Data Visualization in R with ggplot2

class: center, middle, inverse, title-slide

# Data Visualization in R with ggplot2
### Ali Seyhun Saral
### Jdmx 2019
### 2019-06-29

---

### `ggplot2`: What does gg stand for?
* Initiated by Hadley Wickham
* Based on Wilkinson(2005): The Grammar of Graphics

_A statistical graphic is a __mapping__ from data to __aesthetic__ attributes (color, shape, size) of __geometric objects__ (points, lines, bars)_. 
 Wickham - ggplot2 (2016)
--

- Set of independent components:
    - Data
    - Layers
        - Geometric objects (geoms)
        - Statistical Transformations (stats)
    - Scales (scale)
    - Coordinate system (coord)
    - Facets (facet)
    - Theme (theme)

---
### Mappings

* aes() is responsible for defining mappings
.pull-left[

```r
head(my_data)
```

```
##   class hours_studied score
## 1     A            28    63
## 2     A            21    59
## 3     A            14    40
## 4     A            29    78
## 5     A            16    37
## 6     A            28    60
```
]
.pull-right[

<img src="index_files/figure-html/unnamed-chunk-4-1.png" style="display: block; margin: auto;" />
]
---
## Let's get started
* Go to:
### [saral.it/jdmx](https://saral.it/jdmx)

* If you are running on your own computer, please install necessary packages ( `pack.R` )

* Open `ex1.R`

---
### Data

* World Happiness Report by UN Sustainable Development Solutions Network

```r
 head(happiness_data)
```

```
##       country happiness gdp_per_capita social_support life_expectancy
## 1     Finland     7.632          1.305          1.592           0.874
## 2      Norway     7.594          1.456          1.582           0.861
## 3     Denmark     7.555          1.351          1.590           0.868
## 4     Iceland     7.495          1.343          1.644           0.914
## 5 Switzerland     7.487          1.420          1.549           0.927
## 6 Netherlands     7.441          1.361          1.488           0.878
##   freedom generosity corruption continent
## 1   0.681      0.192        106    Europe
## 2   0.686      0.286        102    Europe
## 3   0.683      0.284        107    Europe
## 4   0.677      0.353         77    Europe
## 5   0.660      0.256        103    Europe
## 6   0.638      0.333         98    Europe
```
---
### Our first `ggplot2` graph
The relationship between `happiness` and `gdp_per_capita`.
<img src="index_files/figure-html/unnamed-chunk-7-1.png" width="80%" style="display: block; margin: auto;" />
---
### Initializing the canvas (Step 1)
* Load `ggplot2` library 
* We use `ggplot()` function (not ggplot2)
* Define the data frame
* Define the aesthetic mappings with `aes()`

```r
ggplot(happiness_data, aes( x = gdp_per_capita, y = happiness))
```

<img src="index_files/figure-html/unnamed-chunk-8-1.png" width="40%" style="display: block; margin: auto;" />
---
### Adding `geom_point` (Step 2)
* Elements are added with `+` (must be at the end of the line)
* Create scatterplots and bubble charts
* Can take `x`, `y`, `color`, `size`, `alpha` ...
* Documentation for `geom_point`:   
  https://ggplot2.tidyverse.org/reference/geom_point.html

```r
ggplot(happiness_data, aes( x = gdp_per_capita, y = happiness)) +
  geom_point()
```

<img src="index_files/figure-html/unnamed-chunk-9-1.png" width="30%" style="display: block; margin: auto;" />
* geoms inherit aesthetic mappings from the top level of the plot, which is the `ggplot()` function
---
### Adding `geom_text` 
* Elements are added with `+` (must be at the end of the line)
* Create scatterplots and bubble charts
* Can take `x`, `y`, `color`, `size`, `alpha` ...
* Documentation for `geom_point`:   
  https://ggplot2.tidyverse.org/reference/geom_text.html

```r
ggplot(happiness_data, aes( x = gdp_per_capita, y = happiness)) +
  geom_text(aes(label = country))
```

<img src="index_files/figure-html/unnamed-chunk-10-1.png" width="50%" style="display: block; margin: auto;" />
---
### Coloring vs. Mapping to a Color
* If we want to color a geom, we should add the parameter directly

```r
ggplot(happiness_data, aes( x = gdp_per_capita, y = happiness)) +
  geom_point(color = "red")
```

<img src="index_files/figure-html/unnamed-chunk-11-1.png" width="50%" style="display: block; margin: auto;" />
---
###  Coloring vs. Mapping to a Color
* If we want to map a variable to the color, we use `aes()`

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness,
                           color = continent)) +
  geom_point()
```

<img src="index_files/figure-html/unnamed-chunk-12-1.png" width="50%" style="display: block; margin: auto;" />
---
### Coloring vs. Mapping to a Color (Step 3)
* Note that we could put the color aesthetic in `geom_point()` as well.

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
    geom_point(aes(color = continent))
```

<img src="index_files/figure-html/unnamed-chunk-13-1.png" width="50%" style="display: block; margin: auto;" />
---
### Mapping to a Shape Instead

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
    geom_point(aes(shape = continent))
```

<img src="index_files/figure-html/unnamed-chunk-14-1.png" width="50%" style="display: block; margin: auto;" />
---
### Or both

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
  geom_point(aes(shape = continent, color = continent))
```

<img src="index_files/figure-html/unnamed-chunk-15-1.png" width="50%" style="display: block; margin: auto;" />
---
### We can even add a size mapping

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
  geom_point(aes( color = continent, size = generosity))
```

<img src="index_files/figure-html/unnamed-chunk-16-1.png" width="50%" style="display: block; margin: auto;" />
---
### Let's stick to the previous one

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
    geom_point(aes( color = continent))
```

<img src="index_files/figure-html/unnamed-chunk-17-1.png" width="50%" style="display: block; margin: auto;" />
---
### Setting the axis limits (Step 4 ->)

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
  geom_point(aes( color = continent)) +
  scale_y_continuous( limits = c(0,8)) 
```

<img src="index_files/figure-html/unnamed-chunk-18-1.png" width="50%" style="display: block; margin: auto;" />
---
### Setting the axis breaks (-> Step 4 ->)

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
    geom_point(aes( color = continent)) +
    scale_y_continuous( limits = c(0,8), breaks = 0:8) 
```

<img src="index_files/figure-html/unnamed-chunk-19-1.png" width="50%" style="display: block; margin: auto;" />
`seq()` function can be handy to set axis breaks.
---
### Setting axis names (-> Step 4)

<img src="index_files/figure-html/unnamed-chunk-20-1.png" width="50%" style="display: block; margin: auto;" />
`seq()` function can be handy to set axis breaks.
---

### Scales

* Control how data map to visual properties
* `ggplot2` has well selected defaults, but often customization is needed
* There are several "ready to use" scales for different type of aesthethics

[`scale_x_discrete()`](https://ggplot2.tidyverse.org/reference/scale_discrete.html)  
[`scale_x_continuous()`](https://ggplot2.tidyverse.org/reference/scale_continuous.html)  
[`scale_shape()`](https://ggplot2.tidyverse.org/reference/scale_shape.html)  
[`scale_color_brewer()`](https://ggplot2.tidyverse.org/reference/scale_brewer.html)  More information [Brewer Scale Palettes](http://colorbrewer2.org/#type=qualitative&scheme=Set1&n=3)

* And can be built manually:

[DIY scales](https://ggplot2.tidyverse.org/reference/scale_manual.html)
```
scale_colour_manual() 
scale_fill_manual() 
scale_size_manual() 
scale_shape_manual() 
scale_linetype_manual() 
scale_alpha_manual()
scale_discrete_manual() 
```

---
### Setting color scale : Brewer palette (Step 5)

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
  geom_point(aes( color = continent)) +
  scale_y_continuous(name = "Happiness index", limits = c(0,8), breaks = 0:8) +
  scale_x_continuous(name = "Log GDP per Capita") +
  scale_color_brewer(name = "Continent", palette = "Set1")
```

<img src="index_files/figure-html/unnamed-chunk-21-1.png" width="50%" style="display: block; margin: auto;" />
---
### Setting color scale : Custom

<img src="index_files/figure-html/unnamed-chunk-22-1.png" width="50%" style="display: block; margin: auto;" />
---
### Setting color scale : Custom

<img src="index_files/figure-html/unnamed-chunk-23-1.png" width="45%" style="display: block; margin: auto;" />
---
### Adding `geom_smooth()`(Step 6)

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
  geom_point(aes( color = continent)) +
  geom_smooth() +
  scale_y_continuous(name = "Happiness index", limits = c(0,8), breaks = 0:8) +
  scale_x_continuous(name = "Log GDP per Capita") +
  scale_color_brewer(palette = "Set1")
```

<img src="index_files/figure-html/unnamed-chunk-24-1.png" width="60%" style="display: block; margin: auto;" />
---
### Adding linear `geom_smooth()` (Step 7)

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
  geom_point(aes( color = continent)) +
  geom_smooth(method = "lm") +
  scale_y_continuous(name = "Happiness index", limits = c(0,8), breaks = 0:8) +
  scale_x_continuous(name = "Log GDP per Capita") +
  scale_color_brewer(palette = "Set1")
```

<img src="index_files/figure-html/unnamed-chunk-25-1.png" width="60%" style="display: block; margin: auto;" />
---
### Final Touches (Step 7)

```r
ggplot(happiness_data, aes(x = gdp_per_capita,
                           y = happiness)) +
  geom_point(aes(color = continent), alpha = 0.5) +
  geom_smooth(method = "lm", se= FALSE, color = "black") +
  scale_y_continuous(name = "Happiness index", limits = c(0,8), breaks = 0:8) +
  scale_x_continuous(name = "Log GDP per Capita") +
  scale_color_brewer(name = "", palette = "Set1") +
  theme_bw()
```

<img src="index_files/figure-html/unnamed-chunk-26-1.png" width="60%" style="display: block; margin: auto;" />
---
### Facetting (Bonus Step)

<img src="index_files/figure-html/unnamed-chunk-27-1.png" width="90%" style="display: block; margin: auto;" />
---
### Before we go on:
<img src="./img/googling.png" width="60%" style="display: block; margin: auto;" />
---

<img src="./img/googling2.png" width="80%" style="display: block; margin: auto;" />
---
### Don't get lost
#### Commands and Packages
`help(command_name)`  
`help(package = 'dplyr')`  
`browseVignettes(package=package-name)`

#### Browsing data
`view(data_name)` : Real slow. Only in RStudio  
    `head(data_name` and `tail(data_name)`: First n lines / Last n lines  
`str(data_name)`: Check the structure  
`glimpse(data_name)`: Better structure (IMHO)  
`summary(data_name)`: Summary of the data

#### My problem solving workflow

* Help functions
* Googling it [Don't let anyone google that for you](https://lmgtfy.com/?iie=1&q=rotate+axis+labels+in+ggplot2)
* [Tidyverse.org](https://www.tidyverse.org/)
* [Stackoverflow.com](https://www.stackoverflow.com)

---
### Bar charts in ggplot
* `geom_col()`: 
    * Direct mapping from the data 
    * x: categorical variable
    * y: bar height
    
* `geom_bar()`: 
    * Special case of `geom_col()`
    * x: categorical variable
    * y: no y variable (with exceptions)
---
### Bar charts in ggplot : geom_col()

```r
library(dplyr)
happiness_data_subset  <- happiness_data  %>% filter( country %in% c("Italy", "Germany", "Turkey") )
head(happiness_data_subset)
```

```
##   country happiness gdp_per_capita social_support life_expectancy freedom
## 1 Germany     6.965          1.340          1.474           0.861   0.586
## 2   Italy     6.000          1.264          1.501           0.946   0.281
## 3  Turkey     5.483          1.148          1.380           0.686   0.324
##   generosity corruption continent
## 1      0.273         95    Europe
## 2      0.137         12    Europe
## 3      0.106         64      Asia
```
---
### Bar charts in ggplot : geom_col()

```r
ggplot(happiness_data_subset, aes(x = country, y = happiness)) +
  geom_col()
```

<img src="index_files/figure-html/unnamed-chunk-33-1.png" width="50%" style="display: block; margin: auto;" />
---
### Bar charts in ggplot : geom_col()

```r
library(dplyr)
ggplot(happiness_data_subset, aes(x = country, y = happiness)) +
  geom_point(size = 4) +
  scale_y_continuous(limits = c(0,8)) +
  coord_flip()
```

<img src="index_files/figure-html/unnamed-chunk-34-1.png" width="50%" style="display: block; margin: auto;" />
---
### Nobel Prize Data (Step 1)
* Open ex2.R
* Individual/Organizational level

```r
glimpse(nobel_winners)
```

```
## Observations: 969
## Variables: 20
## $ prize_year           <dbl> 1901, 1901, 1901, 1901, 1901, 1901, 1902, 1…
## $ category             <chr> "Chemistry", "Literature", "Medicine", "Pea…
## $ prize                <chr> "The Nobel Prize in Chemistry 1901", "The N…
## $ motivation           <chr> "\"in recognition of the extraordinary serv…
## $ prize_share          <chr> "1/1", "1/1", "1/1", "1/2", "1/2", "1/1", "…
## $ laureate_id          <dbl> 160, 569, 293, 462, 463, 1, 161, 571, 294, …
## $ laureate_type        <chr> "Individual", "Individual", "Individual", "…
## $ full_name            <chr> "Jacobus Henricus van 't Hoff", "Sully Prud…
## $ birth_date           <date> 1852-08-30, 1839-03-16, 1854-03-15, 1828-0…
## $ birth_city           <chr> "Rotterdam", "Paris", "Hansdorf (Lawice)", …
## $ birth_country        <chr> "Netherlands", "France", "Prussia (Poland)"…
## $ gender               <chr> "Male", "Male", "Male", "Male", "Male", "Ma…
## $ organization_name    <chr> "Berlin University", NA, "Marburg Universit…
## $ organization_city    <chr> "Berlin", NA, "Marburg", NA, NA, "Munich", …
## $ organization_country <chr> "Germany", NA, "Germany", NA, NA, "Germany"…
## $ death_date           <date> 1911-03-01, 1907-09-07, 1917-03-31, 1910-1…
## $ death_city           <chr> "Berlin", "Châtenay", "Marburg", "Heiden", …
## $ death_country        <chr> "Germany", "France", "Germany", "Switzerlan…
## $ birth_year           <dbl> 1852, 1839, 1854, 1828, 1822, 1845, 1852, 1…
## $ age_awarded          <dbl> 49, 62, 47, 73, 79, 56, 50, 85, 45, 69, 59,…
```
---
### Bar Charts: `geom_bar()` (Step 2)
* Note that geom_bar automatically counts the number of instances (default behavior)
* Special case of geom_col()

```r
ggplot(nobel_winners, aes(x = category))+
  geom_bar()
```

---

### Bar Charts: `fill` aesthethics (Step 3)
* Note that we use `fill` instead of `color`
* Color often refers to outline

```r
ggplot(nobel_winners, aes(x = category, fill = gender))+
  geom_bar()
```

<img src="index_files/figure-html/unnamed-chunk-38-1.png" width="50%" style="display: block; margin: auto;" />
---

### Investigating the data
What are the NA's?

```r
nobel_winners  %>% filter(is.na(gender))  %>% select(full_name)
```

```
## # A tibble: 26 x 1
##    full_name                                                               
##    <chr>                                                                   
##  1 Institut de droit international (Institute of International Law)        
##  2 Bureau international permanent de la Paix (Permanent International Peac…
##  3 Comité international de la Croix Rouge (International Committee of the …
##  4 Office international Nansen pour les Réfugiés (Nansen International Off…
##  5 Comité international de la Croix Rouge (International Committee of the …
##  6 Friends Service Council (The Quakers)                                   
##  7 American Friends Service Committee (The Quakers)                        
##  8 Office of the United Nations High Commissioner for Refugees (UNHCR)     
##  9 Comité international de la Croix Rouge (International Committee of the …
## 10 Ligue des Sociétés de la Croix-Rouge (League of Red Cross Societies)    
## # … with 16 more rows
```

```r
library(dplyr)
unique(nobel_winners$laureate_type)
```

```
## [1] "Individual"   "Organization"
```

---

### Let's take individual laureates only  (Step 4 and Step 5)

```r
nobel_winners_ind  <- nobel_winners  %>%
  filter(laureate_type == "Individual")

ggplot(nobel_winners_ind, aes(x = category, fill = gender))+
  geom_bar()
```

---
### Different positionings

```r
ggplot(nobel_winners_ind, aes(x = category, fill = gender))+
  geom_bar(position = "dodge")
```

<img src="index_files/figure-html/unnamed-chunk-42-1.png" width="50%" style="display: block; margin: auto;" />
---

### Different positionings

```r
ggplot(nobel_winners_ind, aes(x = category, fill = gender))+
  geom_bar(position = "fill")
```

<img src="index_files/figure-html/unnamed-chunk-43-1.png" width="50%" style="display: block; margin: auto;" />
---
### Flipping the x and the y axis (Step 7)
* `coord_flip()` does the trick easily

```r
ggplot(nobel_winners_ind, aes(x = category, fill = gender))+
  geom_bar(position = "dodge")+
  coord_flip()
```

<img src="index_files/figure-html/unnamed-chunk-44-1.png" width="50%" style="display: block; margin: auto;" />
---
### Bar ordering (Step 8)
* Bars are ordered by the order of the categorical variable (factor)
* `forcats` package have some useful tools that we can plug directly into the plot

```r
library(forcats)
ggplot(data = nobel_winners_ind, aes(x = fct_relevel(category, c("Economics", "Peace", "Literature", "Chemistry", "Physics", "Medicine")))) +
  geom_bar(aes(fill = gender)) +
  coord_flip()
```

<img src="index_files/figure-html/unnamed-chunk-45-1.png" width="50%" style="display: block; margin: auto;" />
---
# You can try out different bar positionings on the file(Step 9)
---
### A continous and a categorical variable: `geom_boxplot()`
* Summarized by  five parameters

```r
ggplot(nobel_winners, aes(y = age_awarded, x = category)) +
  geom_boxplot()
```

<img src="index_files/figure-html/unnamed-chunk-46-1.png" width="50%" style="display: block; margin: auto;" />
---

### A continuous and a categorical variable: `geom_point()`?

```r
ggplot(nobel_winners, aes(y = age_awarded, x = category)) +
  geom_point()
```

<img src="index_files/figure-html/unnamed-chunk-47-1.png" width="50%" style="display: block; margin: auto;" />
---
### A continuous and a categorical variable: `geom_jitter()`

```r
ggplot(nobel_winners, aes(y = age_awarded, x = category)) +
  geom_jitter()
```

<img src="index_files/figure-html/unnamed-chunk-48-1.png" width="50%" style="display: block; margin: auto;" />
---
### A continous and a categorical variable: `geom_jitter()`
* We have to be careful about misrepresentation

```r
ggplot(nobel_winners, aes(y = age_awarded, x = category)) +
  geom_jitter(height = 0, width = 0.3)
```

<img src="index_files/figure-html/unnamed-chunk-49-1.png" width="50%" style="display: block; margin: auto;" />
---
### Layering up: geom_jitter() and geom_boxplot()

```r
ggplot(nobel_winners, aes(y = age_awarded, x = category)) +
  geom_jitter(color = "blue") +
  geom_boxplot(fill = NA, color = "black", outlier.shape = NA)
```

<img src="index_files/figure-html/unnamed-chunk-50-1.png" width="50%" style="display: block; margin: auto;" />
---

```r
ggplot(nobel_winners, aes(x = age_awarded)) +
  geom_histogram() +
  facet_grid(category ~ .)
```

<img src="index_files/figure-html/unnamed-chunk-51-1.png" width="50%" style="display: block; margin: auto;" />
---

```r
ggplot(nobel_winners, aes(x = age_awarded)) +
  geom_density(fill = "blue") +
  facet_grid(category ~ .)
```

<img src="index_files/figure-html/unnamed-chunk-52-1.png" width="50%" style="display: block; margin: auto;" />
---

```r
nobel_winners  %>%
  filter(!is.na(age_awarded))  %>%
  group_by(prize_year)  %>%
  summarise( mean_age = mean(age_awarded))  %>% 
  ggplot(aes(x = prize_year, y = mean_age )) +
  geom_point() +
  geom_line() +
  scale_x_continuous(breaks = seq(1800,2019,10)) +
  scale_y_continuous(limits = c(15,99))
```

<img src="index_files/figure-html/unnamed-chunk-53-1.png" width="50%" style="display: block; margin: auto;" />
---

```r
nobel_winners  %>%
  filter(!is.na(age_awarded))  %>%
  group_by(prize_year)  %>%
  summarise( mean_age = mean(age_awarded))  %>% 
ggplot(aes(x = prize_year, y = mean_age )) +
geom_point() +
geom_line() +
geom_smooth() +
scale_x_continuous(breaks = seq(1800,2019,10)) +
scale_y_continuous(limits = c(15,99))
```

<img src="index_files/figure-html/unnamed-chunk-54-1.png" width="50%" style="display: block; margin: auto;" />
---

```r
nobel_winners  %>%
  filter(!is.na(age_awarded))  %>%
  group_by(prize_year, category)  %>%
  summarise( mean_age = mean(age_awarded))  %>% 
ggplot(aes(x = prize_year, y = mean_age )) +
geom_point() +
geom_line() +
geom_smooth(method = "lm", se = FALSE) +
scale_x_continuous(breaks = seq(1800,2019,10)) +
scale_y_continuous(limits = c(15,99)) +
facet_grid(category ~ .)
```

<img src="index_files/figure-html/unnamed-chunk-55-1.png" width="50%" style="display: block; margin: auto;" />
---
# Ex3 - Create the plot
<img src="index_files/figure-html/unnamed-chunk-56-1.png" width="60%" style="display: block; margin: auto;" />
---
---
<img src="index_files/figure-html/unnamed-chunk-57-1.png" width="100%" style="display: block; margin: auto;" />
---
### Step 1 - Creating jittered points
<img src="index_files/figure-html/unnamed-chunk-58-1.png" width="70%" style="display: block; margin: auto;" />

---
### Step 2 - Creating the summary statistics 
* Average of all disciplines
* Average for each discipline
* Minimum, Maximum, 
---

### Step 2 - Creating the summary statistics 
* Average of all disciplines

```r
average_age  <- mean(nobel_winners$age_awarded, na.rm = TRUE)
print(average_age)
```

```
## [1] 59.48401
```
* Average for each discipline

```r
nobel_winners_summary  <- nobel_winners %>%
  group_by(category)  %>%
  summarise( mean_age_awarded = mean(age_awarded, na.rm = TRUE))  %>%
  arrange(mean_age_awarded)

print(nobel_winners_summary)
```

```
## # A tibble: 6 x 2
##   category   mean_age_awarded
##   <fct>                 <dbl>
## 1 Physics                55.8
## 2 Medicine               57.9
## 3 Chemistry              58.1
## 4 Peace                  61.4
## 5 Literature             64.7
## 6 Economics              67.2
```
---
### Step 3 - Adding mean per discipline line

```r
ggplot(nobel_winners, aes(y = category, x = age_awarded, color = category))+
    geom_jitter(alpha = 0.3, width = 0.0, height = 0.1, size = 2) +
    geom_point(data = nobel_winners_summary,
               aes(x =mean_age_awarded), size = 10)
```

---
### Step 4 - Adding the point layer for averages

<img src="index_files/figure-html/unnamed-chunk-62-1.png" style="display: block; margin: auto;" />
---
### Step 5 - Adding a text layer

```r
ggplot(nobel_winners, aes(y = category, x = age_awarded, color = category))+
    geom_vline( xintercept = mean(nobel_winners$age_awarded, na.rm = TRUE), color = "gray20", linetype = "dashed") +
    geom_jitter(alpha = 0.3, width = 0.0, height = 0.1, size = 2) +
    geom_point(data = nobel_winners_summary,
               aes(x =mean_age_awarded), size = 10) +
    geom_text_repel(aes(label = full_name), data = filter(nobel_winners, age_awarded == min(nobel_winners$age_awarded, na.rm = TRUE) | age_awarded == max(nobel_winners$age_awarded, na.rm = TRUE)), color = "black")
```

<img src="index_files/figure-html/unnamed-chunk-63-1.png" width="70%" style="display: block; margin: auto;" />
---

### Step 6 - Adding a theme

<img src="index_files/figure-html/unnamed-chunk-64-1.png" width="70%" style="display: block; margin: auto;" />
---
<img src="index_files/figure-html/unnamed-chunk-65-1.png" width="100%" style="display: block; margin: auto;" />
---
# World Map

```r
library(rworldmap)
library(ggplot2)
map_world <- map_data(map="world")

#Add the data you want to map countries by to map.world
#In this example, I add lengths of country names plus some offset

map_world_happiness  <- map_world  %>% rename(country = region)  %>% left_join(happiness_data, by = "country")

map_world_new  <- map_world  %>% rename(country = region)

ggplot() +
  geom_map(data=map_world_happiness, map=map_world, aes(map_id=country, x=long, y=lat, fill=happiness)) +
  coord_equal()
```

<img src="index_files/figure-html/unnamed-chunk-66-1.png" style="display: block; margin: auto;" />
---

```r
library(rworldmap)
library(ggplot2)
map_world <- map_data(map="world")

#Add the data you want to map countries by to map.world
#In this example, I add lengths of country names plus some offset

map_world_happiness  <- map_world  %>% rename(country = region)  %>% left_join(happiness_data, by = "country")

map_world_new  <- map_world  %>% rename(country = region)

ggplot() +
  geom_map(data=map_world_happiness, map=map_world, aes(map_id=country, x=long, y=lat, fill=happiness), color = "black", size =0.2) +
  scale_fill_distiller(direction = 1) +
  coord_equal()
```

<img src="index_files/figure-html/unnamed-chunk-67-1.png" style="display: block; margin: auto;" />
---
### Final Remarks
- `ggplot2` is a powerful, flexible and consistent package that produce aesthetically pleasing graphics
--

- `ggplot2` is straightforward as long as you have the right data structure.

.center[
![Saw](./img/meme1.jpg)

]
---
### Final Remarks
- `ggplot2` is a powerful, flexible and consistent package that produce aesthetically pleasing graphics

- `ggplot2` is straightforward as long as you have the right data structure.

- My workflow is:
    - Sketch the plot
    - Determine the mappings
    - Create necessary side data
    - Layer up!
    
- We didn't cover `facets`, `themes` and `guides`.    Participant is encouraged to refer to the documentation
---
# Thank you

###Ali Seyhun Saral  
#### saral@coll.mpg.de

#### @seyhunsaral