Grammar of Graphics intro

R'de Görselleştirme En İyi Uygulamaları

Nick Strayer

Instructor

What is this course?

What you will learn

How to make better visualizations by thinking deeply about the data at hand.

How you will learn it

Overviews of different data types
Standard visualizations
Alternatives

Course layout

Chapter 1: Proportions of a whole

Chapter 2: Point data

Chapter 3: Single distributions

Chapter 4: Multiple(or conditional) distributions

Warning!

Topics here are not as cut and dry as other programming topics
Every rule will have exceptions
An emphasis on thinking through each problem is given to help you deal with these cases when you get to them

Tools used

R
The 'Tidyverse'
Ggplot2

Data used

Comes from the World Health Organization (WHO)

who_disease

# A tibble: 43,262 x 6
   region countryCode country             disease  year  cases
   <chr>  <chr>       <chr>               <chr>   <int>  <dbl>
 1 EMR    AFG         Afghanistan         measles  2016 638   
 2 EUR    ALB         Albania             measles  2016  17.0 
 3 AFR    DZA         Algeria             measles  2016  41.0 
 4 EUR    AND         Andorra             measles  2016   0   
 5 AFR    AGO         Angola              measles  2016  53.0 
 6 AMR    ATG         Antigua and Barbuda measles  2016   0   
 7 AMR    ARG         Argentina           measles  2016   0   
 8 EUR    ARM         Armenia             measles  2016   2.00
# ... with 43,254 more rows

WHO disease data

# Filter to AMR region 
amr_region <- who_disease %>% 
  filter(region == 'AMR')

# Map x to year and y to cases, color by disease
ggplot(amr_region, aes(x = year, y = cases, color = disease)) + 
  geom_point(alpha = 0.5)

Let's practice!

R'de Görselleştirme En İyi Uygulamaları