# 1 Why R?

The majority of tutorials on this site focus on R. This poses the question of why this is so and why learning and using R is better than sticking to ready-made software such as AntConc, SPSS, and Excel.

R is free, open source, and cutting edge

In contrast to many commercial software packages, R is entirely free and it runs on almost any machine - no matter if you use a PC, a Mac or a Linux machine, R will be up and running after a few seconds without having to pay and fees. R is the most comprehensive statistical analysis environment and new technologies, ideas, and methods often appear first in R before being adapted and sold as commercial software packages.

Also, R is widely used and understood. And due to the creativity and diligence of its users, R is expanding rapidly and becoming more popular and more versatile every day. This is great because if you encounter a problem, there is a huge community of R users that are willing to help - simply type in your problem in your favorite search engine search box and it is highly likely that your issue has already been answered on sites such as <www.stackoverflow.com> or some other R forum or help page. This also means that errors are often very quickly detected and solved as anyone is welcome to provide bug fixes, code enhancements, and new packages.

R is a programming language

In contrast to SPSS, R is not only a statistics “package” but a fully-fledged programming language (or programming environment). This means that R is extremely flexible and can be used not only for statistics but for all kinds of different tasks: from extracting strings from a corpus, to processing and visualizing data to performing statistics, and even for creating apps and websites (this website, for instance, was created in R and is written in R Markdown).

This also means that once you have a working knowledge of R you will be able to use R for all the things that you need when dealing with language. This is a real advantage because you do not need to learn all sorts of different pieces of software for specific aspects of your work anymore - rather, you only need R and once you have good command of R you will be able to modify existing functions or even write new functions that enable you to do things that were not available in R previously.

R is simple and good for jobs

Although it may not seem to be when you first encounter R, R is, in fact, very simple and easy to sue compared to almost any other programming language. This is true because R was specifically designed with human users in mind. This is particularly true when R is compared with C (or its direct daughters such as C++), for instance, which is designed for efficiency and is therefore much faster but also much more difficult to learn.

Furthermore, R is very open minded in the sense that you can access many other pieces of software or programming environments from R. Also, R works without a Compiler and is therefore an interpreted language which makes the development of code much easier. R is a vector language is thus more powerful and faster than most other languages (except for languages which are specifically designed for efficiency).

The most important reason why R is so useful is that it is great for visualization, statistics, and data science as well as handing big data.

Finally, R is becoming so wide-spread that many companies use and require knowledge of R form potential employees. This means that being able to program in R is an advantage on the labor market.

R enables full replication

When you do your research in R, you simply write a script that contains the entire analysis. This allows true and honest replication of research. In the humanities replications is not as deeply ingrained in the cultures as it is in the sciences. As a consequence, research in the humanities is not as reliable because only once research has been successfully replicated can it be considered valid. Unfortunately, the humanities do not consider replication as vital as they should and also because replication in the humanities was not as easy as it has been in the sciences. This can change with R because a script which contains all the steps of an analysis can simply be distributed and can therefore be read, understood, and run by other researchers. This is a huge advantage over using ready-made software in which case replications is almost impossible given the various steps that cannot be described properly in the methods sections of articles and papers.

Since R is open source, the quality of some packages is less than perfect and although errors are often detected and fixed quickly, errors do occur and there is no warranty whatsoever when using R.

As R is designed with the human in mind, code in R can be rather slow and may take up all of the available working memory. While this can be fixed by writing more efficient code in R, often contributors do not optimize their code for efficiency.

Because R is a communal project, it is changing rather quickly and packages that worked some time ago may have become outdated, they can be taken down by the people who have contributed them and the packages may therefore not be available anymore or they may simply be incompatible with a more modern version of R.

Why not Python?

Before reading on: Learning Python is absolutely worthwhile! Python is a beautiful and probably the most useful and most sought-after programming language. Python is also very useful for text analytics and has been ahead of R for many years (this is changing fast, however). Nonetheless, there are aspects to R which make it particularly useful for data analytics.

1. R is growing rapidly in data science while use of Python is receding. While Python is growing in many domains, it is being replaced by R in data science at an enormous rate.

2. R is extremely stable and there are few incompatibilities between different versions of R. In inconsistencies occur (which is rare), then they can easily be fixed by re-installing the outdated libraries with a single line of code. Python is constantly changing and code which has been written in Python version 2 is very frequently not compatible with Python version 3 (the current version) even in very basic functions (e.g. the print command in Python 2 differs from the print command in Python 3).

3. Installing additional libraries and packages is merely a single line of code in R (install.packages()) while the package system in Python is a mess. The official documentation in Python for installing packages encompasses seven pages (I know, this is not quite fair, but worth mentioning).

4. Python is easy to learn but not as principled and thus confusing art a certain stage: in Python, it is often unclear whether to use a function or a [method] (https://stackoverflow.com/questions/8108688/in-python-when-should-i-use-a-function-instead-of-a-method)

5. Neural Networks, one of the core methods used for Machine Learning and in Artificial Intelligence, are much easier to implement and to use in R compared with Python. in the data science arena is shrinking by the day: Neural Networks.

You can find a more exhaustive post on this issue here.

Resources for learning R

# 2 Getting started with R

Start R Studio on your computer. R Studio is a so-called IDE - Integrated Development Environment. The interface provides easy access to R. The advantage of this application is that R programs and files as well as a project directory can be managed easily. The environment is capable of editing and running program code, viewing outputs and rendering graphics. Furthermore, it is possible to view variables and data objects of an R-script directly in the interface. The GUI - Graphical User Interface - that R Studio provides the screen into four areas:

1. File editor
2. Environment variables
3. R console
4. Management panes (File browser, plots, help display and R packages).

Before you actually open R or R Studio, you should prepare by going through the following steps:

1. Create a folder for your project or analysis.
2. In that folder, create the following sub-folders:
• images
• datatables
1. Open R Studio
2. In R Studio go to: File and click on New Project and confirm by clicking OK
3. Now you are in a now R project. In this project, go to: File, click on New File, and click on R Notebook (this R Notebook will be the file in which you do all your work)
4. Change the title of the R Notebook and start describing what you are doing.

# 3 Teaser

Before we talk about the more technical details of R, please simply type the following into the file editor (the upper left panel). Once you have written this code into the file editor panel, simply highlight the three lines and click on Run (to the right on top of the file editor panel). Now, the graph you see below the code should have appeared. So, congrats, you have produced your first graph in R!

Year <- c(1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000)
Frequency <- c(2.7, 3.5, 4.3, 3.6, 4.6, 4.9, 4.0, 5.1)
plot(Year, Frequency, type = "l")

By creating your first graph, you have already intuitively familiarized yourself with basic design features of R.

Let’s go over what exactly you have done. When you typed Year <- c(1930, 1940, 1950, 1960, 1970, 1980, 1990, 2000) you have created a so-called vector which consisted of numbers from 1930 to 2000. You have also given this numeric vector a name: Year. So, you have created an object called “Year”" which contains numbers from 1930 to 2000.

Then, you have created another numeric vector called “Frequency”" which contained numeric frequency values.

When you typed plot(Year, Frequency, type = "l") you have “called” a function - in this case the function plot. The function plot takes arguments (elements which we have to provide the function with so that the function has something to work on) and in our case the arguments are the numeric vectors “Year”" and “Frequency”" and type = "l" which tells the plot function that we want a plot of type “l”" (line). The numeric vectors tell the plot function what we want to have displayed on the x- and the y-axis respectively.

We will now tweak the graph a little bit and then we will talk about what you have done and what this tells you about how R works. Now, write the following into the file editor panel and execute it by clicking on Run selected lines.

plot(Year, Frequency, type = "l", col = "red")

We have now added the argument col = "red" which tells the plot function that you want the lines in the line graph to be colored in red.

To summarize, you have learned the following basic features of R:

Assigning names/labels: You assign labels or names to things using the sequence <-. Assigning names or labels does also work in the other direction with ->.

Functions: Functions have the form functionname(argument1, argument2, ...)

Arguments: Elements functions work on.

## 3.1 Basic Operations

1 + 2      # addition
## [1] 3
sqrt(9)    # square root function
## [1] 3
x <- 1     # assignment
1:10       # sequence
##  [1]  1  2  3  4  5  6  7  8  9 10
# install.packages("tm")
library("tm")
require("tm")
# help
#help(require)
#?require
apropos("nova")
## [1] "anova"            "manova"           "power.anova.test" "stat.anova"
## [5] "summary.manova"

## 3.2 Data types

x <- 10.5
typeof(x)
## [1] "double"
class(x)
## [1] "numeric"
as.integer(x)
## [1] 10
is.integer(x)
## [1] FALSE
is.integer(as.integer(x))
## [1] TRUE
x <- "3.14"
typeof(x)
## [1] "character"
x <- as.double(x)
1:3 == c(1, 2, 3)
## [1] TRUE TRUE TRUE

## 3.3 Vector, matrix and data.frames

options(stringsAsFactors = F)
myvector <- c(1, 2, 3)
names(myvector) <- c("one", "two", "three")
print(myvector)
##   one   two three
##     1     2     3
print(myvector[1:2])
## one two
##   1   2
print(myvector[-1])
##   two three
##     2     3
sum(myvector)
## [1] 6
mean(myvector)
## [1] 2
mymatrix <- matrix(0, nrow=3, ncol=4)
rownames(mymatrix) <- c("one", "two", "three")
colnames(mymatrix) <- c("house", "sun", "tree", ".")
mymatrix[, 1] <- 12
mymatrix[, "sun"] <- 4
mymatrix[3, 4] <- 5
mymatrix[2, 3:4] <- 9
colSums(mymatrix)
## house   sun  tree     .
##    36    12     9    14
rowSums(mymatrix)
##   one   two three
##    16    34    21
colMeans(mymatrix)
##     house       sun      tree         .
## 12.000000  4.000000  3.000000  4.666667
mydatdaframe <- data.frame(v = c(1, 2, 3), c = as.character(myvector), n = c("one", "two", "three"))
mydatdaframe$v ## [1] 1 2 3 mydatdaframe$c
## [1] "1" "2" "3"
mydatdaframe[, "c"]
## [1] "1" "2" "3"
mydatdaframe[1, ]
##   v c   n
## 1 1 1 one
#rowSums(mydatdaframe)

## 3.4 More basic functions

sort(mydatdaframe$n) ## [1] "one" "three" "two" sort(mydatdaframe$n, decreasing = T)
## [1] "two"   "three" "one"
c(myvector, myvector)
##   one   two three   one   two three
##     1     2     3     1     2     3
is.vector(myvector)
## [1] TRUE
is.matrix(mymatrix)
## [1] TRUE
is.matrix(myvector)
## [1] FALSE
mymatrix / 2
##       house sun tree   .
## one       6   2  0.0 0.0
## two       6   2  4.5 4.5
## three     6   2  0.0 2.5
mymatrix / myvector
##       house      sun tree        .
## one      12 4.000000  0.0 0.000000
## two       6 2.000000  4.5 4.500000
## three     4 1.333333  0.0 1.666667
t(mymatrix)
##       one two three
## house  12  12    12
## sun     4   4     4
## tree    0   9     0
## .       0   9     5
mymatrix %*% t(mymatrix)
##       one two three
## one   160 160   160
## two   160 322   205
## three 160 205   185
cbind(mymatrix, myvector)
##       house sun tree . myvector
## one      12   4    0 0        1
## two      12   4    9 9        2
## three    12   4    0 5        3
rbind(mymatrix, myvector)
##          house sun tree .
## one         12   4    0 0
## two         12   4    9 9
## three       12   4    0 5
## myvector     1   2    3 1
data(USArrests)
#View(USArrests)
dim(USArrests)
## [1] 50  4
nrow(USArrests)
## [1] 50
ncol(USArrests)
## [1] 4
length(USArrests)
## [1] 4
max(USArrests)
## [1] 337
which.max(USArrests[, "Murder"])
## [1] 10
o <- order(USArrests[, "Murder"], decreasing = T)
USArrests[o, ]
##                Murder Assault UrbanPop Rape
## Georgia          17.4     211       60 25.8
## Mississippi      16.1     259       44 17.1
## Florida          15.4     335       80 31.9
## Louisiana        15.4     249       66 22.2
## South Carolina   14.4     279       48 22.5
## Alabama          13.2     236       58 21.2
## Tennessee        13.2     188       59 26.9
## North Carolina   13.0     337       45 16.1
## Texas            12.7     201       80 25.5
## Nevada           12.2     252       81 46.0
## Michigan         12.1     255       74 35.1
## New Mexico       11.4     285       70 32.1
## Maryland         11.3     300       67 27.8
## New York         11.1     254       86 26.1
## Illinois         10.4     249       83 24.0
## Alaska           10.0     263       48 44.5
## Kentucky          9.7     109       52 16.3
## California        9.0     276       91 40.6
## Missouri          9.0     178       70 28.2
## Arkansas          8.8     190       50 19.5
## Virginia          8.5     156       63 20.7
## Arizona           8.1     294       80 31.0
## Colorado          7.9     204       78 38.7
## New Jersey        7.4     159       89 18.8
## Ohio              7.3     120       75 21.4
## Indiana           7.2     113       65 21.0
## Wyoming           6.8     161       60 15.6
## Oklahoma          6.6     151       68 20.0
## Pennsylvania      6.3     106       72 14.9
## Kansas            6.0     115       66 18.0
## Montana           6.0     109       53 16.4
## Delaware          5.9     238       72 15.8
## West Virginia     5.7      81       39  9.3
## Hawaii            5.3      46       83 20.2
## Oregon            4.9     159       67 29.3
## Massachusetts     4.4     149       85 16.3
## Nebraska          4.3     102       62 16.5
## Washington        4.0     145       73 26.2
## South Dakota      3.8      86       45 12.8
## Rhode Island      3.4     174       87  8.3
## Connecticut       3.3     110       77 11.1
## Utah              3.2     120       80 22.9
## Minnesota         2.7      72       66 14.9
## Idaho             2.6     120       54 14.2
## Wisconsin         2.6      53       66 10.8
## Iowa              2.2      56       57 11.3
## Vermont           2.2      48       32 11.2
## Maine             2.1      83       51  7.8
## New Hampshire     2.1      57       56  9.5
## North Dakota      0.8      45       44  7.3
murderRates <- USArrests[o[1:5], "Murder"]
names(murderRates) <- rownames(USArrests)[o[1:5]]
barplot(murderRates)