R Training – The Basics

This is the first module of a five-module training on R I conceived and taught to my ex-colleagues back in December 2016. RStudio is the suggested IDE to go through this module. For the raw code, example data and resources visit this repo on GitHub.

Tips

• This notebook mixes plain text and R code (highlighted text in the boxes)
• Everything to the right of ‘#’ symbol is a comment

# this is a comment
x <- 1
y <- 2 # this is also a comment

• R is case sensitive!
• Press CTRL+Enter to run programs from RStudio script editor
• To pull up documentation for a function run ?namefunction
• Remember R does not like back slash in filepaths

R and your machine

Working directory

• Once R is installed in your computer it is able to communicate and interact with your system (create folders, read existing files, etc.)
• First, let’s see where we are

getwd()

• getwd() is a function without arguments returning the filepath to your current working directory
• Working directory is the place which by default R communicate with (save and load file, etc.)

Change working directory

• Create a folder called ‘RFundamentalsWeek1’ with dir.create()

dir.create("C:/Users/pc/Desktop/RFundamentalsWeek1") # fit this path to your machine (e.g. "C:/Users/YOUR-USER-NAME/Desktop/RFundamentalsWeek1")

• set it as your working directory with setwd()

setwd("C:/Users/pc/Desktop/RFundamentalsWeek1")

• Working directory is the folder R interacts with by default then guess what this will produce:

dir.create("sub")

…exactly, a sub-folder in your working directory

Check content folder

• Check what is inside your working directory with dir()

dir() # can you see "sub"?

• dir() search in your WD because no other path is specified
• But you can check any folder in your system

dir("C:/Users")

• Shortcuts ‘.’ and ‘..’ help you navigate in your system

dir("./sub") # "." set the start in your WD
dir("..") # ".." moves you one level up

R workspace

• Workspace is the collection of all objects created during an R session
• list all objects in your workspace with ls() function

ls() # character(0) indicates empty

• Create your first object named ‘x’ taking value 1

x <- 1

• Assignment operator ‘<-’ is used to create obects in R
• Top-right box in RStudio represents your working space (you should see ‘x’ now)
• re-runnig ls() now should return ‘x’ object

Remove objects from workspace

• Let’s create a bunch of objects:

y <- 99; msg <- "Hello"; msg2 <- "Hi"

• let’s remove ‘x’ from the workspace with rm() function

rm("x")

• by concatenating rm() and ls() we can clean-up all workspace

rm(list=ls()) # In R is very common to nest functions

• To understand why we used list argument read documentation

?rm

R basic objects and operators

Objects’ classes in R

• In R there are four important classes of objects:

"Hola" # character, any string within quotes
3.14 # numeric, any real number
4L # integer, any integer number
TRUE # logical, TRUE or FALSE reserved words

• Check the class of these objects with function class()

class("Hello")
class(3.14)
class(4L)
class(4) # without suffix "L" all numbers are numeric by default
class(TRUE)

Arithmetic operators

• given two numeric objects R can perform the most common arithmetic operations:

3 + 4
3 - 4
3 * 4
3 / 4
abs(3 - 4)
3^4 # or 3**4
sqrt(4)

• In R expressions are directly evaluated and the result is returned to the console logical operators
• given a couple of atomic objects R can perform logical operations
• logical operations return a logical value (TRUE, FALSE)…

3 == 4 # equality
"a" == "a"
3 > 4 # greater than
3 <= 4 # lower or equal than
3 != 4 # different from
"hello" == "Hello"

• …which can be combined using AND (&) and OR (|) operators

4 >= 3 & 3==3
4 < 3 | 3==3

Atomic vectors

• Vectors represent the simplest data structure in R
• Even single-elements objects are seen as vectors (of length one)

length("Hello")
length(2)
length(TRUE)

• That’s why we call vectors atomic vectors
• A vector is a collection of elements all of the same class (character, logical, etc.)

More complex data structures

• More complex data structures can be seen as extensions of vectors

Create vectors with ‘combine’ function

• Create vectors of length>1 with c() function

c("Hola", "Ciao", "Hello", "Bonjour") # character vector
c(0.99, 2.4, 1.4, 5.9) # numeric vector
c(1L, 2L, 3L, 4L) # integer vector
c(TRUE, TRUE, FALSE, TRUE) # logical vector

• Check their class:

class(c("Hola", "Ciao", "Hello", "Bonjour"))
class(c(0.99, 2.4, 1.4, 5.9))
class(c(1L, 2L, 3L, 4L))
class(c(TRUE, TRUE, FALSE, TRUE))

Other ways to create vectors

• Create integer vectors with seq() function (or ‘:’ operator)
• the following four expressions all produce the same result:

seq(from = 1, to = 4, by = 1)
seq(from=1, to=4) # by=1 is default
seq(1, 4) # arguments in R can be matched by position
1:4 # common operations in R have shortcuts

• Create vectors using rep() function

rep(x = "a", times = 4) # replicate "a" four times
rep("a", 4) # same as above
rep(c("a", "b"), times = 2) # same but for a vector
rep(c("a", "b"), each = 2) # element-by-element

Subsetting vectors

• [logical index]

x <- 1:10
x >= 5
idx <- (x > 5)
x[idx] # all values of x greater then 5
x[x < 7] # calculate index directly within brackets

• [positive integers index]

x[1] # 1st element
x[c(1,5)] # 1st and 5th element

• [negative integers vector]

x[-1] # all but the 1st
x[-c(1,10)] # all but the 1st and the 10th

Arithmetic and logical operators are vectorized

• we say that a function is vectorized when it works both on vectors (and matrices) and scalars
• What do you expect these expressions will return?

c(1, 2, 3, 4) + c(5, 6, 7, 8)
c(1, 2, 3, 4) / c(5, 6, 7, 8)
sqrt(c(1, 2, 3, 4))
c(1, 2, 3, 4) == c(5, 6, 7, 8)
c(1, 2, 3, 4) != c(5, 6, 7, 8)

• R perform the operation element-by-element and return the vector of results so obtained
• Keep in mind that most funcions in R are vectorized…

Vectorization + Recycling

• we saw operations between vectors of same length:

c(1, 2, 3) + c(5, 6, 7) # simple element-by-element

• but what if length differs?
• In the case when one is multiple of another:

c(1, 2) + c(5, 6, 7, 8) # shortest vector "recycled"
c(1, 2, 1, 2) + c(5, 6, 7, 8)

• The case when one isn’t multiple of another

c(1, 2) + c(5, 6, 7) # recycling + warning
r <- c(1, 2, 1) + c(5, 6, 7)

Useful functions for numerical objects

• summarizing a numerical vector

mynum <- c(3.14, 6, 8.99, 10.21, 10, 56.9, 32.1, 2.3)
sum(mynum)
mean(mynum)
sd(mynum) # standard deviation
median(mynum)

• what if we want the skewness of this vector?
• We could check the formula and write our own function or we could search the internet (google this ‘skeweness function in r’)

Install a package and use its functions

• First Google result mentions a R package called ‘e1071’

install.packages("e1071") # install the package
library(e1071) # load the package

• Now all the functions in this package are available to use:

skewness(mynum)
kurtosis(mynum)

• There are almost 10.000 packages in CRAN (and many others off-CRAN), so just type your problem in google with an R tag and odds are you will find a built-in solution in some package

Useful functions for logical objects

• underlying structure of logical values is TRUE=1 and FALSE=0

mylogic <- c(F, T, F, rep(T, 3))
sum(mylogic)

• obtain the TRUE indices of a logical object with which()

which(mylogic)

• summarizing logical vectors…

any(mylogic) # is at least one of the values TRUE?
all(mylogic) # are all of the values TRUE?

Useful functions for character objects

mychar <- c("201510", "201511", "201512", "201601")

substr(x = mychar, start = 1, stop = 4) # the ubiquitous substring...
nchar("Hello") # number of characters in a string

• concatenate character vectors

paste("I", "m", sep = "'")
paste("N.", 1, sep="") # 1 is coerced to "1"

• find and replace

gsub(pattern = "20", replacement = "", x = mychar)

Implicit coercion

Coercion happens when we force an object to belong to a class – implicit coercion numeric vs CHARACTER

c(1.7, "a")
class(c(1.7, "a"))

• implicit coercion logical vs NUMERICAL

c(FALSE, 2)
class(c(TRUE, 2))

• implicit coercion CHARACTER vs logical

c("a", TRUE)
class(c("a", TRUE))

What’s holding here is a principle of least common denominator…

Explicit coercion

• Family of functions of form as.* coerce explicitly R objects
• consider the following numeric vector

x <- c(0, 1, 2, 3, 4, 5, 6)
class(x)

• Force it to a character or logical (what do you expect to happen?)

as.character(x)
as.logical(x) # 0=FALSE, 1+ = TRUE

• non-sensical coercion returns missing values:

as.numeric(c("a", "b", "c"))
as.logical(c("a", "b", "c"))

Special values in R

Missing values

• NA is a reserved word in R indicating a missing value
• reserved words have special meaning and cannot be used as identifier (variable name, function name, etc.)

NA <- 1 # This will trigger an error!

• You can use the NAs to create a placeholder for a value that exist but you don’t know…

year <- c(2012, 2013, 2014)
gwp <- c(NA, 98.7, 32.5)

• class deduced from non-missing elements

class(gwp)
is.na(gwp) # indicates which elements are missing

Other special values

• For a list of reserved words in R type this:

help(reserved)

• NULL, represents an object which is absent

x <- NULL # useful to initialize objects to be filled later

• Inf, -Inf, NaN (special words for mathematical concepts)

1/0 # infinite
-1/0 # minus infinite
0/0 # undefined number

• bear in mind that NaN is also NA, viceversa is not true

Matrices

Matrix underlying structure

• In R matrices can be seen as vectors with a dimension attribute
• To highlight this idea let’s create a matrix in a not-so-common way:

x <- 1:6 # take a vector
dim(x) # vector do not have dimension attribute
dim(x) <- c(2, 3) # impose a 2x3 dimesion (2 rows, 3 columns)
class(x) # here it is a matrix!
x

m <- matrix(data = 1:6, nrow = 2, ncol = 3)
class(m)
dim(m)

x <- 1:3
y <- 10:12
m1 <- cbind(x,y)
m2 <- rbind(x,y)
class(m1)
class(m2)

m[1,2] # one single element
m[1,] # one full row
m[,3] # one full column
m[,-1] # all columns but one

f <- factor( c("f", "m", "m", "f", "f") )
class(f)

attributes(f)
levels(f)

fo <- factor( c("low", "med", "low", "high"), ordered = TRUE)

levels(fo) <- c("low", "med", "high") # re-order

levels(f) <- c("m", "f") # change alphabetical default

table(f)

Data Frames

df <- data.frame( x = 1:3, y = c("a", "b", "c"),
f = factor( c("m", "f", "m") ) )
class(df)

data("iris") # load the example data in the workspace

str(iris) # returns a compact summary of R objects
summary(iris) # few statistics for each variable
head(iris, n = 20) # visualize first 20 observations
tail(iris) # last 6 observations

iris[1,1]
iris[1,5]

iris$Sepal.Length # using $ operator
iris[, "Sepal.Length"] # quoting variable's name in j slot
iris[, c("Sepal.Length", "Sepal.Width")]

mean(iris$Petal.Length)

mean( iris[ iris$Species =="setosa", "Petal.Length" ] )

with( iris, sum(Petal.Width)/sum(Petal.Length) )

subset( iris, Species=="setosa" )

with( subset(iris, Species=="setosa"),
sum(Petal.Width)/sum(Petal.Length))

i_na <- sample(x = 1:nrow(iris), size = 0.1*nrow(iris) )
j_na <- sample(1:4, size = 0.1*nrow(iris), replace = TRUE)
iris_na <- iris
for(k in 1:length(i_na)) {
  iris_na[i_na[k], j_na[k]] <- NA
}

sum( is.na(iris_na) ) # not surprised ehm?

class(is.na(iris_na))
w <- which( x = is.na(iris_na), arr.ind = TRUE )
head(w)

iris_clean <- subset(iris_na, !is.na(Petal.Width))

good <- complete.cases(iris_na) # "good" is a logical vector
iris_clean <- iris[good,]

iris$new <- sample(1:10, nrow(iris),
replace = TRUE)

cor(iris[,which(lapply(iris, class)=="numeric")])

That’s it for this module! If you have gone through all this code you should have learnt the fundamentals of the R language.

When you’re ready, go ahead with the second module: R training – functions and programming blocks.

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