A relatively short Introduction to R as presented at the Belgian Software Craftmanship meetup group. The goal of this presentation is to give you an introduction to: • The style of the language • It's ecosystem • How common things like data manipulation and visualization work • How to use it for machine learning • Webdevelopment and report generation in R • Integrating R in your system License: Introduction To R by Samuel Bosch To the extent possible under law, the person who associated CC0 with Introduction To R has waived all copyright and related or neighboring rights to Introduction To R. http://creativecommons.org/publicdomain/zero/1.0/

1. Introduction To R
Samuel Bosch (mail@samuelbosch.com)

2. What is R
R is a language and environment for statistical computing and graphics. It
is a GNU project which is similar to the S language.
Created in 1993, license: GNU GPL, current version 3.2.3
Interpreted
C-like syntax
Functional programming language semantics (Lisp, APL)
Object oriented (3 different OO systems)
Garbage collector
Mostly call-by-value
Lexical scope
Function closure
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3. Popularity
TIOBE: 18
Github: 12
Stackoverflow: 117341 questions (Java: 978006, Python: 507653)
Most popular tool for statistical data analysis
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4. Usage
CRAN Task Views: https://cran.r-project.org/web/views/
Statistics (frequentist and bayesian)
Machine learning and data mining
Science (mathematics, chemistry, physics, medical, ecology, genetics,
economy, history, …)
Finance
Natural Language Processing
Data visualization
Analyzing spatial, spatio-temporal data and time series
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5. R Markdown
This is an R Markdown presentation. Markdown is a simple formatting
syntax for authoring HTML, PDF, and MS Word documents. For more
details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that
includes both content as well as the output of any embedded R code
chunks within the document.
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6. Competitors/colleagues
SAS, SPSS, STATA, Mathematica and other statistical software
Python + Numpy + Pandas + matplotlib + …
Matlab/Octave
Julia
K/J and other APL like languages
Java (Weka), Clojure, .NET (F#), …
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7. Calling R
command line
SAS, SPSS, Stata, Statistica, JMP
Java, C++, F#
Python, Perl, Ruby, Julia
PostgreSQL: PL/R
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8. Ecosystem
IDE: RStudio or one of the alternatives (plugins for Eclipse, Visual
Studio, Atom, Sublime Text, Vim, …) Packages: CRAN (6700+ packages),
Bioconductor, RForge, Github
Learning more and getting help:
Built-in documentation (?, help(), F1) and package vignettes
Official manuals: https://cran.r-project.org/manuals.html
Short reference card: https://cran.r-project.org/doc/contrib/Short-
refcard.pdf
(Free) books: Advanced R and R packages by Hadley Wickham
Courses on Edx and Coursera
Stack Overflow and Cross validated (for statistical questions)
mail@samuelbosch.com
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9. View help
?Filter
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10. Operators
+ ‐ * /
^ or ** for exponentiation
%% modulus
%/% integer division
< <= > >= == != !x isTRUE(x) xor(x, y)
# element wise OR and AND
c(FALSE, FALSE) | c(TRUE, FALSE) & c(TRUE, FALSE)
## [1] TRUE FALSE
# first element OR and AND
c(FALSE, FALSE) || c(TRUE, FALSE) && c(TRUE, TRUE)
## [1] TRUE /

11. Vectors
List of elements of the same type
a <‐ c(1,2,5.3,6,‐2,4) # numeric vector
a[c(2,4)] # 2nd and 4th element
## [1] 2 6
names(a) <‐ c("c","d","e","f","g","h")
a
## c d e f g h
## 1.0 2.0 5.3 6.0 ‐2.0 4.0
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12. Vectors
a[a > 3] ## a[c(F, F, T, T, F, T)]
## e f h
## 5.3 6.0 4.0
a[3:5]
## e f g
## 5.3 6.0 ‐2.0
a[‐1]
## d e f g h
## 2.0 5.3 6.0 ‐2.0 4.0
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13. Vectors
a[c("c","d","e")]
## c d e
## 1.0 2.0 5.3
a[a %in% c(1,2)]
## c d
## 1 2
is.null(c()) & is.null(NULL)
## [1] TRUE
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14. Vectors
c(1,2,3)[c(TRUE,FALSE,NA)]
## [1] 1 NA
c(sum=sum(a), sumna=sum(c(a,NA)), sumnona=sum(c(a,NA), na.rm = TRUE),
mean=mean(a), sd=sd(a), max=max(1,2,a))
## sum sumna sumnona mean sd max
## 16.300000 NA 16.300000 2.716667 2.993604 6.000000
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15. Data Types: numeric vectors
Default type for numbers
class(c(1, 2.3))
## [1] "numeric"
c(is.integer(1), is.numeric(1))
## [1] FALSE TRUE
c(seq(from = 1, to = 5, by = 2), rep(c(6,7), times = c(2,3)))
## [1] 1 3 5 6 6 7 7 7
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16. Data Types: integer vectors
as.integer(c(1,2.3,"4.5","bla"))
## Warning: NAs introduced by coercion
## [1] 1 2 4 NA
as.integer(c(TRUE,FALSE))
## [1] 1 0
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17. Factors
Used to encode a vector as a factor ('category'/'enumerated type')
f <‐ factor(c(1,1,2,2,3,3,2,1), levels=c(1,2,3), labels=c("a", "b", "c"))
f
## [1] a a b b c c b a
## Levels: a b c
table(f)
## f
## a b c
## 3 3 2
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18. Factors
as.character(f)
## [1] "a" "a" "b" "b" "c" "c" "b" "a"
as.numeric(f)
## [1] 1 1 2 2 3 3 2 1
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19. Other Vectorial Data Types
Complex
Logical: 1 < 2, TRUE, T, FALSE, F
Character:
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as.character(1.2)
## [1] "1.2"
fizz <‐ paste0("fi", paste(rep("z", 2), collapse = ""))
paste(fizz, "buzz", 1:3, sep="_", collapse = " | ")
## [1] "fizz_buzz_1 | fizz_buzz_2 | fizz_buzz_3"
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20. Matrices
Multiple vector columns of the same type and the same length
m <‐ matrix(1:10, nrow=5, ncol=2, byrow = TRUE)
m[1,] # 1st row
## [1] 1 2
m[,2] # 2nd column
## [1] 2 4 6 8 10
m[1,2] # 1st row, 2nd column
## [1] 2
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21. Matrices
m * 1:5
## [,1] [,2]
## [1,] 1 2
## [2,] 6 8
## [3,] 15 18
## [4,] 28 32
## [5,] 45 50
t(m) %*% m
## [,1] [,2]
## [1,] 165 190
## [2,] 190 220
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22. Matrices
diag(1, nrow = 2, ncol = 2)
## [,1] [,2]
## [1,] 1 0
## [2,] 0 1
sum(c(rowSums(m), colSums(m))) == sum(2*m)
## [1] TRUE
apply(m, MARGIN = 1, function(x) { sum(x) }) == rowSums(m)
## [1] TRUE TRUE TRUE TRUE TRUE
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23. Matrices
head(m, n = 3)
## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
## [3,] 5 6
summary(m)
## V1 V2
## Min. :1 Min. : 2
## 1st Qu.:3 1st Qu.: 4
## Median :5 Median : 6
## Mean :5 Mean : 6
## 3rd Qu.:7 3rd Qu.: 8
## Max. :9 Max. :10 /

24. Arrays
One, two or more dimensions
a <‐ array(data = t(1:24), dim = c(2,3,4))
a[1,,]
## [,1] [,2] [,3] [,4]
## [1,] 1 7 13 19
## [2,] 3 9 15 21
## [3,] 5 11 17 23
a[1,1,1]
## [1] 1
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25. Data frames
A data frame combines columns with the same length and different
data types
d <‐ data.frame(number=1:2, bool=c(TRUE, FALSE), string=c("y", "z"))
d$number
## [1] 1 2
d[1,c(2,3)]
## bool string
## 1 TRUE y
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26. Data frames
d[,"string"]
## [1] y z
## Levels: y z
data.frame(string=c("y", "z"), stringsAsFactors = FALSE)[,1]
## [1] "y" "z"
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27. dplyr
Lots of operators for manipulating local and database data (sqlite,
mysql and postgresql). Basic verbs:
Other goodies:
select
filter
arrange (= sort)
mutate
summarise
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piping (chaining)
database access as lazy as possible
Bigquery support (Google)
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28. dplyr
library(dplyr)
cars <‐ mutate(mtcars, hp_mpg = hp/mpg)
cars %>%
group_by(cyl) %>%
summarise(mean(disp), mean(hp), mean(mpg), mean(hp_mpg))
## Source: local data frame [3 x 5]
##
## cyl mean(disp) mean(hp) mean(mpg) mean(hp_mpg)
## (dbl) (dbl) (dbl) (dbl) (dbl)
## 1 4 105.1364 82.63636 26.66364 3.244667
## 2 6 183.3143 122.28571 19.74286 6.231013
## 3 8 353.1000 209.21429 15.10000 14.419146
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29. dplyr
## options("samuelb@obisdb‐stage.vliz.be" = "<your password here>")
pwd <‐ getOption("samuelb@obisdb‐stage.vliz.be")
src <‐ src_postgres(dbname = "obis", host = "obisdb‐stage.vliz.be",
port = "5432", user="samuelb",
password = pwd, options="‐c search_path=obis")
tbl(src, "positions") %>%
select(id, bottomdepth) %>%
filter(longitude == 0 && latitude == 0) %>%
collect()
## Source: local data frame [1 x 2]
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## id bottomdepth
## (int) (int)
## 1 8667455 4935
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30. Lists
Ordered collection of objects
l <‐ list(name="Samuel", age=33,
workdays=c("Mon","Tues","Wed", "Thurs", "Fri"))
l
## $name
## [1] "Samuel"
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## $age
## [1] 33
##
## $workdays
## [1] "Mon" "Tues" "Wed" "Thurs" "Fri"
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31. Lists
l$name
## [1] "Samuel"
l[["age"]]
## [1] 33
l[[3]]
## [1] "Mon" "Tues" "Wed" "Thurs" "Fri"
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32. Functions
sumf <‐ function(x, na.rm = FALSE) {
x <‐ ifelse(na.rm, na.omit(x), x)
Reduce("+", x)
}
sumf(1:3)
## [1] 1
sumf
## function(x, na.rm = FALSE) {
## x <‐ ifelse(na.rm, na.omit(x), x)
## Reduce("+", x)
## }
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33. Functions
sumf <‐ function(x, na.rm = FALSE) {
if(na.rm) {
x <‐ na.omit(x)
}
sum <‐ 0
for (element in x) {
sum <‐ sum + element
}
sum
}
sumf(c(1, 2, NA))
## [1] NA
sumf(c(1, 2, NA), na.rm = TRUE)
## [1] 3
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34. Errors
try({ stop("Not supported") }, silent = TRUE)
tryCatch(expr = { qwerty + 1 },
error = function (e) str(e),
finally = print("Finally"))
## List of 2
## $ message: chr "object 'qwerty' not found"
## $ call : language doTryCatch(return(expr), name, parentenv, handler)
## ‐ attr(*, "class")= chr [1:3] "simpleError" "error" "condition"
## [1] "Finally"
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35. Files
f <‐ file(filename, open = "r")
on.exit(close(f))
readLines
writeLines
cat
sink
scan
parse
url
gzfile
read.table
read.csv
read.csv2
write.table
write.csv
write.csv2
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36. Short example
aphia_ids <‐ c()
for (file in list.files("demo", pattern="*[.]txt", full.names=T)) {
print(file)
species <‐ read.table(file, header=T, sep="t", quote="", fill=T)
exact_match <‐ species[species$Match.type == "exact",]
aphia_ids <‐ c(aphia_ids, exact_match$AphiaID_accepted)
}
## [1] "demo/corals_red_sea_matched.txt"
## [1] "demo/red_sea_non_coral_invertebrate_1_matched.txt"
## [1] "demo/red_sea_non_coral_invertebrate_2_matched.txt"
## [1] "demo/red_sea_shore_fish_2_matched.txt"
## [1] "demo/red_sea_shore_fish_matched.txt"
paste(na.omit(aphia_ids[1:6]), collapse = ",")
## [1] "216153,216155,216154,286927,216152,210746"
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37. Data visualization
plot(runif(n=1000, 0, 0.5), runif(n=1000, 0, 1), pch=3, col="red",
xlab="", ylab="", xlim=0:1, ylim=0:1)
points(runif(n=50, .5, 1), runif(n=50, 0, 1), pch=20, col="blue")
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38. Data visualization
library(ggplot2)
mtcars$gear <‐ factor(mtcars$gear,levels=c(3,4,5),
labels=c("3gears","4gears","5gears"))
mtcars$cyl <‐ factor(mtcars$cyl,levels=c(4,6,8),
labels=c("4cyl","6cyl","8cyl"))
qplot(mpg, data=mtcars, geom="density", fill=gear, alpha=I(.5),
main="Distribution of Gas Milage", xlab="Miles Per Gallon",
ylab="Density")
## linear regression
qplot(wt, mpg, data=mtcars, geom=c("point", "smooth"),
method="lm", formula=y~x, color=cyl,
main="Regression of MPG on Weight",
xlab="Weight", ylab="Miles per Gallon")
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39. Data visualization
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40. Data visualization
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41. Data visualization
ggplot(movies, aes(x=rating)) +
geom_histogram(binwidth = 0.1, aes(fill = ..count..)) +
scale_fill_gradient("Count", low = "green", high = "red")
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42. Objects
Recommended reading: http://adv-r.had.co.nz/OO-essentials.html
S3: generic function OO, very casual system e.g. drawRect(canvas,
"blue")
S4: similar to S3 but more rigid, has multiple dispatch
Reference classes: message-passing OO (like Java, C++, etc), objects
are mutable
Base classes: defined in C
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43. Debugging
RStudio setting: Debug -> On Error -> Break in code
DEMO
recover()
traceback()
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44. Packages
install.packages("caret") ## installs caret and it's dependencies
devtools::install_github("rstudio/packrat") # install from github
library(caret) # load the library and import all functions
if(!require(raster)) {
print("raster package could not be loaded")
}
dplyr::aggregate ## calling a function without importing the full package
plyr::select ## or handle naming conflicts
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45. Packrat
Per project private package libraries
install.packages("packrat")
packrat::init(project = ".")
install.packages("survival")
packrat::snapshot()
packrat::init()
packrat::snapshot()
packrat::restore()
packrat::clean()
packrat::bundle()
packrat::unbundle()
packrat::on()
packrat::off()
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46. Package development
devtools + roxygen2 + testthat
Advantages:
Disadvantage:
Get started with the book http://r-pkgs.had.co.nz/ by Hadley Wickham
testing
documentation
versioning
distribution
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47. Testing
library(testthat)
test_that("list_datasets result is same as datasets.csv", {
skip_on_cran()
original <‐ read.csv2(data_raw_file("datasets.csv"), stringsAsFactors = FALSE)
df <‐ list_datasets()
expect_equal(nrow(df),nrow(original))
expect_equal(df, original)
})
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48. Performance
Some resources:
compiler package (byte-code compiler)
parallel package
http://www.noamross.net/blog/2013/4/25/faster-talk.html
http://adv-r.had.co.nz/Performance.html
http://adv-r.had.co.nz/Profiling.html
http://adv-r.had.co.nz/memory.html
http://adv-r.had.co.nz/Rcpp.html
https://cran.r-
project.org/web/views/HighPerformanceComputing.html
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49. Parallel
# Calculate the number of cores
no_cores <‐ detectCores() ‐ 1
# Initiate cluster
cl <‐ makeCluster(no_cores)
on.exit(stopCluster(cl))
clusterExport(cl, "species")
clusterExport(cl, "background")
results <‐ parLapply(cl, seq(0.1, 0.9, 0.1), function(beta) {
source("sdmExperiment.R")
kresults <‐ lapply(1:10, function(k) {
data <‐ species[[paste0("beta",beta)]][[paste0("k",k)]]
cbind(beta, k, t(build_sdm_rcew(data, background)))
})
dplyr::rbind_all(kresults)
})
results <‐ dplyr::rbind_all(results) ## combine list of data.frames
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50. Web
Shiny: http://shiny.rstudio.com/
OpenCPU: https://www.opencpu.org/
RServe: https://rforge.net/Rserve/doc.html
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interactive web pages
no need for javascript (at least not for simple things)
reactive programming
typically ui.R and a server.R
example: http://shiny.rstudio.com/gallery/movie-explorer.html
DEMO
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Binary R server-
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51. Machine learning
https://cran.r-project.org/web/views/MachineLearning.html
caret, rattle
specific libraries for the different machine learning algorithms
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52. Machine Learning example
library(e1071)
train_idx <‐ sample(1:nrow(mtcars), nrow(mtcars)/2)
train <‐ mtcars[train_idx,]
test <‐ mtcars[‐train_idx,]
model <‐ svm(hp ~ mpg + cyl + gear, data = train)
train_results <‐ predict(model, train)
test_results <‐ predict(model, test)
rmse <‐ function(error)
{
sqrt(mean(error^2))
}
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53. Machine learning example
print(paste("training rmse",rmse(train_results ‐ train$hp)))
## [1] "training rmse 22.1904078087206"
print(paste("test rmse",rmse(test_results ‐ test$hp)))
## [1] "test rmse 38.6500402049542"
plot_data <‐ data.frame(hp=c(train$hp,test$hp),
predicted=c(train_results,test_results),
split=c("train","test"))
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54. Machine learning example
ggplot(plot_data, aes(hp, predicted)) +
geom_point(aes(colour = factor(split), shape = factor(split)))
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55. Questions
ggplot(data.frame(a="?", x=0, y=0), aes(x=x, y=y, label=a)) +
geom_text(size=100)
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