ETC5510: Introduction to Data Analysis

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<br>

# .monash-blue.outline-text[ETC5510: Introduction to Data Analysis]

<br>

<h2 style="font-weight:900!important;">Data Visualisation</h2>

.bottom_abs.width100[

Lecturer: *Nicholas Tierney & Stuart Lee*

Department of Econometrics and Business Statistics

<span><i class="fas  fa-envelope faa-float animated "></i></span>  ETC5510.Clayton-x@monash.edu

23rd March 2020

<br>
]

]]

---
# Understanding learning

- Growth and fixed mindsets
- Reframe success + failure as opportunities for growth
- Growing area of research by [Carol Dweck of Stanford](https://www.youtube.com/watch?v=hiiEeMN7vbQ)

---
# Reframing

.pull-left[
# From

> "I'll never understand"

> "I just don't get programming"

> "I'm not a maths person"

]

.pull-right[
# To

> "I understand more than I did yesterday"

> "I can learn how to program"

> "Compared to this last week, I've learnt quite a bit!"

]

---
class: transition

# Overview for today

- Going from tidy data to a data plot, using a grammar
- Mapping of variables from the data to graphical elements
- Using different geoms

---
# Example: Tuberculosis data

.left-code[
The case notifications table From [WHO](http://www.who.int/tb/country/data/download/en/).

Data is tidied here, with only counts for Australia.
]

.right-plot[

```r
tb_au
## # A tibble: 192 x 6
##    country   iso3   year count gender age  
##    <chr>     <chr> <dbl> <dbl> <chr>  <chr>
##  1 Australia AUS    1997     8 m      15-24
##  2 Australia AUS    1998    11 m      15-24
##  3 Australia AUS    1999    13 m      15-24
##  4 Australia AUS    2000    16 m      15-24
##  5 Australia AUS    2001    23 m      15-24
##  6 Australia AUS    2002    15 m      15-24
##  7 Australia AUS    2003    14 m      15-24
##  8 Australia AUS    2004    18 m      15-24
##  9 Australia AUS    2005    32 m      15-24
## 10 Australia AUS    2006    33 m      15-24
## # … with 182 more rows
```
]

---
# The "100% charts"

```r
ggplot(tb_au, aes(x = year, y = count, fill = gender)) +
  geom_bar(stat = "identity", position = "fill") +
  facet_grid(~ age) +
  scale_fill_brewer(palette="Dark2")
```

???

100% charts, is what excel names these beasts. What do we learn?

---
class: transition
# Let's unpack a bit.

---
# Data Visualisation

.blockquote[
"The simple graph has brought more information to the data analyst’s mind than any other device." — John Tukey
]

---
# Data Visualisation

- 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

.left-code[
<img src="images/ggplot2-part-of-tidyverse.png" width="80%" style="display: block; margin: auto;" />
]

.right-plot[
- **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 <sup>†</sup>
- A grammar of graphics is a tool that enables us to concisely describe the components of a graphic
- (Source: [BloggoType](http://bloggotype.blogspot.com/2016/08/holiday-notes2-grammar-of-graphics.html))
]

---
background-image: url(images/grammar-of-graphics.png)
background-size: contain
background-position: 50% 50%
class: center, bottom, white

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

---
# Our first ggplot!

.left-code[

```r
library(ggplot2)
ggplot(tb_au)
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-1-out-1.png" width="100%" style="display: block; margin: auto;" />

]

---
# Our first ggplot!

.left-code[

```r
library(ggplot2)
ggplot(tb_au,
*      aes(x = year,
*          y = count))
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-2-out-1.png" width="100%" style="display: block; margin: auto;" />

]

---
# Our first ggplot!

.left-code[

```r
library(ggplot2)
ggplot(tb_au,
       aes(x = year, 
           y = count))  +
* geom_point()
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-3-out-1.png" width="100%" style="display: block; margin: auto;" />

]

---
# Our first ggplot! (what's the data again?)

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> country </th>
   <th style="text-align:left;"> iso3 </th>
   <th style="text-align:right;"> year </th>
   <th style="text-align:right;"> count </th>
   <th style="text-align:left;"> gender </th>
   <th style="text-align:left;"> age </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 1997 </td>
   <td style="text-align:right;"> 8 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 1998 </td>
   <td style="text-align:right;"> 11 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 1999 </td>
   <td style="text-align:right;"> 13 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 2000 </td>
   <td style="text-align:right;"> 16 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 2001 </td>
   <td style="text-align:right;"> 23 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 2002 </td>
   <td style="text-align:right;"> 15 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 2003 </td>
   <td style="text-align:right;"> 14 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 2004 </td>
   <td style="text-align:right;"> 18 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 2005 </td>
   <td style="text-align:right;"> 32 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Australia </td>
   <td style="text-align:left;"> AUS </td>
   <td style="text-align:right;"> 2006 </td>
   <td style="text-align:right;"> 33 </td>
   <td style="text-align:left;"> m </td>
   <td style="text-align:left;"> 15-24 </td>
  </tr>
</tbody>
</table>

---
# Our first ggplot!

.left-code[

```r
library(ggplot2)
ggplot(tb_au,
       aes(x = year, 
           y = count))  +
* geom_col()
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-4-out-1.png" width="100%" style="display: block; margin: auto;" />

]

---
# Our first ggplot!

.left-code[

```r
library(ggplot2)
ggplot(tb_au,
       aes(x = year, 
           y = count,
*          fill = gender))  +
  geom_col() 
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-5-out-1.png" width="100%" style="display: block; margin: auto;" />

]

---
# Our first ggplot!

.left-code[

```r
library(ggplot2)
ggplot(tb_au,
       aes(x = year, 
           y = count,
           fill = gender))  + 
* geom_col(position = "fill")
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-6-out-1.png" width="100%" style="display: block; margin: auto;" />

]

---
# Our first ggplot!

.left-code[

```r
library(ggplot2)
ggplot(tb_au,
       aes(x = year, 
           y = count,
           fill = gender))  + 
  geom_col(position = "fill") +
* scale_fill_brewer(
*   palette = "Dark2"
*   )
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-7-out-1.png" width="100%" style="display: block; margin: auto;" />

]

---
# Our first ggplot!

.left-code[

```r
library(ggplot2)
ggplot(tb_au,
       aes(x = year, 
           y = count,
           fill = gender))  + 
  geom_col(position = "fill") +
  scale_fill_brewer( 
    palette = "Dark2"
    ) +
* facet_wrap(~ age)
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-8-out-1.png" width="100%" style="display: block; margin: auto;" />

]

???

- First argument provided is the name of the data, `tb_au`
- Variable mapping: year is mapped to x, count is mapped to y, gender is mapped to colour, and age is used to subset the data and make separate plots
- The column geom is used, `geom_col`
- We are mostly interested in proportions between gender, over years, separately by age. The `position = "fill"` option in `geom_bar` sets the heights of the bars to be all at 100%. It ignores counts, and emphasizes the proportion of males and females.

---
# The "100% charts"

```r
ggplot(tb_au, aes(x = year, y = count, fill = gender)) +
  geom_bar(stat = "identity", position = "fill") +
  facet_grid(~ age) +
  scale_fill_brewer(palette="Dark2")
```

What do we learn

???

100% charts, is what excel names these beasts. What do we learn?

---
class: transition left
# What do we learn?

- Focus is on **proportion** in each category. 
- Across (almost) all ages, and years, the proportion of males having TB is higher than females
- These proportions tend to be higher in the older age groups, for all years.

---
# Code structure of ggplot

- `ggplot()` is the main function
- Plots are constructed in layers
- Structure of code for plots can often be summarised as

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

---
# How to use ggplot

- To use ggplot2 functions, first load tidyverse

```r
library(tidyverse)
```

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

---
class: transition
# Let's look at some more options to emphasise different features

---
.left-code[

```r
ggplot(tb_au,
       aes(x = year, 
           y = count,
           fill = gender))  + 
  geom_col(position = "fill") +
  scale_fill_brewer( 
    palette = "Dark2"
    ) +
* facet_wrap(~ age)
```

]

.right-plot[
<img src="lecture_3a_files/figure-html/first-gg-9-out-1.png" width="100%" style="display: block; margin: auto;" />

]

---
# Emphasizing different features with ggplot2

```r
ggplot(tb_au, 
       aes(x = year, 
           y = count, 
           fill = gender))  + 
  geom_col(position = "fill") +
  scale_fill_brewer( palette = "Dark2") +
* facet_grid(~ age)
```

---
# Emphasise ... ?

```r
ggplot(tb_au, 
       aes(x = year, 
           y = count, 
           fill = gender))  + 
* geom_col() +
  scale_fill_brewer( palette = "Dark2") +
  facet_grid(~ age)
```

---
# What do we learn?

- `, position = "fill"` was removed
- Focus is on **counts** in each category. 
- Different across ages, and years, counts tend to be lower in middle age (45-64)
- 1999 saw a bit of an outbreak, in most age groups, with numbers doubling or tripling other years.
- Incidence has been increasing among younger age groups in recent years.

---
# Emphasise ... ?

```r
ggplot(tb_au, 
       aes(x = year, 
           y = count, 
           fill = gender)) +
* geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(~ age)
```

---
# What do we learn?

- `, position="dodge"` is used in `geom_col`
- Focus is on **counts by gender**, predominantly male incidence.
- Incidence among males relative to females is from middle age on. 
- There is similar incidence between males and females in younger age groups.

---
# Separate bar charts

```r
ggplot(tb_au, 
       aes(x = year, y = count, fill = gender)) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
* facet_grid(gender ~ age)
```

---
class: transition
# What do we learn?

- `facet_grid(gender ~ age) +` faceted by gender as well as age
- note `facet_grid` vs `facet_wrap`
- Easier to focus separately on males and females.
- 1999 outbreak mostly affected males.
- Growing incidence in the 25-34 age group is still affecting females but seems to be have stablised for males.

---
# ~~Pie charts?~~ Rose Charts

```r
ggplot(tb_au, 
       aes(x = year, y = count, fill = gender)) +
  geom_col() +
  scale_fill_brewer(palette="Dark2") + 
  facet_grid(gender ~ age) +
* coord_polar() +
  theme(axis.text = element_blank())
```

---
# What do we learn?

- Bar charts in polar coordinates produce rose charts. 
- `coord_polar() +` plot is made in polar coordinates, rather than the default Cartesian coordinates
- Emphasizes the middle years as low incidence.

---
# Rainbow charts?

```r
ggplot(tb_au, aes(x = 1, 
                  y = count, 
*                 fill = factor(year))) +
  geom_col(position = "fill") +
  facet_grid(gender ~ age) 
```

---
# What do we see in the code??

- A single stacked bar, in each facet. 
- Year is mapped to colour. 
- Notice how the mappings are different. A single number is mapped to x, that makes a single stacked bar chart.
- year is now mapped to colour (that's what gives us the rainbow charts!)

---
class: transition
# What do we learn?

- Pretty chart but not easy to interpret.

---
# (Actual) Pie charts

```r
ggplot(tb_au, aes(x = 1, y = count, fill = factor(year))) +
  geom_col(position = "fill") +
* facet_grid(gender ~ age) +
* coord_polar(theta = "y") +
  theme(axis.text = element_blank())
```

---
# What is different in the code?

- `coord_polar(theta="y")`  is using the y variable to do the angles for the polar coordinates to give a pie chart.

---
class: transition
# What do we learn?

- Pretty chart but not easy to interpret, or make comparisons across age groups.

---
# Why?

[The various looks of David Bowie](https://www.wired.com/wp-content/uploads/2016/01/DB-Transformation-Colour.gif)

.left-code[
<img src="https://www.wired.com/wp-content/uploads/2016/01/DB-Transformation-Colour.gif" style="width:50%" />

]

- Using named plots, eg pie chart, bar chart, scatterplot, is like seeing animals in the zoo. 
- The grammar of graphics allows you to define the mapping between variables in the data, with elements of the plot. 
- It allows us to see and understand how plots are similar or different. 
- And you can see how variations in the definition create variations in the plot.

---
class: transition
# Your Turn:

- Do the lab exercises
- Take the lab quiz
- Use the rest of the lab time to coordinate with your group on the first assignment.

---

# References

- [Chapter 3 of R for Data Science](https://r4ds.had.co.nz/data-visualisation.html)
- [Data made available from WHO](https://www.who.int/tb/country/data/download/en/)
- [Garret Aden Buie's gentle introduction to ggplot2](https://pkg.garrickadenbuie.com/gentle-ggplot2/#1)
- [Mine Çetinkaya-Rundel's introduction to ggplot using star wars.](https://github.com/rstudio-education/datascience-box/tree/master/slides/u1_d02-data-and-viz)