Note: you can create an html file by running in your console the command:

rmarkdown::render(“CourseSessions/Session1/Session1inclass.Rmd”)

The purpose of this session is to become familiar with:

  1. Basic functionality of R;
  2. Reading/Writing data;
  3. Simple data manipulations;
  4. Simple plots;
  5. The idea of functions

Before starting, make sure you have pulled the session 1 files on your github repository (if you pull the course github repository you also get the session files automatically). To confirm, you can also “source” the file “update_fork.R” which you can find in your main course directory.

Note also that directory paths may sometimes be a frustrating source of problems, so it is recommended that you learn these R commands to find out your current working directory and, if needed, set it where you need to (e.g. where you have the main files for the class). For example, assuming we are now in the “MYDIRECTORY/INSEADAnalytics” directory, we can do these:

# This command shows the directory we are at:
getwd()

# This command can change the directory if we need to:
setwd("CourseSessions/Session1")

Let’s start.

Note: you can always use the help command in Rstudio to find out about any R function (e.g. type help(list.files) to learn what the R function list.files does).


Setting up

First, notice the structure of this file when you open it in your RStudio (the “raw file”). This is a so called Markdown file file (ending with .Rmd). Markdown files are excellent ways to create reproducible, reusable, and easy to modify reports. Effectively one combines text with code within the same file. The code can be inserted either using code chunks which, as you can note below, are effectively blocks of code that start for example as:

“```{r eval = TRUE, echo=TRUE, comment=NA, warning=FALSE, message=FALSE,results=‘markup’}”

or by adding simple code commands in the text like the inline “r colnames(ProjectData)[2]” that you can find further below. When one compiles the file (e.g. using the “rmarkdown::render” command as shown at the beginning of this document) then all code is executed and the output is seamlessly merged within the document.

You can create a new .Rmd by creating a new “R Markdown” file as shown in this image:

“”

In general once you create any new file, you will be asked to give it a name when you try to save it for the first time:

“”

Markdown (ending at .Rmd) files are not like the usual R Script (which end with .R) files. The latter only contain code, such as custom functions we may want to build. Here is an example .R file. One can incorporate such .R files in the .Rmd document by simply “sourcing” these files, like in this example:

source("library.R")

(note that the directory path is defined relative to where the current .Rmd file is located - in this case they are both in the same directory).

Notice also the structure of the Session 1 directory. All it has (other than a few image files) is one .Rmd file, one .R file and one directory where we keep the data (which you can create using the “New Folder” button).

“”

Questions

  1. Please create a new directory within the “CourseSessions/Session1/” directory (e.g. call this directory “MyProject”), and populate it with a new .Rmd file (e.g. save it as “MyProject.Rmd”), a new .R file (e.g. save it as “library.r”), and a sub-directory (name it “data”) where you add a .csv file (for example copy the Boads.csv file from the data directory in Session1).
  2. Please add this code chunk in your .Rmd file
ProjectData <- read.csv(file = "data/Boats.csv", header = TRUE, sep=";")
ncol(ProjectData)
[1] 82
  1. What happens when you create an html file (e.g. using the “rmarkdown::render” command but with your new .Rmd filename as argument)?

Your Answer here:




Adding libraries

One of the major benefits of using open source software is the impressive availability of many functions as well as code people develop and share. There is a very fast growing body of (free) tools you can use (also in your jobs) - so avoid reinventing the wheel and ride the wave.

There are many ways to get new tools. First, “mature/tested” tools are available as “packages” that you can install through your RStudio. Take a look at this list of R packages and see which ones you like.

To install a package in R click on the “Packages” menu and then on “Install” and type the name of the package to install, also selecting “Install dependencies”, as indicated in this figure: