This document provides an introduction to vectors, matrices, and data frames in R. It discusses that in R, everything starts as a vector, including the columns of a data frame. It demonstrates how to create, subset, order, and concatenate vectors. It also shows how to create matrices and discusses their similarities to vectors. Finally, it covers key aspects of working with data frames such as subsetting, joining, and using names(), rownames(), and colnames().

1. Introduction to R for Data Science
Lecturers
dipl. ing Branko Kovač
Data Analyst at CUBE/Data Science Mentor
at Springboard
Institut za savremene nauke
Data Science zajednica Srbije
branko.kovac@gmail.com
dr Goran S. Milovanović
Data Scientist at DiploFoundation
Data Science zajednica Srbije
goran.s.milovanovic@gmail.com
goranm@diplomacy.edu

2. Vectors in R
• No scalars in R; a <- 5 is a vector (length(a)==1)==TRUE 
• Vectorizing your code is a priority in vector programming languages such as R (more
on vectorizing takes part later during this course…)
• !!! - An excellent read: http://www.noamross.net/blog/2014/4/16/vectorization-in-r--
why.htmlwhy.html (a little bit advanced at this point - yet highly recommended)
Intro to R for Data Science
Session 2: Vectors, Matrices & Data Frames
# Introduction to R for Data Science
# SESSION 2 :: 5 May, 2016
char_list <- character(length = 0) #empty character list
> char_list
character(0)
num_list <- numeric(length = 10)
#length can be != 0, but 0 is default value
> num_list
[1] 0 0 0 0 0 0 0 0 0 0
log_list <- logical(length = 3) #default value is FALSE
> log_list
[1] FALSE FALSE FALSE

3. Vectors in R: c(), subsetting
Intro to R for Data Science
Session 2: Vectors, Matrices & Data Frames
# Introduction to R for Data Science
# SESSION 2 :: 5 May, 2016
log_list_2 <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE) # some Ts and Fs
> log_list_2
[1] TRUE FALSE FALSE TRUE TRUE TRUE
# Subsetting is regular-thing-to-do when using R
char_list_2[5] #single element can be selected
log_list_2[2:4] #or some interval
num_list_2[3:length(num_list_2)] #or even length() function

4. Vectors in R: ordering, coercing while concatenating
Intro to R for Data Science
Session 2: Vectors, Matrices & Data Frames
# Introduction to R for Data Science
# SESSION 2 :: 5 May, 2016
# Vector ordering
sort(test, decreasing = T) # using sort() function
test[order(test, decreasing = T)] # or with order() function
# Concatenation
new_num_vect <- c(num_list, num_list_2) #using 2 vectors to create new one
> new_num_vect #?
new_combo_vect <- c(num_list_2, log_list) #combination of num and log vector
new_combo_vect #a ll numbers? false to zero? coercion in action

5. Matrices in R: there are matrices in R, indeed
Intro to R for Data Science
Session 2: Vectors, Matrices & Data Frames
# Introduction to R for Data Science
# SESSION 2 :: 5 May, 2016
# Matrices are available in R
matr <- matrix(data = c(1,3,5,7,NA,11), nrow = 2, ncol = 3) #2x3 matrix
class(matr) # yes, it's matrix
typeof(matr) # double as expected
# Again: R Objects (like matrices) have classes, R Data (like integers)
# have types; the difference between class() and typeof().
• There are many 1e06 things that you can do with matrices in R. Only a few of them will
be discussed in the second (applied statistical modeling) part of the course.
• Matrices and vectors are fast - as fast as R (not quite a Roadrunner, beep-beep…) can
get. On the deepest implementation level, *everything in R is a vector*, in spite of the
wide-spread opinions that “everything in R is a list/an object”…
• Again !!! - An excellent read: http://www.noamross.net/blog/2014/4/16/vectorization-in-
r--why.htmlwhy.html

6. data.frame in R: mastering the Force
Intro to R for Data Science
Session 2: Vectors, Matrices & Data Frames
# Introduction to R for Data Science
# SESSION 2 :: 5 May, 2016
# Think of data frame columns as vectors! Because they are!
mean(cars_data$mpg) #mean of cars_data mpg (miles per galon) column
median(cars_data$cyl) #median of cars_data cyl (cylinders) column
is.list(cars_data[1,]); # but rows are lists!
# Introduction to R for Data Science
# SESSION 2 :: 5 May, 2016
> is.list(mtcars)
[1] TRUE
> length(mtcars)
[1] 11
> length(colnames(mtcars))
[1] 11
• A data.frame is…
• a list…
• whose components are its columns…
• which are, in turn, vectors.
• Consistency, as in any database:
• a column “is about” something –
but only about that one thing.

7. data.frame in R: subsetting data.frames
Intro to R for Data Science
Session 2: Vectors, Matrices & Data Frames
# Introduction to R for Data Science
# SESSION 2 :: 5 May, 2016
cars_data[c(1,3)] #keeping 1st and 3rd column only
cars_data[-c(1,3)] #removing 1st and 3rd column
cars_data[ ,-c(1,3)] #same as the previous line of code
cars_data[!duplicated(cars_data$mpg), ] #maybe we want to remove all cars with same mpg?
#remember it keeps only the first occurence!
subset(cars_data, mpg < 19) #this is one way (and it can be slow!)
cars_data[cars_data$mpg < 19, ] #this is another one (faster)
cars_data[which(cars_data$mpg < 19), ] #and another one (usually even more faster)
cars_data[cars_data$mpg > 20 & cars_data$am == 1, ] #multiple conditions
cars_data[grep("Merc", row.names(cars_data), value=T), ] #filtering by pattern match

8. data.frame in R: separation, joining, names(), rownames(), and
colnames()
Intro to R for Data Science
Session 2: Vectors, Matrices & Data Frames
# Introduction to R for Data Science
# SESSION 2 :: 5 May, 2016
# Separation and joining of data frames
low_mpg <- cars_data[cars_data$mpg < 15, ] #new data frame with mpg < 15
high_mpg <- cars_data[cars_data$mpg >= 15, ] #new data frame with mpg >= 15
mpg_join <- rbind(low_mpg, high_mpg) # we can combine 2 data frames like this
car_condition <- data.frame(sample(c("old","new"), replace = T, size = 32)) #creating random
# data frame with "old" and "new" values
names(car_condition) <- "condition" # for all kinds of objects
colnames(car_condition) <- "condition" # for "matrix-like" objects, but same effect here
rownames(car_condition) <- rownames(cars_data) # use row names of one data frame as row #
names of another
#or combine data frames like this:
mpg_join <- cbind(mpg_join, car_condition)