R Programming for Data Science Tutorial Guide for Beginner

Introduction to R for Data Science

Data science can be defined as the discipline of using raw data as input and extracting knowledge and insights from it.The main objective of “R for data science” is that it help you to learn the most important tools in R that will permit you to do data science.

R programming language, developed by Ross Ihaka and Robert Gentleman in 1993, is widely used for applications related to data science. R provides support for an extensive suite of statistical methods, inference techniques, machine learning algorithms, time series analysis, data analytics, graphical plots to list a few. These features make it a great language for data exploration and investigation. 

Read: What Is Data Science? A Beginners Guide To Data Scientists

Before we proceed further with programming in r for data science and what is r for data science? Let’s first discuss what is data science and what is a data scientist?

What is data science?

Data science is the study of data that involves developing methods of analyzing, recording and storing data to effectively extract useful information.The main aim of data science is to get in-depth knowledge about any type of structured and unstructured data.

What is a data scientist?

A data scientist is one who has technical skills to solve complex problems and who has curiosity to explore what kind of problems are needed to be solved. The main goal of data scientists is to analyze, process, and model data then interpret the outcomes to create actionable plans for companies and other organizations.

Getting started with R for Data Science

R can be downloaded from CRAN , the comprehensive R archive network. CRAN comprises a set of mirror servers distributed around the world and is used to distribute R and R packages. A new major version of R comes out once a year, and there are 2 to 3 minor versions each year. RStudio provides an integrated development environment, or IDE, for R programming.

How to install R Packages?

R functionality is provided in terms of its packages. There are now over 10000 R packages in CRAN.

Packages contain R functions, data, and compiled code in a well-defined format. The directory containing the packages is called the library. R ships with a standard set of packages. Non standard packages are available for download and installation. Examples of R packages include arules,ggplot2,caret,shiny etc. Packages can be installed with the install.packages() function as shown below.

Install.packages(“”)

This command causes R to download the package from CRAN. Once you have a package installed, you can make its contents available to use in your current R session by using the library command:

library("")

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What is Programming in R for Data Science?

Let us take a look at a simple program in R which prints “Hello World”. This can be accomplished either from the command line in the R interpreter or via a R script. Let us look at both mechanisms.

Hello World in R – from the R command prompt:

> print("Hello world!")

[1] "Hello world!"

Creating a HelloWorld.R script in R:

helloStr <- "Hello world!"

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print(helloStr)

The script can be executed using Rscript HelloWorld.R. It will print:

[1] "Hello world!"

What are different BuiltIn DataSets in R?

R comes with a large number of built in datasets.These can be used as demo data for understanding R packages and functions. Data sets in package 'datasets' include:

AirPassengers	Monthly Airline Passenger Numbers 1949-1960
BJsales	Sales Data with Leading Indicator
BJsales.lead (BJsales)	Sales Data with Leading Indicator
BOD	Biochemical Oxygen Demand
CO2	Carbon Dioxide Uptake in Grass Plants
ChickWeight	Weight versus age of chicks on different diets
DNase	Elisa assay of DNase
EuStockMarkets	Daily Closing Prices of Major European Stock Indices, 1991-1998
Formaldehyde	Determination of Formaldehyde
HairEyeColor	Hair and Eye Color of Statistics Students
Harman23.cor	Harman Example 2.3
Harman74.cor	Harman Example 7.4
Indometh	Pharmacokinetics of Indomethacin
InsectSprays	Effectiveness of Insect Sprays
JohnsonJohnson	Quarterly Earnings per Johnson & Johnson Share
LakeHuron	Level of Lake Huron 1875-1972
LifeCycleSavings	Intercountry Life-Cycle Savings Data
Loblolly	Growth of Loblolly pine trees
Nile	Flow of the River Nile
Orange	Growth of Orange Trees
OrchardSprays	Potency of Orchard Sprays
PlantGrowth	Results from an Experiment on Plant Growth
Puromycin	Reaction Velocity of an Enzymatic Reaction
Seatbelts	Road Casualties in Great Britain 1969-84
Theoph	Pharmacokinetics of Theophylline
Titanic	Survival of passengers on the Titanic
ToothGrowth	The impact of Vitamin C on Tooth Growth in Guinea Pigs
UCBAdmissions	Student Admissions at UC Berkeley
UKDriverDeaths	Road Casualties in Great Britain 1969-84
UKgas	UK Quarterly Gas Consumption
USAccDeaths	Accidental Deaths in the US 1973-1978
USArrests	Violent Crime Rates by US State
USJudgeRatings	Ratings of State Judges in the US Superior Court
USPersonalExpenditure	Personal Expenditure Data
UScitiesD	Distances Between European Cities and Between US Cities
VADeaths	Death Rates in Virginia (1940)
WWWusage	Internet Usage per Minute
WorldPhones	The World's Telephones
ability.cov	Ability and Intelligence Tests
airmiles	Passenger Miles on Commercial US Airlines, 1937-1960
airquality	New York Air Quality Measurements
anscombe	Anscombe's Quartet of 'Identical' Simple Linear Regressions
attenu	The Joyner-Boore Attenuation Data
attitude	The Chatterjee-Price Attitude Data
austres	Quarterly Time Series of the Number of Australian Residents
beaver1 (beavers)	Body Temperature Series of Two Beavers
beaver2 (beavers)	Body Temperature Series of Two Beavers
cars	Speed and Stopping Distances of Cars
chickwts	Chicken Weights versus Feed Type
co2	Mauna Loa Atmospheric CO2 Concentration
crimtab	Student's 3000 Criminals Data
discoveries	Yearly Numbers of Important Discoveries
esoph	Smoking, Alcohol and (O)esophageal Cancer
euro	Conversion Rates of Euro Currencies
euro.cross (euro)	Conversion Rates of Euro Currencies
eurodist	Distances Between European Cities and BetweenUS Cities
faithful	Old Faithful Geyser Data
fdeaths (UKLungDeaths)	Monthly Deaths from Lung Diseases in the UK
freeny	Freeny's Revenue Data
freeny.x (freeny)	Freeny's Revenue Data
freeny.y (freeny)	Freeny's Revenue Data
infert	Infertility after Spontaneous and Induced Abortion
iris	Edgar Anderson's Iris Data
iris3	Edgar Anderson's Iris Data
islands	Areas of the World's Major Landmasses
ldeaths (UKLungDeaths)	Monthly Deaths from Lung Diseases in the UK
lh	Luteinizing Hormone in Blood Samples
longley	Longley's Economic Regression Data
lynx	Annual Canadian Lynx trappings 1821-1934
mdeaths (UKLungDeaths)	Monthly Deaths from Lung Diseases in the UK
morley	Michelson Speed of Light Data
mtcars	Motor Trend Car Road Tests
nhtemp	Average Yearly Temperatures in New Haven
nottem	Average Monthly Temperatures at Nottingham, 1920-1939
npk	Classical N, P, K Factorial Experiment
occupationalStatus	Occupational Status of Fathers and their Sons
precip	Annual Precipitation in US Cities
presidents	Quarterly Approval Ratings of US Presidents
pressure	Vapor Pressure of Mercury as a Function of Temperature
quakes	Locations of Earthquakes off Fiji
randu	Random Numbers from Congruential Generator RANDU
rivers	Lengths of Major North American Rivers
rock	Measurements on Petroleum Rock Samples
sleep	Student's Sleep Data
stack.loss (stackloss)	Brownlee's Stack Loss Plant Data
stack.x (stackloss)	Brownlee's Stack Loss Plant Data
stackloss	Brownlee's Stack Loss Plant Data
state.abb (state)	US State Facts and Figures
state.area (state)	US State Facts and Figures
state.center (state)	US State Facts and Figures
state.division (state)	US State Facts and Figures
state.name (state)	US State Facts and Figures
state.region (state)	US State Facts and Figures
state.x77 (state)	US State Facts and Figures
sunspot.month	Monthly Sunspot Data, from 1749 to "Present"
sunspot.year	Yearly Sunspot Data, 1700-1988
sunspots	Monthly Sunspot Numbers, 1749-1983
swiss	Swiss Fertility and Socioeconomic Indicators (1888) Data
treering	Yearly Treering Data, -6000-1979
trees	Width, Height and Volume for Cherry Trees
uspop	Populations Recorded by the US Census
volcano	Topographic Information on Auckland's Maunga Whau Volcano
warpbreaks	The Number of Yarn Breaks during Weaving
women	Average Heights and Weights for American Women

We can view the contents of any of these datasets using the following commands:

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# Loading 
data() 
# Print the first n rows 
head(, )
For example, lets explore the iris datset:
data(iris)
head(iris,3)

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
1	5.1	3.5	1.4	0.2  setosa
2	4.9	3	1.4	0.2  setosa
3	4.7	3.2	1.3	0.2  setosa

What is Data ingestion in R?

R is used for data processing and analysis tasks. The first step in this activity is importing data. Base R provides several functions for this purpose. Let us explore some of these for a better understanding:

read.table : Used for importing tab delimited tabular data.

e.g.,

1	2	3
4	5	6
7	8	9
A	b	c
D	e	f

  > df <- read.table("data.txt", header = FALSE)

> df

	V1	V2	V3
1	1	2	3
2	4	5	6
3	7	8	9
4	A	b	c
5	D	e	f

read.csv : Used for importing csv file with comma(,) delimiter.

e.g.,

1	2	3
4	5	6
7	8	9
A	b	c
D	e	f

read.csv2 : Used for importing csv file with semicolon(;) delimiter.

read.delim : Used for importing delimited file with any arbitrary delimiter.

Other library functions are available for importing data of specific format:

e.g.,

• readxl:read_excel for reading excel files
• rjson:fromJSON for reading JSON data
• XML:xmlTreeParse for xml data.
• RCurl:readHTMLTable for reading HTML table data

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What is Data Preparation and Cleansing in R?

Eighty percent of data analysis is spent on the cleaning and preparation of data. It is not just the first step, but may need to be repeated many times over the course of analysis.

In tidy data:

• Each variable forms a column.
• Each observation forms a row.
• Each type of observation unit forms a table.

Tidy represents a standard way of structuring a dataset. Real world datasets need not necessarily be available in tidy format:

• Column headers may not be variable names.
• Multiple variables might be stored in one column.
• Variables might be stored in rows.
• Unrelated observations might be stored in the same table.
• A single observational unit might be stored across multiple tables.

R provides a package tidyr for converting data into tidy format. tidyr provides three main functions for tidying up messy data:

• gather(),
• separate()and
• spread().

gather() takes multiple columns, and organizes them into key-value pairs.

For example, consider the dataset below which represents the test scores in 2 tests(a and b) for 3 individuals named Amar, Akbar and Anthony:

library(tidyr)

messy <- data.frame( name = c("Amar", "Akbar", "Anthony"), a = c(56, 91, 88), b = c(72, 64, 60) ) messy #>    name    a  b
#> 1  Amar   56 72
#> 2 Akbar   91 64
#> 3 Anthony 88 60

But ths dataset is currently not in a tidy format (Variables must correspond to columns). For it to be converted it into column format the data must be represented as name , test , score.

Let us see how we can use tidyr package to convert the existing dataset into tidy form.

messy %>%
  gather(test, score, a:b)
#>      name   test     score
#> 1   Amar    a        56
#> 2   Akbar   a        91
#> 3   Anthony a        88
#> 4   Amar    b        72
#> 5   Akbar   b        64
#> 6   Anthony b        60

Here we used the pipe operator %>%. The pipe operator allows you to pipe the output from one function to the input of another function. In our case the messy dataframe is piped as input to the gather function.

Similarly, separate function allows us to separate two variables are clumped together in one column.

spread(), takes two columns (key-value pair) and spreads them in to multiple columns, making data wider.

The tidied dataset can then be transformed as per the requirement of analysis.

R provides several packages for data transformation. Let us look at one of these – dplyr.

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Below are some of the functions which are useful for this purpose:

• filter : Pick observations by their values
• arrange : Reordering the rows
• select : Pick variables by their names
• mutate : Create new variables in terms of functions of existing variables
• summarise : Create a single summary value from multiple given values
• group_by() : grouping operations in the “split-apply-combine” concept

For example let us determine all the entries in the iris datset with Species as ‘virginica’ and Sepal.Width>3:

> library(dplyr)

> filter(iris,Species=="virginica",Sepal.Width>3)

Sepal.Length Sepal.Width Petal.Length Petal.Width   Species

1	6.3	3.3	6	2.5 virginica
2	7.2	3.6	6.1	2.5 virginica
3	6.5	3.2	5.1	2.0 virginica
4	6.4	3.2	5.3	2.3 virginica
5	7.7	3.8	6.7	2.2 virginica
6	6.9	3.2	5.7	2.3 virginica
7	6.7	3.3	5.7	2.1 virginica
8	7.2	3.2	6	1.8 virginica
9	7.9	3.8	6.4	2.0 virginica
10	6.3	3.4	5.6	2.4 virginica
11	6.4	3.1	5.5	1.8 virginica
12	6.9	3.1	5.4	2.1 virginica
13	6.7	3.1	5.6	2.4 virginica
14	6.9	3.1	5.1	2.3 virginica
15	6.8	3.2	5.9	2.3 virginica
16	6.7	3.3	5.7	2.5 virginica
17	6.2	3.4	5.4	2.3 virginica

What is Data Modeling in R?

A model provides a simple low-dimensional summary of a given dataset. R provides inbuilt functions that make fitting statistical models very simple.

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The function to fit linear models is called lm. It is very useful for regression analysis of dataset.The generic syntax is as follows:

> fitted_model <- lm(formula, data = data.frame) For example: > fitted_model <- lm(y ~ x1 + x2 + x3, data = production) Will fit a multiple regression model of dependent variable y on independent variables x1,x2 and x3. Generalized linear models extend linear models to distributions such as gaussian, binomial, poisson, inverse gaussian,gamma and quasi-likelihood models. > fitted_model <- glm(formula, family=family.generator, data=data.frame) `For example: > fitted_model <- glm(y ~ x1 + x2, family = gaussian, data = production) Or > fitted_model <- glm(y ~ x, family = binomial(link=probit), data = mydata) Or > fitted_model <- glm(y ~ x1 + x2 - 1, family = quasi(link=inverse, variance=constant), data = testdata) The model parameters can be visualized by calling > summary(fitted_model)

Example:
## 
## Call:
## glm(formula = formula, family = "binomial", data = mydata)
## ## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6456  -0.5858  -0.2609  -0.0651   3.1982  
## 
## Coefficients:
##                           Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                0.07882    0.21726   0.363  0.71675    
## age                        0.41119    0.01857  22.146  

Besides these R also provides support for other models such as :

• Classification and Regression model – caret package
• Mixed models – nlme package
• Robust Regression – package MASS ( removes outliers)
• Additive models – package acepack
• Tree models – package rpart,tree

What is Data Visualization in R?

Data visualization is an important aid in data analysis and decision making.ggplot2 is a data visualization package for R. ggplot2 is an implementation of Grammar of Graphics(gg)—a general scheme for data visualization which breaks up graphs into components such as scales and layers. In contrast to base R graphics using plot function, ggplot2 allows the user to add, remove or alter components in a plot at a high level of abstraction.

ggplot(dat, aes(year, lifeExp)) + geom_point()

This will create a graph between year and life expectancy data from the dataset dat and depict it using geometric points on the graph.

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Different types of plots can be created by making use of additional graphing primitives such as geom_lines(),geom_boxplot(),geom_smooth() etc.

qplot is a convenient wrapper on tip of ggplot2 for creating a number of different types of plots .

The generic syntax for qplot is :

qplot(x, y, ..., data, facets = NULL, margins = FALSE, geom = "auto", xlim = c(NA, NA), ylim = c(NA, NA), log = "", main = NULL, xlab = NULL, ylab = NULL, asp = NA, stat = NULL,position = NULL)

where,

x, y, ...	Aesthetics passed into each layer
data	Data frame to use (optional). If not specified, will create one.
facets	faceting formula to use.
margins	See facet_grid: display marginal facets?
geom	Character vector specifying geom(s) to draw. Default: "point" if both x and y are specified, "histogram" if only x is specified.
xlim, ylim	X and y axis limits
log	variables to log transform ("x", "y", or "xy")
main, xlab, ylab	Character vector/expression giving plot title, x axis label, and y axis label.
asp	The y/x aspect ratio
stat, position	DEPRECATED.

e.g.,

qplot(mpg, wt, data = mtcars)

f <- function() {
   a <- 1:10
   b <- a ^ 3
   qplot(a, b)
}
f()

This will plot a curve with a[1-10] on x-axis and b=a^3 on y axis and the (x,y) pairs being represented by points.  

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Conclusion:

R as a language is developed from ground up for data analysis and data interpretation. As is rightly said, data represents power in the new economy. But we need appropriate tools to harness the power inherent in raw data. R programming for data science provides us with this power. With an ever growing user community and expanding package list covering all facets of data science, R is a language of choice for data science. This post provides a brief introduction to R and its capabilities so that readers can get started quickly and begin exploring further all the powerful features available for data modelling and interpretation.

Read: What Qualifications Are Required To Become Data Scientist?

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