R programming tutorial

This R programming tutorial was orignally created by the uWaterloo stats club and MSFA with the purpose of providing the basic information to quickly get students hands dirty using R. Fine the original here

Contents:

R Programming 101 (Beginner Tutorial): Introduction to R Presentation
Presentation in html / Presentation in pdf / Source (r markdown)

R Programming Reference: complilation of useful information and code snippets
Reference in html / Reference in pdf / Source (r markdown) ## Useful Resources:

Learn more R using swirl

R for Statisitical Computing

Download R from CRAN and install

Recommended supplement but not necessary: RStudio from http://www.rstudio.com/products/rstudio/download/

• R Studio: A (very matlab like) IDE for R

Comments

Comments are typed with hashtags ‘#’

# This is a comment
cat("This is not a comment")

No block comments. So sad. =(

Data Types

### Integers & Numerics Examples: 1,2.0,1.1,pi

c(1,2.0,1.1,pi)

• Inf can also be used in calculations

1/Inf

Complex Numbers

We can even use complex numbers.

complex(real = 1, imaginary = 2)
8+6i

Characters

Example: ‘One’, ‘1’, ‘pi’

c('One', '1', 'pi')

Boolian (Logical) Values

Boolian values can take only two values: TRUE (T) or FALSE (F).

c(TRUE, FALSE, TRUE)

Factors

A factor is a categorical variable that can take on only a finite set of values. i.e. Sex, Faculty in University

factor(c('Male','Female','Male','Male'))

Everything is an object, including functions!

Vectors

Most common objects are called vectors.
Examples: vector of numbers

a1 <- c(1,2,3)
a1

a2 <- c('one','two','three')
a2

a3 <- c('1','2','3')
a3

You can also create a range of values using start:end

4:10
4:-3
0.1:4

Basic Numerical Operations: +, -, *, /, ^

Numerical operations are: +, -, *, /, ^
- These operate elementwise between vectors.

c(1,2,3) * c(4,5,6)

Note: They don’t have to have the same length. If they don’t then the vector will recycle though the shorter vector. The longer has to be a multiple of the shorter vector.

c(1,2,3) ^ c(1,2,3,4,5,6)

Logical Operators

• Return a boolan value of TRUE or FALSE

c(TRUE, FALSE) | c(FALSE, FALSE)

c(1,2,3) < c(2,1,4)

Pro Tip: When interacting with number, boolians are converted to an integer: 0, or 1.

Type check

is.(typename)

Example: is.vector, is.integer, is.numeric, is.data.frame, is.matrix

• Not sure which type? Use typeof() !

is.numeric(a1)
is.vector(a1)
is.data.frame(a1)

Assignment Operator

Assignment can come in 3 forms:
var_name <- evaluation
var_name = evaluation
evaluation -> var_name

x <- 1
x

Be careful: <- is not the same as < -

x < -1

y = "string"
y

"This isn't used much" -> z
z

Concatenating Vectors

They are different vectors! To concatenate two vectors, use c(vector.1, vector.2)

b12 <- c(a1,a2)
b12

b23 <- c(a2,a3)
b23
b13 <- c(a1,a3)
b13

b21 <- c(a2,a1)
b21

Notice that when combined with characters, numerics are changed into characters automatically. So b23 == b21.

b23 == b21

b123 <- c(a1,a2,a3)

Dot Product

To use dot product of two vectors (instead of elementwise) use %*%

a1 %*% a1
c(1,4,5) %*% c(6,7,2)

##Exercise 1. What are the datatypes available in R? 2. What datatype would the vector c(1,2,"three") be? 3. What is the vector c(3,4,5,6) to the power of 4? 4. What elements of c(3,4,5,6) greater than 4?

##Answer 1. What are the datatypes available in R? - Numeric - Integer - Complex - Character - Boolian - Factor

1. What datatype would the vector c(1,2,"three") be?
   • Character

class(c(1,2,"three"))

1. What is the vector c(3,4,5,6) to the power of 4?

c(3,4,5,6) ^ 4

1. What elements of c(3,4,5,6) greater than 4?

c(3,4,5,6) > 4

Lists

Different from vectors, they allow us to put multiple structures in a list.
- Useful when we need to store a list of objects with different datatypes

l12 <- list(a1,a2)
l12

l23 <- list(a2,a3)
l23
l13 <- list(a1,a3)
l13

Notice they are stored in two ‘different arrays’

as.vector, as.list can interchange list to vectors and vectors to list via as.vector and as.list

as.vector(l23)
as.list(a1)

Exercise

1. Generate a vector of 1 to 10, a list of characters 2.1 to 2.5 separated by 0.1
2. Add the list to the vector and return a vector
3. Define 2 vectors, x1, x2, by using rnorm(7,mean = 13, sd = 5)
4. Do the inner product of x1,x2

Answer

q1 = 1:10 #Question 1
q1c = as.list(as.character(seq(2.1,2.5,0.1)))

q1 + as.numeric(q1c) # Question 2

x1 = rnorm(7,mean = 13, sd = 5) #Question 3
x2 = rnorm(7,mean = 13, sd = 5)

x1 %*% x2 #Question 4

Matrix

• Each column needs to contain same type.
• Like a high level vector.

M1 <- matrix(c(1,2,3,4,5,6,7,8,9),nrow=3,ncol=3)
M1
M2 <- matrix(9:1 ,3 ,3)
M2

M3 <- matrix(c(a1,a2),2,3)
M3

Data Frames

• Generalised matrix. Now we can store different data types in different columns! =)
• Like high level list

df1 <- data.frame(a1,a2,a3)
df1

Attributes

length(a1)

names(a1) = c("a","b","c")
a1

names(df1) = c("var_1","var_2","var_3")
df1

dim(M1)

Data Manipulation

Indices, just like linear algebra, for vectors, specify thy entry, and matrix row first then column.

a1[2] # Second entry
M1[1,2] #First row second column
df1[2,3] # Second row third column

M1[1,] # First row
M1[,3] # Third column

You can also Boolian values to get a subset of values:

a1[a1 <= 2]

Accessing the elements of a list is slightly different. Use double [[]] notation:

l13[[1]]

Assigning names to data.frame and matrices

rownames(M1) <- c('Ein','Zwei','Drei')
colnames(M1) <- c('Un','Deux','Trois')
M1

rownames(df1) <- c('Uno','Dos','Tres')
colnames(df1) <- c('yi','er','san')
df1

Adding new rows or columns into matrix or data.frame

rbind(): Add new row to rbind, cbind

M1.rbind <- rbind(M1,M1)
M1.rbind

M2.rbind <- rbind(M1,M2) # Notice the names of columns and rows
M2.rbind

M1.cbind <- cbind(M1,M1)
M1.cbind

Calling by Column Names

df1$yi
df1$er
df1$san

Reading csv/delim files

read.file_type(file = "Name.file_type", header = TRUE, sep = "")

Useful functions

attach(iris)
head(iris)

summary(iris)

print(iris)

apply, sapply, lapply

• sapply, lapply takes in vectors/list
• sapply(lapply) returns a vector(list) of same length

WARNING: DO NOT USE ANY OF lapply OR sapply under normal circumstances

sapply(iris$Sepal.Width,floor)

lapply(iris$Sepal.width,floor)

floor(iris$Sepal.Width) 

Notice that this returns same thing as sapply, so there is no reason to use sapply under most of the cases.

• apply is a function to apply a function to a matrix by row or column or both

apply(M2,1,min)# Minimum for each row

apply(M2,2,min)# Minimum for each column

apply(M2,c(1,2),min)# Minimum of all entries, same as min(M2)

User define functions

Can predefine default value for argument(s) - Can take in vectors instead of scalars

random.walk <- function(n=1000,p=0.5, start = 0,min = 0, max = 1){
  rand <- runif(n = n,  min = min, max = max)/(max - min);
  
  steps <- sign(2*(rand - p));
  out <- start + cumsum(steps);
  
  return(out)
}

plot(random.walk(),type = "l")

Exercise

Use the iris dataset in R and build a matrix call iris.matrix with the followings:
1. Columns and rows of iris corresponds to columns and rows of iris.matrix
2. Change the Species column of iris.matrix into the following indicator variables
- 1 - setosa, 2 - versicolor, 3 - virginica
3. Get the mean of every column except for Species column
4. Take away respective mean from each column except for the Species column
5. Produce the summary of the new matrix

Futher Notes

• browser() is useful to help debugging, talk about this later
• ?function_name is useful to look up what a function does

Interesting reads:

ggplot2: Plot package based around “the grammer of graphics” data.table: Package showcasing a faster version of data.frame

Next Steps:

R Programming Reference: http://rpubs.com/uwaterloodatateam/r-programming-reference

Contribute useful code snippets: https://github.com/uWaterlooDataTeam/r-programming-tutorial