Some windows. Olympus XA, Portra 800. Photo by Nicholas Tierney

The new R version 4.5.0 is out, and you should get it!

I’ve read through the NEWS file, which details every change – there are many! I would recommend having a skim.

If you’d like to have a skim of the news file, you even use the news() R function, which will open the NEWS file to your help file pane in RStudio. This can also tell us how many news bullets were added:

news_4_5_0 <- news(Version == "4.5.0")
# just show the first 5
head(news_4_5_0)
#>                         Changes in version 4.5.0                        
#> 
#> NEW FEATURES
#> 
#>     o   as.integer(rl) and hence as.raw(rl) now work for a list of
#>   raw(1) elements, as proposed by Michael Chirico's PR#18696.
#> 
#>     o   graphics' grid() gains optional argument nintLog.
#> 
#>     o   New functions check_package_urls() and check_package_dois() in
#>   package tools for checking URLs and DOIs in package sources.
#> 
#>     o   New head() and tail() methods for class "ts" time series,
#>   proposed by Spencer Graves on R-devel.
#> 
#>     o   New qr.influence() function, a "bare bones" interface to the
#>   lm.influence() leave-one-out diagnostics computations; wished
#>   for in PR#18739.
#> 
#>     o   Package citation() results auto-generated from the package
#>   metadata now also provide package DOIs for CRAN and
#>   Bioconductor packages.

The work of this release involved many people, and there are 150 changes. I think it’s worthwhile reading over these changes, and I am incredibly grateful to all the amazing volunteers who put their time and energy into continuing to make R stable, and awesome.

I thought I’d share just a couple of the changes that jumped out at me as I was reading.

Installation is now faster

install.packages() and download.packages() download packages simultaneously using libcurl, significantly reducing download times when installing or downloading multiple packages.

This is cool! Installing packages is now much faster! I love this. Generally, I use pak, which I believe already does this. But it’s a nice win for vanilla R to know I can download things faster.

Data

There are two new datasets added to R! The last time (that I could find) a new dataset was added to R was in R version 3.0.2, which, going to the release dates, was in September 2013. So, nearly 12 years ago! That NEWS item read:

Dataset npk has been copied from MASS to allow more tests to be run without recommended packages being installed.

You might be familiar with one of these new datasets, the penguins data (!) from the palmerpenguins R package.

artwork by Allison Horst

The NEWS item reads:

New datasets penguins and penguins_raw thanks to Ella Kaye, Heather Turner, and Kristen Gorman.

This is huge! Well, I think it is. Now we don’t need to write library(palmerpenguins) if you want to use the penguins or penguins_raw data. Looking at the examples, we can see the following base visualisations:

## bill depth vs. length by species (color) and sex (symbol):
## positive correlations for all species, males tend to have bigger bills
head(penguins)
#>   species    island bill_len bill_dep flipper_len body_mass    sex year
#> 1  Adelie Torgersen     39.1     18.7         181      3750   male 2007
#> 2  Adelie Torgersen     39.5     17.4         186      3800 female 2007
#> 3  Adelie Torgersen     40.3     18.0         195      3250 female 2007
#> 4  Adelie Torgersen       NA       NA          NA        NA   <NA> 2007
#> 5  Adelie Torgersen     36.7     19.3         193      3450 female 2007
#> 6  Adelie Torgersen     39.3     20.6         190      3650   male 2007
names(penguins)
#> [1] "species"     "island"      "bill_len"    "bill_dep"    "flipper_len"
#> [6] "body_mass"   "sex"         "year"
sym <- c(1, 16)
pal <- c(
  "darkorange",
  "purple",
  "cyan4"
  )
plot(
  bill_dep ~ bill_len, 
  data = penguins, 
  pch = sym[sex], 
  col = pal[species]
  )

It is worth noting that the variable names are slightly different in the new base R version - bill_len (not bill_length) bill_dep (not bill_depth), and flipper_len (not flipper_length), which they note in the helpfile: “The penguins data has some shorter variable names than the palmerpenguins version, for compact code and data display.”

There has been a bit of work done on the penguins data, and I wanted to share the relevant references:

• Gorman, K. B., Williams, T. D. and Fraser, W. R. (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9, 3, e90081; doi:10.1371/journal.pone.0090081.

• Horst, A. M., Hill, A. P. and Gorman, K. B. (2022) Palmer Archipelago Penguins Data in the palmerpenguins R Package - An Alternative to Anderson’s Irises. R Journal 14, 1; doi:10.32614/RJ-2022-020.

• Kaye, E., Turner, H., Gorman, K. B., Horst, A. M. and Hill, A. P. (2025) Preparing the Palmer Penguins Data for the datasets Package in R. doi:10.5281/zenodo.14902740.

And there’s ANOTHER dataset!

New dataset gait thanks to Heather Turner and Ella Kaye, used in examples.

I hadn’t come across this before, the helpfile (?gait) reads:

Hip and knee angle (in degrees) through a 20 point movement cycle for 39 boys.

Which is stored as a 3D array, as stated in the helpfile:

A 3-dimensional array with dimensions c(20, 39, 2) giving the “Hip Angle” and “Knee Angle” (in degrees) for 39 repetitions of a 20 point gait cycle (over standardized gait times).

head(gait)
#> , , Variable = Hip Angle
#> 
#>        Subject
#> Time    boy1 boy2 boy3 boy4 boy5 boy6 boy7 boy8 boy9 boy10 boy11 boy12 boy13
#>   0.025   37   47   46   37   20   57   46   46   46    35    38    35    34
#>   0.075   36   46   44   36   18   48   38   46   42    34    37    31    31
#>   0.125   33   42   39   27   11   44   33   43   37    29    33    29    27
#>   0.175   29   34   34   20    8   35   25   40   34    28    29    26    23
#>   0.225   23   27   33   15    7   31   18   36   31    19    26    22    19
#>   0.275   18   21   27   15    5   27   15   30   25    15    20    19    15
#>        Subject
#> Time    boy14 boy15 boy16 boy17 boy18 boy19 boy20 boy21 boy22 boy23 boy24 boy25
#>   0.025    43    43    40    51    52    36    35    46    43    55    39    37
#>   0.075    41    37    41    49    46    33    37    38    41    51    38    34
#>   0.125    36    35    36    45    41    28    33    30    37    47    31    30
#>   0.175    31    28    32    39    35    22    27    23    30    41    27    27
#>   0.225    26    26    27    31    31    18    22    17    24    35    21    26
#>   0.275    20    21    20    23    24    13    14    13    16    30    14    19
#>        Subject
#> Time    boy26 boy27 boy28 boy29 boy30 boy31 boy32 boy33 boy34 boy35 boy36 boy37
#>   0.025    36    36    42    38    46    54    52    32    46    46    48    44
#>   0.075    33    33    40    34    47    48    44    28    41    44    42    41
#>   0.125    28    30    40    30    44    44    44    26    38    40    42    38
#>   0.175    22    28    34    23    37    37    33    22    31    35    35    32
#>   0.225    18    21    23    17    29    30    28    19    25    31    30    24
#>   0.275    13    15    15    12    23    27    27    13    20    25    23    18
#>        Subject
#> Time    boy38 boy39
#>   0.025    55    48
#>   0.075    56    50
#>   0.125    51    47
#>   0.175    46    42
#>   0.225    41    37
#>   0.275    36    29
#> 
#> , , Variable = Knee Angle
#> 
#>        Subject
#> Time    boy1 boy2 boy3 boy4 boy5 boy6 boy7 boy8 boy9 boy10 boy11 boy12 boy13
#>   0.025   10   16   18    5    2   15   13   14   15     9    13     7     9
#>   0.075   15   25   27   14    6   17   16   17   20    22    24     8    14
#>   0.125   18   28   32   16    6   23   22   18   23    25    27    11    16
#>   0.175   18   25   32   17    6   23   17   19   26    21    23    12    15
#>   0.225   15   18   28   10    5   20   12   19   25    10    18     8    15
#>   0.275   14   12   23    8    6   19    9   15   21     9    13     6    12
#>        Subject
#> Time    boy14 boy15 boy16 boy17 boy18 boy19 boy20 boy21 boy22 boy23 boy24 boy25
#>   0.025    15     6    11    24    16    16     7    21    11    12     8    11
#>   0.075    20    11    19    32    20    20    13    24    14    17    12    20
#>   0.125    22    20    30    35    21    22    14    25    14    20    14    22
#>   0.175    22    18    28    33    20    21    17    21    11    20    13    21
#>   0.225    21    13    25    29    18    20    14    16     8    18    12    21
#>   0.275    19     9    17    24    14    20     8     9     5    12     9    17
#>        Subject
#> Time    boy26 boy27 boy28 boy29 boy30 boy31 boy32 boy33 boy34 boy35 boy36 boy37
#>   0.025    16    19    13    11    17    20    18     9     8     9    13    19
#>   0.075    20    26    23    15    25    20    18    12    10    18    18    23
#>   0.125    22    28    30    19    30    22    25    16    17    19    27    26
#>   0.175    21    28    28    20    30    16    23    15    16    19    26    25
#>   0.225    20    24    19    18    27    10    18    14    12    19    25    21
#>   0.275    20    18    10    17    22    10    19    11    10    15    18    18
#>        Subject
#> Time    boy38 boy39
#>   0.025    16    14
#>   0.075    23    25
#>   0.125    28    32
#>   0.175    28    34
#>   0.225    25    30
#>   0.275    21    20

With the examples having a nifty data visualisation:

plot(
  gait[, 1, ], 
  type = "b",
  xlim = range(gait[,,1]), 
  ylim = range(gait[,,2]), 
  xlab = "Hip Angle", 
  ylab = "Knee Angle", 
  main = "'gait' data : Boy 1"
)
mtext(
  "all other boys", 
  col = "thistle"
  )
grid()
matlines(
  gait[, -1, 1], 
  gait[, -1, 2], 
  type = "l", 
  lty = 1, 
  col = adjustcolor("thistle", 1/3)
  )

It is worth noting the references for this data:

• Olshen, R. A., Biden, E. N., Wyatt, M. P., and Sutherland, D. (1989) Gait Analysis and the Bootstrap. Annals of Statistics 17, 4, 1419–1440. doi:10.1214/AOS/1176347372

• Ramsay, J. O., and Silverman, B. W. (2006) Functional Data Analysis, 2nd ed., New York: Springer.

• Ramsay, J. (2023) fda: Functional Data Analysis. R package version 6.1.4, https://CRAN.R-project.org/package=fda.

BLAS and LAPACK update

The bundled BLAS and LAPACK sources have been updated to those shipped as part of January 2025’s LAPACK 3.12.1.

These help power many linear algebra operations in R, and there will likely be speed improvements and other efficiencies. So, worth installing for that alone.

A note on reading NEWS files

I have tried to read the R NEWS files since about 2017 or 2018. I believe I heard Roger Peng say he read the R NEWS files on the Not So Standard Deviations podcast with Hilary Parker. It seemed like a good idea, so I have tried to do the same.

It usually involves me reading over the NEWS file and looking out for things that seem relevant to me and my work. I don’t always understand everything, in fact, I would say I don’t understand a lot of it. But every now and again something comes up that is important to me. For example, the NEWS item for R 4.0.0 about matrices and arrays:

matrix objects now also inherit from class “array”, so e.g., class(diag(1)) is c(“matrix”, “array”). This invalidates code incorrectly assuming that class(matrix_obj)) has length one.

S3 methods for class “array” are now dispatched for matrix objects.

Resulted in a breaking change in greta, that I was able to identify and fix.

Another time that I find it useful to know how to look up the NEWS file is remembering when some key new features were introduced. For example, knowing that the native pipe |> was introduced in 4.1.0, but the underscore _ placeholder was only introduced in 4.2.0 is really useful if I’m developing packages to know which version of R to depend on.

I also often visit the “Previous releases of R for Windows” page to see when a version of R was released. This is for my (somewhat) arbitrary rule that

Yes, it is annoying to reinstall R packages again.

When you install a new minor version of R (that is, when you change its minor version, for example going from 4.1.0 to 4.2.0, not 4.1.0 to 4.1.1), you need to install all your R packages again.

Sure, it’s annoying. But I see it as an opportunity to start afresh. I usually do something like the following

install.packages("pak")
library(pak)
pkg_install("tidyverse", dependencies = TRUE)
pkg_install("devtools", dependencies = TRUE)
pkg_install("naniar", dependencies = TRUE)
pkg_install("blogdown", dependencies = TRUE)
pkg_install("greta", dependencies = TRUE)
pkg_install("brolgar", dependencies = TRUE)
pkg_install("targets", dependencies = TRUE)
pkg_install("ropensci/geotargets", dependencies = TRUE)
pkg_install("milesmcbain/datapasta", dependencies = TRUE)

And this gets me 90% of the way there.

It is worth noting that if you’re a windows user, you can use the installr package to update R and other dependencies.

And on that note it’s ALSO worth mentioning the rig command line utility. This allows you to more easily install different, and release versions of R. If you’ve seen Bob Rudis’s RSwitch software, this is the updated version of this (Bob has archived RSwitch).