Introduction to R and data exploration

• RStudio and R
• R as calculator
• Variables
• Functions and help
• Importing data in R in a dataframe
• Accessing rows and columns
• Adding columns to the data
• Goal of statistics
• Data exploration (descriptive statistics)
  • Numerical measures
  • Visual exploration

# Addition (this is a comment: preceded by '#')
5 + 5

# [1] 10

# Multiplication
5 * 3

# [1] 15

# Division
5/3

# [1] 1.6667

a <- 5  # store a single value; instead of '<-' you can also use '='
a  # display the value

# [1] 5

b <- c(2, 4, 6, 7, 8)  # store a series of values in a vector
b

# [1] 2 4 6 7 8

b[4] <- a  # assign value 5 (stored in 'a') to the 4th element of vector b
b[1] <- NA  # assign NA (missing) to the first element of vector b
b <- b * 10  # multiply all values in vector b with 10
b

# [1] NA 40 60 50 80

mn <- mean(b)  # calculating the mean and storing in variable mn
mn

# [1] NA

# mn is NA (missing) as one of the values is missing
mean(b, na.rm = TRUE)  # we can use the function parameter na.rm to ignore NAs

# [1] 57.5

# But which parameters does a function have: use help!
help(mean)  # alternatively: ?mean

• There are many resources for R which you can easily find online
• Here we use “swirl” an online platform for interactive R courses
• Start RStudio, install and start swirl:
  

install.packages("swirl", repos = "http://cran.rstudio.com/")
library(swirl)
swirl()

• Follow the prompts and install the course R programming: The basics of programming in R
• Choose that course to start with and finish Lesson 1 of that course

setwd("C:/Users/Martijn/Desktop/Statistics/Intro-R")  # set working directory
dat <- read.csv2("thnl.csv")  # read.csv2 reads Excel csv file from work dir
str(dat)  # shows structure of the data frame dat (note: wide format)

# 'data.frame': 19 obs. of  4 variables:
#  $ Participant : chr  "VENI-NL_1" "VENI-NL_10" "VENI-NL_11" "VENI-NL_12" ...
#  $ Sex         : chr  "M" "M" "M" "M" ...
#  $ Frontness.T : num  0.781 0.766 0.884 0.748 0.748 ...
#  $ Frontness.TH: num  0.738 0.767 0.879 0.761 0.774 ...

dim(dat)  # number of rows and columns of data set

# [1] 19  4

head(dat)  # show first few rows of dat 

#   Participant Sex Frontness.T Frontness.TH
# 1   VENI-NL_1   M     0.78052      0.73801
# 2  VENI-NL_10   M     0.76621      0.76685
# 3  VENI-NL_11   M     0.88366      0.87871
# 4  VENI-NL_12   M     0.74757      0.76094
# 5  VENI-NL_13   M     0.74761      0.77420
# 6  VENI-NL_14   M     0.75186      0.74913

dat[1, ]  # values in first row

#   Participant Sex Frontness.T Frontness.TH
# 1   VENI-NL_1   M     0.78052      0.73801

dat[1:2, c(2, 3)]  # values of first two rows for second and third column

#   Sex Frontness.T
# 1   M     0.78052
# 2   M     0.76621

dat[c(1, 2, 3), "Participant"]  # values of first three rows for column 'Participant'

# [1] "VENI-NL_1"  "VENI-NL_10" "VENI-NL_11"

tmp <- dat[5:8, c(1, 3)]  # store columns 1 and 3 for rows 5 to 8 in tmp

tmp <- dat[dat$Sex == "M", ]  # only observations for male participants
head(tmp, n = 2)  # show first two rows

#   Participant Sex Frontness.T Frontness.TH
# 1   VENI-NL_1   M     0.78052      0.73801
# 2  VENI-NL_10   M     0.76621      0.76685

# more advanced subsetting: include rows for which frontness for the T sound is
# higher than 0.74 AND participant is either 1 or 2 N.B. use '|' instead of '&' for
# logical OR
dat[dat$Frontness.T > 0.74 & dat$Participant %in% c("VENI-NL_1", "VENI-NL_2"), ]

#   Participant Sex Frontness.T Frontness.TH
# 1   VENI-NL_1   M     0.78052      0.73801

# new column Diff containing difference between TH and T positions
dat$Diff <- dat$Frontness.TH - dat$Frontness.T

# new column DiffClass, initially all observations set to TH0
dat$DiffClass <- "TH0"

# observations with Diff larger than 0.02 are categorized as TH1, negative as TH-
dat[dat$Diff > 0.02, ]$DiffClass <- "TH1"
dat[dat$Diff < 0, ]$DiffClass <- "TH-"

dat$DiffClass <- factor(dat$DiffClass)  # convert string variable to factor

head(dat, 2)

#   Participant Sex Frontness.T Frontness.TH        Diff DiffClass
# 1   VENI-NL_1   M     0.78052      0.73801 -0.04250668       TH-
# 2  VENI-NL_10   M     0.76621      0.76685  0.00064245       TH0

• Run swirl() and finish the following lessons of the R Programming course:
  • Lesson 6: Subsetting vectors
  • Lesson 12: Looking at data

• Goal of statistics is to gain understanding from data
  • Descriptive statistics (this lecture): describe data without further conclusions
  • Inferential statistics: describe data (sample) and its relation to larger group (population)

mean(dat$Diff)  # mean

# [1] 0.016263

median(dat$Diff)  # median

# [1] 0.01093

min(dat$Diff)  # minimum value

# [1] -0.042507

max(dat$Diff)  # maximum value

# [1] 0.10346

sd(dat$Diff)  # or: sqrt((1/(length(dat$Diff)-1)) * sum((dat$Diff - mean(dat$Diff))^2))

# [1] 0.038213

var(dat$Diff)  # or: sd(dat$Diff)^2

# [1] 0.0014603

quantile(dat$Diff)  # quantiles

#         0%        25%        50%        75%       100% 
# -0.0425067 -0.0038419  0.0109299  0.0248903  0.1034607

summary(dat$Diff)  # summary

#     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
# -0.04251 -0.00384  0.01093  0.01626  0.02489  0.10346

table(dat$Sex)

# 
#  F  M 
#  9 10

with(dat, table(Sex))  # alternative

# Sex
#  F  M 
#  9 10

table(dat$DiffClass)

# 
# TH- TH0 TH1 
#   6   7   6

# correlation: relation between two numerical variables
cor(dat$Frontness.T, dat$Frontness.TH)

# [1] 0.71054

# crosstable: relation between two categorical variables
table(dat$Sex, dat$DiffClass)  # or: with(dat, table(Sex,DiffClass))

#    
#     TH- TH0 TH1
#   F   1   3   5
#   M   5   4   1

# means per category: relation between numerical and categorical variable
c(mean(dat[dat$Sex == "M", ]$Diff), mean(dat[dat$Sex == "F", ]$Diff))

# [1] -0.0034299  0.0381446

• Many basic visualization options are available in R
  • boxplot() for a boxplot
  • hist() for a histogram
  • qqnorm() and qqline() for a quantile-quantile plot
  • plot() for many types of plots (scatter, line, etc.)
  • barplot() for a barplot (plotting frequencies)

par(mfrow = c(1, 2))  # set graphics option: 2 graphs side-by-side
boxplot(dat$Diff, main = "Difference")  # boxplot of difference values
boxplot(dat[, c("Frontness.T", "Frontness.TH")])  # frontness per group

hist(dat$Diff, main = "Difference histogram")

qqnorm(dat$Diff)  # plot actual values vs. theoretical quantiles
qqline(dat$Diff)  # plot reference line of normal distribution

plot(dat$Frontness.T, dat$Frontness.TH, col = "blue")

counts <- table(dat$Sex)  # frequency table for sex
barplot(counts, ylim = c(0, 15))

counts <- table(dat$Sex, dat$DiffClass)
barplot(counts, col = c("pink", "lightblue"), legend = rownames(counts), ylim = c(0, 10))

• Run swirl() and finish the following lesson of the R Programming course:
  • Lesson 15: Base graphics

• In this lecture, we've covered the basics of R
• Now you should be able (with help of this presentation) to use R for:
  • Data manipulation, exploration and visualization
• Associated lab session and additional swirl resources:
  • https://www.let.rug.nl/wieling/Statistics/Intro-R/lab
  • Install swirl course Exploratory Data Analysis
    • install_from_swirl('Exploratory_Data_Analysis')
    • Finish Lessons 1 - 5 (download associated slides)
    • If interested, you can finish the full Exploratory Data Analysis course