Visualizing Distributions

Visualizing Distributions - Sketchnote by @nitya

In the previous lesson, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by their maximum length.

Pre-lecture quiz

Explore the birds dataset

Another way to dig into data is by looking at its distribution, or how the data is organized along an axis. Perhaps, for example, you’d like to learn about the general distribution, for this dataset, of the maximum wingspan or maximum body mass for the birds of Minnesota.

Let’s discover some facts about the distributions of data in this dataset. In your R console, import ggplot2 and the database. Remove the outliers from the database just like in the previous topic.

library(ggplot2)

birds <- read.csv("../../data/birds.csv",fileEncoding="UTF-8-BOM")

birds_filtered <- subset(birds, MaxWingspan < 500)
head(birds_filtered)

	Name	ScientificName	Category	Order	Family	Genus	ConservationStatus	MinLength	MaxLength	MinBodyMass	MaxBodyMass	MinWingspan	MaxWingspan
0	Black-bellied whistling-duck	Dendrocygna autumnalis	Ducks/Geese/Waterfowl	Anseriformes	Anatidae	Dendrocygna	LC	47	56	652	1020	76	94
1	Fulvous whistling-duck	Dendrocygna bicolor	Ducks/Geese/Waterfowl	Anseriformes	Anatidae	Dendrocygna	LC	45	53	712	1050	85	93
2	Snow goose	Anser caerulescens	Ducks/Geese/Waterfowl	Anseriformes	Anatidae	Anser	LC	64	79	2050	4050	135	165
3	Ross’s goose	Anser rossii	Ducks/Geese/Waterfowl	Anseriformes	Anatidae	Anser	LC	57.3	64	1066	1567	113	116
4	Greater white-fronted goose	Anser albifrons	Ducks/Geese/Waterfowl	Anseriformes	Anatidae	Anser	LC	64	81	1930	3310	130	165

In general, you can quickly look at the way data is distributed by using a scatter plot as we did in the previous lesson:

ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) +
  geom_point() +
  ggtitle("Max Length per order") + coord_flip()

This gives an overview of the general distribution of body length per bird Order, but it is not the optimal way to display true distributions. That task is usually handled by creating a Histogram.

Working with histograms

ggplot2 offers very good ways to visualize data distribution using Histograms. This type of chart is like a bar chart where the distribution can be seen via a rise and fall of the bars. To build a histogram, you need numeric data. To build a Histogram, you can plot a chart defining the kind as ‘hist’ for Histogram. This chart shows the distribution of MaxBodyMass for the entire dataset’s range of numeric data. By dividing the array of data it is given into smaller bins, it can display the distribution of the data’s values:

ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + 
  geom_histogram(bins=10)+ylab('Frequency')

As you can see, most of the 400+ birds in this dataset fall in the range of under 2000 for their Max Body Mass. Gain more insight into the data by changing the bins parameter to a higher number, something like 30:

ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency')

This chart shows the distribution in a bit more granular fashion. A chart less skewed to the left could be created by ensuring that you only select data within a given range:

Filter your data to get only those birds whose body mass is under 60, and show 30 bins:

birds_filtered_1 <- subset(birds_filtered, MaxBodyMass > 1 & MaxBodyMass < 60)
ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
  geom_histogram(bins=30)+ylab('Frequency')

✅ Try some other filters and data points. To see the full distribution of the data, remove the ['MaxBodyMass'] filter to show labeled distributions.

The histogram offers some nice color and labeling enhancements to try as well:

Create a 2D histogram to compare the relationship between two distributions. Let’s compare MaxBodyMass vs. MaxLength. ggplot2 offers a built-in way to show convergence using brighter colors:

ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) +
  geom_bin2d() +scale_fill_continuous(type = "viridis")

There appears to be an expected correlation between these two elements along an expected axis, with one particularly strong point of convergence:

Histograms work well by default for numeric data. What if you need to see distributions according to text data?

Explore the dataset for distributions using text data

This dataset also includes good information about the bird category and its genus, species, and family as well as its conservation status. Let’s dig into this conservation information. What is the distribution of the birds according to their conservation status?

✅ In the dataset, several acronyms are used to describe conservation status. These acronyms come from the IUCN Red List Categories, an organization that catalogs species’ status.

• CR: Critically Endangered

• EN: Endangered

• EX: Extinct

• LC: Least Concern

• NT: Near Threatened

• VU: Vulnerable

These are text-based values so you will need to do a transform to create a histogram. Using the filteredBirds dataframe, display its conservation status alongside its Minimum Wingspan. What do you see?

birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'EX'] <- 'x1' 
birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'CR'] <- 'x2'
birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'EN'] <- 'x3'
birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'NT'] <- 'x4'
birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'VU'] <- 'x5'
birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'LC'] <- 'x6'

ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) +
  geom_histogram(position = "identity", alpha = 0.4, bins = 20) +
  scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern"))

There doesn’t seem to be a good correlation between minimum wingspan and conservation status. Test other elements of the dataset using this method. You can try different filters as well. Do you find any correlation?

Density plots

You may have noticed that the histograms we have looked at so far are ‘stepped’ and do not flow smoothly in an arc. To show a smoother density chart, you can try a density plot.

Let’s work with density plot’s now!

ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + 
  geom_density()

You can see how the plot echoes the previous one for Minimum Wingspan data; it’s just a bit smoother. If you wanted to revisit that jagged MaxBodyMass line in the second chart you built, you could smooth it out very well by recreating it using this method:

ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
  geom_density()

If you wanted a smooth, but not too smooth line, edit the adjust parameter:

ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
  geom_density(adjust = 1/5)

✅ Read about the parameters available for this type of plot and experiment!

This type of chart offers beautifully explanatory visualizations. With a few lines of code, for example, you can show the max body mass density per bird Order:

ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) +
  geom_density(alpha=0.5)

🚀 Challenge

Histograms are a more sophisticated type of chart than basic scatterplots, bar charts, or line charts. Go on a search on the internet to find good examples of the use of histograms. How are they used, what do they demonstrate, and in what fields or areas of inquiry do they tend to be used?

Post-lecture quiz

Review & Self Study

In this lesson, you used ggplot2 and started working to show more sophisticated charts. Do some research on geom_density_2d() a “continuous probability density curve in one or more dimensions”. Read through the documentation to understand how it works.

Assignment

Apply your skills