ETC1010: Introduction to Data Analysis

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# .monash-blue.outline-text[ETC1010: Introduction to Data Analysis]

<h2 style="font-weight:900!important;">Advanced topics in data visualisation</h2>

.bottom_abs.width100[

Lecturer: *Nicholas Tierney*

Department of Econometrics and Business Statistics

ETC1010.Clayton-x@monash.edu

April 2020

]

]]

---
class: transition
# While the song is playing...

Draw a mental model / concept map of last lectures content on joins.

---
class: refresher
# recap

- Joins

---

# Joins with a person and a coat, by [Leight Tami](https://twitter.com/leigh_tami18/status/1021471889309487105/photo/1)

---
# Upcoming Due Dates

- Assignment 1: Due April 8 at 5pm (Today!)

---

# Exploring life expectancy and income

We want to plot life expectancy vs income, but there's a problem:

.pull-left[

```r
gap_life_au
## # A tibble: 9 x 3
## country year life_expectancy
## <chr> <dbl> <dbl>
## 1 Australia 2012 82.5
## 2 Australia 2013 82.6
## 3 Australia 2014 82.5
## 4 Australia 2015 82.5
## 5 Australia 2016 82.5
## 6 Australia 2017 82.4
## 7 Australia 2018 82.5
## 8 Australia 2019 82.7
## 9 Australia 2020 82.8
```
]

.pull-right[

```r
gap_income_au
## # A tibble: 9 x 3
## country year gdp
## <chr> <dbl> <dbl>
## 1 Australia 2012 42800
## 2 Australia 2013 43200
## 3 Australia 2014 43700
## 4 Australia 2015 44100
## 5 Australia 2016 44600
## 6 Australia 2017 44900
## 7 Australia 2018 45400
## 8 Australia 2019 45500
## 9 Australia 2020 45800
```
]

---

# We need them in the same dataframe!

We could try `bind_cols()`, to bind dataframes columns together

```r
bind_cols(gap_life_au,
 gap_income_au)
## # A tibble: 9 x 6
## country year life_expectancy country1 year1 gdp
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Australia 2012 82.5 Australia 2012 42800
## 2 Australia 2013 82.6 Australia 2013 43200
## 3 Australia 2014 82.5 Australia 2014 43700
## 4 Australia 2015 82.5 Australia 2015 44100
## 5 Australia 2016 82.5 Australia 2016 44600
## 6 Australia 2017 82.4 Australia 2017 44900
## 7 Australia 2018 82.5 Australia 2018 45400
## 8 Australia 2019 82.7 Australia 2019 45500
## 9 Australia 2020 82.8 Australia 2020 45800
```

---

# But this has problems:

1. It produces messy output (country1, year1)
2. It doesn't work if the data doesn't have the same number of rows

```
## # A tibble: 9 x 6
## country year life_expectancy country1 year1 gdp
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Australia 2012 82.5 Australia 2012 42800
## 2 Australia 2013 82.6 Australia 2013 43200
## 3 Australia 2014 82.5 Australia 2014 43700
## 4 Australia 2015 82.5 Australia 2015 44100
## 5 Australia 2016 82.5 Australia 2016 44600
## 6 Australia 2017 82.4 Australia 2017 44900
## 7 Australia 2018 82.5 Australia 2018 45400
## 8 Australia 2019 82.7 Australia 2019 45500
## 9 Australia 2020 82.8 Australia 2020 45800
```

---

# How to bind data?

For example, how do we add this co2 data to income or life?

```r
gap_co2_au
## # A tibble: 3 x 3
## country year co2
## <chr> <dbl> <dbl>
## 1 Australia 2012 17 
## 2 Australia 2013 16.1
## 3 Australia 2014 15.4
```

---

# How to bind data?

We can't use `bind_cols()`

```r
bind_cols(gap_co2_au,
          gap_income_au)
```

```
Error: Argument 2 must be length 3, not 9
```

We could think about a more complex approach using `filter`, and so on...

But surely this must be a problem that we encounter in data analysis?

Someone must have thought of a solution to this before?

They did! **Joins**!

---

# Joins!

We can use ` left_join()` to combine the income and life expectancy data

```r
left_join(x = gap_income_au,
 y = gap_life_au,
 by = c("country", "year"))
## # A tibble: 9 x 4
## country year gdp life_expectancy
## <chr> <dbl> <dbl> <dbl>
## 1 Australia 2012 42800 82.5
## 2 Australia 2013 43200 82.6
## 3 Australia 2014 43700 82.5
## 4 Australia 2015 44100 82.5
## 5 Australia 2016 44600 82.5
## 6 Australia 2017 44900 82.4
## 7 Australia 2018 45400 82.5
## 8 Australia 2019 45500 82.7
## 9 Australia 2020 45800 82.8
```

---

# Add co2 data with another join:

We get missings for co2, because we don't have c02 values for 2015 and beyond.

```r
left_join(x = gap_income_au,
 y = gap_life_au,
 by = c("country", "year")) %>% 
 left_join(gap_co2_au,
 by = c("country", "year"))
## # A tibble: 9 x 5
## country year gdp life_expectancy co2
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Australia 2012 42800 82.5 17 
## 2 Australia 2013 43200 82.6 16.1
## 3 Australia 2014 43700 82.5 15.4
## 4 Australia 2015 44100 82.5 NA 
## 5 Australia 2016 44600 82.5 NA 
## 6 Australia 2017 44900 82.4 NA 
## 7 Australia 2018 45400 82.5 NA 
## 8 Australia 2019 45500 82.7 NA 
## 9 Australia 2020 45800 82.8 NA
```

---

# So now we can combine that together like so:

```r
gap_au <- left_join(x = gap_income_au,
 y = gap_life_au,
 by = c("country", "year")) %>% 
 left_join(gap_co2_au,
 by = c("country", "year"))

gap_au
## # A tibble: 9 x 5
## country year gdp life_expectancy co2
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Australia 2012 42800 82.5 17 
## 2 Australia 2013 43200 82.6 16.1
## 3 Australia 2014 43700 82.5 15.4
## 4 Australia 2015 44100 82.5 NA 
## 5 Australia 2016 44600 82.5 NA 
## 6 Australia 2017 44900 82.4 NA 
## 7 Australia 2018 45400 82.5 NA 
## 8 Australia 2019 45500 82.7 NA 
## 9 Australia 2020 45800 82.8 NA
```

---

# Now we can make a plot!

```r
ggplot(gap_au,
       aes(x = gdp,
           y = life_expectancy)) + 
  geom_point()
```

---

# Your Turn: go to exercises on rstudio.cloud

open "joins.Rmd"

Discuss with your partner why these two joins produce different results?

.pull-left[

```r
left_join(gap_co2_au,
 gap_life_au)
## # A tibble: 3 x 4
## country year co2 life_expectancy
## <chr> <dbl> <dbl> <dbl>
## 1 Australia 2012 17 82.5
## 2 Australia 2013 16.1 82.6
## 3 Australia 2014 15.4 82.5
```
]

.pull-right[

```r
left_join(gap_life_au,
 gap_co2_au)
## # A tibble: 9 x 4
## country year life_expectancy co2
## <chr> <dbl> <dbl> <dbl>
## 1 Australia 2012 82.5 17 
## 2 Australia 2013 82.6 16.1
## 3 Australia 2014 82.5 15.4
## 4 Australia 2015 82.5 NA 
## 5 Australia 2016 82.5 NA 
## 6 Australia 2017 82.4 NA 
## 7 Australia 2018 82.5 NA 
## 8 Australia 2019 82.7 NA 
## 9 Australia 2020 82.8 NA
```
]

---
class: transition
# Your Turn:

What happens when we add data from New Zealand into the mix?

How can you join that data together?

---
class: transition

# Making effective data plots

1. Principles / science of data visualisation
2. Features of graphics

---
# Principles / science of data visualisation

- Palettes and colour blindness
  - change blindness
  - using proximity
  - hierarchy of mappings

---
# Features of graphics

- Layering statistical summaries
- Themes
- adding interactivity

---
# Palettes and colour blindness

There are three main types of colour palette:

- Qualitative: categorical variables
- Sequential: low to high numeric values
- Diverging: negative to positive values

---
# Qualitative: categorical variables

---
# Sequential: low to high numeric values

---
# Diverging: negative to positive values

---
# Example: TB data

```
## # A tibble: 157,820 x 5
## country year count gender age 
## <chr> <dbl> <dbl> <chr> <chr>
## 1 Afghanistan 1980 NA m 04 
## 2 Afghanistan 1981 NA m 04 
## 3 Afghanistan 1982 NA m 04 
## 4 Afghanistan 1983 NA m 04 
## 5 Afghanistan 1984 NA m 04 
## 6 Afghanistan 1985 NA m 04 
## 7 Afghanistan 1986 NA m 04 
## 8 Afghanistan 1987 NA m 04 
## 9 Afghanistan 1988 NA m 04 
## 10 Afghanistan 1989 NA m 04 
## # … with 157,810 more rows
```

---
# Example: TB data: adding relative change

```
## # A tibble: 219 x 4
## country `2002` `2012` reldif
## <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan 6509 13907 1.14 
## 2 Albania 225 185 -0.178 
## 3 Algeria 8246 7510 -0.0893
## 4 American Samoa 1 0 -1 
## 5 Andorra 2 2 0 
## 6 Angola 17988 22106 0.229 
## 7 Anguilla 0 0 0 
## 8 Antigua and Barbuda 4 1 -0.75 
## 9 Argentina 5383 4787 -0.111 
## 10 Armenia 511 316 -0.382 
## # … with 209 more rows
```

---
# Example: Sequential colour with default palette

```r
ggplot(tb_map) + geom_polygon(aes(x = long, y = lat, group = group, fill = reldif)) +
  theme_map()
```

---
# Example: (improved) sequential colour with default palette

```r
library(viridis)
ggplot(tb_map) +
  geom_polygon(aes(x = long, y = lat, group = group, fill = reldif)) +
  theme_map() + scale_fill_viridis(na.value = "white")
```

---
# Example:  Diverging colour with better palette

```r
ggplot(tb_map) +
  geom_polygon(aes(x = long, y = lat, group = group, fill = reldif)) +
  theme_map() +
  scale_fill_distiller(palette = "PRGn", na.value = "white", limits = c(-7, 7))
```

---
# Summary on colour palettes

- Different ways to map colour to values:
  - Qualitative: categorical variables
  - Sequential: low to high numeric values
  - Diverging: negative to positive values

---
# Colour blindness

- About 8% of men (about 1 in 12), and 0.5% women (about 1 in 200) population have difficulty distinguishing between red and green. 
- Several colour blind tested palettes: RColorbrewer has an associated web site [colorbrewer.org](http://colorbrewer2.org) where the palettes are labelled. See also `viridis`, and `scico`.

---
# Plot of two coloured points: Normal Mode

---
# Plot of two coloured points: dicromat mode

---
# Showing all types of colourblindness

---
# Impact of colourblind-safe palette

```r
p2 <- p + scale_colour_brewer(palette = "Dark2")
p2
```

---
# Impact of colourblind-safe palette
<img src="lecture_4b_files/figure-html/cb-grid-1.png" width="100%" style="display: block; margin: auto;" />

---
# Impact of colourblind-safe palette

```r
p3 <- p + scale_colour_viridis_d()
p3
```

---
# Impact of colourblind-safe palette
<img src="lecture_4b_files/figure-html/cb-grid-viridis-1.png" width="100%" style="display: block; margin: auto;" />

---
# Summary colour blindness

- Apply colourblind-friendly colourscales
  - `+ scale_colour_viridis()`
  - `+ scale_colour_brewer(palette = "Dark2")`
  - `scico` R package

---
# Pre-attentiveness: Find the odd one out?

---
# Pre-attentiveness: Find the odd one out?

---
class: idea
# Using proximity in your plots

Basic rule: place the groups that you want to compare close to each other

---
# Which plot answers which question?

- "Is the incidence similar for males and females in 2012 across age groups?"
- "Is the incidence similar for age groups in 2012, across gender?"

---
# incidence similar for: (M and F) or (age, across gender) ?"

???

Here are two different arrangements of the tb data. To answer the question "Is the incidence similar for males and females in 2012 across age groups?" the first arrangement is better. It puts males and females right beside each other, so the relative heights of the bars can be seen quickly. The answer to the question would be "No, the numbers were similar in youth, but males are more affected with increasing age."

The second arrangement puts the focus on age groups, and is better to answer the question "Is the incidence similar for age groups in 2012, across gender?" To which the answer would be "No, among females, the incidence is higher at early ages. For males, the incidence is much more uniform across age groups."

---
# "Incidence similar for M & F in 2012 across age?"

- Males & females next to each other: relative heights of bars is seen quickly. 
- Auestion answer: "No, the numbers were similar in youth, but males are more affected with increasing age."

---
# "Incidence similar for age in 2012, across gender?"

- Puts the focus on age groups 
- Answer to the question: "No, among females, the incidence is higher at early ages. For males, the incidence is much more uniform across age groups."

---
# Proximity wrap up

- Facetting of plots, and proximity are related to change blindness, an area of study in cognitive psychology. 
- There are a series of fabulous videos illustrating the effects of making a visual break, on how the mind processes it by Daniel Simons lab. 
- Here's one example:  
[The door study](https://www.youtube.com/watch?v=FWSxSQsspiQ)

---
# Layering

- *Statistical summaries:* It is common to layer plots, particularly by adding statistical summaries, like a model fit, or means and standard deviations. The purpose is to show the **trend** in relation to the **variation**. 
- *Maps:* Commonly maps provide the framework for data collected spatially. One layer for the map, and another for the data.

---
# `geom_point()`

```r
ggplot(df, aes(x = x, y = y1)) + geom_point()
```

---
# `geom_smooth(method = "lm", se = FALSE)`

```r
ggplot(df, aes(x = x, y = y1)) + geom_point() +
  geom_smooth(method = "lm", se = FALSE)
```

---
# `geom_smooth(method = "lm")`

```r
ggplot(df, aes(x = x, y = y1)) + geom_point() +
  geom_smooth(method = "lm")
```

---
# `geom_point()`

```r
ggplot(df, aes(x = x, y = y2)) + geom_point()
```

---
# `geom_smooth(method = "lm", se = FALSE)`

```r
ggplot(df, aes(x = x, y = y2)) + geom_point() +
  geom_smooth(method = "lm", se = FALSE)
```

---
`geom_smooth(se = FALSE)`

```r
ggplot(df, aes(x = x, y = y2)) + geom_point() +
  geom_smooth(se = FALSE)
```

---
`geom_smooth(se = FALSE, span = 0.05)`

```r
ggplot(df, aes(x = x, y = y2)) + geom_point() +
  geom_smooth(se = FALSE, span = 0.05)
```

---
# `geom_smooth(se = FALSE, span = 0.2)`

```r
p1 <- ggplot(df, aes(x = x, y = y2)) + geom_point() +
 geom_smooth(se = FALSE, span = 0.2)
p1
```

---
# Interactivity with magic plotly

```r
library(plotly)
ggplotly(p1)
```

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---
# Themes: Add some style to your plot

.left-code[

```r
p <- ggplot(mtcars) +
 geom_point(aes(x = wt, 
 y = mpg, 
 colour = factor(gear))) +
 facet_wrap(~am)
p

```
]

.right-plot[
<img src="lecture_4b_files/figure-html/mtcars-out-1.png" width="100%" style="display: block; margin: auto;" />
]

---
# Theme: theme_minimal

.left-code[

```r
p + 
  theme_minimal()
```
]

.right-plot[
<img src="lecture_4b_files/figure-html/mtcars-minimal-out-1.png" width="100%" style="display: block; margin: auto;" />
]
---
# Theme: ggthemes `theme_few()`

.left-code[

```r
p + 
  theme_few() + 
  scale_colour_few()
```
]

.right-plot[
<img src="lecture_4b_files/figure-html/mtcars-theme-few-out-1.png" width="100%" style="display: block; margin: auto;" />
]

---
# Theme: ggthemes `theme_excel()` 😷

.left-code[

```r
p + 
  theme_excel() + 
  scale_colour_excel()
```
]

.right-plot[
<img src="lecture_4b_files/figure-html/mtcars-theme-excel-out-1.png" width="100%" style="display: block; margin: auto;" />
]
---
# Theme: for fun

.left-code[

```r
library(wesanderson)
p + 
  scale_colour_manual(
    values = wes_palette("Royal1")
    )

```
]

.right-plot[
<img src="lecture_4b_files/figure-html/theme-wes-out-1.png" width="100%" style="display: block; margin: auto;" />
]

---
# Summary: themes

- The `ggthemes` package has many different styles for the plots. 
- Other packages such as `xkcd`, `skittles`, `wesanderson`, `beyonce`, `ochre`, ....

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

---
# Hierarchy of mappings

1. Position - common scale (BEST): axis system
2. Position - nonaligned scale: boxes in a side-by-side boxplot
3. Length, direction, angle: pie charts, regression lines, wind maps
4. Area: bubble charts
5. Volume, curvature: 3D plots
6. Shading, color (WORST): maps, points coloured by numeric variable

- [Di's crowd-sourcing expt](http://visiphilia.org/2016/08/03/CM-hierarchy)
- Nice explanation by [Peter Aldous](http://paldhous.github.io/ucb/2016/dataviz/week2.html)
- [General plotting advice and a book from Naomi Robbins](https://www.forbes.com/sites/naomirobbins/#2b1e20082a6a)

---
# Your Turn:

- lab quiz open (requires answering questions from Lab exercise)
- go to rstudio.cloud and check out exercise 4-B
- If you want to use R / Rstudio on your laptop:
  - Install R + Rstudio (see [Stuart Lee's instructions](https://github.com/sa-lee/installr))
  - open R
  - type the following:
  ```r
  # install.packages("usethis")
  library(usethis)
  use_course("https://ida.numbat.space/exercises/4b/ida-exercise-4b.zip")
  ```

---

# Resources

- Kieran Healy [Data Visualization](http://socviz.co/index.html)
- Winston Chang (2012) [Cookbook for R](graphics cookbook)
- Antony Unwin (2014) [Graphical Data Analysis](http://www.gradaanwr.net)
- Naomi Robbins (2013) [Creating More Effective Charts](http://www.nbr-graphs.com)