaviour of using all variables when none are specified remains - this note only applies if you select some variables).

  • The select helper functions starts_with(), ends_with() etc are now real exported functions. This means that you'll need to import those functions if you're using from a package where dplyr is not attached. i.e. dplyr::select(mtcars, starts_with("m")) used to work, but now you'll need dplyr::select(mtcars, dplyr::starts_with("m")).

  • The long deprecated chain(), chain_q() and %.% have been removed. Please use %>% instead.

  • id() has been deprecated. Please use group_indices() instead (#808).

  • rbind_all() and rbind_list() are formally deprecated. Please use bind_rows() instead (#803).

  • Outdated benchmarking demos have been removed (#1487).

  • Code related to starting and signalling clusters has been moved out to multidplyr.

  • coalesce() finds the first non-missing value from a set of vectors. (#1666, thanks to @krlmlr for initial implementation).

  • case_when() is a general vectorised if + else if (#631).

  • if_else() is a vectorised if statement: it's a stricter (type-safe), faster, and more predictable version of ifelse(). In SQL it is translated to a CASE statement.

  • na_if() makes it easy to replace a certain value with an NA (#1707). In SQL it is translated to NULL_IF.

  • near(x, y) is a helper for abs(x - y) < tol (#1607).

  • recode() is vectorised equivalent to switch() (#1710).

  • union_all() method. Maps to UNION ALL for SQL sources, bind_rows() for data frames/tbl_dfs, and combine() for vectors (#1045).

  • A new family of functions replace summarise_each() and mutate_each() (which will thus be deprecated in a future release). summarise_all() and mutate_all() apply a function to all columns while summarise_at() and mutate_at() operate on a subset of columns. These columuns are selected with either a character vector of columns names, a numeric vector of column positions, or a column specification with select() semantics generated by the new columns() helper. In addition, summarise_if() and mutate_if() take a predicate function or a logical vector (these verbs currently require local sources). All these functions can now take ordinary functions instead of a list of functions generated by funs() (though this is only useful for local sources). (#1845, @lionel-)

  • select_if() lets you select columns with a predicate function. Only compatible with local sources. (#497, #1569, @lionel-)

All data table related code has been separated out in to a new dtplyr package. This decouples the development of the data.table interface from the development of the dplyr package. If both data.table and dplyr are loaded, you'll get a message reminding you to load dtplyr.

Functions to related to the creation and coercion of tbl_dfs, now live in their own package: tibble. See vignette("tibble") for more details.

  • $ and [[ methods that never do partial matching (#1504), and throw an error if the variable does not exist.

  • all_equal() allows to compare data frames ignoring row and column order, and optionally ignoring minor differences in type (e.g. int vs. double) (#821). The test handles the case where the df has 0 columns (#1506). The test fails fails when convert is FALSE and types don't match (#1484).

  • all_equal() shows better error message when comparing raw values or when types are incompatible and convert = TRUE (#1820, @krlmlr).

  • add_row() makes it easy to add a new row to data frame (#1021)

  • as_data_frame() is now an S3 generic with methods for lists (the old as_data_frame()), data frames (trivial), and matrices (with efficient C++ implementation) (#876). It no longer strips subclasses.

  • The internals of data_frame() and as_data_frame() have been aligned, so as_data_frame() will now automatically recycle length-1 vectors. Both functions give more informative error messages if you attempting to create an invalid data frame. You can no longer create a data frame with duplicated names (#820). Both check for POSIXlt columns, and tell you to use POSIXct instead (#813).

  • frame_data() properly constructs rectangular tables (#1377, @kevinushey), and supports list-cols.

  • glimpse() is now a generic. The default method dispatches to str() (#1325). It now (invisibly) returns its first argument (#1570).

  • lst() and lst_() which create lists in the same way that data_frame() and data_frame_() create data frames (#1290).

  • print.tbl_df() is considerably faster if you have very wide data frames. It will now also only list the first 100 additional variables not already on screen - control this with the new n_extra parameter to print() (#1161). When printing a grouped data frame the number of groups is now printed with thousands separators (#1398). The type of list columns is correctly printed (#1379)

  • Package includes setOldClass(c("tbl_df", "tbl", "data.frame")) to help with S4 dispatch (#969).

  • tbl_df automatically generates column names (#1606).

  • new as_data_frame.tbl_cube() (#1563, @krlmlr).

  • tbl_cubes are now constructed correctly from data frames, duplicate dimension values are detected, missing dimension values are filled with NA. The construction from data frames now guesses the measure variables by default, and allows specification of dimension and/or measure variables (#1568, @krlmlr).

  • Swap order of dim_names and met_name arguments in as.tbl_cube (for array, table and matrix) for consistency with tbl_cube and as.tbl_cube.data.frame. Also, the met_name argument to as.tbl_cube.table now defaults to "Freq" for consistency with as.data.frame.table (@krlmlr, #1374).

  • as_data_frame() on SQL sources now returns all rows (#1752, #1821, @krlmlr).

  • compute() gets new parameters indexes and unique_indexes that make it easier to add indexes (#1499, @krlmlr).

  • db_explain() gains a default method for DBIConnections (#1177).

  • The backend testing system has been improved. This lead to the removal of temp_srcs(). In the unlikely event that you were using this function, you can instead use test_register_src(), test_load(), and test_frame().

  • You can now use right_join() and full_join() with remote tables (#1172).

  • src_memdb() is a session-local in-memory SQLite database. memdb_frame() works like data_frame(), but creates a new table in that database.

  • src_sqlite() now uses a stricter quoting character, `, instead of ". SQLite "helpfully" will convert "x" into a string if there is no identifier called x in the current scope (#1426).

  • src_sqlite() throws errors if you try and use it with window functions (#907).

  • filter.tbl_sql() now puts parens around each argument (#934).

  • Unary - is better translated (#1002).

  • escape.POSIXt() method makes it easier to use date times. The date is rendered in ISO 8601 format in UTC, which should work in most databases (#857).

  • is.na() gets a missing space (#1695).

  • if, is.na(), and is.null() get extra parens to make precendence more clear (#1695).

  • pmin() and pmax() are translated to MIN() and MAX() (#1711).

  • Window functions:

    • Work on ungrouped data (#1061).

    • Warning if order is not set on cumulative window functions.

    • Multiple partitions or ordering variables in windowed functions no longer generate extra parentheses, so should work for more databases (#1060)

This version includes an almost total rewrite of how dplyr verbs are translated into SQL. Previously, I used a rather ad-hoc approach, which tried to guess when a new subquery was needed. Unfortunately this approach was fraught with bugs, so in this version I've implemented a much richer internal data model. Now there is a three step process:

  1. When applied to a tbl_lazy, each dplyr verb captures its inputs and stores in a op (short for operation) object.

  2. sql_build() iterates through the operations building to build up an object that represents a SQL query. These objects are convenient for testing as they are lists, and are backend agnostics.

  3. sql_render() iterates through the queries and generates the SQL, using generics (like sql_select()) that can vary based on the backend.

In the short-term, this increased abstraction is likely to lead to some minor performance decreases, but the chance of dplyr generating correct SQL is much much higher. In the long-term, these abstractions will make it possible to write a query optimiser/compiler in dplyr, which would make it possible to generate much more succinct queries.

If you have written a dplyr backend, you'll need to make some minor changes to your package:

  • sql_join() has been considerably simplified - it is now only responsible for generating the join query, not for generating the intermediate selects that rename the variable. Similarly for sql_semi_join(). If you've provided new methods in your backend, you'll need to rewrite.

  • select_query() gains a distinct argument which is used for generating queries for distinct(). It loses the offset argument which was never used (and hence never tested).

  • src_translate_env() has been replaced by sql_translate_env() which should have methods for the connection object.

There were two other tweaks to the exported API, but these are less likely to affect anyone.

  • translate_sql() and partial_eval() got a new API: now use connection + variable names, rather than a tbl. This makes testing considerably easier. translate_sql_q() has been renamed to translate_sql_().

  • Also note that the sql generation generics now have a default method, instead methods for DBIConnection and NULL.

  • Avoiding segfaults in presence of raw columns (#1803, #1817, @krlmlr).

  • arrange() fails gracefully on list columns (#1489) and matrices (#1870, #1945, @krlmlr).

  • count() now adds additional grouping variables, rather than overriding existing (#1703). tally() and count() can now count a variable called n (#1633). Weighted count()/tally() ignore NAs (#1145).

  • The progress bar in do() is now updated at most 20 times per second, avoiding uneccessary redraws (#1734, @mkuhn)

  • distinct() doesn't crash when given a 0-column data frame (#1437).

  • filter() throws an error if you supply an named arguments. This is usually a type: filter(df, x = 1) instead of filter(df, x == 1) (#1529).

  • summarise() correctly coerces factors with different levels (#1678), handles min/max of already summarised variable (#1622), and supports data frames as columns (#1425).

  • select() now informs you that it adds missing grouping variables (#1511). It works even if the grouping variable has a non-syntactic name (#1138). Negating a failed match (e.g. select(mtcars, -contains("x"))) returns all columns, instead of no columns (#1176)

    The select() helpers are now exported and have their own documentation (#1410). one_of() gives a useful error message if variables names are not found in data frame (#1407).

  • The naming behaviour of summarise_each() and mutate_each() has been tweaked so that you can force inclusion of both the function and the variable name: summarise_each(mtcars, funs(mean = mean), everything()) (#442).

  • mutate() handles factors that are all NA (#1645), or have different levels in different groups (#1414). It disambiguates NA and NaN (#1448), and silently promotes groups that only contain NA (#1463). It deep copies data in list columns (#1643), and correctly fails on incompatible columns (#1641). mutate() on a grouped data no longer droups grouping attributes (#1120). rowwise() mutate gives expected results (#1381).

  • one_of() tolerates unknown variables in vars, but warns (#1848, @jennybc).

  • print.grouped_df() passes on ... to print() (#1893).

  • slice() correctly handles grouped attributes (#1405).

  • ungroup() generic gains ... (#922).

  • bind_cols() matches the behaviour of bind_rows() and ignores NULL inputs (#1148). It also handles POSIXcts with integer base type (#1402).

  • bind_rows() handles 0-length named lists (#1515), promotes factors to characters (#1538), and warns when binding factor and character (#1485). bind_rows()` is more flexible in the way it can accept data frames, lists, list of data frames, and list of lists (#1389).

  • bind_rows() rejects POSIXlt columns (#1875, @krlmlr).

  • Both bind_cols() and bind_rows() infer classes and grouping information from the first data frame (#1692).

  • rbind() and cbind() get grouped_df() methods that make it harder to
    create corrupt data frames (#1385). You should still prefer bind_rows() and bind_cols().

  • Joins now use correct class when joining on POSIXct columns (#1582, @joel23888), and consider time zones (#819). Joins handle a by that is empty (#1496), or has duplicates (#1192). Suffixes grow progressively to avoid creating repeated column names (#1460). Joins on string columns should be substantially faster (#1386). Extra attributes are ok if they are identical (#1636). Joins work correct when factor levels not equal (#1712, #1559), and anti and semi joins give correct result when by variable is a factor (#1571).

  • inner_join(), left_join(), right_join(), and full_join() gain a suffix argument which allows you to control what suffix duplicated variable names recieve (#1296).

  • Set operations (intersect(), union() etc) respect coercion rules (#799). setdiff() handles factors with NA levels (#1526).

  • There were a number of fixes to enable joining of data frames that don't have the same encoding of column names (#1513), including working around bug 16885 regarding match() in R 3.3.0 (#1806, #1810, @krlmlr).

  • combine() silently drops NULL inputs (#1596).

  • Hybrid cummean() is more stable against floating point errors (#1387).

  • Hybrid lead() and lag() received a considerable overhaul. They are more careful about more complicated expressions (#1588), and falls back more readily to pure R evaluation (#1411). They behave correctly in summarise() (#1434). and handle default values for string columns.

  • Hybrid min() and max() handle empty sets (#1481).

  • n_distinct() uses multiple arguments for data frames (#1084), falls back to R evaluation when needed (#1657), reverting decision made in (#567). Passing no arguments gives an error (#1957, #1959, @krlmlr).

  • nth() now supports negative indices to select from end, e.g. nth(x, -2) selects the 2nd value from the end of x (#1584).

  • top_n() can now also select bottom n values by passing a negative value to n (#1008, #1352).

  • Hybrid evaluation leaves formulas untouched (#1447).

dplyr 0.4.3

Until now, dplyr's support for non-UTF8 encodings has been rather shaky. This release brings a number of improvement to fix these problems: it's probably not perfect, but should be a lot better than the previously version. This includes fixes to arrange() (#1280), bind_rows() (#1265), distinct() (#1179), and joins (#1315). print.tbl_df() also recieved a fix for strings with invalid encodings (#851).

  • frame_data() provides a means for constructing data_frames using a simple row-wise language. (#1358, @kevinushey)

  • all.equal() no longer runs all outputs together (#1130).

  • as_data_frame() gives better error message with NA column names (#1101).

  • [.tbl_df is more careful about subsetting column names (#1245).

  • arrange() and mutate() work on empty data frames (#1142).

  • arrange(), filter(), slice(), and summarise() preserve data frame meta attributes (#1064).

  • bind_rows() and bind_cols() accept lists (#1104): during initial data cleaning you no longer need to convert lists to data frames, but can instead feed them to bind_rows() directly.

  • bind_rows() gains a .id argument. When supplied, it creates a new column that gives the name of each data frame (#1337, @lionel-).

  • bind_rows() respects the ordered attribute of factors (#1112), and does better at comparing POSIXcts (#1125). The tz attribute is ignored when determining if two POSIXct vectors are comparable. If the tz of all inputs is the same, it's used, otherwise its set to UTC.

  • data_frame() always produces a tbl_df (#1151, @kevinushey)

  • filter(x, TRUE, TRUE) now just returns x (#1210), it doesn't internally modify the first argument (#971), and it now works with rowwise data (#1099). It once again works with data tables (#906).

  • glimpse() also prints out the number of variables in addition to the number of observations (@ilarischeinin, #988).

  • Joins handles matrix columns better (#1230), and can join Date objects with heterogenous representations (some Dates are integers, while other are numeric). This also improves all.equal() (#1204).

  • Fixed percent_rank() and cume_dist() so that missing values no longer affect denominator (#1132).

  • print.tbl_df() now displays the class for all variables, not just those that don't fit on the screen (#1276). It also displays duplicated column names correctly (#1159).

  • print.grouped_df() now tells you how many groups there are.

  • mutate() can set to NULL the first column (used to segfault, #1329) and it better protects intermediary results (avoiding random segfaults, #1231).

  • mutate() on grouped data handles the special case where for the first few groups, the result consists of a logical vector with only NA. This can happen when the condition of an ifelse is an all NA logical vector (#958).

  • mutate.rowwise_df() handles factors (#886) and correctly handles 0-row inputs (#1300).

  • n_distinct() gains an na_rm argument (#1052).

  • The Progress bar used by do() now respects global option dplyr.show_progress (default is TRUE) so you can turn it off globally (@jimhester #1264, #1226).

  • summarise() handles expressions that returning heterogenous outputs, e.g. median(), which that sometimes returns an integer, and other times a numeric (#893).

  • slice() silently drops columns corresponding to an NA (#1235).

  • ungroup.rowwise_df() gives a tbl_df (#936).

  • More explicit duplicated column name error message (#996).

  • When "," is already being used as the decimal point (getOption("OutDec")), use "." as the thousands separator when printing out formatted numbers (@ilarischeinin, #988).

  • db_query_fields.SQLiteConnection uses build_sql rather than paste0 (#926, @NikNakk)

  • Improved handling of log() (#1330).

  • n_distinct(x) is translated to COUNT(DISTINCT(x)) (@skparkes, #873).

  • print(n = Inf) now works for remote sources (#1310).

  • Hybrid evaluation does not take place for objects with a class (#1237).

  • Improved $ handling (#1134).

  • Simplified code for lead() and lag() and make sure they work properly on factors (#955). Both repsect the default argument (#915).

  • mutate can set to NULL the first column (used to segfault, #1329).

  • filter on grouped data handles indices correctly (#880).

  • sum() issues a warning about integer overflow (#1108).

dplyr 0.4.2

This is a minor release containing fixes for a number of crashes and issues identified by R CMD CHECK. There is one new "feature": dplyr no longer complains about unrecognised attributes, and instead just copies them over to the output.

  • lag() and lead() for grouped data were confused about indices and therefore produced wrong results (#925, #937). lag() once again overrides lag() instead of just the default method lag.default(). This is necesary due to changes in R CMD check. To use the lag function provided by another package, use pkg::lag.

  • Fixed a number of memory issues identified by valgrind.

  • Improved performance when working with large number of columns (#879).

  • Lists-cols that contain data frames now print a slightly nicer summary (#1147)

  • Set operations give more useful error message on incompatible data frames (#903).

  • all.equal() gives the correct result when ignore_row_order is TRUE (#1065) and all.equal() correctly handles character missing values (#1095).

  • bind_cols() always produces a tbl_df (#779).

  • bind_rows() gains a test for a form of data frame corruption (#1074).

  • bind_rows() and summarise() now handles complex columns (#933).

  • Workaround for using the constructor of DataFrame on an unprotected object (#998)

  • Improved performance when working with large number of columns (#879).

dplyr 0.4.1

  • Don't assume that RPostgreSQL is available.

dplyr 0.4.0

  • add_rownames() turns row names into an explicit variable (#639).

  • as_data_frame() efficiently coerces a list into a data frame (#749).

  • bind_rows() and bind_cols() efficiently bind a list of data frames by row or column. combine() applies the same coercion rules to vectors (it works like c() or unlist() but is consistent with the bind_rows() rules).

  • right_join() (include all rows in y, and matching rows in x) and full_join() (include all rows in x and y) complete the family of mutating joins (#96).

  • group_indices() computes a unique integer id for each group (#771). It can be called on a grouped_df without any arguments or on a data frame with same arguments as group_by().

  • vignette("data_frames") describes dplyr functions that make it easier and faster to create and coerce data frames. It subsumes the old memory vignette.

  • vignette("two-table") describes how two-table verbs work in dplyr.

  • data_frame() (and as_data_frame() & tbl_df()) now explicitly forbid columns that are data frames or matrices (#775). All columns must be either a 1d atomic vector or a 1d list.

  • do() uses lazyeval to correctly evaluate its arguments in the correct environment (#744), and new do_() is the SE equivalent of do() (#718). You can modify grouped data in place: this is probably a bad idea but it's sometimes convenient (#737). do() on grouped data tables now passes in all columns (not all columns except grouping vars) (#735, thanks to @kismsu). do() with database tables no longer potentially includes grouping variables twice (#673). Finally, do() gives more consistent outputs when there are no rows or no groups (#625).

  • first() and last() preserve factors, dates and times (#509).

  • Overhaul of single table verbs for data.table backend. They now all use a consistent (and simpler) code base. This ensures that (e.g.) n() now works in all verbs (#579).

  • In *_join(), you can now name only those variables that are different between the two tables, e.g. inner_join(x, y, c("a", "b", "c" = "d")) (#682). If non-join colums are the same, dplyr will add .x and .y suffixes to distinguish the source (#655).

  • mutate() handles complex vectors (#436) and forbids POSIXlt results (instead of crashing) (#670).

  • select() now implements a more sophisticated algorithm so if you're doing multiples includes and excludes with and without names, you're more likely to get what you expect (#644). You'll also get a better error message if you supply an input that doesn't resolve to an integer column position (#643).

  • Printing has recieved a number of small tweaks. All print() method methods invisibly return their input so you can interleave print() statements into a pipeline to see interim results. print() will column names of 0 row data frames (#652), and will never print more 20 rows (i.e. options(dplyr.print_max) is now 20), not 100 (#710). Row names are no never printed since no dplyr method is guaranteed to preserve them (#669).

    glimpse() prints the number of observations (#692)

    type_sum() gains a data frame method.

  • summarise() handles list output columns (#832)

  • slice() works for data tables (#717). Documentation clarifies that slice can't work with relational databases, and the examples show how to achieve the same results using filter() (#720).

  • dplyr now requires RSQLite >= 1.0. This shouldn't affect your code in any way (except that RSQLite now doesn't need to be attached) but does simplify the internals (#622).

  • Functions that need to combine multiple results into a single column (e.g. join(), bind_rows() and summarise()) are more careful about coercion.

    Joining factors with the same levels in the same order preserves the original levels (#675). Joining factors with non-identical levels generates a warning and coerces to character (#684). Joining a character to a factor (or vice versa) generates a warning and coerces to character. Avoid these warnings by ensuring your data is compatible before joining.

    rbind_list() will throw an error if you attempt to combine an integer and factor (#751). rbind()ing a column full of NAs is allowed and just collects the appropriate missing value for the column type being collected (#493).

    summarise() is more careful about NA, e.g. the decision on the result type will be delayed until the first non NA value is returned (#599). It will complain about loss of precision coercions, which can happen for expressions that return integers for some groups and a doubles for others (#599).

  • A number of functions gained new or improved hybrid handlers: first(), last(), nth() (#626), lead() & lag() (#683), %in% (#126). That means when you use these functions in a dplyr verb, we handle them in C++, rather than calling back to R, and hence improving performance.

    Hybrid min_rank() correctly handles NaN values (#726). Hybrid implementation of nth() falls back to R evaluation when n is not a length one integer or numeric, e.g. when it's an expression (#734).

    Hybrid dense_rank(), min_rank(), cume_dist(), ntile(), row_number() and percent_rank() now preserve NAs (#774)

  • filter returns its input when it has no rows or no columns (#782).

  • Join functions keep attributes (e.g. time zone information) from the left argument for POSIXct and Date objects (#819), and only only warn once about each incompatibility (#798).

  • [.tbl_df correctly computes row names for 0-column data frames, avoiding problems with xtable (#656). [.grouped_df will silently drop grouping if you don't include the grouping columns (#733).

  • data_frame() now acts correctly if the first argument is a vector to be recycled. (#680 thanks @jimhester)

  • filter.data.table() works if the table has a variable called "V1" (#615).

  • *_join() keeps columns in original order (#684). Joining a factor to a character vector doesn't segfault (#688). *_join functions can now deal with multiple encodings (#769), and correctly name results (#855).

  • *_join.data.table() works when data.table isn't attached (#786).

  • group_by() on a data table preserves original order of the rows (#623). group_by() supports variables with more than 39 characters thanks to a fix in lazyeval (#705). It gives meaninful error message when a variable is not found in the data frame (#716).

  • grouped_df() requires vars to be a list of symbols (#665).

  • min(.,na.rm = TRUE) works with Dates built on numeric vectors (#755)

  • rename_() generic gets missing .dots argument (#708).

  • row_number(), min_rank(), percent_rank(), dense_rank(), ntile() and cume_dist() handle data frames with 0 rows (#762). They all preserve missing values (#774). row_number() doesn't segfault when giving an external variable with the wrong number of variables (#781)

  • group_indices handles the edge case when there are no variables (#867)


  • Fixed problem with test script on Windows.

dplyr 0.3

  • between() vector function efficiently determines if numeric values fall in a range, and is translated to special form for SQL (#503).

  • count() makes it even easier to do (weighted) counts (#358).

  • data_frame() by @kevinushey is a nicer way of creating data frames. It never coerces column types (no more stringsAsFactors = FALSE!), never munges column names, and never adds row names. You can use previously defined columns to compute new columns (#376).

  • distinct() returns distinct (unique) rows of a tbl (#97). Supply additional variables to return the first row for each unique combination of variables.

  • Set operations, intersect(), union() and setdiff() now have methods for data frames, data tables and SQL database tables (#93). They pass their arguments down to the base functions, which will ensure they raise errors if you pass in two many arguments.

  • Joins (e.g. left_join(), inner_join(), semi_join(), anti_join()) now allow you to join on different variables in x and y tables by supplying a named vector to by. For example, by = c("a" = "b") joins x.a to y.b.

  • n_groups() function tells you how many groups in a tbl. It returns 1 for ungrouped data. (#477)

  • transmute() works like mutate() but drops all variables that you didn't explicitly refer to (#302).

  • rename() makes it easy to rename variables - it works similarly to select() but it preserves columns that you didn't otherwise touch.

  • slice() allows you to selecting rows by position (#226). It includes positive integers, drops negative integers and you can use expression like n().

  • You can now program with dplyr - every function that does non-standard evaluation (NSE) has a standard evaluation (SE) version ending in _. This is powered by the new lazyeval package which provides all the tools needed to implement NSE consistently and correctly.

  • See vignette("nse") for full details.

  • regroup() is deprecated. Please use the more flexible group_by_() instead.

  • summarise_each_q() and mutate_each_q() are deprecated. Please use summarise_each_() and mutate_each_() instead.

  • funs_q has been replaced with funs_.

  • %.% has been deprecated: please use %>% instead. chain() is defunct. (#518)

  • filter.numeric() removed. Need to figure out how to reimplement with new lazy eval system.

  • The Progress refclass is no longer exported to avoid conflicts with shiny. Instead use progress_estimated() (#535).

  • src_monetdb() is now implemented in MonetDB.R, not dplyr.

  • show_sql() and explain_sql() and matching global options dplyr.show_sql and dplyr.explain_sql have been removed. Instead use show_query() and explain().

  • Main verbs now have individual documentation pages (#519).

  • %>% is simply re-exported from magrittr, instead of creating a local copy (#496, thanks to @jimhester)

  • Examples now use nycflights13 instead of hflights because it the variables have better names and there are a few interlinked tables (#562). Lahman and nycflights13 are (once again) suggested packages. This means many examples will not work unless you explicitly install them with install.packages(c("Lahman", "nycflights13")) (#508). dplyr now depends on Lahman 3.0.1. A number of examples have been updated to reflect modified field names (#586).

  • do() now displays the progress bar only when used in interactive prompts and not when knitting (#428, @jimhester).

  • glimpse() now prints a trailing new line (#590).

  • group_by() has more consistent behaviour when grouping by constants: it creates a new column with that value (#410). It renames grouping variables (#410). The first argument is now .data so you can create new groups with name x (#534).

  • Now instead of overriding lag(), dplyr overrides lag.default(), which should avoid clobbering lag methods added by other packages. (#277).

  • mutate(data, a = NULL) removes the variable a from the returned dataset (#462).

  • trunc_mat() and hence print.tbl_df() and friends gets a width argument to control the deafult output width. Set options(dplyr.width = Inf) to always show all columns (#589).

  • select() gains one_of() selector: this allows you to select variables provided by a character vector (#396). It fails immediately if you give an empty pattern to starts_with(), ends_with(), contains() or matches() (#481, @leondutoit). Fixed buglet in select() so that you can now create variables called val (#564).

  • Switched from RC to R6.

  • tally() and top_n() work consistently: neither accidentally evaluates the the wt param. (#426, @mnel)

  • rename handles grouped data (#640).

  • Correct SQL generation for paste() when used with the collapse parameter targeting a Postgres database. (@rbdixon, #1357)

  • The db backend system has been completely overhauled in order to make it possible to add backends in other packages, and to support a much wider range of databases. See vignette("new-sql-backend") for instruction on how to create your own (#568).

  • src_mysql() gains a method for explain().

  • When mutate() creates a new variable that uses a window function, automatically wrap the result in a subquery (#484).

  • Correct SQL generation for first() and last() (#531).

  • order_by() now works in conjunction with window functions in databases that support them.

  • All verbs now understand how to work with difftime() (#390) and AsIs (#453) objects. They all check that colnames are unique (#483), and are more robust when columns are not present (#348, #569, #600).

  • Hybrid evaluation bugs fixed:

    • Call substitution stopped too early when a sub expression contained a $ (#502).

    • Handle :: and ::: (#412).

    • cumany() and cumall() properly handle NA (#408).

    • nth() now correctly preserve the class when using dates, times and factors (#509).

    • no longer substitutes within order_by() because order_by() needs to do its own NSE (#169).

  • [.tbl_df always returns a tbl_df (i.e. drop = FALSE is the default) (#587, #610). [.grouped_df preserves important output attributes (#398).

  • arrange() keeps the grouping structure of grouped data (#491, #605), and preserves input classes (#563).

  • contains() accidentally matched regular expressions, now it passes fixed = TRUE to grep() (#608).

  • filter() asserts all variables are white listed (#566).

  • mutate() makes a rowwise_df when given a rowwise_df (#463).

  • rbind_all() creates tbl_df objects instead of raw data.frames.

  • If select() doesn't match any variables, it returns a 0-column data frame, instead of the original (#498). It no longer fails when if some columns are not named (#492)

  • sample_n() and sample_frac() methods for data.frames exported. (#405, @alyst)

  • A grouped data frame may have 0 groups (#486). Grouped df objects gain some basic validity checking, which should prevent some crashes related to corrupt grouped_df objects made by rbind() (#606).

  • More coherence when joining columns of compatible but different types, e.g. when joining a character vector and a factor (#455), or a numeric and integer (#450)

  • mutate() works for on zero-row grouped data frame, and with list columns (#555).

  • LazySubset was confused about input data size (#452).

  • Internal n_distinct() is stricter about it's inputs: it requires one symbol which must be from the data frame (#567).

  • rbind_*() handle data frames with 0 rows (#597). They fill character vector columns with NA instead of blanks (#595). They work with list columns (#463).

  • Improved handling of encoding for column names (#636).

  • Improved handling of hybrid evaluation re $ and @ (#645).

  • Fix major omission in tbl_dt() and grouped_dt() methods - I was accidentally doing a deep copy on every result :(

  • summarise() and group_by() now retain over-allocation when working with data.tables (#475, @arunsrinivasan).

  • joining two data.tables now correctly dispatches to data table methods, and result is a data table (#470)

  • summarise.tbl_cube() works with single grouping variable (#480).

dplyr 0.2

dplyr now imports %>% from magrittr (#330). I recommend that you use this instead of %.% because it is easier to type (since you can hold down the shift key) and is more flexible. With you %>%, you can control which argument on the RHS recieves the LHS by using the pronoun .. This makes %>% more useful with base R functions because they don't always take the data frame as the first argument. For example you could pipe mtcars to xtabs() with:

mtcars %>% xtabs( ~ cyl + vs, data = .)

Thanks to @smbache for the excellent magrittr package. dplyr only provides %>% from magrittr, but it contains many other useful functions. To use them, load magrittr explicitly: library(magrittr). For more details, see vignette("magrittr").

%.% will be deprecated in a future version of dplyr, but it won't happen for a while. I've also deprecated chain() to encourage a single style of dplyr usage: please use %>% instead.

do() has been completely overhauled. There are now two ways to use it, either with multiple named arguments or a single unnamed arguments. group_by() + do() is equivalent to plyr::dlply, except it always returns a data frame.

If you use named arguments, each argument becomes a list-variable in the output. A list-variable can contain any arbitrary R object so it's particularly well suited for storing models.

models <- mtcars %>% group_by(cyl) %>% do(lm = lm(mpg ~ wt, data = .))
models %>% summarise(rsq = summary(lm)$r.squared)

If you use an unnamed argument, the result should be a data frame. This allows you to apply arbitrary functions to each group.

mtcars %>% group_by(cyl) %>% do(head(., 1))

Note the use of the . pronoun to refer to the data in the current group.

do() also has an automatic progress bar. It appears if the computation takes longer than 5 seconds and lets you know (approximately) how much longer the job will take to complete.

dplyr 0.2 adds three new verbs:

  • glimpse() makes it possible to see all the columns in a tbl, displaying as much data for each variable as can be fit on a single line.

  • sample_n() randomly samples a fixed number of rows from a tbl; sample_frac() randomly samples a fixed fraction of rows. Only works for local data frames and data tables (#202).

  • summarise_each() and mutate_each() make it easy to apply one or more functions to multiple columns in a tbl (#178).

  • If you load plyr after dplyr, you'll get a message suggesting that you load plyr first (#347).

  • as.tbl_cube() gains a method for matrices (#359, @paulstaab)

  • compute() gains temporary argument so you can control whether the results are temporary or permanent (#382, @cpsievert)

  • group_by() now defaults to add = FALSE so that it sets the grouping variables rather than adding to the existing list. I think this is how most people expected group_by to work anyway, so it's unlikely to cause problems (#385).

  • Support for MonetDB tables with src_monetdb() (#8, thanks to @hannesmuehleisen).

  • New vignettes:

    • memory vignette which discusses how dplyr minimises memory usage for local data frames (#198).

    • new-sql-backend vignette which discusses how to add a new SQL backend/source to dplyr.

  • changes() output more clearly distinguishes which columns were added or deleted.

  • explain() is now generic.

  • dplyr is more careful when setting the keys of data tables, so it never accidentally modifies an object that it doesn't own. It also avoids unnecessary key setting which negatively affected performance. (#193, #255).

  • print() methods for tbl_df, tbl_dt and tbl_sql gain n argument to control the number of rows printed (#362). They also works better when you have columns containing lists of complex objects.

  • row_number() can be called without arguments, in which case it returns the same as 1:n() (#303).

  • "comment" attribute is allowed (white listed) as well as names (#346).

  • hybrid versions of min, max, mean, var, sd and sum handle the na.rm argument (#168). This should yield substantial performance improvements for those functions.

  • Special case for call to arrange() on a grouped data frame with no arguments. (#369)

  • Code adapted to Rcpp > 0.11.1

  • internal DataDots class protects against missing variables in verbs (#314), including the case where ... is missing. (#338)

  • all.equal.data.frame from base is no longer bypassed. we now have all.equal.tbl_df and all.equal.tbl_dt methods (#332).

  • arrange() correctly handles NA in numeric vectors (#331) and 0 row data frames (#289).

  • copy_to.src_mysql() now works on windows (#323)

  • *_join() doesn't reorder column names (#324).

  • rbind_all() is stricter and only accepts list of data frames (#288)

  • rbind_* propagates time zone information for POSIXct columns (#298).

  • rbind_* is less strict about type promotion. The numeric Collecter allows collection of integer and logical vectors. The integer Collecter also collects logical values (#321).

  • internal sum correctly handles integer (under/over)flow (#308).

  • summarise() checks consistency of outputs (#300) and drops names attribute of output columns (#357).

  • join functions throw error instead of crashing when there are no common variables between the data frames, and also give a better error message when only one data frame has a by variable (#371).

  • top_n() returns n rows instead of n - 1 (@leondutoit, #367).

  • SQL translation always evaluates subsetting operators ($, [, [[) locally. (#318).

  • select() now renames variables in remote sql tbls (#317) and
    implicitly adds grouping variables (#170).

  • internal grouped_df_impl function errors if there are no variables to group by (#398).

  • n_distinct did not treat NA correctly in the numeric case #384.

  • Some compiler warnings triggered by -Wall or -pedantic have been eliminated.

  • group_by only creates one group for NA (#401).

  • Hybrid evaluator did not evaluate expression in correct environment (#403).

dplyr 0.1.3

  • select() actually renames columns in a data table (#284).

  • rbind_all() and rbind_list() now handle missing values in factors (#279).

  • SQL joins now work better if names duplicated in both x and y tables (#310).

  • Builds against Rcpp 0.11.1

  • select() correctly works with the vars attribute (#309).

  • Internal code is stricter when deciding if a data frame is grouped (#308): this avoids a number of situations which previously causedd .

  • More data frame joins work with missing values in keys (#306).

dplyr 0.1.2

  • select() is substantially more powerful. You can use named arguments to rename existing variables, and new functions starts_with(), ends_with(), contains(), matches() and num_range() to select variables based on their names. It now also makes a shallow copy, substantially reducing its memory impact (#158, #172, #192, #232).

  • summarize() added as alias for summarise() for people from countries that don't don't spell things correctly ;) (#245)

  • filter() now fails when given anything other than a logical vector, and correctly handles missing values (#249). filter.numeric() proxies stats::filter() so you can continue to use filter() function with numeric inputs (#264).

  • summarise() correctly uses newly created variables (#259).

  • mutate() correctly propagates attributes (#265) and mutate.data.frame() correctly mutates the same variable repeatedly (#243).

  • lead() and lag() preserve attributes, so they now work with dates, times and factors (#166).

  • n() never accepts arguments (#223).

  • row_number() gives correct results (#227).

  • rbind_all() silently ignores data frames with 0 rows or 0 columns (#274).

  • group_by() orders the result (#242). It also checks that columns are of supported types (#233, #276).

  • The hybrid evaluator did not handle some expressions correctly, for example in if(n() > 5) 1 else 2 the subexpression n() was not substituted correctly. It also correctly processes $ (#278).

  • arrange() checks that all columns are of supported types (#266). It also handles list columns (#282).

  • Working towards Solaris compatibility.

  • Benchmarking vignette temporarily disabled due to microbenchmark problems reported by BDR.

dplyr 0.1.1

  • new location() and changes() functions which provide more information about how data frames are stored in memory so that you can see what gets copied.

  • renamed explain_tbl() to explain() (#182).

  • tally() gains sort argument to sort output so highest counts come first (#173).

  • ungroup.grouped_df(), tbl_df(), as.data.frame.tbl_df() now only make shallow copies of their inputs (#191).

  • The benchmark-baseball vignette now contains fairer (including grouping times) comparisons with data.table. (#222)

  • filter() (#221) and summarise() (#194) correctly propagate attributes.

  • summarise() throws an error when asked to summarise an unknown variable instead of crashing (#208).

  • group_by() handles factors with missing values (#183).

  • filter() handles scalar results (#217) and better handles scoping, e.g. filter(., variable) where variable is defined in the function that calls filter. It also handles T and F as aliases to TRUE and FALSE if there are no T or F variables in the data or in the scope.

  • select.grouped_df fails when the grouping variables are not included in the selected variables (#170)

  • all.equal.data.frame() handles a corner case where the data frame has NULL names (#217)

  • mutate() gives informative error message on unsupported types (#179)

  • dplyr source package no longer includes pandas benchmark, reducing download size from 2.8 MB to 0.5 MB.

Reference manual

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0.5.0 by Hadley Wickham, 8 months ago


Report a bug at https://github.com/hadley/dplyr/issues

Browse source code at https://github.com/cran/dplyr

Authors: Hadley Wickham [aut, cre], Romain Francois [aut], RStudio [cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports assertthat, utils, R6, Rcpp, tibble, magrittr, lazyeval, DBI

Suggests RSQLite, RMySQL, RPostgreSQL, testthat, knitr, microbenchmark, ggplot2, mgcv, Lahman, nycflights13, methods, rmarkdown, covr, dtplyr

Linking to Rcpp, BH

Imported by ACDm, ADPclust, AIG, ARTool, AutoModel, CARBayesST, CollapsABEL, DMwR2, DataCombine, DeLorean, DiagrammeR, DisimForMixed, DiversityOccupancy, EFDR, FSA, FedData, GSODR, GerminaR, GetHFData, HTSSIP, HydeNet, IAT, IATscores, IDmining, IMP, IRISMustangMetrics, IncucyteDRC, InformativeCensoring, KraljicMatrix, LBSPR, Lahman, LendingClub, LocFDRPois, MIAmaxent, MazamaSpatialUtils, Momocs, NFP, NPC, NestedCategBayesImpute, NetworkRiskMeasures, PAC, Plasmidprofiler, PopED, RCMIP5, REDCapR, RNHANES, RNeXML, RSSL, RmarineHeatWaves, RtutoR, SWMPr, ShinyTester, SpaCCr, SpaDES, Tmisc, WHO, WRTDStidal, WikidataQueryServiceR, WufooR, Zelig, ZeligChoice, ZeligEI, abjutils, adegenet, admixturegraph, aemo, alakazam, alphabetr, apa, assertr, backtestGraphics, bayesplot, bib2df, bibliometrix, bigrquery, binomen, bioinactivation, biomartr, bkmr, blkbox, bootnet, boxr, broom, bsam, carpenter, cdcfluview, choroplethr, chromer, clustRcompaR, clustrd, cofeatureR, cometExactTest, compareDF, condformat, coreSim, countytimezones, countyweather, cricketr, d3r, darksky, dat, dataRetrieval, datacheckr, datadr, dataonderivatives, datastepr, dbfaker, ddpcr, denovolyzeR, descriptr, diffrprojects, diffrprojectswidget, dmutate, docxtools, docxtractr, dotwhisker, downsize, dtplyr, eAnalytics, easyformatr, ecoengine, edeaR, eechidna, eemR, electionsBR, emil, emuR, engsoccerdata, epidata, episheet, estatapi, europepmc, explor, extdplyr, eyetrackingR, ezec, ezsummary, fbar, feedeR, filesstrings, finreportr, fitcoach, flora, foghorn, forestmodel, freqweights, funModeling, funrar, futureheatwaves, fuzzyjoin, gender, geomnet, geoparser, getCRUCLdata, ggCompNet, ggalt, ggforce, ggfortify, ggguitar, gglogo, ggmosaic, ggpmisc, ggraph, ggraptR, ggspectra, ggvis, gistr, gitgadget, gitlabr, glycanr, gogamer, goldi, googleAnalyticsR, googlesheets, graphTweets, graphicalVAR, grattan, groupdata2, gunsales, gutenbergr, gwdegree, harrietr, heemod, highcharter, hurricaneexposure, hypothesisr, idbr, imfr, imputeTestbench, incadata, inferr, inlmisc, internetarchive, interplot, ipft, janitor, jpmesh, kntnr, kokudosuuchi, laketemps, lexRankr, livechatR, lmeresampler, longurl, lookupTable, loopr, lplyr, lvnet, macleish, makeFlow, mason, mdsr, metacoder, metricTester, mglR, mixOmics, mlVAR, modelr, morse, mousetrap, mplot, mscstexta4r, mtconnectR, muir, myTAI, nasadata, ncappc, ncar, networkreporting, nlstimedist, notifyme, nullabor, observer, openadds, openair, opencage, pRF, packagetrackr, parsemsf, patternplot, peptider, performanceEstimation, petrinetR, photobiology, photobiologyInOut, phylopath, pinnacle.API, pixiedust, plater, platetools, pleiades, plotly, plotrr, pmc, poio, pollen, poppr, prcr, prepdat, prophet, ptstem, purrr, qdap, quadmesh, queuecomputer, qwraps2, r2glmm, rPref, radiant.basics, radiant.design, radiant.model, radiant.multivariate, rbgm, rbison, rccmisc, rchess, rcicr, rcrossref, rdrop2, rebird, refund.shiny, replyr, rerddap, resumer, rfishbase, rgho, rmcfs, rnoaa, ropenaq, rpcdsearch, rpdo, rplexos, rplos, rprev, rrr, rscorecard, rtide, rtimes, rtrends, rvertnet, rwunderground, saeSim, scholar, sejmRP, shazam, simPH, simmer.plot, sjPlot, sjmisc, sjstats, slackr, solrium, sophisthse, sorvi, sp500SlidingWindow, sparklyr, sparsediscrim, spbabel, spellcheckr, sqlscore, srvyr, ss3sim, statar, stationaRy, statip, stormwindmodel, stplanr, superheat, taber, tadaatoolbox, textmining, textreuse, tidyRSS, tidyjson, tidyquant, tidyr, tidytext, tidyverse, tigger, timelineS, traits, trelloR, uaparserjs, ubeR, unpivotr, useful, valr, vcfR, vdmR, wakefield, wallace, wand, wec, wfindr, wordbankr, worldmet, wrangle, yorkr, ztype.

Depended on by ANLP, GenCAT, PogromcyDanych, SEERaBomb, TeachBayes, VWPre, anametrix, braQCA, censusr, chunked, cleanNLP, corrr, dggridR, efreadr, etl, ggmcmc, manifestoR, merTools, mosaic, neuropsychology, poplite, quickpsy, radiant.data, rmdHelpers, sfc, sourceR, spdplyr, surveybootstrap, tcR, texmexseq, treeplyr, turfR, vqtl.

Suggested by DepthProc, Greg, JacobiEigen, MonetDBLite, PKNCA, R6Frame, RDML, ROpenFIGI, RPresto, SimDesign, TH.data, Tcomp, VIM, afex, alluvial, ameco, archivist, assertive.types, bannerCommenter, bayesGDS, binford, blscrapeR, bmlm, bodenmiller, bossMaps, causaldrf, cbsodataR, climbeR, codingMatrices, codyn, crawl, decoder, describer, ecb, europop, eurostat, eyelinker, feather, fiftystater, fivethirtyeight, fractional, fueleconomy, gapminder, geoknife, ggRandomForests, ggenealogy, ggmap, ggswissmaps, growthcurver, hdr, highlightHTML, hydrostats, imager, infuser, janeaustenr, labelled, largeVis, lucid, medicare, metricsgraphics, mixpack, monkeylearn, mosaicData, mpoly, neurobase, nycflights13, nzelect, padr, pitchRx, qualvar, randomizr, raptr, rattle, raw, rclinicaltrials, readODS, recexcavAAR, reval, rex, rivr, robotstxt, rpivotTable, rslp, rsparkling, rtable, rtdists, sf, shinyAce, simmer, simputation, sparseHessianFD, statisticalModeling, sticky, tempcyclesdata, testassay, tictactoe, tigris, titanic, tmap, trelliscope, unvotes, vaersNDvax, vaersvax, vkR, vtreat, wikipediatrend, wrswoR, wrswoR.benchmark.

Enhanced by MonetDB.R, repr.

See at CRAN