Getting Started

• R is a high-level language and an environment for data analysis and graphics
• influenced by S (Becker, Chamber, Wilks) and Scheme (Sussman)
  • Trevor Hastie
  • 1980s
  • AT&T Laboratories

• and created by Ross Ihaka and Robert Gentleman at the university of Auckland
  • released for genereal use 1999
  • for nearly all operating systems
  • now managed by the R Development Core Team (University of Vienna)

• R is free.
• R is open source.
• R is a dialect of S system.

• wide range of statistical methods
• powerful graphics
• data wrangling facilities

• R is free and R is open source
• community
• most of your problems are already solved and with a high probability the solution is available from one of the repositories (as package)
• the language is easy to learn and also intuitive
• the graphics capabilities are impressive

• there is a help browser in the right lower panel
• there are at leat three R related search engines:
  • http://www.rdocumentation.org/
  • http://rseek.org/ (google wrapper)
  • http://finzi.psych.upenn.edu/search.html
• google your question (English!!!): good sites are stackoverflow, stackexchange
• R taskviews: https://cran.r-project.org/web/views/

• type ?command e.g.

?lm

• use apropos() e.g.

apropos("linear")

Besides all LIFE Publication the group has a statistics section: https://www.zotero.org/groups/unimedleipzig/items

• the group library entries are public
• for full text access you have to be registered (feel free to add documents, hints, comments, links to screencasts, etc…)

• to work with the original R console is tedious
• over time many UIs were developed
  • Rattle
  • JGR
  • Notepad++
  • SciViews
  • RGui
  • etc pp…

• Hadley Wickham says:

[…] Rstudio, which I believe is the best development environment for most R users. The only real competitor is Emacs Speaks Statistics (ESS).

• Has everybody a working installation? (Start R Studio)

• Has everybody the current R version? (type version)

version

               _                           
platform       x86_64-pc-linux-gnu         
arch           x86_64                      
os             linux-gnu                   
system         x86_64, linux-gnu           
status                                     
major          3                           
minor          3.0                         
year           2016                        
month          05                          
day            03                          
svn rev        70573                       
language       R                           
version.string R version 3.3.0 (2016-05-03)
nickname       Supposedly Educational      

• Has everybody the current version of RStudio? Type:

RStudio.Version()

• you can check for newer versions via the Help menu -> Check for Updates

• in the console you can type commands and R will answer you
• for example you can use R like a calulator

2 + 2

[1] 4

sqrt(4)

[1] 2

• everything you can type in the console you can also type in a script file
• go to the menu: file -> new file and choose R script
• a script file can be saved (everything you typed in the console is much more difficult to recover) so use script files! for your analyses
• in a script file hit Control + Enter simultaneously will send the line to the console
• mark a region and hit Control + Enter will send the marked region

• an object can be as simple as a single number
• it can be a table
• or a connection to a data base
• it can be everything

• to work with objects (i.e. assess or use the containing information, manipulate them etc) we give a name to an object (assign), e.g.

x <- 2
y <- 2

• you do not see any result but if you now type

x 

[1] 2

y 

[1] 2

x + y

[1] 4

• in the beginning the underscore was used for assigning
• then it changed to an arrow (the head of the arrow points to the name, that's what I am using)
• you can also use a equal sign (it is easier to type)
  • it is not the same,
  • and it makes a difference,
  • but if you do not plan to become a R developer - it is ok…

• all your created objects are listed (together with some basic information) in the Enviroment tab of the upper right panel (alternatively you use the menu: View -> Show Environment or use Control-8)

• we have seen how numbers are assigned to variables
• there are other types, namely character and logical

• a variable of type character can be a single letter, a word, a sentence, or a whole book:

a <- "b"
b <- "Wort"
sentence <- "Hello world" 

a 

[1] "b"

b

[1] "Wort"

sentence

[1] "Hello world"

• a variable of type logical can have two values: TRUE and FALSE
• we can turn logical into numerical values, then FALSE will become a 0 and TRUE a 1
• these types of variables are mostly used for filtering, e.g. we can ask

x > 3

[1] FALSE

x == 3

[1] FALSE

x < 3

[1] TRUE

• it also works with character

a == "a"

[1] FALSE

a == "b"

[1] TRUE

• bad news: there are 24 types
• good news: the three above are all 99.9 percent of R users will ever need to know about

• combining several single values of the same type yields a vector
  • a vector of numbers (i.e. a numeric vector)
  • a vector of characters (i.e. a character vector)
  • a vector consisting of TRUEs and FALSEs (i.e. a logical vector)
• a vector can be seen like a column of a data table

• one of the most important functions in R is the c()
• c is for c oncatenate or c ombine

v <- c(1,4,7,2)
v

[1] 1 4 7 2

• so v has more than one element
• we can use length() to count the elements contained in a vector

length(v)

[1] 4

There are some build in vectors in R, containing information frequently used by users. Here are some of them:

• letters
• LETTERS
• month.abb
• month.name

Get the length of these vectors!

The function

colors()

produces a vector containing the names of all predefined colors available in R.

• Use this function in combination with length() to find the number of named colors!
  
• What type is the produced vector (remember our three types)?

• after vectors we come to DATA FRAMEs

• in R an object representing a conventional data table is called data frame
• a data frame is a rectangular data structure of rows and columns where
  • the columns are containing the variables
  • the rows are containing the observations

• as the built-in vectors we seen above R also comes with a lot of example data (https://vincentarelbundock.github.io/Rdatasets/datasets.html)
• go to the console and type

data(iris)
summary(iris)

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       Species  
 setosa    :50  
 versicolor:50  
 virginica :50  

• the data() command loads the data (or promise to do it)
• and summary() gives a short summary of the object

• for data frame RStudio provides some special functionalities
• one of them is the data viewer:
  • go to the enviromnent tab
  • now you should see two sections Data and Values
  • in the data section there should be an entry for our iris data frame
  • you can click on the blue bullet on the left of the data frame's name
  • you can click on the table symbols on the right -> the data viewer should open

The data viewer provides:

• filtering
• sorting
• detaching

Cave: the number of rows as well as the number of columns shown in the data viewer are limited!

Type the following commands. What do they do?

head(iris)
names(iris)
?iris
nrow(iris)
ncol(iris)
plot(iris)

The first way to access a column of a data frame is typing the name of the data frame followed by a dollar sign followed by the name of the column. HINT: RStudio provides very comprehensiv autocompletion functionality by hitting the TAB key

iris$Petal.Length

  [1] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 1.5 1.6 1.4 1.1 1.2 1.5 1.3
 [18] 1.4 1.7 1.5 1.7 1.5 1.0 1.7 1.9 1.6 1.6 1.5 1.4 1.6 1.6 1.5 1.5 1.4
 [35] 1.5 1.2 1.3 1.4 1.3 1.5 1.3 1.3 1.3 1.6 1.9 1.4 1.6 1.4 1.5 1.4 4.7
 [52] 4.5 4.9 4.0 4.6 4.5 4.7 3.3 4.6 3.9 3.5 4.2 4.0 4.7 3.6 4.4 4.5 4.1
 [69] 4.5 3.9 4.8 4.0 4.9 4.7 4.3 4.4 4.8 5.0 4.5 3.5 3.8 3.7 3.9 5.1 4.5
 [86] 4.5 4.7 4.4 4.1 4.0 4.4 4.6 4.0 3.3 4.2 4.2 4.2 4.3 3.0 4.1 6.0 5.1
[103] 5.9 5.6 5.8 6.6 4.5 6.3 5.8 6.1 5.1 5.3 5.5 5.0 5.1 5.3 5.5 6.7 6.9
[120] 5.0 5.7 4.9 6.7 4.9 5.7 6.0 4.8 4.9 5.6 5.8 6.1 6.4 5.6 5.1 5.6 6.1
[137] 5.6 5.5 4.8 5.4 5.6 5.1 5.1 5.9 5.7 5.2 5.0 5.2 5.4 5.1

• we have already seen how plot() works with data frames
• we also can give plot other objects (like one or two vectors), it tries to guess to figure out the best way to plot the objects given to it

plot(iris$Sepal.Length)

• Run the following lines using autocompletion (TAB!!!!)!
• What kinds of graphics are produced?

plot(iris$Sepal.Length,iris$Petal.Length)

plot(iris$Species,iris$Petal.Length)

plot(iris$Species)

plot(iris$Petal.Length,iris$Species)

plot(iris$Sepal.Length,iris$Petal.Length,col=iris$Species)

• we've already seen how to access columns using the $-sign
• we also can use numbers (so called indices)
• first we try to extract the 20th element from our letters vector from above
  • we need the 20
  • and square brackets
  • and of course the vector

letters[20]

[1] "t"

• extract rows 11-15
• extract rows 2,4,and 100
• use the summary() function on the Sepal.Width column. What is the mean of Sepal.Width. (How did you access the respective column, which of the ways did you choose?)
• use the hist() function to plot a histogram of Sepal.Width

R can import data of different forms, from different file types, etc. It is important to remember: for almost every kind of file type you have to use a different function, e.g.

All these functionalities are also available via the Import Dataset dialog. (Menu: Tools -> Import Dataset or via the Environment tab).

For now we want only use one of these functions to import a data set. Run the following line of code or use the data import menu…

education <- read.csv("http://www.barrolee.com/data/BL_v2.1/BL2013_MF1599_v2.1.csv")

Run summary() on the data frame.

• What is the highest percentage of no schooling (lu)
• Plot lp (percentage of population with primary school as highest level) vs. lpc (percentage which had primary school completed)
• Try indicate different contries by colour
• add a line through (0,0) with a slope of 1 using the command abline(0,1)
  • The majority of points are below the line, some are on the line. Why aren't any points above the line?