Understanding learning

• Growth and fixed mindsets
• Reframe success + failure as opportunities for growth
• Growing area of research by Carol Dweck of Stanford

Reframing

Example: Tuberculosis data

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"

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

Data Visualisation

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 ∈ tidyverse

Our first ggplot!

library(ggplot2)
ggplot(tb_au)

Our first ggplot!

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

Our first ggplot!

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

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

Our first ggplot!

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

Our first ggplot!

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

Our first ggplot!

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

Our first ggplot!

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

Our first ggplot!

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

The "100% charts"

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

Code structure of ggplot

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

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

library(tidyverse)

• For help with the ggplot2, see ggplot2.tidyverse.org

Emphasizing different features with ggplot2

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

Emphasise ... ?

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 ... ?

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

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

Pie charts? Rose Charts

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?

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!)

(Actual) Pie charts

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.

Why?

The various looks of David Bowie

• 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.

References

• Chapter 3 of R for Data Science
• Data made available from WHO
• Garret Aden Buie's gentle introduction to ggplot2
• Mine Çetinkaya-Rundel's introduction to ggplot using star wars.