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Install packages from Mastering Software Development in R

R, Data1 min read

I'm currently reading a really nice book "Mastering Software Development in R" — but also a complete book — about R language. It was written by Roger D. Peng, Sean Kross and Brooke Anderson

Mastering Software Development in R cover - Arthur Camberlein

Mastering Software Development in R

In the introduction of this book, authors give you an insight for all the packages they will use — and also invite you to install them — If you familiar with R, it will be no big deal ;-) And maybe a lot of theses packages are already installed on you R environement — ... but familiar or not I will give you some advice to be more efficient, because that's 45 packages.

Because time is what we are looking for with software like R: automation of data analyses to get more time of "real consulting" !

Installing packages with R

If you know just a little of R you could try :


But really 45 times, are you insane ? ;-)

If you just have to install a few packages, you could try to install several — dependents — packages in one :


One-liner to install packages with R

But what I recommend you to do is to install all packages at once, with this line (example) :

1install.packages(c("package_one", "package_two", "...", "package_n"))

Note: it could take time ... time that you could use to comment this article and take a coffee/a tea!

All the packages

And with all the packages :

1install.packages(c("choroplethr", "choroplethrMaps", "data.table", "datasets", "devtools", "dlnm", "dplyr", "faraway", "forcats", "GGally", "ggmap", "ggplot2", "ggthemes", "ghit", "GISTools", "grid", "gridExtra", "httr", "knitr", "leaflet", "lubridate", "magrittr", "methods", "microbenchmark", "package", "pander", "plotly", "profvis", "pryr", "purrr", "rappdirs", "raster", "RColorBrewer", "readr", "rmarkdown", "sp", "stats", "stringr", "testthat", "tidyr", "tidyverse", "tigris", "titanic", "viridis"))

With a variable

You could also create a variable including all packages, like that :

1packages <- c("choroplethr", "choroplethrMaps", "data.table", "datasets", "devtools", "dlnm", "dplyr", "faraway", "forcats", "GGally", "ggmap", "ggplot2", "ggthemes", "ghit", "GISTools", "grid", "gridExtra", "httr", "knitr", "leaflet", "lubridate", "magrittr", "methods", "microbenchmark", "package", "pander", "plotly", "profvis", "pryr", "purrr", "rappdirs", "raster", "RColorBrewer", "readr", "rmarkdown", "sp", "stats", "stringr", "testthat", "tidyr", "tidyverse", "tigris", "titanic", "viridis")

And then call them directly on a variable ;-)

Listing all the packages from "Mastering Software Development in R"

The list of all the packages are :

  • choroplethr
  • choroplethr
  • Maps
  • data.table
  • datasets
  • devtools
  • dlnm
  • dplyr
  • faraway
  • forcats
  • GGally
  • ggmap
  • ggplot2
  • ggthemes
  • ghit
  • GISTools
  • grid
  • gridExtra
  • httr
  • knitr
  • leaflet
  • lubridate
  • magrittr
  • methods
  • microbenchmark
  • package
  • pander
  • plotly
  • profvis
  • pryr
  • purrr
  • rappdirs
  • raster
  • RColorBrewer
  • readr
  • rmarkdown
  • sp
  • stats
  • stringr
  • testthat
  • tidyr
  • tidyverse
  • tigris
  • titanic
  • viridis

If you've understand how it works, it would be the same to call several libraries :

1library(c("choroplethr", "choroplethrMaps", "data.table", "datasets", "devtools", "dlnm", "dplyr", "faraway", "forcats", "GGally", "ggmap", "ggplot2", "ggthemes", "ghit", "GISTools", "grid", "gridExtra", "httr", "knitr", "leaflet", "lubridate", "magrittr", "methods", "microbenchmark", "package", "pander", "plotly", "profvis", "pryr", "purrr", "rappdirs", "raster", "RColorBrewer", "readr", "rmarkdown", "sp", "stats", "stringr", "testthat", "tidyr", "tidyverse", "tigris", "titanic", "viridis"))

Hope this article helped you or interesed you, if it's the case you can share it and give a thumb up!


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