Basic introduction into R

Quantitave research methods
Data analysis workﬂow
Statistical Software
Installing R and RStudio
Getting help
Introduction into R
Part 1A
Richard L. Zijdeman
2016-06-15
Richard L. Zijdeman Introduction into R

Getting help
1 Quantitave research methods
2 Data analysis workﬂow
3 Statistical Software
4 Installing R and RStudio
5 Getting help

Getting help

Getting help
Why
To answer descriptive and explanatory questions on populations

Getting help
Workﬂow: PTE
problem (research question)
theory (hypothesis)
empirical test . . . with loops between T-E and P-T-E

Getting help
Research Questions
descriptive (to what extent. . . )
comparative (comparing two entities)
trend (comparison over time)
explanatory (focus on mechanism at hand)

Getting help
Theory
deductive reasoning
explanans
general mechanism
condition
explanandum (hypothesis)

Getting help
Empirical test
sample vs. population
random vs. stratiﬁed samples
testing technique, e.g.:
T-test, correlation, regression
Software required for faster analysis

Getting help

Getting help
Empirical testings has its own workﬂow
Grolemund & Wickham, 2016, Creative Commons
Attribution-NonCommercial-NoDerivs 4.0.

Getting help

Getting help
The dangers of analysing with spreadsheets
(e.g. MS Excel)
tempting to input and clean data and analyse in the same sheet
di cult to track cleaning rules
defaults mess up your data (e.g. 01200 -> 1200)

Getting help
Why use syntax (scripting)
E ciency (really)
Quality (error checking)
Replicatability
Communication

Getting help
R
R is open source, which is good and bad:
anybody can contribute (check, improve, create code)
free of charge
but: R depends on collective action
cannot ‘demand’ support
sprawl of packages

Getting help
RStudio
browser for R
provides easy access to:
scripts
data
plots
manual

Getting help

Getting help
Download R
Instructions via http://www.r-project.org
Choose a CRAN mirror
http://cran.r-project.org/mirrors.html
close, but active too!
Romania hasn’t gone (yet!)
Click on ‘Download R for Windows’
Follow usual installation procedure
Double click on R
You should now have a working session!
Close the session, do not save workspace image

Getting help
Packages and libraries
base R (core product)
additional packages
CRAN repository
spread through ‘mirrors’
choose a local, but active mirror
Github
packages not on CRAN
development versions of CRAN libraries

Getting help
RStudio
RStudio is found on http://www.rstudio.com
Download the version for your OS (e.g. windows)
http://www.rstudio.com/products/rstudio/download/
Install by double clicking on the downloaded ﬁle
Start RStudio by double clicking on the icon
You do not need to start R, before starting RStudio

Getting help
Getting help

Getting help
Build-in help: “?”
?[function] / ?[package]
e.g. “?plot” or “?graphics”
check the index for user guides and vignettes

Getting help
Cran website
Manuals
R FAQ
R Journal

Getting help
Online communities
Stackoverﬂow
Instance of Stackexchange
Reputation based Q&A
Speciﬁc lists for packages, e.g.:
ggplot2
R-sig-mixed-models

Getting help
Asking a question Getting an answer
Search the web: others must have had this problem too
If you raise a question:
be polite
be concise
short background
replicatable example
debrief your e orts sofar

Introducing RStudio and R
Introducing base R
Data visualization using ggplot2
Introduction into R
Part 1B
Richard L. Zijdeman
2016-06-15

Introducing base R
1 Introducing RStudio and R
2 Introducing base R
3 Data visualization using ggplot2

Introducing base R

Introducing base R
RStudio
Rstudio is sort of a ‘viewer’ on R
helps to organize input and output:
editor (upper left)
console (lower left)
environment (upper right)
output (lower right)

Introducing base R
R script
series of ))commands to manipulate data
always save your script, NEVER change your data
original data + script = reproducable research

Introducing base R
Packages
Build your R system using packages
‘Base R’ is basic. Add packages for your speciﬁc needs
Packages are found on servers, called ‘mirrors’
Make sure to select a mirror ﬁrst
https://cran.r-project.org/mirrors.html%5Bhttps:
//cran.r-project.org/mirrors.html%5D
## To permanently add the mirror, type:
options(repos=structure(
c(CRAN="http://cran.xl-mirror.nl")))
## replace http://... with your favorite mirror

Introducing base R
Packages for book (see 1.4.2)
pkgs <- c(
"broom", "dplyr", "ggplot2", "jpeg", "jsonlite",
"knitr", "Lahman", "microbenchmark", "png", "pryr",
"purrr", "rcorpora", "readr", "stringr", "tibble",
"tidyr"
)
install.packages(pkgs)

Introducing base R
R Session
contains scripts, data, functions
can be saved ‘workspace image’
prefer not to:
sessions are usually cluttered
only useful if running script takes time
Suggested tweak:
Options: uncheck “Restore .RData into workspace at startup”
Options: Save workspace to .RData on exit, select ‘never’

Introducing base R
Introducing base R

Introducing base R
base R: assignment and print()
‘attach’ values to an object (e.g. a variable)
x <- 5
y <- 4
z <- x * y
print(z)
## [1] 20

Introducing base R
base R: assignment and print() (II)
Try and imagine the potential of assignment
x <- c(4, 3, 2, 1, 0, 27, 34, 35)
# c for concatenate values
y <- -1
z <- x*y
print(z)
## [1] -4 -3 -2 -1 0 -27 -34 -35

Introducing base R
base R: data.frame
basically a table
contains columns (variables)
contains rows (cases)
“ﬂat table” in Kees’ terminology
my.df <- data.frame(x,z)
str(my.df) # show STRucture
## data.frame : 8 obs. of 2 variables:
## $ x: num 4 3 2 1 0 27 34 35
## $ z: num -4 -3 -2 -1 0 -27 -34 -35
There’s much more, but let’s keep that for tomorrow

Introducing base R

Introducing base R
Visualizing your data
Not just for analyses!
Data quality
representativeness
missing data

Introducing base R
plot() in base R
library(help = "datasets") # all datasets in R
?mtcars # show help on mtcars dataset
df <- mtcars()
str(mtcars) # display STRucture of an object
plot(mtcars$hp, mtcars$mpg)
plot(df)

Introducing base R
plot() is like . . .
plot() is like latex:
Forge it in anyway you want
Heterogeneous approach though
Takes quite some time to get it right

Introducing base R
ggplot() as alternative
ggplot is but one of many graph packages ggplot is nice bc, of:
similar approach to various types of graphs
easy build up for basic graphs
can get quite complex too
(but cannot do it all)

Introducing base R
ggplot() and the canvas metaphore
ggplot() consists of two elements
canvas
(multiple) layers of paint

Introducing base R
mapping and geom layers
ggplot() consists of two elements
canvas:
data
mapping (aesthetic)
(multiple) layers of paint
geom layers
ggplot(data = <DATASET>,
mapping = aes(x = <X-VAR>, y = <Y-VAR>)) +
geom_<TYPE>

Introducing base R
our ﬁrst ggplot
install.packages("ggplot2")
library(ggplot2)
df <- mtcars
ggplot(data = df, aes(x = hp, y = mpg)) +
geom_point()

Introducing base R
geom_ features
? geom_point
install.packages("ggplot2")
library(ggplot2)
df <- mtcars
geom_point(fill = "white", colour = "blue",
shape = 21, size = 4)

Introducing base R
Adding characteristics to your plot
Add variables to explain a pattern
geom_point(aes(colour = wt), size = 4)
NB: notice the di erence?
geom_point(aes(colour = wt, size = 4))

Introducing base R
Multiple geom’s
Add variables to explain a pattern
geom_point(aes(colour = as.factor(am)),
size = 6) + # increase size bc overlap
geom_point(aes(shape = as.factor(vs)),
size = 3)
#V/S whether V8 (0) or Straight (European) (1)

Introducing base R
Adding facets
Facets help reduce complexity
geom_point(aes(colour = as.factor(am)),
size = 4) +
facet_wrap( ~ vs)

Introducing base R
Things to consider with geom(_point)
ﬁll only works where shape actually can be ﬁlled
consider order of geoms
mind overlap:
decrease size
use alpha
use ‘open’ shapes
geom_jitter

Introducing base R
ggplot and titles
Various ways to add titlex to axes and stu
Can get quite complex
Here’s the basiscs
geom_point() +
labs(title = "Nice graph", x = "Horse Power",
y = "Miles per Gallon" )

Introducing base R
Themes and size
geom_point() +
labs(title = "Nice graph", x = "Horse Power",
y = "Miles per Gallon" ) +
theme_bw(base_size = 16)

Introducing base R
Much more to learn
not just about ggplot()
axes
legend (guides)
geoms
also about dataviz in general
general do’s and don’ts
which problem ﬁts which graph
it’s a science! (Graph theory)

Data wrangling
bit about NA
Introduction into R
Part 2A, 2B
Richard L. Zijdeman
2016-06-16

Data wrangling
bit about NA
1 Data wrangling
2 bit about NA

Data wrangling
bit about NA
Data wrangling

Data wrangling
bit about NA

Data wrangling
bit about NA
dplyr package
# install.packages("dplyr") # 1 time only
library(dplyr)
install.packages("nycflights13")
library(nycflights13)
print(flights)

Data wrangling
bit about NA
tibble or data_frame vs data.frame
str(mtcars)
class(mtcars)
mtcars_tbl <- as_data_frame(mtcars)
str(mtcars)
class(mtcars)

Data wrangling
bit about NA
ﬁlter
filter(mtcars, am == 1, vs == 0)
some.cars <- filter(mtcars, am == 1, vs == 0)
some.cars
(some.cars2 <- filter(mtcars, am == 1, vs == 0))

Data wrangling
bit about NA
filter and using or
filter(mtcars, gear == 3 | gear == 4) # !! not like this:
filter(mtcars, gear == 3 | 4)

Data wrangling
bit about NA
bit about NA

Data wrangling
bit about NA
Arrange
arrange(flights, dep_time)
arrange(flights, year, month, day) # ascending order
arrange(flights, desc(day))
# NB: missing values come at end

Data wrangling
bit about NA
Select
df <- select(flights, year, month, day)
names(flights)
df <- select(flights, tailnum:dest)
df <- select(flights, -(tailnum:dest))
df
df <- select(flights, starts_with("arr_"))
df <- select(flights, ends_with("e"))
df <- select(flights, contains("a"))

Data wrangling
bit about NA
rename
df <- rename(flights, Y_ear = year)
df <- mutate(flights, year1 = year+1)
select(df, year, year1)
df <- mutate(flights, year1 = year + 1, year2 = year1+1)
select(df, contains("year"))
df <- transmute(flights, year1 = year + 1, year2 = year1+1)
# only maintains the newly created variables

Data wrangling
bit about NA
group_by
by_day <- group_by(flights, year, month, day)
summarise(by_day)
cars <- mtcars
cars <- as_data_frame(mtcars)
summarise(cars, mean_hp = mean(hp, na.rm = TRUE))
mean(cars$hp, na.rm = TRUE)

Data wrangling
bit about NA
the pipe: %>%
cars_grp <- group_by(cars, carb)
class(cars)
class(cars_grp)
summarise(cars_grp, mmpg = mean(mpg, na.rm = TRUE))
cars_grp_sum <- summarise(cars_grp,
mmpg = mean(mpg, na.rm = TRUE),
count = n())
cars_grp_sum
plot <- ggplot(cars_grp_sum,
aes(x = carb, y = mmpg,
label = carb)) +
geom_point(aes(size = count)) +
geom_text(colour = "cyan")
plot Richard L. Zijdeman Introduction into R

Data wrangling
bit about NA
more pipe, adding a ﬁlter
cars_grp_sum3 <- cars %>%
group_by(carb) %>%
summarise(mmpg = mean(mpg, na.rm = TRUE),
count = n()) %>%
filter(count > 3)
ggplot(cars_grp_sum3, aes(x = carb, y = mmpg, label = carb)
geom_point(aes(size = count)) +
geom_text(colour = "cyan") +
labs(title = "figure with %>% and count > 3")

Session management
Basic data manipulation
Introduction into R
Part 3A
Richard L. Zijdeman
2016-06-17

Session management
1 Session management
2 Basic data manipulation

Session management
Session management

Session management
Maintaining your workspace

Session management
Setting up a session
clear your Environment
check sessionInfo() for loaded packages
detach obsolete packages under ‘other attached packages’
set your directory (“" on windows and”/" for linux/mac)
load libraries (install new ones)
load your data

Session management
Example session setup
rm(list = ls())
sessionInfo() # check for other attached packages
detach("package:nycflights13", unload = TRUE)
setwd("/Users/RichardZ/Dropbox/
Summer school 2016/Richard Zijdeman/")
getwd() # to see whether you re in the right directory
dir() # shows what s in your directory

Session management
Loading your data
read.table() (generic function)
read.csv()
library(foreign) # e.g. SPSS and Stata
library(readxl) # fast excel-package

Session management
Reading in data
Di erent functions for di erent ﬁles:
Base R: read.table() (read.csv())
foreign package: read.spss(), read.dta(), read.dbf()
readxl
alternatives packages:
xlsx(Java required)
gdata (perl-based)
openxlsx package: read.xlsx()

Session management
read.csv()
ﬁle: your ﬁle, including directory
header: variable names or not?
sep: seperator
read.csv default: “,”
read.csv2 default: “;”
skip: number of rows to skip
nrows: total number of rows to read
stringsAsFactors
encoding (e.g. “latin1” or “UTF-8”)

Session management
read_excel from readxl package
path: your ﬁle, including directory
sheet: name or number of sheet
col_names: col names in 1st row?
col_types: specify type
na: what’s the sign for missing values
skip: how many rows to skip before data starts

Session management
Example session loading your csv data
# setwd() to set your working directory
hmar100 <- read.csv("./Datafiles_HSN/HSN_marriages.csv",
stringsAsFactors = FALSE,
encoding = "latin1",
header = TRUE,
nrows = 100) # just first 100 rows

Session management
Example session loading your excel data
# setwd() to set your working directory
install.packages("readxl")
library("readxl")
hmar <- read_excel("./Datafiles_HSN/HSN_marriages_awful.xls
col_names = TRUE,
skip = 3) # empty lines not counted!!!

Session management

Session management
Change case of text
tolower()
toupper()
tolower("CaN we pleASe jUSt have LOWER cases?")
names(hmar) <- tolower(names(hmar))

Session management
length()
Used to count how many instances there are
length(names(hmar))
# shows number of variables in hmar

Basic statistical techniques
Introduction into R
Part 3B
Richard L. Zijdeman
2016-06-17

1 Basic statistical techniques

Box and whisker plot
Distribution of data
Median: 50% of the cases above and below
Box: 1st and 3rd quartile
Interquartile range (IQR): Q3-Q1
Outliers (Tukey, 1977):
x < Q1 - 1.5*IQR
x > Q3 + 1.5*IQR

p <- ggplot(hmar, aes(sign_groom, age_groom))
p + geom_boxplot()

hmar <- mutate(hmar, sign_groomD = (sign_groom == "h" & !(i
p <- ggplot(hmar, aes(sign_groomD, age_groom))
p + geom_boxplot()

hmar <- mutate(hmar, sign_groomD = (sign_groom == "h" & !(i
p <- ggplot(hmar, aes(sign_groomD, age_groom))
p + geom_boxplot() + geom_jitter(shape = 24, width = 0.2)

library(stats)
var.test(age_groom ~ sign_groomD, data = hmar)
t.test(age_groom ~ sign_groomD, data = hmar)
# NB: always check for variances

A small PTE project
Look at the variables in the HSN ﬁles
Think of a research question
Provide a general mechanism and hypothesis
Plot your results

Basic introduction into R

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