Fundamentals of data and data visualization 📊

# Fundamentals of data and data visualization <br> 📊
### Dr. Çetinkaya-Rundel

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<span>
Dr. Mine Çetinkaya-Rundel -
<a href="https://introds.org" target="_blank">introds.org
</a>
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</div>

---

## Week 2

- Preparing for tomorrow's workshop: Make sure you have completed Lab 01, and ask questions if you get stuck, complete survey asap
- Workshop tomorrow: Individual to give you some more practice on your own + hold off on setting teams till after drop/add
- Due this week: HW 01 (Wednesday) + OQ 01 (Friday)
  - Neither of these are graded, but you're strongly recommended to complete them
- Student hours for this week only: After class today + no student hours on Tuesday (but I'll be answering questions on Piazza)
- Thanks to all asking and answering questions on Piazza!

---

# Getting help in R

---

## Reading help files

---

## Asking good questions

.pull-left[
- **Good:** Describe your intention and include your code and the error
- **Better:** Describe your intention and create a minimum working example
- **Best:** Write a **rep**roducible **ex**ample (reprex) -- we'll introduce this concept more formally and teach you the tools for it a little later in the semester
]
--
.pull-right[
![](https://media.giphy.com/media/uRb2p09vY8lEs/giphy.gif)
- Use code formatting
- For issues with R code: copy / paste your code and resulting error, don't use screenshots 
]

---

# Exploratory data analysis

---

## What is EDA?

- Exploratory data analysis (EDA) is an aproach to analyzing data sets to summarize its main characteristics.
- Often, this is visual. That's what we're focusing on today.
- But we might also calculate summary statistics and perform data wrangling/manipulation/transformation at (or before) this stage of the 
analysis. That's what we'll focus on next.

---

# 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.
- There are many tools for visualizing data (R is one of them), and 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%" />
]
.pull-right[
- **ggplot2** is tidyverse's data visualization package
- The `gg` in "ggplot2" stands for Grammar of Graphics
- It is inspired by the book **Grammar of Graphics** by Leland Wilkinson
]
---

## Grammar of Graphics

A grammar of graphics is a tool that enables us to concisely describe the components of a graphic

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

---

```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).
```

---

.question[
- What are the functions doing the plotting? 
- What is the dataset being plotted? 
- Which variable is on the x-axis and which variable is on the y-axis? 
- What does the warning mean?
]

```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).
```

---

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

---

## Hello ggplot2!

- `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
```
- To use ggplot2 functions, first load tidyverse

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

---

# Visualizing Star Wars

---

## Dataset terminology

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

```r
starwars
```

```
## # A tibble: 87 x 13
##    name  height  mass hair_color skin_color eye_color birth_year gender
##    <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
##  1 Luke…    172    77 blond      fair       blue            19   male  
##  2 C-3PO    167    75 <NA>       gold       yellow         112   <NA>  
##  3 R2-D2     96    32 <NA>       white, bl… red             33   <NA>  
##  4 Dart…    202   136 none       white      yellow          41.9 male  
##  5 Leia…    150    49 brown      light      brown           19   female
##  6 Owen…    178   120 brown, gr… light      blue            52   male  
##  7 Beru…    165    75 brown      light      blue            47   female
##  8 R5-D4     97    32 <NA>       white, red red             NA   <NA>  
##  9 Bigg…    183    84 black      light      brown           24   male  
## 10 Obi-…    182    77 auburn, w… fair       blue-gray       57   male  
## # … with 77 more rows, and 5 more variables: homeworld <chr>,
## #   species <chr>, films <list>, vehicles <list>, starships <list>
```
]

---

## Luke Skywalker

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

---

## What's in the Star Wars data?

Take a `glimpse` at the data:

```r
glimpse(starwars)
```

```
## Observations: 87
## Variables: 13
## $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "L…
## $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, …
## $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.…
## $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "bro…
## $ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "lig…
## $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", "blue", "…
## $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, …
## $ gender     <chr> "male", NA, NA, "male", "female", "male", "female", N…
## $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaa…
## $ species    <chr> "Human", "Droid", "Droid", "Human", "Human", "Human",…
## $ films      <list> [<"Revenge of the Sith", "Return of the Jedi", "The …
## $ vehicles   <list> [<"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <…
## $ starships  <list> [<"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanc…
```

---

## What's in the Star Wars data?

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

```r
?starwars
```

---

## Mass vs. height

```r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point()
```

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

---

## What's that warning?

- Not all characters have height and mass information (hence 28 of them not plotted)

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

- Going forward I'll supress the warning to save room on slides, but it's important to note it

---

## Labels

```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)")
```

<img src="w2_d1-dataviz_files/figure-html/unnamed-chunk-15-1.png" width="70%" />
]

---

## Mass vs. height

.question[
How would you describe this relationship? 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!

---

## Additional variables

We can map additional variables to various features of the plot:

- aesthetics
    - shape
    - colour
    - size
    - alpha (transparency)
- faceting: small multiples displaying different subsets

---

# Aesthetics

---

## Aesthetics options

Visual characteristics of plotting characters that can be **mapped to a specific variable**
in the data are

- `color`
- `size`
- `shape`
- `alpha` (transparency)

---

## Mass vs. height + gender

```r
ggplot(data = starwars, 
       mapping = aes(x = height, y = mass, color = gender)) +
  geom_point()
```

---

## Mass vs. height + gender

```r
ggplot(data = starwars, 
       mapping = aes(x = height, y = mass, color = gender, 
*                    size = birth_year)) +
  geom_point()
```

---

## Mass vs. height + gender

Let's now increase the size of all points **not** based on the values of a 
variable in the data:

```r
ggplot(data = starwars, mapping = aes(x = height, y = mass, color = gender)) +
* geom_point(size = 2)
```

---

## Aesthetics summary

- Continuous variable are measured on a continuous scale
- Discrete variables are measured (or often counted) on a discrete scale

aesthetics    | discrete                 | continuous
------------- | ------------------------ | ------------
color         | rainbow of colors        | gradient
size          | discrete steps           | linear mapping between radius and value
shape         | different shape for each | shouldn't (and doesn't) work

- Use aesthetics for mapping features of a plot to a variable, define the 
features in the geom for customization **not** mapped to a variable

---

# Faceting

---

## Faceting

- Smaller plots that display different subsets of the data
- Useful for exploring conditional relationships and large data

---

```r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
* facet_grid(. ~ gender) +
  geom_point() +
  labs(title = "Mass vs. height of Starwars characters",
*      subtitle = "Faceted by gender")
```

---

## Dive further...

.question[
In the next few slides describe what each plot displays. Think about
how the code relates to the output.
]

.alert[
The plots in the next few slides do not have proper titles, axis labels, etc. 
because we want you to figure out what's happening in the plots. But you 
should always label your plots!
]

---

```r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  facet_grid(gender ~ .)
```

---

```r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  facet_grid(. ~ gender)
```

---

```r
ggplot(data = starwars, mapping = aes(x = height, y = mass)) +
  geom_point() +
  facet_wrap(~ eye_color)
```

---

## Facet summary

- `facet_grid()`: 
    - 2d grid
    - `rows ~ cols`
    - use `.` for no split
- `facet_wrap()`: 1d ribbon wrapped into 2d

---

# Identifying variables

---

## Number of variables involved

* Univariate data analysis - distribution of single variable
* Bivariate data analysis - relationship between two variables
* Multivariate data analysis - relationship between many variables at once, usually focusing on the relationship between two while conditioning for others

---

## Types of variables

- **Numerical variables** can be classified as **continuous** or **discrete** based on whether or not the variable can take on an infinite number of values or only non-negative whole numbers, respectively. 
- If the variable is **categorical**, we can determine if it is **ordinal** based on whether or not the levels have a natural ordering.

---

# Visualizing numerical data

---

## Describing shapes of numerical distributions

* shape:
    * skewness: right-skewed, left-skewed, symmetric (skew is to the side of the longer tail)
    * modality: unimodal, bimodal, multimodal, uniform
* center: mean (`mean`), median (`median`), mode (not always useful)
* spread: range (`range`), standard deviation (`sd`), inter-quartile range (`IQR`)
* unusal observations

---

## Histograms

```r
ggplot(data = starwars, mapping = aes(x = height)) +
  geom_histogram(binwidth = 10)
```

```
## Warning: Removed 6 rows containing non-finite values (stat_bin).
```

---

## Density plots

```r
ggplot(data = starwars, mapping = aes(x = height)) +
  geom_density()
```

```
## Warning: Removed 6 rows containing non-finite values (stat_density).
```

---

## Side-by-side box plots

```r
ggplot(data = starwars, mapping = aes(y = height, x = gender)) +
  geom_boxplot()
```

```
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
```

---

# Visualizing categorical data

---

## Bar plots

```r
ggplot(data = starwars, mapping = aes(x = gender)) +
  geom_bar()
```

---

## Segmented bar plots, counts

```r
ggplot(data = starwars, mapping = aes(x = gender, fill = hair_color)) +
  geom_bar()
```

---

## Recode hair color

```r
starwars <- starwars %>%
  mutate(hair_color2 = 
           fct_other(hair_color, 
                     keep = c("black", "brown", "brown", "blond")
                     )
         )
```

---

## Segmented bar plots, counts

```r
ggplot(data = starwars, 
       mapping = aes(x = gender, fill = hair_color2)) +
  geom_bar() +
  coord_flip()
```

---

## Segmented bar plots, proportions

```r
ggplot(data = starwars, 
       mapping = aes(x = gender, fill = hair_color2)) +
  geom_bar(position = "fill") +
  coord_flip()
```

```r
  labs(y = "proportion")
```

```
## $y
## [1] "proportion"
## 
## attr(,"class")
## [1] "labels"
```

---

.question[
Which bar plot is a more useful representation for visualizing the relationship between gender and hair color?
]