R is a fun and versatile language for statistical analysis, visualization, and data exploration. Target audience are software engineers/programmers who can code comfortably in another language. Emphasis in this lesson is on data structures, and light on analysis examples (to be covered at later date) but you are exposed to the basic concepts and commands. Email me for the pptx file which has notes.

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INTRODUCTION TO R
STACY IRWIN
FEB 2017

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•What is R?
•Data Structures and Types
•Syntax
•Statistics
•Visualizations
•File I/O
•Packages
•Finding Help
•More Syntax & Common Functions
OVERVIEW

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• Presentation steps thru code and responses (Quick Starts)
user.input() Input at the prompt, typed by you
#> console output Results produced after hitting enter
• Followed by slides with functions and descriptions (Basics)
... More data was produced, but not displayed here for space
### Comment, note, tip
FORMAT

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WHAT IS R?

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• R, the [interpreted] language
• 800,000 lines of code
• 45% C
• 19% R
• 17% Fortran
• R, the implementation(s)
• GNU-R is most poular implementation
• Open source version (GNU) of the S language and environment
• Developed by Bell Labs by John Chambers, et al.
• Licensed under the GNU General Public License (GPL)
https://www.r-project.org/about.html
WHAT IS R?

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WHY R?
WHY NOT R?

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• R is easy to learn, intuitive
• R was made for statistics
• R makes great graphics, publication quality
WHY R?

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WHY R?

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WHY R?

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WHY R?

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WHY R?

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• R is easy to learn, intuitive
• R was made for statistics
• R makes great graphics, pub quality
• R is optimized to work with tabular data structures
• R – it’s fast
• R is versatile – thousands of packages on CRAN alone
• R is open source
BUT…
• Memory limitations***
• Some data wrangling problems are clumsy
WHY R?
…enough!

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• GNU-R 3.3.2
• https://cran.r-project.org
• Microsoft R Open / MRO
• https://mran.microsoft.com/download/
• RStudio dev environment
• https://www.rstudio.com/
…and many other implementations
HOW TO GET R?

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DATA STRUCTURES AND TYPES

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• Vector 1-dimensional array of elements of the same kind
• Scalar?
• Matrix 2D array of elements of the same kind
• Array multi-dimensional structure of one kind of value
• List Something that holds something else (e.g. another list)
• Data frame 2D structure of possibly different types of columns
DATA STRUCTURES

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• Logical TRUE/FALSE or T/F
• Integers …-1,0,1,2,3…
• Double or “numeric” 1.0, 3.14, 4.002e-6
• Character "Hello", '123abc'
Some query and coercion functions
Informative: typeof( )
T/F Testing: is.numeric( )
is.character( )
Coersion: as.numeric("02")
as.character(3.1415)
DATA VALUE TYPES

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1:5 ### integer sequence
#> [1] 1 2 3 4 5
x <- 1:5 ### assignment
x ### evaluation. Try: x+x
#> [1] 1 2 3 4 5
y = x^2
print(y)
#> [1] 1 4 9 16 25
y == x ### comparison. What results?
#> [1] TRUE FALSE FALSE FALSE FALSE
QUICK START: NUMERICS

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"Hello, World!" ### char string
#> [1] "Hello, World!"
c("Smart", "Data") -> x ### vector of strings
LETTERS
#> [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J"…
LETTERS[1:5] -> myABC
c(x, myABC)
#> [1] "Smart" "Data" "A" "B" "C" "D" "E"
c(x, y)
#> [1] "Smart" "Data" "1" "4" "9" "16" "25"
QUICK START: CHARACTERS

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• Command line: Evaluates the line entered, or if the line is incomplete,
it waits for the end of an expression
• Variable names: Consist of numbers, letters, underscores, and periods
• Must start with a letter or a period+letter …CASE sensitive!
• Assignment: <- , = right to left
-> left to right
• Comparison: == != < > <= >=
SYNTAX BASICS
preferred

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; Ends a statement
x <- 8; print(x)
# Comments out anything following
#but only on that line
( ) Groups expressions, enclosing function arguments
print(2*(6+c((1:4)+x)))
{ } Encloses groups of expressions, loops, if/then
if (x) { print(c("x^2 =", x^2))
print("Done.") }
[ [[ $ Subsets elements of a data object
SYNTAX BASICS

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? <fxn> Opens help page for the command/concept/constant
?? <term> Lists help pages with term in their content
typeof( ) Identifies the data type of the object
str( ) Shows structure of data object, types of cols in df
length( ), nchar( )
Counts elements in vectors, letters in string
dim( ), nrow( ), ncol( )
Returns dimensions of data object
names( ), colnames( ), rownames( )
Displays only list/column/row names
SELF-HELP BASICS

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NA missing values
NaN not a number
Inf infinity
NULL empty/nothing
• NA has a type, determined at time of assignment
• Mixed types are coerced into the most flexible type
x <- c(TRUE, 1, 4.4, NA) ; typeof(x[4])
#> [1] "double"
• Predefined constants in base R:
Letters, LETTERS, month.abb, month.name, pi
SPECIAL VALUES

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list(11:15)
#> [[1]]
#> [1] 11 12 13 14 15
X <- list(A = 1:5, B = c("Yes", "No")); X
#> $A
#> [1] 1 2 3 4 5
#> $B
#> [1] "Yes" "No"
QUICK START: LISTS

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Y <- list(a = list(b = list(c = "R"))); str(Y)
#> List of 1
#> $ a:List of 1
#> ..$ b:List of 1
#> .. ..$ c: chr "R"
Y[[1]][[1]][[1]]
#> [1] "R"
### also: Y$a$b$c
### also: Y[['a']][['b']][['c']]
c(X, Y) ### try it!
QUICK START: LISTS

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[[, $ [
Simplifying subset Preserving subset
Returns simplest data structure Output structure == Input structure
str(X)
#> List of 2
#> $ a: int [1:5] 1 2 3 4 5
#> $ b: chr [1:2] "Yes" "No"
SUBSETTING LISTS

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[[, $ [
Simplifying subset Preserving subset
Returns simplest data structure Output structure == Input structure
X[[1]] returns int vector X[1] returns list holding int vector
X[[1]]
#> [1] 1 2 3 4 5 ### vector
X[1]
#> $A ### list...
#> [1] 1 2 3 4 5 ### holding a vector
SUBSETTING LISTS

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mtcars
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> ...
Row and Column Names are not data ... just referential meta-data
QUICK START: DATAFRAMES
column names
row names

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mtcars
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> ...
mtcars[3,2] # [row, col] ### What will this return?
#> [1] 4
QUICK START: DATAFRAMES

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mtcars[1]
#> mpg
#> Mazda RX4 21.0
#> Mazda RX4 Wag 21.0
#> Datsun 710 22.8
#> Hornet 4 Drive 21.4
#>...
mtcars[[1]] # also: mtcars$mpg or mtcars[,'mpg']
#> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
mtcars[,1]
#> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
mtcars[1,]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4
QUICK START: DATAFRAMES
Recall:
# [row, col]
first list element

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[[, $ [
Simplifying subset Preserving subset
Simplest data structure Output structure == Input structure
mtcars[[1]] returns contents of 1st col mtcars[1] returns first col as list: $mpg
mtcars[[1]]
#> [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 ...
mtcars[1]
#> mpg
#> Mazda RX4 21.0
#> Mazda RX4 Wag 21.0
#> Datsun 710 22.8
#> Hornet 4 Drive 21.4
#> ...
SUBSETTING DATAFRAMES
data.frame columns ARE lists

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str(mtcars)
#> 'data.frame': 32 obs. of 11 variables:
#> $ mpg : num 21 21 22.8 21.4 18.7 18.1...
#> $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
#> ...
### BUT WAIT ###
typeof(mtcars)
#> [1] "list"
EVEN DATAFRAMES ARE LISTS!

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mtcars[mtcars$cyl == 4 & mtcars$gear == 4, ]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
MT <- mtcars[mtcars$gear > 4, c(1,4,6)] ; MT # only show mpg, hp, wt
#> mpg hp wt
#> Porsche 914-2 26.0 91 2.140
#> Lotus Europa 30.4 113 1.513
#> Ford Pantera L 15.8 264 3.170
#> Ferrari Dino 19.7 175 2.770
#> Maserati Bora 15.0 335 3.570
QUICK START: DATAFRAMES
“which” also returns an index vector:
mtcars[which(mtcars$gear > 4), …]

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tf <- c(T,F)
df <- data.frame(x = 1:5, y = letters[1:10], z = tf)
df
#> x y z
#> 1 1 a TRUE
#> 2 2 b FALSE
#> 3 3 c TRUE
#> 4 4 d FALSE
#> 5 5 e TRUE
#> 6 1 f FALSE
#> 7 2 g TRUE
#> 8 3 h FALSE
#> 9 4 i TRUE
#> 10 5 j FALSE
QUICK START: DATAFRAMES
Combining and amending:
cbind( ), rbind( )
merge( )
data.frame(df, tf)
df$newCol <- NA
Deleting column x:
df[-1] -> df
df[,-1] -> df
df$x <- NULL

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• With what you know about lists and dataframes…
• What happens when we execute these lines?
c(df, df)
typeof(c(df, df))
str(c(df, df))
c(df, mtcars)
DATAFRAMES QUIZ

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BASIC STATISTICS

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summary
round, ceiling, floor
sin, cos, …, exp, log, log10, log2
sum, diff, filter
union, intersect
mean, sd, var, weighted.mean
median, Mode, quartile, fivenum
min, max, range
&, |, !, xor
all, any
SELECTED STATS FUNCTIONS

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mymodel <- lm(MT$mpg ~ MT$hp) ### y ~ x
mymodel
#>
#> Call:
#> lm(formula = y ~ x)
#>
#> Coefficients:
#> (Intercept) x
#> 32.77745 -0.05827
LINEAR MODEL EXAMPLE

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VISUALIZATIONS

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plot(x <- mtcars$hp, y <- mtcars$mpg,
xlab = "HP", ylab = "MPG", pch = 20)
### assignment can occur inside plot call
### also: plot(y ~ x)
BASIC PLOTS: SCATTER PLOT

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SCATTER PLOT

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plot(x <- mtcars$hp, y <- mtcars$mpg,
xlab = "HP", ylab = "MPG", pch = 20)
### assignment can occur inside plot call
### also: plot(y ~ x)
mymodel <- lm(y ~ x)
abline(mymodel, col="red", lwd=2)
BASIC PLOTS: SCATTER PLOT

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TREND LINE

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• We can place multiple plots on one window:
par(mfrow = c(1, 2)) ### request 1x2 layout
hist(mtcars$hp, xlab = "Horsepower",
main = "Histogram of HP")
hist(mtcars$mpg, xlab = "Miles per gallon",
breaks = 10, main = "Histogram of MPG")
BASIC PLOTS: HISTOGRAM

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HISTOGRAMS

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FILE I/O

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readLines(file = "C:/Users/stacy/text.txt")
returns vector of character strings
read(file = "…") returns data.frame of rows & cols
read.csv(file = "…") read( ) with sep = ","
read.table(file = "…") read( ) with sep = "t"
writeLines(data = …, file = "…")
write(data = …, file = "…")
write.csv(x, "…")
write.table(x, "…")
FILE I/O BASICS

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mydata <- readLines("http://ford.com", n = 10) ; mydata
#> [1] "<!DOCTYPE HTML>"
#> [2] "<html>"
#> [3] " <!-- appVersion: 09302016 rel_21.0 -->"
#> [4] " <!-- htmllint preset="$none" -->"
#> [5] " <head>"
#> [6] " <meta http-equiv="content-type" content="text/htm
#> [7] " "
#> [8] " <meta name="keywords">"
#> [9] " <meta name="description" content="The Official Fo
#>[10] ""
save(mydata, file = "…")
load("…")
FILE I/O BASICS

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PACKAGES

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•Package-related commands:
library() Lists installed packages
search() Lists loaded packages
install.packages("mylib") Installs package called “mylib”
library(mylib) Loads mylib into current env
require(mylib) …used inside other functions
https://cran.r-project.org/web/packages/
PACKAGE MANAGEMENT

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plyr Tools for splitting, applying and combining data
data.table Extension of Data.frame, highly optimized
ggplot2 An Implementation of the Grammar of Graphics
colorspace Color Space Manipulation
shiny Web Application Framework for R
chron Chronological Objects which handle dates and times
RCurl General Network (HTTP/FTP/...) Client Interface for R
wordcloud Make Word Clouds
rjson, RJSONIO JSON tools for R
htmltools Tools for HTML
pdftools Extract Text and Data from PDF Documents
xlsx Read, write, format Excel 2007 and Excel 97/2000/XP/2003 files
XML Tools for Parsing and Generating XML Within R and S-Plus
xtable Export Tables to LaTeX or HTML
MY "MUST-HAVE" & OTHER POPULAR PACKAGES

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FINDING HELP

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• Beginning R (Wiley) FREE Chapter 1 online
• Mark Gardner
• R Graphics Cookbook (O'Reilly) FREE full text online
• Winston Chang
• Main focus is on ggplot2 package. Problem-Solution format
• R for Data Science (O'Reilly) FREE full text online
• Hadley Wickham and Garrett Grolemund
• Advanced R (CRC Press) FREE full text online
• Hadley Wickham
RECOMMENDED BOOKS

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• StackExchange
• StackOverflow http://stackoverflow.com/tags/r
• CrossValidated http://stats.stackoverflow.com
• Post questions with MWE = Minumum Working Example
• R-bloggers https://www.r-bloggers.com
• News, Tutorials, Jobs … common issues often documented clearly
• R help mailing list https://stat.ethz.ch/mailman/listinfo/r-help
• Quick-R (2014) http://www.statmethods.net/
• Robert I. Kabacoff, Ph.D.
RECOMMENDED ONLINE HELP & TUTORIALS

54. www.modusoperandi.com
709 South Harbor City Blvd., Suite 400
Melbourne, FL 32901-1936
321-473-1400
Stacy Irwin
sirwin@modusoperandi.com
sirwin@gmail.com

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MORE COMMANDS & SYNTAX

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ls(), rm( ) list, remove objects from memory
getwd(), setwd("…") get, set working directory
grep, lgrep, gsub grep family
match, identical, setdiff, unique, %in%
matching functions
• String manipulation:
nchar, strsplit, unlist, paste0, pmatch
toupper, tolower, sub, strtrim, strtoi
help.search(keyword = "character")
COMMON FUNCTIONS

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for(i in 1:100) ...
for(myLetter in LETTERS) ...
while(i < 100) i <- i+5
if(this == that) <do_something>
if(this %in% that) {
<do_this1>
<do_this2>
} else {
<do_that1>
<do_that2>
}
LOOPS AND CONDITIONAL FUNCTIONS