Introduction to R for data science

for Data Science
September 2017
Long Nguyen

R for Data Science | Long Nguyen | Sep 20172
Why R?
• R is the most preferred programming tool for statisticians, data scientists, data analysts and data architects
• Easy to develop your own model.
• R is freely available under GNU General Public License
• R has over 10,000 packages (a lot of available algorithms) from multiple repositories.
http://www.burtchworks.com/2017/06/19/2017-sas-r-python-flash-survey-results/

R & Rstudio IDE
Go to:
• https://www.r-project.org/
• https://www.rstudio.com/products/rstudio/download/

Essentials of R Programming
• Basic computations
• Five basic classes of objects
– Character
– Numeric (Real Numbers)
– Integer (Whole Numbers)
– Complex
– Logical (True / False)
• Data types in R
– Vector: a vector contains object of same class
– List: a special type of vector which contain
elements of different data types
– Matrix: A matrix is represented by set of rows
and columns.
– Data frame: Every column of a data frame acts
like a list
2+3
sqrt(121)
myvector<- c("Time", 24, "October", TRUE, 3.33)
my_list <- list(22, "ab", TRUE, 1 + 2i)
my_list[[1]]
my_matrix <- matrix(1:6, nrow=3, ncol=2)
df <- data.frame(name = c("ash","jane","paul","mark"), score =
c(67,56,87,91))
df
name score
1 ash NA
2 jane NA
3 paul 87
4 mark 91

Essentials of R Programming
• Control structures
– If (condition){
Do something
}else{
Do something else
}
• Loop
– For loop
– While loop
• Function
– function.name <- function(arguments) {
computations on the arguments some
other code }
x <- runif(1, 0, 10)
if(x > 3) {
y <- 10
} else {
y <- 0
}
for(i in 1:10) {
print(i)
}
mySquaredFunc<-function(n){
# Compute the square of integer `n`
n*n
}
mySquaredVal(5)

Useful R Packages
• Install packages: install.packages('readr‘, ‘ggplot2’, ‘dplyr’, ‘caret’)
• Load packages: library(package_name)
Importing Data
•readr
•data.table
•Sqldf
Data Manipulation
•dplyr
•tidyr
•lubridate
•stringr
Data Visualization
•ggplot2
•plotly
Modeling
•caret
•lm,
•randomForest, rpart
•gbm, xgb
Reporting
•RMarkdown
•Shiny

Importing Data
• CSV file
mydata <- read.csv("mydata.csv") # read csv file
library(readr)
mydata <- read_csv("mydata.csv") # 10x faster
• Tab-delimited text file
mydata <- read.table("mydata.txt") # read text file
mydata <- read_table("mydata.txt")
• Excel file:
library(XLConnect)
wk <- loadWorkbook("mydata.xls")
df <- readWorksheet(wk, sheet="Sheet1")
• SAS file
library(sas7bdat)
mySASData <- read.sas7bdat("example.sas7bdat")
• Other files:
– Minitab, SPSS(foreign),
– MySQL (RMySQL)
Col1,Col2,Col3
100,a1,b1
200,a2,b2
300,a3,b3
100 a1 b1
200 a2 b2
300 a3 b3
400 a4 b4

Data Manipulation with ‘dplyr’
• Some of the key “verbs”:
– select: return a subset of the columns of
a data frame, using a flexible notation
– filter: extract a subset of rows from a
data frame based on logical conditions
– arrange: reorder rows of a data frame
– rename: rename variables in a data
frame
library(nycflights13)
flights
select(flights, year, month, day)
select(flights, year:day)
select(flights, -(year:day))
jan1 <- filter(flights, month == 1, day == 1)
nov_dec <- filter(flights, month %in% c(11, 12))
filter(flights, !(arr_delay > 120 | dep_delay > 120))
filter(flights, arr_delay <= 120, dep_delay <= 120)
arrange(flights, year, month, day)
arrange(flights, desc(arr_delay))
rename(flights, tail_num = tailnum)

Data Manipulation with ‘dplyr’
– mutate: add new variables/columns or
transform existing variables
– summarize: generate summary statistics
of different variables in the data frame
– %>%: the “pipe” operator is used to
connect multiple verb actions together
into a pipeline
flights_sml <- select(flights, year:day, ends_with("delay"),
distance, air_time )
mutate(flights_sml, gain = arr_delay - dep_delay,
speed = distance / air_time * 60 )
by_dest <- group_by(flights, dest)
delay <- summarise(by_dest,
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
)
delay <- filter(delays, count > 20, dest != "HNL")
ggplot(data = delay, mapping = aes(x = dist, y = delay)) +
geom_point(aes(size = count), alpha = 1/3) +
geom_smooth(se = FALSE)

library(tidyr)
tidy4a <- table4a %>%
gather(`1999`, `2000`, key = "year", value = "cases")
tidy4b <- table4b %>%
gather(`1999`, `2000`, key = "year", value = "population")
left_join(tidy4a, tidy4b)
spread(table2, key = type, value = count)
separate(table3, year, into = c("century", "year"), sep = 2)
separate(table3, rate, into = c("cases", "population"))
unite(table5, "new", century, year, sep = "")
Data Manipulation with ‘tidyr’
– gather: takes multiple columns, and gathers
them into key-value pairs
– spread: takes two columns (key & value) and
spreads in to multiple columns
– separate: splits a single column into multiple
columns
– unite: combines multiple columns into a
single column

Data Visualization with ‘ggplot2’
• Scatter plot library(ggplot2)
ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() +
geom_smooth(method="lm")
ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state, size=popdensity)) +
geom_smooth(method="loess", se=F) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) +
labs(subtitle="Area Vs Population",
y="Population",
x="Area",
title="Scatterplot",
caption = "Source: midwest")

• Correlogram library(ggplot2)
library(ggcorrplot)
# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)
# Plot
ggcorrplot(corr, hc.order = TRUE,
type = "lower",
lab = TRUE,
lab_size = 3,
method="circle",
colors = c("tomato2", "white", "springgreen3"),
title="Correlogram of mtcars",
ggtheme=theme_bw)

• Histogram on Categorical Variables
ggplot(mpg, aes(manufacturer)) +
geom_bar(aes(fill=class), width = 0.5) +
theme(axis.text.x = element_text(angle=65,
vjust=0.6)) +
labs(title="Histogram on Categorical Variable",
subtitle= "Manufacturer across Vehicle
Classes")

• Density plot ggplot(mpg, aes(cty)) +
geom_density(aes(fill=factor(cyl)), alpha=0.8) +
labs(title="Density plot", subtitle="City Mileage
Grouped by Number of cylinders",
caption="Source: mpg", x="City Mileage",
fill="# Cylinders")
Other plots:
• Box plot
• Pie chart
• Time-series plot

Interactive Visualization with ‘plotly’
library(plotly)
d <- diamonds[sample(nrow(diamonds), 1000), ]
plot_ly(d, x = ~carat, y = ~price, color = ~carat,
size = ~carat, text = ~paste("Clarity: ", clarity))
Plotly library makes interactive, publication-quality graphs online. It supports line plots, scatter plots, area
charts, bar charts, error bars, box plots, histograms, heat maps, subplots, multiple-axes, and 3D charts.

Data Modeling - Linear Regression
data(mtcars)
mtcars$am = as.factor(mtcars$am)
mtcars$cyl = as.factor(mtcars$cyl)
mtcars$vs = as.factor(mtcars$vs)
mtcars$gear = as.factor(mtcars$gear)
#Dropping dependent variable
mtcars_a = subset(mtcars, select = -c(mpg))
#Identifying numeric variables
numericData <- mtcars_a[sapply(mtcars_a, is.numeric)]
#Calculating Correlation
descrCor <- cor(numericData)
# Checking Variables that are highly correlated
highlyCorrelated = findCorrelation(descrCor, cutoff=0.7)
highlyCorCol = colnames(numericData)[highlyCorrelated]
#Remove highly correlated variables and create a new dataset
dat3 = mtcars[, -which(colnames(mtcars) %in% highlyCorCol)]
#Build Linear Regression Model
fit = lm(mpg ~ ., data=dat3)
#Extracting R-squared value
summary(fit)$r.squared
library(MASS) #Stepwise Selection based on AIC
step <- stepAIC(fit, direction="both")
summary(step)

Data modeling with ‘caret’
• Loan prediction problem
• Data standardization and imputing missing values using kNN
preProcValues <- preProcess(train, method = c("knnImpute","center","scale"))
library('RANN')
train_processed <- predict(preProcValues, train)
• One-hot encoding for categorical variables
dmy <- dummyVars(" ~ .", data = train_processed,fullRank = T)
train_transformed <- data.frame(predict(dmy, newdata = train_processed))
• Prepare training and testing set
index <- createDataPartition(train_transformed$Loan_Status, p=0.75, list=FALSE)
trainSet <- train_transformed[ index,]
testSet <- train_transformed[-index,]
• Feature selection using rfe
predictors<-names(trainSet)[!names(trainSet) %in% outcomeName]
Loan_Pred_Profile <- rfe(trainSet[,predictors], trainSet[,outcomeName], rfeControl = control)

• Take top 5 variables
predictors<-c("Credit_History", "LoanAmount", "Loan_Amount_Term", "ApplicantIncome", "CoapplicantIncome")
• Train different models
model_gbm<-train(trainSet[,predictors],trainSet[,outcomeName],method='gbm')
model_rf<-train(trainSet[,predictors],trainSet[,outcomeName],method='rf')
model_nnet<-train(trainSet[,predictors],trainSet[,outcomeName],method='nnet')
model_glm<-train(trainSet[,predictors],trainSet[,outcomeName],method='glm')
• Variable important
plot(varImp(object=model_gbm),main="GBM - Variable Importance")
plot(varImp(object=model_rf),main="RF - Variable Importance")
plot(varImp(object=model_nnet),main="NNET - Variable Importance")
plot(varImp(object=model_glm),main="GLM - Variable Importance")
• Prediction
predictions<-predict.train(object=model_gbm,testSet[,predictors],type="raw")
confusionMatrix(predictions,testSet[,outcomeName])
#Confusion Matrix and Statistics
#Prediction 0 1
# 0 25 3
# 1 23 102
#Accuracy : 0.8301
Data modeling with ‘caret’

Reporting
R Markdown files are designed: (i) for communicating to decision
makers, (ii) collaborating with other data scientists, and (iii) an
environment in which to do data science, where you can capture
what you were thinking.
Text formatting
*italic* or _italic_
**bold** __bold__
`code`
superscript^2^ and subscript~2~
Headings
# 1st Level Header
## 2nd Level Header
### 3rd Level Header
Lists
* Bulleted list item 1
* Item 2
* Item 2a
* Item 2b
1. Numbered list item 1
2. Item 2. The numbers are incremented automatically in the output.
Links and images
<http://example.com>[linked phrase](http://example.com)![optional
caption text](path/to/img.png)

Thank you!

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