(This article was first published on

**The Exactness of Mind**, and kindly contributed to R-bloggers)Welcome to Introduction to R for Data Science Session 2! The course is co-organized by Data Science Serbia and Startit. You will find all course material (R scripts, data sets, SlideShare presentations, readings) on these pages.

**Lecturers**

- dipl. ing Branko Kovač, Data Analyst at CUBE, Data Science Mentor at Springboard, Institut savremenih nauka, Data Science Serbia
- Goran S. Milovanović, Phd, [email protected], Data Science Serbia

**Summary of Session 2, 05. may 2016 :: Introduction to R: vectors, matrices, and data frames**

*Introduction to vectors, matrices, and data frames in R. R is a vector programming language, which means you will be using vectors, matrices, and n-dimensional arrays a lot. Vectorizing your code means enhanced performance in terms of speed. Data frame objects in R are elementary carriers of most of your data in R; unlike vectors and matrices, data frames can encompass various data types. *

**Intro to R for Data Science SlideShare :: Session 2**

**R script :: Session 2**

######################################################## # Introduction to R for Data Science # SESSION 2 :: 5 May, 2016 # Data Science Community Serbia + Startit # :: Goran S. Milovanović and Branko Kovač :: ######################################################## # clear all rm(list=ls()); # Let's start with some vectors char_list <- character(length = 0) #empty character list num_list <- numeric(length = 10) #length can be != 0, but 0 is default value log_list <- logical(length = 3) #default value is FALSE # But you can always use good ol' c() for the same purpose log_list_2 <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE) #some Ts and Fs num_list_2 <- c(1, 4, 12, NA, 101, 999) #numb char_list_2 <- c("abc", "qwerty", "test", "data", "science") # Factor vectors are also part of R fac_list <- gl(n = 4, k = 1, length = 8, ordered = T, labels = c("low", "med", "high", "very high")) #only mentioning now 🙂 # 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 # New objects can be created when subsetting test <- num_list_2[-c(2,4)] #or somthing like this - displays all but 2nd and 4th element test_2 <- num_list_2 %in% test #operator %in% can be very useful not_na <- num_list_2[!is.na(num_list_2)] #removing NAs using operator ! and is.na() function # Vector ordering sort(test, decreasing = T) #using sort() function test[order(test, decreasing = T)] #or with order() function # Vector sequences seq(1,22,by = 2) #we already mentioned seq() rep(1, 4) #but rep() is something new 🙂 rep(num_list_2, 2) #replicate num_list_2, 2 times # 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 #all numbers? false to zero? coercion in action new_combo_vect_2 <- c(char_list_2, num_list_2) #works as well new_combo_vect_2 #where are the numbers? class(new_combo_vect_2) #all characters # 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 matr[,2] #2nd column matr[3,] #oops, out of bounds, there's no 3rd row matr[2,3] #element in 2nd row and 3rd column matr_2 <- matrix(data = c(1,3,5,"7",NA,11), nrow = 2, ncol = 3) #another 2x3 matrix class(matr_2) #matrix again typeof(matr_2) #but not double anymore, type conversion in action! t(matr_2) #transponed matr_2 # What can we do if a matrix needs to encompass different types of data? # Introducing data frame! library(datasets) #there are some datasets in base R like mtcars cars_data <- mtcars # Some useful information about data frames str(cars_data) #lets see what we have here summary(cars_data) #more information about mtcars dataset names(cars_data) #column names ?mtcars #dataset documentation is *very* important # 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! # Lets do some data frame subsetting cars_data[-1, ] # first row out cars_data[ ,-1] # first column out 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$am == 1, ] #multiple conditions cars_data[grep("Merc", row.names(cars_data), value=T), ] #filtering by pattern match # Data frame transformations cars_data$trans <- ifelse(cars_data$am == 0, "automatic", "manual") #we can add new colums cars_data$trans <- NULL #or we can remove them cars_data[c(1:3,11,4,7,5:6,8:10)] #this way we change column order # 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 other mpg_join <- cbind(mpg_join, car_condition) #or combine data frames like this

**Readings :: Session 3 [12. May, 2016, @Startit.rs, 19h CET]**

Chapters 1 – 5, **The Art of R Programming, Norman Matloff**

- Intro to R
- Vectors and Matrics
- Lists

**Session 2 Photos**

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