I’m very pleased to announce that dplyr 0.4.0 is now available from CRAN. Get the latest version by running:

install.packages("dplyr")

dplyr 0.4.0 includes over 80 minor improvements and bug fixes, which are described in detail in the release notes. Here I wanted to draw your attention to two areas that have particularly improved since dplyr 0.3, two-table verbs and data frame support.

Two table verbs

dplyr now has full support for all two-table verbs provided by SQL:

  • Mutating joins, which add new variables to one table from matching rows in another: inner_join(), left_join(), right_join(), full_join(). (Support for non-equi joins is planned for dplyr 0.5.0.)
  • Filtering joins, which filter observations from one table based on whether or not they match an observation in the other table: semi_join(), anti_join().
  • Set operations, which combine the observations in two data sets as if they were set elements: intersect(), union(), setdiff().

Together, these verbs should allow you to solve 95% of data manipulation problems that involve multiple tables. If any of the concepts are unfamiliar to you, I highly recommend reading the two-table vignette (and if you still don’t understand, please let me know so I can make it better.)

Data frames

dplyr wraps data frames in a tbl_df class. These objects are structured in exactly the same way as regular data frames, but their behaviour has been tweaked a little to make them easier to work with. The new data_frames vignette describes how dplyr works with data frames in general, and below I highlight some of the features new in 0.4.0.

Printing

The biggest difference is printing: print.tbl_df() doesn’t try and print 10,000 rows! Printing got a lot of love in dplyr 0.4 and now:

  • All print() method methods invisibly return their input so you can interleave print() statements into a pipeline to see interim results.
  • If you’ve managed to produce a 0-row data frame, dplyr won’t try to print the data, but will tell you the column names and types:
    data_frame(x = numeric(), y = character())
    #> Source: local data frame [0 x 2]
    #> 
    #> Variables not shown: x (dbl), y (chr)
  • dplyr never prints row names since no dplyr method is guaranteed to preserve them:
    df <- data.frame(x = c(a = 1, b = 2, c = 3))
    df
    #>   x
    #> a 1
    #> b 2
    #> c 3
    df %>% tbl_df()
    #> Source: local data frame [3 x 1]
    #> 
    #>   x
    #> 1 1
    #> 2 2
    #> 3 3

    I don’t think using row names is a good idea because it violates one of the principles of tidy data: every variable should be stored in the same way.

    To make life a bit easier if you do have row names, you can use the new add_rownames() to turn your row names into a proper variable:

    df %>% 
      add_rownames()
    #>   rowname x
    #> 1       a 1
    #> 2       b 2
    #> 3       c 3

    (But you’re better off never creating them in the first place.)

  • options(dplyr.print_max) is now 20, so dplyr will never print more than 20 rows of data (previously it was 100). The best way to see more rows of data is to use View().

Coercing lists to data frames

When you have a list of vectors of equal length that you want to turn into a data frame, dplyr provides as_data_frame() as a simple alternative to as.data.frame(). as_data_frame() is considerably faster than as.data.frame() because it does much less:

l <- replicate(26, sample(100), simplify = FALSE)
names(l) <- letters
microbenchmark::microbenchmark(
  as_data_frame(l),
  as.data.frame(l)
)
#> Unit: microseconds
#>              expr      min        lq   median        uq      max neval
#>  as_data_frame(l)  101.856  112.0615  124.855  143.0965  254.193   100
#>  as.data.frame(l) 1402.075 1466.6365 1511.644 1635.1205 3007.299   100

It’s difficult to precisely describe what as.data.frame(x) does, but it’s similar to do.call(cbind, lapply(x, data.frame)) – it coerces each component to a data frame and then cbind()s them all together.

The speed of as.data.frame() is not usually a bottleneck in interactive use, but can be a problem when combining thousands of lists into one tidy data frame (this is common when working with data stored in json or xml).

Binding rows and columns

dplyr now provides bind_rows() and bind_cols() for binding data frames together. Compared to rbind() and cbind(), the functions:

  • Accept either individual data frames, or a list of data frames:
    a <- data_frame(x = 1:5)
    b <- data_frame(x = 6:10)
    
    bind_rows(a, b)
    #> Source: local data frame [10 x 1]
    #> 
    #>    x
    #> 1  1
    #> 2  2
    #> 3  3
    #> 4  4
    #> 5  5
    #> .. .
    bind_rows(list(a, b))
    #> Source: local data frame [10 x 1]
    #> 
    #>    x
    #> 1  1
    #> 2  2
    #> 3  3
    #> 4  4
    #> 5  5
    #> .. .

    If x is a list of data frames, bind_rows(x) is equivalent to do.call(rbind, x).

  • Are much faster:
    dfs <- replicate(100, data_frame(x = runif(100)), simplify = FALSE)
    microbenchmark::microbenchmark(
      do.call("rbind", dfs),
      bind_rows(dfs)
    )
    #> Unit: microseconds
    #>                   expr      min        lq   median        uq       max
    #>  do.call("rbind", dfs) 5344.660 6605.3805 6964.236 7693.8465 43457.061
    #>         bind_rows(dfs)  240.342  262.0845  317.582  346.6465  2345.832
    #>  neval
    #>    100
    #>    100

(Generally you should avoid bind_cols() in favour of a join; otherwise check carefully that the rows are in a compatible order).

List-variables

Data frames are usually made up of a list of atomic vectors that all have the same length. However, it’s also possible to have a variable that’s a list, which I call a list-variable. Because of data.frame()s complex coercion rules, the easiest way to create a data frame containing a list-column is with data_frame():

data_frame(x = 1, y = list(1), z = list(list(1:5, "a", "b")))
#> Source: local data frame [1 x 3]
#> 
#>   x        y         z
#> 1 1 <dbl[1]> <list[3]>

Note how list-variables are printed: a list-variable could contain a lot of data, so dplyr only shows a brief summary of the contents. List-variables are useful for:

  • Working with summary functions that return more than one value:
    qs <- mtcars %>%
      group_by(cyl) %>%
      summarise(y = list(quantile(mpg)))
    
    # Unnest input to collpase into rows
    qs %>% tidyr::unnest(y)
    #> Source: local data frame [15 x 2]
    #> 
    #>    cyl    y
    #> 1    4 21.4
    #> 2    4 22.8
    #> 3    4 26.0
    #> 4    4 30.4
    #> 5    4 33.9
    #> .. ...  ...
    
    # To extract individual elements into columns, wrap the result in rowwise()
    # then use summarise()
    qs %>% 
      rowwise() %>% 
      summarise(q25 = y[2], q75 = y[4])
    #> Source: local data frame [3 x 2]
    #> 
    #>     q25   q75
    #> 1 22.80 30.40
    #> 2 18.65 21.00
    #> 3 14.40 16.25
  • Keeping associated data frames and models together:
    by_cyl <- split(mtcars, mtcars$cyl)
    models <- lapply(by_cyl, lm, formula = mpg ~ wt)
    
    data_frame(cyl = c(4, 6, 8), data = by_cyl, model = models)
    #> Source: local data frame [3 x 3]
    #> 
    #>   cyl            data   model
    #> 1   4 <S3:data.frame> <S3:lm>
    #> 2   6 <S3:data.frame> <S3:lm>
    #> 3   8 <S3:data.frame> <S3:lm>

dplyr’s support for list-variables continues to mature. In 0.4.0, you can join and row bind list-variables and you can create them in summarise and mutate.

My vision of list-variables is still partial and incomplete, but I’m convinced that they will make pipeable APIs for modelling much eaiser. See the draft lowliner package for more explorations in this direction.

Bonus

My colleague, Garrett, helped me make a cheat sheet that summarizes the data wrangling features of dplyr 0.4.0. You can download it from RStudio’s new gallery of R cheat sheets.

Data wrangling cheatsheet