Summary Advanced data anaylsis

CRM Advanced data analysis: Exam preparation Mar’2024




EXERCICE 1
BEFORE YOU START

 Create a new script file  File  New File  R Script. The screen is split into 4 quadrants:

o The script editor (top left) : visualizing the script file with your R-commands.

o The command prompt (bottom left) : Here you can directly type in commands at the prompt. Easy
for a quick check or calculation that you don’t want to include into the code.

o The workspace (top right) : This stores all objects you have defined (see later).

o Output panel (bottom right) : Here you can see plots, look for help, select packages (see later)

TELLING WHERE IS THE DIRECTORY

 Using the setwd() command you can see where the current working directory is:


o Need to change the direction of the slashes  from "\" to "/"

o Do not forget the quotes (" ") inside the command

 To change the working directory, go to Session/Set working
directory/Choose directory and browse to the C:\temp. The
function setwd() does exactly the same thing (using forward slashes!):

 Check that the working directory has changed.

 Using the list.files(getwd())function, you can get an overview
of the files that are in your working directory. Set or get???  així
tens una vista dels documents, I pots veure el nom amb els que estan guradats  t’estalvies
haver-ho d’escriure !!! fer-ho com a segon pas abans de (read.table)

READING IN A TABLE

EXCEL FILE PREPARATI ON

Prepare the excel making sure that:

 Add a header (ID) in the first column (identification of the subjects)  header =TRUE
 Cells with an excel formula: better the calculated value by its real value.

1

,CRM Advanced data analysis: Exam preparation Mar’2024


 We need (.) instead of comas (,) need to be specified  already by default.
 Delete brackets
 Avoid white spaces (in this case on the names), delete it instead  sep
 Missing value indicator (?) need to be specified  na.strings = "?"


ID-variable has been interpreted as a factor with 12 levels. Hence, internally the ID will be considered as a
number. We’d rather have it as a character vector  myData$ID <- as.character(myData$ID)

to make sure it has changed:

 class(myData$ID)
str(myData)


º X

 To create a tab-delimited (.txt) file, go to File\ save as\ Save as type text (Tab


delimited)(.txt).

 You get a warning that the document may contain features not compatible with the .txt
format, and that you are only saving the active sheet. Click OK/yes. Quit the excel
workbook without further saving (you already did save your workbook before).

 check if the .txt document has been created.

 The R-function to read in the table is read.table().At the command prompt (bottom left


panel)

 File (mandatory, the name) within quotes.First argument is the name of the file: (data(1).txt in this
case).
 header: first column names: header=TRUE
 stringsAsFactors: indication that there are text strings that need to be read in as factors. 
StringAsFactors=TRUE

 sep: indication that there as white spaces. In case you get the warning “More columns than column
names”  sep=”\t”
 Indicates that there is a missing value, that need to be considered (by default is “na”  na.strings
= "?"

2

,CRM Advanced data analysis: Exam preparation Mar’2024


 We need (.) instead of comas (,) need to be specified  dec = "."

 Ask for help on the by typing ?read.table.

SAVING AN OBJECT

 Using the assignment operator (<-), you have given a name to the data you’ve read in. This object
is called “myData” appear in the top right panel of R Studio.




o myData <- read.table(file="data1.txt",sep="\t",header=T)

 If you want to see your saved object: Type the word “myData” at the prompt (bottom left panel in
RStudio). Remember, R is case sensitive!! “myData” is not the same as “MYDATA” or “mydata”!!

CALCULATIONS

Object: myX<- seq(from=-3,to=3,by=0.5)  vector

 From (start)
 To (final)
 By (increment)  how large the steps are.

 mean(myX)  calculate the mean
 sum(myX)  calculate the summation

A vector is the elementary structure for data handling in R. It is a set of
simple elements, all being objects of the same class. For example, a
simple vector of the numbers one to three can be constructed by one of
the following commands:



DATA STRUCTURE

 What kind of data structure would it be? Use the class function. class(myData)

 The variables in the data frame myData are of different data types:

o factor (=categorical variable with limited number of levels, ordered of not.

o num(eric) and int(eger)  both of which represent numeric variables.

3

, CRM Advanced data analysis: Exam preparation
Mar’2024

o character (=text string)

o logical (TRUE or FALSE).


 The gender of the persons in our dataset are stored as a factor  class
(myData$gender)

 The levels of the factor can be extracted using the levels function  levels(myData$gender)




 A more comprehensive overview of the current data structure  str(myData)

 The output tells you again that ‘myData’ is an object of class data frame, how many
observations and variables, and what type of data the different variables represent.

 In R, the names of the variables are an entire part of the data frame  names(myData)




 The dimensions of the table  dim(myData)

 Number of rows and columns can be found using:

 nrow(myData)

 ncol(myData)

DATA TYPES AND COERCION

Changes in data type to one another:

 as.numeric()
 as.character()
 as.logical()
 as.factor()

 The individual variables are also objects, and belong to a class
class(myData$exam)

4