Updating to 4.0.0 on MacOS

Mixed emotions

Wow! Has it been a year? Another major update from The R Foundation (the recent 4.0.0 release in April). I’m always happy to see the continuing progress and the combination of new features and bug fixes, but I also dread the upgrade because it means I have to address the issue of what to do about the burgeoning number of packages (libraries) I have installed. I wrote a fairly comprehensive post about it last year. I just took the plunge this year and almost everything seems to still work. Vindication!

The details are here in the old post but since this is timely I republish the basics.

I’m aware that there are full-fledged package managers like packrat and checkpoint and even a package designed to manage the upgrade for you on windows, but I’m a Mac user and wanted to do things my own way and I don’t need that level of sophistication.

So I set out to do the following:

1. Capture a list of everything I had installed under R 3.6.x and, very importantly, as much as I could about where I got the package e.g.  CRAN or GitHub or ???
2. Keep a copy for my own edification and potential future use.
3. Do a clean R 4.0.0 install and not copy any library directories manually or create symlinks or any other thing at the OS level.
4. Take a look at the list I produced in #1 above but mainly to just download and install the exact same packages if I can find them.
5. Make the process mainly scripted and automatic and available again for the future – it worked this year let’s hope it works again next.

Before you upgrade!

Let’s load tidyverse to have access to all it’s various functions and features and then build a dataframe called allmypackages with the basic information about the packages I currently have installed in R 3.6.3.

Note – I’m writing this after already upgrading so there will be a few inconsistencies in the output

• This could just as easily be a tibble but I chose as.data.frame
• I am deliberately removing base packages from the dataframe by filter
• I am eliminating columns I really don’t care about with select

require(tidyverse)

allmypackages <- as.data.frame(installed.packages())

allmypackages <- allmypackages %>%
  filter(Priority != "base" | is.na(Priority)) %>%
  select(-c(Enhances:MD5sum, LinkingTo:Suggests)) %>%
  droplevels()

str(allmypackages)

A function to do the hard work

As I mentioned above the stack overflow post was a good start but I wanted more information from the function. Rather than TRUE/FALSE to is it github I would like as much information as possible about where I got the package. The package~source function will be applied to the Package column for each row of our dataframe. For example as.character(packageDescription("ggplot2")$Repository) will get back “CRAN”, and as.character(packageDescription("CHAID")$Repository) will yield “R-Forge”. For GitHub packages the result is character(0) which has a length of zero. So we’ll test with an if else clause. If we get an answer like “CRAN” we’ll just return it. If not, we’ll see if there is a GitHub repo listed with as.character(packageDescription(pkg)$GithubRepo) as well as a GitHub username as.character(packageDescription(pkg)$GithubUsername). If they exist we’ll concatenate and return. If not we’ll return “Other”. Besides being good defensive programming this may catch the package you have built for yourself as is the case for me.

package_source <- function(pkg){
  x <- as.character(packageDescription(pkg)$Repository)
  if (length(x) == 0) {
    y <- as.character(packageDescription(pkg)$GithubRepo)
    z <- as.character(packageDescription(pkg)$GithubUsername)
    if (length(y) == 0) {
      return("Other")
    } else {
      return(str_c("GitHub repo = ",
                   z,
                   "/",
                   y))
    }
  } else {
    return(x)
  }
}

# show the first 60 as an example
head(sapply(allmypackages$Package,
            package_source),
     60)

What’s in your libraries?

Now that we have the package_source function we can add a column to our data frame and do a little looking.

allmypackages$whereat <- sapply(allmypackages$Package,
                                package_source)
str(allmypackages)

table(allmypackages$whereat)
allmypackages %>%
  filter(whereat == "Other") %>%
  select(Package, Version)

And just to be on the safe side we’ll also write a copy out as a csv file so we have it around in case we ever need to refer back.

write.csv(allmypackages, "mypackagelistApril2020.csv")

Go ahead and install R 4.0.0

At this point we have what we need, so go ahead and download and install R 4.0.0. At the end of the installation process you’ll have a pristine copy with a new (mostly empty) library directory (on my system it’s /Library/Frameworks/R.framework/Versions/4.0/). When next you restart R and R Studio you’ll see a clean new version. Let’s make use of our data frame to automate most of the process of getting nice clean copies of the libraries we want.

We’ll start by getting the entire tidyverse since we need several parts and because installing it will trigger the installation of quite a few dependencies and bootstrap our work.

# post upgrade with output surpessed
install.packages("tidyverse")
library(tidyverse)

Now we have R 4.0.0 and some additional packages. Let’s see what we can do. First let’s create two dataframes, one with our old list and one with what we have right now. Then we can use anti_join to make a dataframe that lists the differences thediff. We can use filter and pull to generate a vector of just the the packages that are on CRAN we want to install.

oldpackages <- read.csv("mypackagelistApril2020.csv")
allmypackages <- as.data.frame(installed.packages())
allmypackages <- allmypackages %>%
  filter(Priority != "base" | is.na(Priority)) %>%
  select(-c(Enhances:MD5sum, LinkingTo:Suggests))
thediff <- anti_join(oldpackages,
                     allmypackages, 
                     by = "Package")

thediff <- droplevels(thediff)
thediff %>%
  filter(whereat == "CRAN") %>%
  pull(Package) %>%
  as.character

Just do it!

Now that you have a nice automated list of everything that is a CRAN package you can give it a final look and see if there is anything else you’d like to filter out. Once you are sure the list is right one final pipe will set the process in motion.

thediff %>%
  filter(whereat == "CRAN") %>%
  pull(Package) %>%
  as.character %>%
  install.packages

Depending on the speed of your network connection and the number of packages you have that will run for a few minutes.

That takes care of our CRAN packages. What about GitHub? Here’s another chance to review what you have and whether you still want need these packages. You can automate the process and once again feed the right vector to devtools::install_github().

# Manual peek
thediff %>%
  filter(str_detect(whereat, "GitHub repo")) %>%
  select(Package, Version, NeedsCompilation, whereat)

# if you want to automate
thediff %>%
  filter(str_detect(whereat, "GitHub repo")) %>%
  pull(whereat) %>%
  as.character %>%
  str_remove("GitHub repo = ") %>%
  devtools::install_github()

Same with the one package I get from R-Forge…

allmypackages %>%
  filter(str_detect(whereat, "R-Forge")) %>%
  select(Package, Version, NeedsCompilation, whereat)

install.packages("CHAID", repos="http://R-Forge.R-project.org")

At the end of this process you should have a nice clean R install that has all the packages you choose to maintain as well as a detailed listing of what those are.