Chapter 2 Getting Started

2.1 Packages

When you start R studio, basic functionality is initially available. However, in most projects we will want to use some special code and functions contained in packages that are not initially available whe R starts. Before packages can be used in our analyses, they must be installed in our R workspace. We presume that the R Studio development environment is being used by our students. Any package can be installed by clicking the “Packages” tab in the lower right panel of the R Studio workspace. Then click “Install” to produce an entry bar where you type the name of the desired package.

Once a package is installed into your R Studio environment, you make it available by loading with the library() command. For this document, some additional packages are needed, and are loaded in the next code block. The knitr and tidyverse packages have been previously installed. If you attempt to load a package (in this case zelig) that has not been installed, you will get an error message similar to this:

Error in library(zelig) : there is no package called ‘zelig’

# this block loads R packages that may be needed for the analysis.
library(knitr)
library(tidyverse)

2.2 The tidyverse Package

The tidyverse package is very special - it is a package of other packages. The tidyverse website tidyverse describes the tidyverse as: The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

The most important packages inside the tidyverse package for this document are: dplyr, magrittr, and ggplot2.

2.3 Gapminder Data:

This dataset (named gapminder) is contained in an R package called gapminder, and needs to be loaded before the dataset can be used.

library(gapminder)

2.4 Set Working Directory

In the “Files” tab in the lower right portion of the R Studio work area, you can choose where you want to store files and conduct your work by navigating to a suitable folder by making folders and sub-folders and then clicking to navigate to a suitable work area.

You should notify R Studio and the R software to this location called the working directory. Once you have navigated to where you want files, data, and results to reside, you notify R Studio by clicking the blue “More” gear and choose “set working directory.” This will help R understand where to expect files and dat to be located.

One of the most common problems students experience is that they work on files in a location not specified as the “working directory.”

2.5 Reading Data From a CSV File

The most common way to read data into R is from an excel spreadsheet that has been saved into a comma-separated-values (csv) file. This means that data elements are separated from each other by commas “,”.

We consider a data file named (file.csv) that contains variable names in the first row of the file. Place this file in your working directory and read,

dataframe <- read.csv("file.csv",header=TRUE)

A frequent issue with read.csv is that character variables are automatically converted to factor/categorical variables. This may not be a good choice in many instances. To gain full control of how this is handled, you can prevent this kind of auto-conversion by using the stringsAsFactors option.

cardata <- read.csv(file = 'carspeeds.csv', stringsAsFactors = FALSE)

The readr package inside the tidyverse family of packages has a slightly nicer read csv function you should know about. We use the readr:: prefix to inform readers that the read_csv function resides in the readr package. This read function will not auto-convert character variables to category/factor variables.

dataframe <- readr::read_csv("file.csv",col_names = TRUE)

Reading data directly from excel spreadsheets is more complex and you should read documentation for the readxl package.