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  3. Universiteit Leiden (UL)
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  5. Data analysis with R

Samenvatting

BW3 Data Science with R summary
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Samenvatting van de Opbouw en basiskennis van het werken met het R softwarepakket en programmeertaal voor data analyse. Inhoud: > R en R studio: introduction, calculator, variable, projects, scripts > Data structures: install/use package, vectors, factors, list, formulas > Data manipu...

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  • 6 oktober 2022
  • 16
  • 2021/2022
  • Samenvatting

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Voorbeeld van de inhoud

Summary Data Science with R

R and RStudio

INTRODUCTION R (15/11)
https://lumc.github.io/rcourse/B3DS_202111/S01L02l_introduction0.html
Statisticians develop new methods and make them first available as packages in R (no need to wait until these
methods are programmed into SPSS)
Now R is known as “a free software environment for statistical computing and graphics.
CRAN (Comprehensive R Archive Network) = a central repository for R language interpreter and R packages.
It also contains manuals and mailing lists (well indexed on google)
Rstudio = an open source integrated development environment for programming in R language. It provides
useful features to help in development of the R code and in organization of projects.

CALCULATOR (15/11)
https://lumc.github.io/rcourse/B3DS_202111/S01L03l_basic_calculator0.html
RStudio consists of various panes which are parts of a window, namely the console, source,
environment(workspace) and files (directory).

The console is the place to type & execute commands
● The “#” sign = comment. All text after it is simply ignored
● The “>” sign = prompt, meaning that one van type any expression. One can press enter to see the
result (output) of the expression.
● R can be used as a simple calculator with arithmetic operators:
○ Addition: +
○ Subtraction: -
○ Multiplication: *
○ Division: /
○ Exponentiation: ^
○ Use (...) for the correct order
■ Multiline commands: If (.... too much → R expects the rest is still coming → no error
or result but a + instead of > symbol on next line which means the command is not
finished yet.
If error → press ESCape button or ctrl C stops the demand.
○ Absolute number: |x| = abs(x)
○ Squareroot: √x = sqrt(x)
○ Decimal separator: one has to use a dot instead of a comma for decimals.
● Useful console keystrokes:
○ Ctrl + L clears the console pane, but not the history.
○ Ctrl + R shows the history ( can be checked with history()) in the environment pane.
■ The environment can be set to list
○ Use up-arrow and down-arrow to scroll through the history of the commands types before;
click one to To replay command
● Getting help by typing ?name in the console

VARIABLE (15/11)

,https://lumc.github.io/rcourse/B3DS_202111/S01L04l_basic_variables0.html
A variable is an argument that stores a value or result.
Choose the names of variables freely. They are case sensitive so do use underscore “_” instead of dot.
Numbers are allowed except for at the beginning of the variable.
Typing the variables in the console creates a change in the environment pane after each ENTER.
The symbol “<-” is the assignment operator that puts a value/number/… to a variable.
fe: x <- 5 puts the number 5 in variable x
(one can also use the = sign but this is less common and one has to be consistent)
if the variable is the outcome of a calculation: x <- (calculation)

The variables are stored in R memory and RStudio shows them in the Environment pane (top right).
The scripts provide reproducibility (shows the calculation + answer).

PROJECTS (15/11)
https://lumc.github.io/rcourse/B3DS_202111/S01L05l_basic_projects0.html
At the start of every new data analysis project a new project is created in RStudio.
For every data analysis multiple files (input files/scripts/reports) have to be placed in the new R project folder
in order to use these.
Create a new project: Menu → File → new project → new directory → new project
- Give the Directory a name
- Choose where to create/store the directory
- (or→ New project: right upper corner)
Copy input files:
- download the datafiles to your computer
- go to verkenner and copy-paste the downloaded files into the project map
- the downloaded files should appear in the bottomright pane. (or otherwise search for the files in
‘files’ panel)


SCRIPTS (15/11)
https://lumc.github.io/rcourse/B3DS_202111/S01L06l_basic_scripts0.html
R script is a simple text file containing commands written in R language.
R Markdown document
An R Markdown document is an extension of an R script that enables the development of elegant and
reproducible reports.
The file is created through:
1. Menu → File → New file - R markdown - name it, html → (switch old/new view with the letter A top
right)
2. Save the file with name in the same map as the project map (and it will be shown in the file panel)
3. Knit the document: converts an R markdown document into a report file which is shown in the
bottom right pane.
→ When typing in console: R script stores all executed commands (typed in console) in a text
file directly in memory of R (local machine/computer). Sourcing = running a file in R
language.
→ When typing in markdown it does not appear in R environment; when pressing knit it is
copied in another document on computer.
Environment and knitting are not combined/do not communicate with each other
Knitting is the process of recalculating all lines/steps and checks the reproducibility (but is
very slow) and allows you to create a report (text document with commands in R language
and free text).

, - (Make sure tidyverse is installed)
R markdown is mixture of pretext and R code:
● Titles are indicated with # 1
● Subtitles are indicated with ## or ###
● - (minus) introduces bullet list
● Hyperlinked words [word](link) enables the isnertion of a datalink.
● ** … ** = bold words
● R code is typed in chunks
○ insert chunk (shortcut: Cntr + Alt + I)
○ Start with ```{r} and end with ``` (in between areas should become grey)
○ Vectors: v <-
○ Library(…)
■ Every time you want to run a program/package (make sure tidyverse is installed!!)
○ ```{r warning=FALSE,message=FALSE} to no longer see error messages
○ To ‘open’ csv files in markdown: read_csv (or read.csv)
○ To make new csv files use: write_csv(...)




Data structures

INSTALL/USE PACKAGES (15/11)
https://lumc.github.io/rcourse/B3DS_202111/S02L04l_packages0.html
R packages are a collection of related functions, possibly with data, built to tackle a specific problem. R comes
with several pre-installed packages such as base, stats, datasets etc. that cover basic data science exercises.
The Comprehensive R Archive Network, CRAN for short, is possibly the only one you need to know for now.
How to install packages
- type it into console pane: install.packages(“your-package-name”)
- or look it up in lower right pane ‘package’ searchbar
- or look at menu-tools and install package
How to load a package
- type in command in console: library(haven)

VECTORS (INTRODUCTION) (15/11)
https://lumc.github.io/rcourse/B3DS_202111/S02L03l_basic_vectors0a.html
A vector is a container of (multiple) elements at the same time:
- all elements are of the same type
- elements are kept at numbered positions
- elements might be given names
Types of data:
- Numerical: a vector of numbers (height of students)
- Character: a vector of texts (names of students)
- Logical: a vector of FALSE/TRUE values (if students have siblings)
- Factor: a vector of values from a limited choice list (eye color of students)

General:



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