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Introduction - Me

Felix Haass

ABI Freiburg / GIGA Hamburg

Political economy; conflict, democratization, United Nations

felixhaass

http://felixhaass.github.io

haass@giga-hamburg.de

Introduction - You

Briefly introduce yourself!

Your Name

Your Research

Your Motivation for Participation in the Workshop

Learning Objectives

• Learn principles of how to use the ggplot2 package to create informative data visualizations
  
• We will learn mechanics of the software package, rather than good visualization principles. For infos on that, see course page (watch out for the dataviz ninja, though!).
  
• Know where to find help

Course Principles

• Ask questions at any time!
• Collaborate with your neighbours!
• Individual preferences/data visualization problems are most welcome!

Outline of the Course

R Refresher - What is R?

• R is a programming language for statistical analysis
  • “Programming language”: repeat tedious tasks; replicability; connect to unusual data sources (Twitter, web sites, text documents, …)
  • “statistical analysis”: many packages to do any statistical analysis you want
• RStudio is the interactive software with which we write and execute R code, plot things, view the R memory environment (…and much more)

R Refresher - Libraries

R uses different libraries or packages to load specific functions (read excel files, talk to Twitter, generate plots, …): https://cran.r-project.org/. You load a package or a library with the command

library(read_excel) # read_excel is the package name (without quotation marks)

If a command throws an error, chances are you either

• forgot to load the respective library
• have a syntax error - R is case sensitive!

To install a package we use:

install.packages("gapminder") # with quotation marks!

R Refresher - Assignment

In R, we assign stuff (numbers, characters, data frames) to things (objects)

url <- "http://gmi.bicc.de/index.php?page=ranking-table"

• url: object, in this case: a character vector
• "http://gmi.bicc.de/index.php?page=ranking-table": “stuff” (URL, could be any text or number)
  
• <-: assign command, type < and - (shortcut: alt + - in RStudio)

R Refresher - Objects

In R, everything is an object–and you can have multiple objects in your memory at the same time!

# 1st object: assign numbers to a vector
numbers <- 1:5

# 2nd object: read data from an excel sheet
sipri <- read_excel("./data/SIPRI-Milex-data-1949-2016_cleaned.xlsx", 
                    sheet = 5, 
                    na = c("xxx", ". ."))

Executing this command yields to objects in memory, numbers the vector of numbers and the data frame sipri.

R Refresher - Data Frames

Data frames are rectangular data tables, like an Excel spreadsheet.

library(gapminder)
library(tidyverse)

gapminder

## # A tibble: 1,704 x 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fctr>      <fctr>    <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333       779
##  2 Afghanistan Asia       1957    30.3  9240934       821
##  3 Afghanistan Asia       1962    32.0 10267083       853
##  4 Afghanistan Asia       1967    34.0 11537966       836
##  5 Afghanistan Asia       1972    36.1 13079460       740
##  6 Afghanistan Asia       1977    38.4 14880372       786
##  7 Afghanistan Asia       1982    39.9 12881816       978
##  8 Afghanistan Asia       1987    40.8 13867957       852
##  9 Afghanistan Asia       1992    41.7 16317921       649
## 10 Afghanistan Asia       1997    41.8 22227415       635
## # ... with 1,694 more rows

Review: R Refresher

• Libraries are your friend! library() (load) them or install.packages() them!
• Assign stuff. Use <- for assignments!
• Multiple objects can and should exist in memory (if you lose track of the objects you’re juggling with, check the environemnt panel at the upper right of RStudio)
• Data frames are the most important type of object. Don’t stop until you have your data in a data frame (check data type with class())!
• Bonus tip: use the help function help(command_name) if you can’t remember a command’s options.

Organizing your Code

Having a structured way to organize your R code is useful for reproducibility (and your future sanity!)

There are two ways to improve your R code organization:

1. Folder Structure
2. RStudio Projects

Organizing your Code: Folder Structure

A useful way to organize your project folders:

project_name/       # name of your project
|-- code/           # here go all the .R script files
|-- data/           # here's your data
|   |-- input/       # raw input data file (experimental results, existing datasets)
    |-- output/      # transformed and cleaned datasets for analysis
|-- manuscript/     # your manuscript, i.e. .docx or LaTeX files
|-- figures/        # your figures as separate files 
|-- output/         # tables

Organizing your Code: RStudio Projects

An RStudio project takes care of several useful steps in your project. When you load an RStudio project, the following steps are taken:

• Sets the working directory to the project directory
• A new R session (process) is started (with no libraries)
• Previously edited source documents are restored into editor tabs
• Other RStudio settings (e.g. active tabs, splitter positions, etc.) are restored to where they were the last time the project was closed.
• […and others, see https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects]

Reading Data

To read .csv files, the the read_csv() function in the readr package is useful (automatically loaded through library(tidyverse)).

To read Excel files, use the read_excel() function from the readxl package, which needs to be loaded separately.

To read files from Stata or SPSS, use read_dta() or read_spss() from the haven package, which needs to be loaded separately.

Example:

library(tidyverse)
library(readxl)

sipri <- read_excel("./data/SIPRI-Milex-data-1949-2016_cleaned.xlsx", 
                    sheet = 5, na = c("xxx", ". ."))

To read R files (.rda or .rdata), simply use load("name_of_my_file.rda")

Also useful: the rio package!

Exercise

1. Create organized folder structure
2. Download the SIPRI data from the course website
3. Create an R script to read the data (hint: remember that the Excel file has several sheets!)

Solution

library(tidyverse)
library(readxl)

## Warning: package 'readxl' was built under R version 3.4.3

sipri <- read_excel("./data/SIPRI-Milex-data-1949-2016_cleaned.xlsx", 
                    sheet = 5, na = c("xxx", ". ."))

sipri_plot <- sipri %>%
  # from wide to long format with the `gather function
  gather(key = year, 
         value = military_expenditure, 
         -Country) %>% 
  ggplot(., aes(x = year, 
                y = military_expenditure, 
                group = Country)) + 
  geom_line(alpha = 0.5)

Solution

print(sipri_plot)