Jen Looper
4 years ago
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# Visualizing Quantities
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| ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)|
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|:---:|
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| Visualizing Quantities - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
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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.
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## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/16)
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## Observe wingspan with ggplot2
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An excellent library to create both simple and sophisticated plots and charts of various kinds is [ggplot2](https://cran.r-project.org/web/packages/ggplot2/index.html). 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.
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`ggplot2` is a system for declaratively creating graphics, based on The Grammar of Graphics. The [Grammar of Graphics](https://en.wikipedia.org/wiki/Ggplot2) 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.
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> ✅ Plot = Data + Aesthetics + Geometry
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> - Data refers to the dataset
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> - Aesthetics indicate the variables to be studied (x and y variables)
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> - Geometry refers to the type of plot (line plot, bar plot, etc.)
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Choose the best geometry (type of plot) according to your data and the story you want to tell through the plot.
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> - To analyze trends: line, column
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> - To compare values: bar, column, pie, scatterplot
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> - To show how parts relate to a whole: pie
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> - To show distribution of data: scatterplot, bar
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> - To show relationships between values: line, scatterplot, bubble
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✅ You can also checkout this descriptive [cheatsheet](https://nyu-cdsc.github.io/learningr/assets/data-visualization-2.1.pdf) for ggplot2.
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## Build a line plot about bird wingspan values
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Open the R console and import the dataset.
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> Note: The dataset is stored in the root of this repo in the `/data` folder.
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Let's import the dataset and observe the head (top 5 rows) of the data.
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```r
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birds <- read.csv("../../data/birds.csv",fileEncoding="UTF-8-BOM")
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head(birds)
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```
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The head of the data has a mix of text and numbers:
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| | Name | ScientificName | Category | Order | Family | Genus | ConservationStatus | MinLength | MaxLength | MinBodyMass | MaxBodyMass | MinWingspan | MaxWingspan |
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| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: |
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| 0 | Black-bellied whistling-duck | Dendrocygna autumnalis | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Dendrocygna | LC | 47 | 56 | 652 | 1020 | 76 | 94 |
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| 1 | Fulvous whistling-duck | Dendrocygna bicolor | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Dendrocygna | LC | 45 | 53 | 712 | 1050 | 85 | 93 |
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| 2 | Snow goose | Anser caerulescens | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 64 | 79 | 2050 | 4050 | 135 | 165 |
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| 3 | Ross's goose | Anser rossii | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 57.3 | 64 | 1066 | 1567 | 113 | 116 |
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| 4 | Greater white-fronted goose | Anser albifrons | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 64 | 81 | 1930 | 3310 | 130 | 165 |
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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.
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```r
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install.packages("ggplot2")
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library("ggplot2")
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ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
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geom_line()
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```
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Here, you install the `ggplot2` package and then import it into the workspace using the `library("ggplot2")` command. To plot any plot in ggplot, the `ggplot()` function is used and you specify the dataset, x and y variables as attributes. In this case, we use the `geom_line()` function since we aim to plot a line plot.
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What do you notice immediately? There seems to be at least one outlier - that's quite a wingspan! A 2000+ centimeter wingspan equals more than 20 meters - are there Pterodactyls roaming Minnesota? Let's investigate.
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While you could do a quick sort in Excel to find those outliers, which are probably typos, continue the visualization process by working from within the plot.
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Add labels to the x-axis to show what kind of birds are in question:
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```r
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ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
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geom_line() +
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theme(axis.text.x = element_text(angle = 45, hjust=1))+
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xlab("Birds") +
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ylab("Wingspan (CM)") +
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ggtitle("Max Wingspan in Centimeters")
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```
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We specify the angle in the `theme` and specify the x and y axis labels in `xlab()` and `ylab()` respectively. The `ggtitle()` gives a name to the graph/plot.
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Even with the rotation of the labels set to 45 degrees, there are too many to read. Let's try a different strategy: label only those outliers and set the labels within the chart. You can use a scatter chart to make more room for the labeling:
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```r
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ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
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geom_point() +
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geom_text(aes(label=ifelse(MaxWingspan>500,as.character(Name),'')),hjust=0,vjust=0) +
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theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank())
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ylab("Wingspan (CM)") +
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ggtitle("Max Wingspan in Centimeters") +
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```
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What's going on here? You used the `geom_point()` function to plot scatter points. With this, you added labels for birds who had their `MaxWingspan > 500` and also hid the labels on the x axis to declutter the plot.
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What do you discover?
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## Filter your data
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Both the Bald Eagle and the Prairie Falcon, while probably very large birds, appear to be mislabeled, with an extra 0 added to their maximum wingspan. It's unlikely that you'll meet a Bald Eagle with a 25 meter wingspan, but if so, please let us know! Let's create a new dataframe without those two outliers:
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```r
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birds_filtered <- subset(birds, MaxWingspan < 500)
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ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) +
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geom_point() +
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ylab("Wingspan (CM)") +
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xlab("Birds") +
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ggtitle("Max Wingspan in Centimeters") +
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geom_text(aes(label=ifelse(MaxWingspan>500,as.character(Name),'')),hjust=0,vjust=0) +
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theme(axis.text.x=element_blank(), axis.ticks.x=element_blank())
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```
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We made a new dataframe `birds_filtered` and then plotted a scatter plot. By filtering out outliers, your data is now more cohesive and understandable.
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Now that we have a cleaner dataset at least in terms of wingspan, let's discover more about these birds.
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While line and scatter plots can display information about data values and their distributions, we want to think about the values inherent in this dataset. You could create visualizations to answer the following questions about quantity:
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> How many categories of birds are there, and what are their numbers?
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> How many birds are extinct, endangered, rare, or common?
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> How many are there of the various genus and orders in Linnaeus's terminology?
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## Explore bar charts
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Bar charts are practical when you need to show groupings of data. Let's explore the categories of birds that exist in this dataset to see which is the most common by number.
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Let's create a bar chart on filtered data.
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```r
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install.packages("dplyr")
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install.packages("tidyverse")
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library(lubridate)
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library(scales)
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library(dplyr)
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library(ggplot2)
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library(tidyverse)
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birds_filtered %>% group_by(Category) %>%
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summarise(n=n(),
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MinLength = mean(MinLength),
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MaxLength = mean(MaxLength),
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MinBodyMass = mean(MinBodyMass),
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MaxBodyMass = mean(MaxBodyMass),
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MinWingspan=mean(MinWingspan),
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MaxWingspan=mean(MaxWingspan)) %>%
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gather("key", "value", - c(Category, n)) %>%
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ggplot(aes(x = Category, y = value, group = key, fill = key)) +
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geom_bar(stat = "identity") +
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scale_fill_manual(values = c("#D62728", "#FF7F0E", "#8C564B","#2CA02C", "#1F77B4", "#9467BD")) +
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xlab("Category")+ggtitle("Birds of Minnesota")
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```
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In the following snippet, we install the [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) and [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) packages to help manipulate and group data in order to plot a stacked bar chart. First, you group the data by the `Category` of bird and then summarise the `MinLength`, `MaxLength`, `MinBodyMass`,`MaxdyMass`,`MinWingspan`,`MaxWingspan` columns. Then, plot the bar chart using `ggplot2` package and specify the colours for the different category and the labels.
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This bar chart, however, is unreadable because there is too much non-grouped data. You need to select only the data that you want to plot, so let's look at the length of birds based on their category.
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Filter your data to include only the bird's category.
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Since there are many categories, you can display this chart vertically and tweak its height to account for all the data:
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```r
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birds_count<-dplyr::count(birds_filtered, Category, sort = TRUE)
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birds_count$Category <- factor(birds_count$Category, levels = birds_count$Category)
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ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip()
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```
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You first count unique values in the `Category` column and then sort them into a new dataframe `birds_count`.This sorted data is then factored in the same level so that it is plotted in the sorted way. Using `ggplot2` you then plot the data in a bar chart. The `coord_flip()` plots horizontal bars.
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This bar chart shows a good view of the number of birds in each category. In a blink of an eye, you see that the largest number of birds in this region are in the Ducks/Geese/Waterfowl category. Minnesota is the 'land of 10,000 lakes' so this isn't surprising!
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✅ Try some other counts on this dataset. Does anything surprise you?
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## Comparing data
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You can try different comparisons of grouped data by creating new axes. Try a comparison of the MaxLength of a bird, based on its category:
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```r
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birds_grouped <- birds_filtered %>%
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group_by(Category) %>%
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summarise(
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MaxLength = max(MaxLength, na.rm = T),
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MinLength = max(MinLength, na.rm = T)
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) %>%
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arrange(Category)
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ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_flip()
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```
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We group the `birds_filtered` data by `Category` and then plot a bar graph.
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Nothing is surprising here: hummingbirds have the least MaxLength compared to Pelicans or Geese. It's good when data makes logical sense!
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You can create more interesting visualizations of bar charts by superimposing data. Let's superimpose Minimum and Maximum Length on a given bird category:
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```r
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ggplot(data=birds_grouped, aes(x=Category)) +
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geom_bar(aes(y=MaxLength), stat="identity", position ="identity", fill='blue') +
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geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+
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coord_flip()
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```
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## 🚀 Challenge
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This bird dataset offers a wealth of information about different types of birds within a particular ecosystem. Search around the internet and see if you can find other bird-oriented datasets. Practice building charts and graphs around these birds to discover facts you didn't realize.
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## [Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/17)
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## Review & Self Study
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This first lesson has given you some information about how to use `ggplot2`to visualize quantities. Do some research around other ways to work with datasets for visualization. Research and lookout for datasets that you could visualize using other packages like [Lattice](https://stat.ethz.ch/R-manual/R-devel/library/lattice/html/Lattice.html) and [Plotly](https://github.com/plotly/plotly.R#readme)
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## Assignment
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[Lines, Scatters, and Bars](assignment.md)
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# Lines, Scatters and Bars
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## Instructions
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In this lesson, you worked with line charts, scatterplots, and bar charts to show interesting facts about this dataset. In this assignment, dig deeper into the dataset to discover a fact about a given type of bird. For example, create a script visualizing all the interesting data you can uncover about Snow Geese. Use the three plots mentioned above to tell a story in your notebook.
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## Rubric
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Exemplary | Adequate | Needs Improvement
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--- | --- | -- |
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A script is presented with good annotations, solid storytelling, and attractive graphs | The script is missing one of these elements | The script is missing two of these elements
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# Visualizing Distributions
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| ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)|
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|:---:|
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| Visualizing Distributions - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
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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.
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## [Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/18)
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## Explore the birds dataset
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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.
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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.
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```r
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library(ggplot2)
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birds <- read.csv("../../data/birds.csv",fileEncoding="UTF-8-BOM")
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birds_filtered <- subset(birds, MaxWingspan < 500)
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head(birds_filtered)
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```
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| | Name | ScientificName | Category | Order | Family | Genus | ConservationStatus | MinLength | MaxLength | MinBodyMass | MaxBodyMass | MinWingspan | MaxWingspan |
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| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: |
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| 0 | Black-bellied whistling-duck | Dendrocygna autumnalis | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Dendrocygna | LC | 47 | 56 | 652 | 1020 | 76 | 94 |
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| 1 | Fulvous whistling-duck | Dendrocygna bicolor | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Dendrocygna | LC | 45 | 53 | 712 | 1050 | 85 | 93 |
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| 2 | Snow goose | Anser caerulescens | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 64 | 79 | 2050 | 4050 | 135 | 165 |
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| 3 | Ross's goose | Anser rossii | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 57.3 | 64 | 1066 | 1567 | 113 | 116 |
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| 4 | Greater white-fronted goose | Anser albifrons | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 64 | 81 | 1930 | 3310 | 130 | 165 |
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In general, you can quickly look at the way data is distributed by using a scatter plot as we did in the previous lesson:
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```r
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ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) +
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geom_point() +
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ggtitle("Max Length per order") + coord_flip()
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```
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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.
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## Working with histograms
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`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:
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```r
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ggplot(data = birds_filtered, aes(x = MaxBodyMass)) +
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geom_histogram(bins=10)+ylab('Frequency')
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```
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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:
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```r
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ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency')
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```
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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:
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Filter your data to get only those birds whose body mass is under 60, and show 30 `bins`:
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```r
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birds_filtered_1 <- subset(birds_filtered, MaxBodyMass > 1 & MaxBodyMass < 60)
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ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) +
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geom_histogram(bins=30)+ylab('Frequency')
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```
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✅ Try some other filters and data points. To see the full distribution of the data, remove the `['MaxBodyMass']` filter to show labeled distributions.
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The histogram offers some nice color and labeling enhancements to try as well:
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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:
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```r
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ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) +
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geom_bin2d() +scale_fill_continuous(type = "viridis")
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```
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There appears to be an expected correlation between these two elements along an expected axis, with one particularly strong point of convergence:
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Histograms work well by default for numeric data. What if you need to see distributions according to text data?
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## Explore the dataset for distributions using text data
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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?
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> ✅ In the dataset, several acronyms are used to describe conservation status. These acronyms come from the [IUCN Red List Categories](https://www.iucnredlist.org/), an organization that catalogs species' status.
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>
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> - CR: Critically Endangered
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> - EN: Endangered
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> - EX: Extinct
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> - LC: Least Concern
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> - NT: Near Threatened
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> - VU: Vulnerable
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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?
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```r
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birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'EX'] <- 'x1'
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birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'CR'] <- 'x2'
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birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'EN'] <- 'x3'
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birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'NT'] <- 'x4'
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birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'VU'] <- 'x5'
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birds_filtered_1$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!
|
||||
|
||||
```r
|
||||
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:
|
||||
|
||||
```r
|
||||
ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) +
|
||||
geom_density()
|
||||
```
|
||||
|
||||
|
||||
If you wanted a smooth, but not too smooth line, edit the `adjust` parameter:
|
||||
|
||||
```r
|
||||
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:
|
||||
|
||||
```r
|
||||
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](https://red-water-0103e7a0f.azurestaticapps.net/quiz/19)
|
||||
|
||||
## 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](https://ggplot2.tidyverse.org/reference/geom_density_2d.html) to understand how it works.
|
||||
|
||||
## Assignment
|
||||
|
||||
[Apply your skills](assignment.md)
|
||||
|
||||
|
||||
@ -0,0 +1,10 @@
|
||||
# Apply your skills
|
||||
|
||||
## Instructions
|
||||
|
||||
So far, you have worked with the Minnesota birds dataset to discover information about bird quantities and population density. Practice your application of these techniques by trying a different dataset, perhaps sourced from [Kaggle](https://www.kaggle.com/). Build a R script to tell a story about this dataset, and make sure to use histograms when discussing it.
|
||||
## Rubric
|
||||
|
||||
Exemplary | Adequate | Needs Improvement
|
||||
--- | --- | -- |
|
||||
A script is presented with annotations about this dataset, including its source, and uses at least 5 histograms to discover facts about the data. | A script is presented with incomplete annotations or bugs. | A script is presented without annotations and includes bugs.
|
||||
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After Width: | Height: | Size: 19 KiB |
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After Width: | Height: | Size: 16 KiB |
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Reference in new issue