Data and visualisation

# Data and visualisation
## <br><br> Introduction to Data Science with R and Tidyverse
### based on datasciencebox.org

---

layout: true
  
<div class="my-footer">
<span>
Introduction to Data Science with R and Tidyverse | Lukas Jürgensmeier, Matteo Fina, Jan Bischoff | based on
<a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a>
</span>
</div>

---

# What is in a dataset?

---

## Dataset terminology

- Each row is an **observation**
- Each column is a **variable**

```r
starwars
```

```
## # A tibble: 87 x 14
##   name  height  mass hair_~1 skin_~2 eye_c~3 birth~4 sex   gender
##   <chr>  <int> <dbl> <chr>   <chr>   <chr>     <dbl> <chr> <chr> 
## 1 Luke~    172    77 blond   fair    blue       19   male  mascu~
## 2 C-3PO    167    75 <NA>    gold    yellow    112   none  mascu~
## 3 R2-D2     96    32 <NA>    white,~ red        33   none  mascu~
## 4 Dart~    202   136 none    white   yellow     41.9 male  mascu~
## 5 Leia~    150    49 brown   light   brown      19   fema~ femin~
## 6 Owen~    178   120 brown,~ light   blue       52   male  mascu~
## # ... with 81 more rows, 5 more variables: homeworld <chr>,
## #   species <chr>, films <list>, vehicles <list>,
## #   starships <list>, and abbreviated variable names
## #   1: hair_color, 2: skin_color, 3: eye_color, 4: birth_year
```

]

---

## Luke Skywalker

![luke-skywalker](img/luke-skywalker.png)

---

## What's in the Star Wars data?

Take a `glimpse` at the data:

```r
glimpse(starwars)
```

```
## Rows: 87
## Columns: 14
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth V~
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 1~
## $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, ~
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, gr~
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "lig~
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", ~
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, N~
## $ sex        <chr> "male", "none", "none", "male", "female", "m~
## $ gender     <chr> "masculine", "masculine", "masculine", "masc~
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine",~
## $ species    <chr> "Human", "Droid", "Droid", "Human", "Human",~
## $ films      <list> <"The Empire Strikes Back", "Revenge of the~
## $ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <~
## $ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TI~
```

---

.question[
How many rows and columns does this dataset have?
What does each row represent?
What does each column represent?
]

```r
?starwars
```

---

```r
nrow(starwars) # number of rows
```

```
## [1] 87
```

```r
ncol(starwars) # number of columns
```

```
## [1] 14
```

```r
dim(starwars)  # dimensions (row column)
```

```
## [1] 87 14
```
]

---

# Exploratory data analysis

---

## What is EDA?

- **Exploratory data analysis (EDA)** is an approach to analysing data sets to summarize its main characteristics
- Often, this is **visual** — this is what we'll focus on first
- But we might also calculate summary statistics and perform data wrangling/manipulation/transformation at (or before) this stage of the analysis — this is what we'll focus on next

---

## Mass vs. height

.question[ 
How would you describe the **relationship between mass and height** of Starwars characters?
What other variables would help us understand data points that don't follow the overall trend?
Who is the not so tall but really chubby character?
]

---

## Jabba!

---

# Data visualization

---

## Data visualization

> *"The simple graph has brought more information to the data analyst's mind than any other device." — John Tukey*

- Data visualization is the creation and study of the visual representation of data
- Many tools for visualizing data — R is one of them
- Many approaches/systems within R for making data visualizations — **ggplot2** is one of them, and that's what we're going to use

---

## ggplot2 `$\in$` tidyverse

.pull-left[
<img src="img/ggplot2-part-of-tidyverse.png" width="80%" style="display: block; margin: auto;" />
] 
.pull-right[ 
- **ggplot2** is tidyverse's data visualization package 
- `gg` in "ggplot2" stands for Grammar of Graphics 
- Inspired by the book **Grammar of Graphics** by Leland Wilkinson
]

---

## Grammar of Graphics

.pull-left-narrow[
A grammar of graphics is a tool that enables us to concisely describe the components of a graphic
]
.pull-right-wide[
<img src="img/grammar-of-graphics.png" width="100%" style="display: block; margin: auto;" />
]

.footnote[ Source: [BloggoType](http://bloggotype.blogspot.com/2016/08/holiday-notes2-grammar-of-graphics.html)]

---

## Mass vs. height

```r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
```

---

.question[ 
- What are the functions doing the plotting?
- What is the dataset being plotted?
- Which variables map to which features (aesthetics) of the plot?
- What does the warning mean?<sup>+</sup>
]

```r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
```

```
## Warning: Removed 28 rows containing missing values
## (`geom_point()`).
```

---

## Hello ggplot2!

.pull-left-wide[
- `ggplot()` is the main function in ggplot2
- Plots are constructed in layers
- Structure of the code for plots can be summarized as

```r
ggplot(data = [dataset], 
       mapping = aes(x = [x-variable], y = [y-variable])) +
   geom_xxx() +
   other options
```

- The ggplot2 package comes with the tidyverse

```r
library(tidyverse)
```

- For help with ggplot2, see [ggplot2.tidyverse.org](http://ggplot2.tidyverse.org/)
]

---

# Why do we visualize?

---

## Age at first kiss

---

## Facebook visits