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tibble 1.2.0

Hadley Wickham Headshot
Written by Hadley Wickham
2016-08-29

Please note that the information presented in this post reflects the package as it stood when initially released, and may now be outdated. For the most up-to-date information, kindly refer to https://tibble.tidyverse.org/.

We’re proud to announce version 1.2.0 of the tibble package. Tibbles are a modern reimagining of the data frame, keeping what time has shown to be effective, and throwing out what is not. Grab the latest version with:

install.packages("tibble")

This is mostly a maintenance release, with the following major changes:

  • More options for adding individual rows and (new!) columns

  • Improved function names

  • Minor tweaks to the output

There are many other small improvements and bug fixes: please see the release notes for a complete list.

Thanks to Jenny Bryan for add_row() and add_column() improvements and ideas, to William Dunlap for pointing out a bug with tibble’s implementation of all.equal(), to Kevin Wright for pointing out a rare bug with glimpse(), and to all the other contributors. Use the issue tracker to submit bugs or suggest ideas, your contributions are always welcome.

Adding rows and columns

There are now more options for adding individual rows, and columns can be added in a similar way, illustrated with this small tibble:

df <- tibble(x = 1:3, y = 3:1)
df
#> # A tibble: 3 × 2
#>       x     y
#>   <int> <int>
#> 1     1     3
#> 2     2     2
#> 3     3     1

The add_row() function allows control over where the new rows are added. In the following example, the row (4, 0) is added before the second row:

df %>%
  add_row(x = 4, y = 0, .before = 2)
#> # A tibble: 4 × 2
#>       x     y
#>   <dbl> <dbl>
#> 1     1     3
#> 2     4     0
#> 3     2     2
#> 4     3     1

Adding more than one row is now fully supported, although not recommended in general because it can be a bit hard to read.

df %>%
  add_row(x = 4:5, y = 0:-1)
#> # A tibble: 5 × 2
#>       x     y
#>   <int> <int>
#> 1     1     3
#> 2     2     2
#> 3     3     1
#> 4     4     0
#> 5     5    -1

Columns can now be added in much the same way with the new add_column() function:

df %>%
  add_column(z = -1:1, w = 0)
#> # A tibble: 3 × 4
#>       x     y     z     w
#>   <int> <int> <int> <dbl>
#> 1     1     3    -1     0
#> 2     2     2     0     0
#> 3     3     1     1     0

It also supports .before and .after arguments:

df %>%
  add_column(z = -1:1, .after = 1)
#> # A tibble: 3 × 3
#>       x     z     y
#>   <int> <int> <int>
#> 1     1    -1     3
#> 2     2     0     2
#> 3     3     1     1

df %>%
  add_column(w = 0:2, .before = "x")
#> # A tibble: 3 × 3
#>       w     x     y
#>   <int> <int> <int>
#> 1     0     1     3
#> 2     1     2     2
#> 3     2     3     1

The add_column() function will never alter your existing data: you can’t overwrite existing columns, and you can’t add new observations.

Function names

frame_data() is now tribble(), which stands for “transposed tibble”. The old name still works, but will be deprecated eventually.

tribble(
  ~x, ~y,
   1, "a",
   2, "z"
)
#> # A tibble: 2 × 2
#>       x     y
#>   <dbl> <chr>
#> 1     1     a
#> 2     2     z

Output tweaks

We’ve tweaked the output again to use the multiply character × instead of x when printing dimensions (this still renders nicely on Windows.) We surround non-semantic column with backticks, and dttm is now used instead of time to distinguish POSIXt and hms (or difftime) values.

The example below shows the new rendering:

tibble(`date and time` = Sys.time(), time = hms::hms(minutes = 3))
#> # A tibble: 1 × 2
#>       `date and time`     time
#>                <dttm>   <time>
#> 1 2016-08-29 16:48:57 00:03:00

Expect the printed output to continue to evolve in next release. Stay tuned for a new function that reconstructs tribble() calls from existing data frames.

Hadley Wickham Headshot

Hadley Wickham

Chief Scientist, Posit
Hadley is Chief Scientist at Posit PBC, winner of the 2019 COPSS award, and a member of the R Foundation. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (like the tidyverse, which includes ggplot2, dplyr, and tidyr)and principled software development (e.g. roxygen2, testthat, and pkgdown). He is also a writer, educator, and speaker promoting the use of R for data science.

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