An Introduction to R and Data Analysis

(Guest lecturer for the Tigers - Tracy Teal, Executive Director of Data Carpentry!)

For this, we will be working exclusively in RStudio! Try to connect to a running RStudio Web server instance – you can get the Web address by running this command:

echo My RStudio Web server is running at: http://$(hostname):8787/

Install RStudio Web server

If you cannot connect, you’ll need to install the prerequisite software:

sudo apt-get update && sudo apt-get install -y gdebi-core r-base r-base-dev

After that finishes, download and install RStudio itself.

sudo gdebi -n rstudio-server-1.0.143-amd64.deb 

And, finally, change the password to something you can remember:

sudo passwd tx160085

Install the tidyverse packages

As per these installation instructions, we can install the so-called tidyverse packages like so:

sudo apt-key adv --keyserver --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9

and then upgrade r-base and install r-base-dev:

sudo apt-get install -y --allow-unauthenticated r-base r-base-dev \
    libxml2-dev libcurl4-openssl-dev

Now we’ll want to install tidyverse at the command line:

cd ~/
cat > install.R <<EOF
install.packages('tidyverse', repos='')
sudo Rscript install.R
echo 'done!'

this will take a long time to run - while it’s running you can switch to the RStudio Web tab in your browser and start working with R!


From this point on, we will be using the standard lesson from Data Carpentry on R: R for Data Analysis.