R is a free and open-source programming language and software environment for statistical analysis and graphics. RStudio is a popular integrated development environment (IDE) for R that provides a convenient graphical user interface. This document introduces R and RStudio, covering how to install them, their basic layout and features, and how to get help when working with R. Key functions and concepts discussed include loading and installing packages, working directories, and calling functions.

1. Introduction to Data Analysis and Graphics
using R
Introduction to Data Analysis and Graphics us-
ing R
Hellen Gakuruh
2017-03-06
Introduction to R and RStudio
Outline
• Introduction to base R and RStudio
• Download and Install both R and RStudio
• Layout (Windows and Panes)
• Interactively work with R and RStudio’s console
• Global environment/working space and history files
• Code using scripts
• Install R Packages
• Working directory and RStudio’s Projects
• Getting Help
Introduction to base R and RStudio
In this session we get to know a bit about R and Rstudio, like what they are,
how they differ and where we can get them.
About R
• A system for statistical computation and graphics (R Documentation)
• A Programming Language
• A dialect of the S language and started in 1983
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2. • Partly named after the initials of first names of the leading founders Ross
Ihaka and Robert Gentleman and partly to bear similarity with S
• Began as a program for teaching statistics at a university
• Has since grown with diverse user from all over the world
• A collaborative project with many contributors to the base package as well
as extensions (packages)
Why R?
• It’s absolutely free
• Has magnificent graphing capabilities: R’s greatest strength.
• As a programming language, it’s highly extensible; it allows user defined
functions and packages (add-on)
• Growing number of packages to facilitate documentations (Word, PDF,
HTML) and reproducible analysis (Rmarkdown, bookdown, blogdown)
• Innovative packages for interactive statistical application (apps) like shiny
• A growing number of users
Why R? (cont.)
• A growing number of free and commercial Integrated Development Envi-
ronment (IDE’s) [1].
• To distinguish R from it’s IDE’s, it is usually referred to as base R.
• With good foundation, R is easy to work with.
[1]: IDE's are software’s which ease coding process, they include RStudio, and
Revolution R.
What is RStudio?
• One of R’s Integrated Development Environment(IDE)'s. Some of it’s
key advantages over base R are:
– Well thought out and organized layout(panes), making it easy to see
all the core windows at the same time.
– It has a syntax-highlighting editor that supports direct code execution,
– Workspace management.
What is RStudio (cont)
• Makes data importation easier
• Incorporation of R markdown files makes documentation and reproducible
analysis easier.
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3. • It is available for open source and commercial editions and runs on the
desktop (Windows, Mac, and Linux) or in a browser connected to RStudio
Server or RStudio Server Pro (Debian/Ubuntu, RedHat/CentOS, and
SUSE Linux) RStudio.
Download and install both programs
In this sub-session we discuss and demonstrate how to install and download R
and Rstudio.
Downloading base R
• Base R is available from Comprehensive R Archives Network (CRAN)
• CRAN is a collection of web servers that stores identical and up to date
version of code and documentation of R
• There are multiple mirrors (copies) of CRAN located across the globe. It
is recommended to download from one of the mirrors closest to your area
• Select a platform most suitable to your computer specification (OS, 32/64
bit)
• Now lets download and start it up (live demonstration)
Downloading RStudio
• Download from RStudio
• Select platform specific version
• Click the executable file to begin the installation process
(live demonstration)
Layout (Windows and Panes)
In this sub-session we quickly look at R’s tool bar and windows.
Base R layout
Tool bar (live demo)
• File: Open/create script, load/save workspace and history, print and exit.
Workspace and history files to be discussed later in this session
• Edit: Usual edit options plus run options and GUI preference (appearance)
options
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4. • Packages: selecting mirrors, loading, installing and uninstalling package.
Packages are add-no’s
• Windows: How to arrange the windows
• Help: Manuals and help documentation
Console
• Also called Command Line Interface
• It’s an interactive platform (acts just like a calculator)
• Enter an input and it would be evaluated and results outputted immediately
(interactive)
• As a window, it can be minimize, maximize, or re-size
• Most suitable for single lines
Console (cont)
Interactive session on console (live demo)
# simple arithmetric
1 + 1
[1] 2
# Exponential
exp(1)
[1] 2.718282
# Some geometry
2 * pi * (90/360)
[1] 1.570796
# Some trigonometry
cos(90 - 32)
[1] 0.1191801
atan(28/63)
[1] 0.4182243
Console (cont)
Note:
• R is case sensitive, so Cos and cos are not the same.
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5. • To clear console Ctrl+L
• Use up and down arrow to go back to a previous code
• Console prompt “+” indicates an incomplete syntax; R is waiting for
completion
R script
• Scripts are text files used to write code
• They are more suitable for reproducibility and multiple line of code (like
creating a program)
• Text editor (program for writing scripts) in base R, need to be loaded as a
window from file > Scripts
• Not interactive (don’t give instant results) like console
• To output results, must click edit then run all/selection, alternatively
ctrl_R
R Script (cont)
Live demonstration on scripting in base R
Global environment/workspace
• Environment in R is a place with list of object names and location of their
associated values. It is also a list of parent environments as environment
have a hierarchical nature.
• Environments themselves do not store objects (values/data), they only
point to where it’s located.
• First environment to be used is called a global environment or the
workspace.
• Global environment is searched first when an object name is given in a
code, if not found, it’s parent environment is searched (this will become
clear as we create objects)
• This is not visible in base R (use function ls())
RStudio Layout
• Very user friendly
• Has four panes with multiple tabs
• Tool bar is similar to base R but with some additions
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6. RStudio Panes
• Top left usually Script/text editor and data viewer tabs
• Top right usually global environment, history (logs) and additional tabs
(like Build, Git and Presentations)
• Bottom left usually console tab
• Bottom right files, plots, packages, help and viewer tabs
• However, these can be re-arranged
Interactive session on Rstudio
• Not different than base R’s console
• Input code click enter and output generated
Live demonstration
Scripting in RStudio
• Also like Base R with added bonus that it has it’s own tab with easily
accessible run button.
• There are a variety of scripts that work well in R’s script editor like .R (for
pure R code), .Rmd (for reproducibility, text and code), md (markdown
files), html and css.
Live demo (.R file)
Function Calls in R
• Everything that happens in R is as a result of a function call
• Functions are actions performed by R: commands
• A function is is denoted by parenthesis: ()
• Within parenthesis are arguments or parameters. Arguments are name-
value pair used to give input or specify how a function should be done
• There two types of functions, named functions and anonymous functions.
Named functions include mean (to compute mean), median (to locate
median), and read.table (to import data)
• Functions can also be considered to be high or low level. High level
functions are commonly used commands while low level commands are
those called by high level functions
• “Function call” means using a function to perform and action
• When making function call, argument can be named or unnamed and it
could have default values or not
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7. Function call example: mean function
• To access documentation of this and any other function; type ?function
name e.g. ?mean
• mean has arguments (x, trim = 0, na.rm = FALSE, . . . )
• Arguments trim and na.rm have default values 0 and FALSE. These can
be changed as need be but if okay, don’t include in call.
• Argument x has no default value hence it must be given.
• When a function call is made with both name and value e.g. mean(x =
data) it’s a named argument call. If it’s mean(data), then it’s unnamed
argument call
• A function call with named arguments can be specified in any order without
a problem (though not really good practice)
• But unnamed argument call needs to be ordered the same way as function
definition (Live demo)
Errors, warnings and messages
• An error in R means something is not available, for example a data set
is specified in a call yet it’s unavailable in R. Errors are fatal, they stop
execution
• A warnings are information of possible problems, they do not stop execution.
It good to check why this happens to pre-empt a possible problem
• Messages are useful information, they have nothing to do with a problem,
good examples are messages to inform on installation progress
(Live demo)
R Packages
• Package is simply a mechanism for loading optional code, data and docu-
mentation.
• All R functions and data sets are stored in packages and base R distribution
includes about 30 packages [2].
• Out of these 30 packages, there those packages that are considered part of
R source code and therefore automatically loaded
• The others are installed (exist), but are not available for use, they must
be loaded with function library to make them available
• Many other contributed packages exist in CRAN (Comprehensive R Net-
work)
• Generally, base R packages are sufficient to perform most basic statistics,
but if specialized functions are needed, then check CRAN (start with tasks
view )
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8. [2]:
R Core Team (2016). R: A language and environment for statistical com-
putation. R Foundation for Statistical Computing, Vienna, Austria. URL
https://www.R-project.org/.
R Session and Working Directory
• R session is a current active session, it begins when R is logged in and end
when logged off
• A working directory is a folder used during an R session to source and save
files
• It is important to specify working directory for each R session or globally
change R to start from a folder considered as a working directory
Getting and setting working directory
• To check current working directory, call getwd()
• To set another working directory call setwd(dir)
• Argument dir is a path (location) name), it can either be relative or
absolute
• Example: set("~/Data Mania Inc/Data_Mgt_Analysis_and_Graphics_R")
RStudio Projects
• One of the recent feature in RStudio
• Enables working with different yet inter-related work and document each
with it’s own working directory (folder)
• Can be created in a new or an existing working directory or from a cloned
version controlled repository )
Getting Help
• Important to initially read internal help documentation like function
documentation: access with either ?function or help(function) e.g.
?read.table or help("read.table"). Note, later case has to be quoted
(“”).
• R core team has invested a lot of time and energy to develop a number of
user manual: access with help.start() (no arguments)
• Manuals to read as a beginner in R are:
– An introduction to R and
– R Installation and Administration
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9. • Under references, Search Engine & Keywords can be handy in locating
certain write-up
• Under Miscellaneous Materials, “Frequently Asked Questions is a must
read”
• Other documentation in this page can be read in bits as they become
relevant
Beyond R documentation
• Do a Google search, specifically using R seek
• Ask a knowledgeable (and helpful) friend
• Search through R’s help mailing list
• Search through stackoverflow Q&A
• Finally post a question to either Stackoverflow or R’s mailing list (consider
the latter) but never the same question to both sites
Useful resources
• RStudio’s getting help: https://support.rstudio.com/hc/en-us/articles/
200552336-Getting-Help-with-R) in R documentation
• A PDF version of R-project’s “An Introduction to R: https://cran.r-
project.org/doc/manuals/R-intro.pdf
• R-projects’s ref-card: https://cran.r-project.org/doc/contrib/Short-
refcard.pdf
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