##### R Introduction (based on Leemann and Mykhaylov, PUBLG100)

Let’s get acquainted with R.

Take a look at R Studio. See the 4 windows:

• Upper-Left : R-script.
• Lower-Left : Console.
• Upper-Right : Environment (data, variables, …)
• Lower-Right : Plots, help file, packages etc.

We begin by walking through the steps for creating and saving an R script.

• Create a new R script and save it as lab1.R to your StatisticalLearning directory.
• Now type the following commands in the new file you just created:
# check working directory
getwd()
## [1] "C:/Users/phili/Documents/ML2017.io"

Save your script, and re-open it to make sure your changes are still there. Then check your workspace.

# check workspace
ls()
# delete variable 'a' from workspace
rm(a)
# delete everything from workspace
rm( list = ls() )

# to clear console window press Crtl+l on Win or Command+l on Mac

### Creating and manipulating variables, vectors, data frames

# Create a numeric and a character variable
a <- 5
typeof(a) # a is a numeric variable
## [1] "double"
a
## [1] 5
b <- "Yay stats class"
typeof(b) # b is a string variable
## [1] "character"
b
## [1] "Yay stats class"
# Create a vector
my.vector <- c(10,7,99,34,0,5) # a vector
my.vector
## [1] 10  7 99 34  0  5
length(my.vector) # how many elements?
## [1] 6
# subsetting
my.vector[1] # 1st vector element
## [1] 10
my.vector[-1] # all elements but the 1st
## [1]  7 99 34  0  5
my.vector[2:4] # the 2nd to the 4th elements
## [1]  7 99 34
my.vector[c(2,5)] # 2nd and 5th element
## [1] 7 0
my.vector[length(my.vector)] # the last element
## [1] 5
# calculating in R
# element-wise operations
my.vector + 2 
## [1]  12   9 101  36   2   7
my.vector * 2
## [1]  20  14 198  68   0  10
my.vector / 2
## [1]  5.0  3.5 49.5 17.0  0.0  2.5
my.vector ^2
## [1]  100   49 9801 1156    0   25
sqrt(my.vector)
## [1] 3.162278 2.645751 9.949874 5.830952 0.000000 2.236068
log(my.vector)
## [1] 2.302585 1.945910 4.595120 3.526361     -Inf 1.609438

Use the ? to get help on R functions. E.g. ?rep will open the help for the rep() function.

# creating longer vectors and sequences
na.vector <- rep(NA, 10)
na.vector
##  [1] NA NA NA NA NA NA NA NA NA NA
id.var <- seq(from = 1, to = length(na.vector), by = 1)
# combine vectors to data frame
my.df <- data.frame(id.var, na.vector)
my.df
##    id.var na.vector
## 1       1        NA
## 2       2        NA
## 3       3        NA
## 4       4        NA
## 5       5        NA
## 6       6        NA
## 7       7        NA
## 8       8        NA
## 9       9        NA
## 10     10        NA
# create a matrix
my.matrix1 <- matrix(data = c(1,2,30,40,500,600), nrow = 3, ncol = 2, byrow = FALSE,
dimnames = NULL)
my.matrix1
##      [,1] [,2]
## [1,]    1   40
## [2,]    2  500
## [3,]   30  600
# subsetting a matrix
my.matrix1[1,2] # element in row 1 and column 2
## [1] 40
my.matrix1[2,1] # element in row 2 and column 1
## [1] 2
my.matrix1[,1] # 1st column only
## [1]  1  2 30
my.matrix1[1:2,] # rows 1 to 2
##      [,1] [,2]
## [1,]    1   40
## [2,]    2  500
my.matrix1[c(1,3),] # rows 1 and 3 
##      [,1] [,2]
## [1,]    1   40
## [2,]   30  600

Download the foreigners data set here. Copy it to your working directory and then load the data set using the load() function.

# load, inspect, and manipulate data set
# variable names
names(data2)
##  [1] "IMMBRIT"       "over.estimate" "RSex"          "RAge"
##  [5] "Househld"      "Cons"          "Lab"           "SNP"
##  [9] "Ukip"          "BNP"           "GP"            "party.other"
## [13] "paper"         "WWWhourspW"    "religious"     "employMonths"
## [17] "urban"         "health.good"   "HHInc"
# summary stats of all variables
summary(data2)
##     IMMBRIT       over.estimate         RSex            RAge
##  Min.   :  0.00   Min.   :0.0000   Min.   :1.000   Min.   :17.00
##  1st Qu.: 10.00   1st Qu.:0.0000   1st Qu.:1.000   1st Qu.:36.00
##  Median : 25.00   Median :1.0000   Median :2.000   Median :49.00
##  Mean   : 29.03   Mean   :0.7235   Mean   :1.544   Mean   :49.75
##  3rd Qu.: 40.00   3rd Qu.:1.0000   3rd Qu.:2.000   3rd Qu.:62.00
##  Max.   :100.00   Max.   :1.0000   Max.   :2.000   Max.   :99.00
##     Househld          Cons             Lab              SNP
##  Min.   :1.000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000
##  1st Qu.:1.000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000
##  Median :2.000   Median :0.0000   Median :0.0000   Median :0.00000
##  Mean   :2.392   Mean   :0.2707   Mean   :0.2669   Mean   :0.01525
##  3rd Qu.:3.000   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:0.00000
##  Max.   :8.000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000
##       Ukip              BNP                GP           party.other
##  Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.0000
##  1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000
##  Median :0.00000   Median :0.00000   Median :0.00000   Median :0.0000
##  Mean   :0.02955   Mean   :0.03051   Mean   :0.02193   Mean   :0.3651
##  3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:1.0000
##  Max.   :1.00000   Max.   :1.00000   Max.   :1.00000   Max.   :1.0000
##      paper          WWWhourspW        religious       employMonths
##  Min.   :0.0000   Min.   :  0.000   Min.   :0.0000   Min.   :  1.00
##  1st Qu.:0.0000   1st Qu.:  0.000   1st Qu.:0.0000   1st Qu.: 72.00
##  Median :0.0000   Median :  2.000   Median :0.0000   Median : 72.00
##  Mean   :0.4538   Mean   :  5.251   Mean   :0.4929   Mean   : 86.56
##  3rd Qu.:1.0000   3rd Qu.:  7.000   3rd Qu.:1.0000   3rd Qu.: 72.00
##  Max.   :1.0000   Max.   :100.000   Max.   :1.0000   Max.   :600.00
##      urban        health.good        HHInc
##  Min.   :1.000   Min.   :0.000   Min.   : 1.000
##  1st Qu.:2.000   1st Qu.:2.000   1st Qu.: 6.000
##  Median :3.000   Median :2.000   Median : 9.000
##  Mean   :2.568   Mean   :2.044   Mean   : 9.586
##  3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:13.000
##  Max.   :4.000   Max.   :3.000   Max.   :17.000
# external excel-style explorer
#fix(data2)
# variable types in data frame
str(data2)
## 'data.frame':    1049 obs. of  19 variables:
##  $IMMBRIT : num 1 50 50 15 20 30 60 7 30 2 ... ##$ over.estimate: num  0 1 1 1 1 1 1 0 1 0 ...
##  $RSex : num 1 2 2 2 2 1 2 1 1 1 ... ##$ RAge         : num  50 18 60 77 67 30 56 49 40 61 ...
##  $Househld : num 2 3 1 2 1 4 2 1 4 3 ... ##$ Cons         : num  0 0 0 0 0 0 0 0 0 1 ...
##  $Lab : num 1 0 0 0 0 0 0 0 0 0 ... ##$ SNP          : num  0 0 0 0 0 0 1 0 1 0 ...
##  $Ukip : num 0 0 0 0 0 0 0 0 0 0 ... ##$ BNP          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $GP : num 0 0 0 0 0 0 0 0 0 0 ... ##$ party.other  : num  0 1 1 1 1 1 0 1 0 0 ...
##  $paper : num 0 0 0 1 0 1 0 1 0 1 ... ##$ WWWhourspW   : num  1 4 1 2 1 14 5 8 3 0 ...
##  $religious : num 0 0 0 1 1 0 1 0 1 1 ... ##$ employMonths : num  72 72 456 72 72 72 180 156 264 72 ...
##  $urban : num 4 4 3 1 3 1 1 4 2 1 ... ##$ health.good  : num  1 2 3 3 3 2 2 2 2 3 ...
##  $HHInc : num 13 3 9 8 9 9 13 14 11 8 ... # indexing in data sets # first 5 rows and first 4 columns (similar to matrix indexing) data2[1:5, 1:4]  ## IMMBRIT over.estimate RSex RAge ## 1 1 0 1 50 ## 2 50 1 2 18 ## 3 50 1 2 60 ## 4 15 1 2 77 ## 5 20 1 2 67 # indexing with names using the$-sign
data2$RSex[1:5] ## [1] 1 2 2 2 2 # indexing with names using square brackets data2[1:6, c("RAge", "WWWhourspW")] ## RAge WWWhourspW ## 1 50 1 ## 2 18 4 ## 3 60 1 ## 4 77 2 ## 5 67 1 ## 6 30 14 # dimension of a data set dim(data2) ## [1] 1049 19 # number of rows nrow(data2) ## [1] 1049 # number of columns ncol(data2) ## [1] 19 # delete a variable data2$SNP <- NULL
# rename a variable
names(data2)[ names(data2) == "RAge" ] <- "age"
names(data2)
##  [1] "IMMBRIT"       "over.estimate" "RSex"          "age"
##  [5] "Househld"      "Cons"          "Lab"           "Ukip"
##  [9] "BNP"           "GP"            "party.other"   "paper"
## [13] "WWWhourspW"    "religious"     "employMonths"  "urban"
## [17] "health.good"   "HHInc"
# creating a new (dummy) variable
data2$old <- ifelse( data2$age > 30, yes = 1, no = 0)
# frequency table of new variable
table(data2$old) ## ## 0 1 ## 176 873 # create subesets df.cons <- data2[ data2$Cons == 1 , ]
df.not_cons <- data2[ data2$Cons != 1, ] # pick observations randomly #?sample pick <- sample(nrow(data2), size = as.integer(.33 * nrow(data2)), replace = FALSE) df2 <- data2[ pick, ] df3 <- data2[ -pick, ] ### Plotting # scaterplot plot(WWWhourspW ~ age, data = data2, main = "scatterplot") # boxplot plot(HHInc ~ as.factor(Ukip), data = data2, main = "boxplot", xlab = "Ukip party affiliation", ylab = "income", frame.plot = FALSE) # density plot( density(data2$employMonths), bty = "n", main = "density plot")

# histogram
hist( data2$employMonths, main = "histogram") Download a useful cheat-sheet for R if you are not already familiar with the essentials of R. https://www.rstudio.com/wp-content/uploads/2016/06/r-cheat-sheet.pdf ### Exercises 1 1. Create a new file called “assignment1.R” in your StatisticalLearning folder and write all the solutions in it. 2. Clear the workspace and set the working directory to your StatisticalLearning folder. 3. Load the High School and Beyond dataset. Remember to load any necessary packages. 4. Calculate the final score for each student by averaging the read, write, math, science, and socst scores and save it in a column called final_score. 5. Calculate the mean, median and mode for the final_score. 6. Create a factor variable called school_type from schtyp using the following codes: • 1 = Public schools • 2 = Private schools 7. How many students are from private schools and how many are from public schools? 8. Calculate the variance and standard deviation for final_score from each school type. 9. Find out the ID of the students with the highest and lowest final_score from each school type. 10. Find out the 20th, 40th, 60th and 80th percentiles of final_score. 11. Create box plot for final_score grouped by the school_type factor variable to show the difference between final_score at public schools vs. private schools. ## Packages #### What are packages and why should I care? Packages are bundled pieces of software that extend the functionality of R far beyond what’s available when you install R for the first time. Just as smartphone “apps” add new features or make existing features easier to use, packages add new functionality or provide convenient functions for tasks that otherwise would be cumbersome to do using base R packages. Some R packages are designed to carry out very specific tasks while others are aimed at offering a general purpose set of functions. We will get a chance to work with both specific and generic type of packages over the next several days. A small number of core packages come pre-installed with R but thousands of extremely useful packages are available for download with just a few keystrokes within R. The strength of R comes not just from the language itself but from the vast array of packages that you can download at no cost. #### Installing Packages Recall from earlier that we used the read.csv() function to read a file in Comma Separated Values (CSV) format. While CSV is an extremely popular format, the dataset we’re using in this seminar is only available in Microsoft Excel format. In order to load this dataset we need a package called readxl. We will install the readxl package with the install.packages() function. The install.packages() function downloads the package from a central repository so make sure you’ve internet access before attempting to install it. install.packages("readxl") Watch out for errors and warning messages when installing or loading packages. #### Removing Packages On rare occasions, you might have to remove a package from R. Although we will not demonstrate removing packages in this seminar, it is worth noting that the remove.packages() function can be used to remove a package if necessary. #### Using Packages Once a package is installed, it must be loaded in R using the library() function. Let’s load the readxl package so we can use the functions it provides for reading a file an Excel file. The library() function takes the name of the package as an argument and makes the functionality from that package available to us in R. library(readxl) ## Warning: package 'readxl' was built under R version 3.4.1 Now that the readxl package is loaded, we can load our dataset. In this seminar, we’re using a small subset of High School and Beyond survey conducted by the National Center of Education Statistics in the U.S. Our dataset includes observations from 200 students with variables including each student’s race, gender, socioeconomic status, school type, and scores in reading, writing, math, science and social studies. First, we need to download the dataset and save it to our StatisticalLearning folder. If you haven’t set your working directory yet, make sure to follow the Getting Started instructions from the top of this page. Next we load the dataset using the read_excel() function from the readxl package. # make sure you've downloaded the dataset from http://uclspp.github.io/PUBLG100/data/hsb2.xlsx # to your StatisticalLearning working directory. student_data <- read_excel("./data/hsb2.xlsx") ### Factor Variables Categorical (or nominal) variables are variables that take a fixed number of distinct values with no ordering. Some common examples of categorical variables are colors (red, blue, green), occupation (doctor, lawyer, teacher), and countries (UK, France, Germany). In R, when categorical variables are stored as numeric data (e.g. 1 for male, 2 for female), we must convert them to factor variables to ensure that categorical data are handled correctly in functions that implement statistical models, tables and graphs. Datasets from public sources such the U.N, World Bank, etc often encode categorical variables with numerical values so it is important to convert them to factor variable before running any data analysis. The High School and Beyond dataset that we’ve been using is one such example where categorical variable such as race, gender and socioeconomic status are coded as numeric data and must be converted to factor variables. We’ll use the following code book to create categorical variables for gender, race, and socioeconomic status. Categorical Variable New Factor Variable Levels female gender 0 - Male 1 - Female ses socioeconomic_status 1 - Low 2 - Middle 3 - High race racial_group 1 - Black 2- Asian 3 - Hispanic 4 - White We can convert categorical variables to factor variables using the factor() function. The factor() function needs the categorical variable and the distinct labels for each category (such as “Male”, “Female”) as the two arguments for creating factor variables. student_data$gender <- factor(student_data$female, labels = c("Male", "Female")) student_data$socioeconomic_status <- factor(student_data$ses, labels = c("Low", "Middle", "High")) student_data$racial_group <- factor(student_data\$race, labels = c("Black", "Asian", "Hispanic", "White")) 

Let’s quickly verify that the factor variables were created correctly.

head(student_data)
## # A tibble: 6 x 14
##      id female  race   ses schtyp  prog  read write  math science socst
##   <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> <dbl>
## 1    70      0     4     1      1     1    57    52    41      47    57
## 2   121      1     4     2      1     3    68    59    53      63    61
## 3    86      0     4     3      1     1    44    33    54      58    31
## 4   141      0     4     3      1     3    63    44    47      53    56
## 5   172      0     4     2      1     2    47    52    57      53    61
## 6   113      0     4     2      1     2    44    52    51      63    61
## # ... with 3 more variables: gender <fctr>, socioeconomic_status <fctr>,
## #   racial_group <fctr>