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Statistical analysis with R

NOTE: same tutorial as in the repository ORCRI –>(Online Ressources for Crime Reporting and Investigation).

Credits: notes from the course Introduction à la statistique avec R, Paris Saclay

Statistics beginners course

Review some notions: https://www.khanacademy.org/math/statistics-probability/analyzing-categorical-data

Books

RStudio : https://rstudio-education.github.io/hopr/starting.html
R and data sciences: https://r4ds.had.co.nz/

Data

For this tutorial here

Fancy

R Markdown to produce beautiful reports: https://shiny.rstudio.com/articles/rmarkdown.html

Download and installation (Ubuntu 20)

Download and install the last release of R from the official website : https://cran.r-project.org/

There is a apt for it:

sudo apt install r-base

There also a links for precompiled binary versions, available for Windows, Mac and Linux.

Rstudio

As Rstudio for Ubuntu 20 isn’t available, download the one for Ubuntu18 Jessie.

dpkg -i rstudio-1.3.1073-amd64.deb

You might need to run another command to fix dependancies issues.

sudo apt --fix-broken install

Launch R interpreter, run Rstudio

Start Rstudio from the launchpad of Ubuntu

or/and

$R runs the R interpreter in your terminal

If you need a graphic interface, dl Rstudio https://rstudio.com/products/rstudio/download/

Run RStudio

First install packages you’ll need to draw plots:

    Tools -> Install Packages... ->  and select package you want to install in the dialog box; here "gplots"

Starting key points

In addition to the visual interface, you can launch instructions directly in the R interpretor (the console) and see the output (crucial for errors)
Graphs are generated in another window.
R scripts have the extention .r
you hit a script by running the instruction source()
You call the help menu by running the instruction help().

Variables

variables names are Case-senSitiVe
Missing value is encoded such as NA
mode() display the variable type numeric or character and lenght() the number of item in the variable
factor() and levels() are helps to qualify a variable: factor() for what qualitative value of the variable, labels= for labelling the variable

to keep in mind : Variable = (element1, element2, element2, element3,…) it’s like supervariable= [] and a lot of things inthere.

Load data

tips: Cat your file before to see what is the separator used in your data set. For example with a .csv file a “,” or a “;” as separator differs the input syntax: read.csv for “,” and read.csv2 for “;”

   > smp.c <-read.csv2[file path] 
   # smp here is the file in which I lead a set of data with "<-" and the .c method and read from a file.

So with our data: > smp.c <-read.csv2(“data/smp1.csv”)

To check the data integrity :

    > str(smp.c)  ~~~R  'data.frame':	799 obs. of  9 variables:  $ age      : int  31 49 50 47 23 34 24 52 42 45 ...  $ prof     : Factor w/ 8 levels "agriculteur",..: 3 NA 7 6 8 6 3 2 6 6 ...  $ dep.cons : int  0 0 0 0 1 0 1 0 1 0 ...  $ scz.cons : int  0 0 0 0 0 0 0 0 0 0 ...  $ grav.cons: int  1 2 2 1 2 1 5 1 5 5 ...  $ n.enfant : int  2 7 2 0 1 3 5 2 1 2 ...  $ rs       : int  2 2 2 2 2 1 3 2 3 2 ...  $ ed       : int  1 2 3 2 2 2 3 2 3 2 ...  $ dr       : int  1 1 2 2 2 1 2 2 1 2 ...  ~~~

Visualizations

barplot

    > barplot(table(smp.c$prof))

The same thing as table in the console:

    > table(smp.c$prof)  ~~~R
  agriculteur            artisan              autre              cadre            employe            ouvrier prof.intermediaire 
             6                 90                 31                 24                135                227                 58 
   sans emploi 
           222   ~~~

### pie

    > pie(table(smp.c$prof))

histogram

    > hist(smp.c$age) ![img](/pictures/hist.png)

vocabulary:

http://vita.had.co.nz/papers/layered-grammar.pdf

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