R for Beginners

HMC Scientific Computing Workshop Series, Spring 2017

https://github.com/jehopark/r-workshop-for-beginners-sp2017.git

• Slides at http://bit.ly/hmc-r-workshop-slides-sp2017
• R files at http://bit.ly/hmc-r-workshop-github-sp2017
• Sign-in at http://bit.ly/hmc-r-workshop-sign-in
• Digital badge requirements
  • Attend the workshop (Sign-in above)
  • Your work in RStudio (R file submission)

• Create basic R objects such as a vector, a matrix, a list, a dataframe.
• Tell the differences between those data objects/types
• Import CSV files into R environment
• Export R objects in your environment to R data image
• Subset data to create a subset of given data

• What is R?
• What is not R?
• Then Why R?
• What is RStudio?
• What can you do with it? (some math example)

• Demos, search, and help documents
• Workspace of R
• R Objects: Vector, Matrix, List, Data Frame, Factor, Function
• Converting between different types
• Working with Data: Import/Export, subsetting

• R is a statical programming language/environment.
• R is open source/free.
• R is widely used/prefered.
• R is cross-platform.
• R is hard to learn (really?).

• S: R's ancestor
• S-Plus: Commercial; modern implementation of S
• SAS: Commercial; widely used in the commercial analytics.
  
• SPSS: Commercial; easy to use; widely used in Social Science.
• MATLAB: Commercial; can do some Stats.
• Python: Also can do some Stats; good in text data manipulation.

• Rseek meta search engine: http://rseek.org/
• R-help listserv: https://www.r-project.org/mail.html
• Stack Overflow: http://stackoverflow.com/questions/tagged/r
• Cross-Validated: the statistics Q&A site http://stats.stackexchange.com/
• Contact CIS: helpdesk@hmc.edu or jepark@hmc.edu
• HMC R Users Group: r-users-l@g.hmc.edu

• packages are add on features to R that include data, new functions and methods, and extended capabilities. Think of them as “apps'' on your phone. We've already installed several!
• console is the main window of R where you enter commands
• scripts are where you store commands to be run in the terminal later, like syntax files in SPSS or .do files in Stata
• functions are commands that do something to an object in R
• workspace is the working memory of R where all objects are stored
• vector is the basic unit of data in R
• dataframe is the main element for statistical purposes, an object with rows and columns that includes numbers, factors, and other data types

• Integrated Development Environment for R
• Nice combination of GUI and CLI
• Free and commercial version
• 4 main windows, tabs, etc
• Version control: Git and VPN
• R Markdown
• R Presentation

• Open RStudio!

1+1

2+runif(1,0,1)

2+runif(1,min=0,max=1)

3^2

3*3

sqrt(3*3) # comments

# comments are preceded by hash sign

• R can take integrals and derivatives, for example:

Numerical Integral of

\( \displaystyle\int_0^{\infty} \frac{1}{(x+1)\sqrt{x}}dx \)

integrand <- function(x) {1/((x+1)*sqrt(x))} ## define the function

integrate(integrand, lower=0, upper=Inf) ## integrate the function from 0 to infinity

3.141593 with absolute error < 2.7e-05

demo() # display available demos

demo(graphics) # try graphics demo

library() # show available packages on the computer

search() # show loaded packages

?hist # search for the usage of hist function

??histogram # search for package documents containing the word "histogram"

R workspace stores objects like vecors, datasets and functions in memory (the available space for calculation is limited to the size of the RAM).

a <- 5 # notice a in your Environment window

A <- "text" 

a
A
ls()
print(c(a,A))
print(a,A)

VECTOR (homogeneous)
A vector is an array object of the same type data elements.

class(a)

class(A)

B <- c(a,A) # concatenation

print(B)

class(B) # why?

R has five basic or “atomic” classes of objects:

• character
• numeric (real numbers)
• integer
• complex
• logical (True/False)

A vector contains a set of data in any one of the atomic classes.

Matrices (homogeneous)
A matrix is a two-dimensional rectangular object of the same type (homogeneous) data elements.

mat <- matrix(rnorm(6), nrow = 3, ncol = 2) 
mat # a matrix
dim(mat) # dimension
t(mat) 
summary(mat) 

LIST (heterogeneous)
A list is an object that can store different types of vectors.

aList <- list(name=c("Joseph"), married=T, kids=2)
aList
aList$kids <- aList$kids+1
aList$kids
aList2 <- list(numeric_data=a,character_data=A)
aList2
allList <- list(aList, aList2)
allList # a list of lists

Data Frame (heterogeneous and homogeneous)
A data frame is used for storing data tables. It is a list of vectors of equal length.

n <- c(2, 3, 5) # a vector 
s <- c("aa", "bb", "cc") # a vector
b <- c(TRUE, FALSE, TRUE) # a vector
df <- data.frame(n, s, b) # a data frame
df
mtcars # a built-in (attached) data frame
mtcars$mpg

Data Frame (cont.)

myFrame <- data.frame(y1=rnorm(100),y2=rnorm(100), y3=rnorm(100))
head(myFrame) # display first few lines of data
names(myFrame) # display column names
summary(myFrame) # output depends on the data types
plot(myFrame)
myFrame2 <- read.table(file="http://scicomp.hmc.edu/data/R/Rtest.txt", header=T, sep=",")
myFrame2

FACTOR

• Factors are a special compoud object used to represent categorical data such as gender, social class, etc.
• Factors have 'levels' attribute. They may be nominal or ordered.

v <- c("a","b","c","c","b")
x <- factor(v) # turn the character vector into a factor object
z <- factor(v, ordered = TRUE) # ordered factor
x
z
table(x)

Function
Functions are also objects in R environment

fun <- function(a,b) {
  a*b
}

fun   
fun(2,3) # a function call

Use of the as() family of functions. Type as. and wait to see the list of as() functions.

integers <- 1:10
as.character(integers)
as.numeric(c('3.7', '4.8'))

indices <- c(1.7, 2.3)
integers[indices] # sometimes R is too generous
integers[0.999999999] # close to 1 but...

df <- as.data.frame(mat)
df

• Working with raw data (text files)
• Data import and export
• Subsetting
• Using data frames vs. matrices

• Use read.table() to read text files into R
• Try help document for read.table()

• read.csv() is a special case of read.table()
• Data import from your local folder

cpds <- read.csv(file.path('.', 'data', 'cpds.csv'))
head(cpds) # good to look at a few lines
class(cpds) # data.frame

• Data import from the Internet

data <- read.table(file="http://scicomp.hmc.edu/data/R/normtemp.txt", header=T)
tail(data)

rta <- read.table("./data/RTADataSub.csv", sep = ",", head = TRUE)
dim(rta)
rta[1:5, 1:5]
class(rta)
class(rta$time) # what? let's see ?read.table more carefully

rta2 <- read.table("./data/RTADataSub.csv", sep = ",", head = TRUE, stringsAsFactors = FALSE)
class(rta2$time)

• Use write.table() to write data to a CSV file

write.csv(data, file = "temp.csv", row.names = FALSE) 

• Save all the objects in current environmet

save.image(file="myenv.RData") 
# Q: How to load them back later?

Operators that can be used to extract subsets of R objects.

• '[' and ']' always returns an object of the same class as the original; can be used to select more than one element.
• '[[' and ']]' is used to extract elements of a list or a data frame; it can only be used to extract a single element.
• $ is used to extract elements of a list or data frame by name.

x <- c("a", "b", "c", "c", "d", "a")
x[1]
x[1:4]
x[x > "a"] 
u <- x > "a" # what's u here?
u
x[u] # subsetting using a boolean vector
y <- list(foo=x, bar=x[u]) 
y
y[[1]]
y$bar

subset(mtcars, gear == 5) # use of subset function for data frames

Consider the following:

• Same types or different types? Numeric or other type?
• Convenient using $ with col names?
• Data size too big? (memory efficiency and size)

m = matrix(1:400000, 2, 200000) # esp. for a large number of columns!
d = as.data.frame(m)
object.size(m) # 1600200 bytes
object.size(d) # 22400568 bytes

• Conversion between data frame and matrix
  • as.data.frame()
  • as.matrix() or data.matrix() # consider coercion

Please submit your R environemnt file* at http://bit.ly/hmc-r-workshop-sign-in (digital badge requirement).

*You should know how to export the objetcs in your environment to a Rdata file.

Further Study!

• Online learning resources: https://www.rstudio.com/online-learning/#R
• Swirl (self-paced interactive learning) package: http://swirlstats.com/