Data-Science-For-Beginners/3-Data-Visualization/R/09-visualization-quantities

Visualizing Quantities

Visualizing Quantities - Sketchnote by @nitya

In this lesson you will explore how to use some of the many available R packages libraries to learn how to create interesting visualizations all around the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you can learn many interesting facts about local wildlife.

Pre-lecture quiz TO BE LINKED

Observe wingspan with ggplot2

An excellent library to create both simple and sophisticated plots and charts of various kinds is ggplot2. In general terms, the process of plotting data using these libraries includes identifying the parts of your dataframe that you want to target, performing any transforms on that data necessary, assigning its x and y axis values, deciding what kind of plot to show, and then showing the plot.

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. The Grammar of Graphics is a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. In other words, the ease of creating plots and graphs for univariate or multivariate data with little code makes ggplot2 the most popular package used for visualizations in R. The user tells ggplot2 how to map the variables to aesthetics, the graphical primitives to use, and ggplot2 takes care of the remaining.

✅ Plot = Data + Aesthetics + Geometry

• Data refers to the dataset
• Aesthetics indicate the variables to be studied (x and y variables)
• Geometry refers to the type of plot (line plot, bar plot, etc.)

Choose the best geometry (type of plot) according to your data and the story you want to tell through the plot.

• To analyze trends: line, column
• To compare values: bar, column, pie, scatterplot
• To show how parts relate to a whole: pie
• To show distribution of data: scatterplot, bar
• To show relationships between values: line, scatterplot, bubble

✅ You can also checkout this descriptive cheatsheet for ggplot2.

Build a line plot about bird wingspan values

Open the R console and import the dataset.

Note: The dataset is stored in the root of this repo in the /data folder.

Let's import the dataset and observe the head (top 5 rows) of the data.

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

The head of the data has a mix of text and numbers:

	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

Let's start by plotting some of the numeric data using a basic line plot. Suppose you wanted a view of the maximum wingspan for these interesting birds.

install.packages("ggplot2")
library("ggplot2")
ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1))
+     geom_line()
+     theme(axis.text.x = element_text(angle = 45, hjust=1))
+     xlab("Birds")
+     ylab("Wingspan (CM)") +
+     ggtitle("Max Wingspan in Centimeters")