Vidushi Gupta
4 years ago
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# Visualizing Quantities
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<em> Sketchnote comes here </em>
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In this lesson you will explore how to use the [ggplot2](https://cran.r-project.org/web/packages/ggplot2/index.html) package to create meaningful visualizations around the concept of quantity. Using a clean dataset about the number of [COVID-19 cases in the world](https://docs.microsoft.com/en-in/azure/open-datasets/dataset-bing-covid-19?tabs=azure-storage), you can make these visualizations and explore the trends of the COVID-19 numbers. This dataset has many variables including the number of confirmed, recovered and deceased cases all over the world from 21-01-2020 and is updated regularly.
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## [Pre-lecture quiz](_) <em> yet to be linked </em>
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## Observe COVID-19 trends with ggplot2
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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 on the number of COVID-19 cases
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In this section, you would be plotting a line graph to analyse the trends of the confirmed, recovered and deceased COVID-19 cases in the world.
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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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df_covid <- read.csv("../../data/covid.csv")
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head(df_covid)
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```
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The head of the data has a mix of numbers and text:
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|id|updated|confirmed|confirmed_change|deaths|deaths_change|recovered|recovered_change|load_time|
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| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: |
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|338995|21-01-2020|262|NA|0|NA|NA|NA|09:54.1|
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|338996|22-01-2020|313|51|0|0|NA|NA|09:54.1|
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|338997|23-01-2020|578|265|0|0|NA|NA|09:54.1|
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|338998|24-01-2020|841|263|0|0|NA|NA|09:54.1|
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|338999|25-01-2020|1320|479|0|0|NA|NA|09:54.1|
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The data type for the "updated" column is a`chr`(character), but we would want it in a date-time format for analysing and vidualizing. In order to convert the values to the date-time format in the updated column, the [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) package is used. To install, load and convert the data type to date-time, enter the following in the R console.
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```r
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install.packages("lubridate")
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library("lubridate")
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df_covid$updated<-as.Date(parse_date_time(df_covid$updated, c('ymd', 'ymd')))
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```
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Using the command above, we convert the "updated" column in the "df_covid" dataset into a date-time format. We specify the existing format of the dates to be yyyy-mm-dd and the desired format as yyyy-mm-dd
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Next, to get started with visualizations, let's install and load`ggplot2`package using the R console.
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```r
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install.packages("ggplot2")
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library("ggplot2")
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```
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Let's start visualizing the data we imported by plotting a line graph of the confirmed, deceased and the recovered cases.
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```r
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ggplot(df_covid,aes(x=updated, y=confirmed)) +
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geom_line()
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```
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We use the`ggplot2`package for plotting a line graph of the confirmed covid cases for the range of dates starting January 2020 to November 2021. The`geom_line()`function is used for plotting a line graph, with the dataset being the first argument in`ggplot()` The 'aes' specifies the variables for the x and y axes which would be 'updated' and 'confirmed' respectively.
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The graph looks like this:
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To make the plot understandable, we can change the Y-axis scales to numbers instead of abbreviated points (abbreviated to e) using the ['scales'](https://scales.r-lib.org/) package. We can also add points, title, customized labels on the line graph for a better presentation.
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```r
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install.packages("scales")
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library("scales")
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ggplot(df_covid,aes(x=updated, y=confirmed)) +
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geom_line(colour="red") +
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geom_point() +
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xlab("Month") +
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ylab("Confirmed Cases") +
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ggtitle("Confirmed COVID-19 cases from Jan 2020 to Nov 2021") +
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scale_y_continuous(labels=comma)
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```
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You have now successfully added customized labels to the axes, titled the plot, changed the colour of the line to red and points on the y-axis are now in the numerical format instead of the abbreviated format.
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Similarly, you can now plot the line graphs for the recovered and the deceased cases.
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```r
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ggplot(df_covid,aes(x=updated, y=recovered)) +
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geom_line(colour="red") +
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geom_point() +
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xlab("Month") +
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ylab("Recovered Cases") +
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ggtitle("Recovered COVID-19 cases from Jan 2020 to Nov 2021") +
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scale_y_continuous(labels=comma)
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ggplot(df_covid,aes(x=updated, y=deaths)) +
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geom_line(colour="red") +
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geom_point() +
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xlab("Month") +
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ylab("Deceased Cases") +
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ggtitle("Deceased COVID-19 cases from Jan 2020 to Nov 2021") +
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scale_y_continuous(labels=comma)
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```
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## Exploring bar graphs
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Let's start off with creating a simple bar chart of the confirmed COVID-19 cases
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```r
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ggplot(data=df, aes(x=updated, y=confirmed, fill=confirmed)) +
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geom_bar(stat="identity") +
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xlab("Month") +
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ylab("Confirmed Cases") +
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ggtitle("Confirmed COVID-19 cases from Jan 2020 to Nov 2021") +
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scale_y_continuous(labels=comma) +
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scale_fill_gradient(low = "green", high = "red",labels = comma)
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```
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Similarly, we can also make the bar charts for the recovered and the deceased cases in different colours.
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### Stacked Bar Chart
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You can now analyse the data using a stacked bar chart. Each bar is divided into a number of sub-bars which get stacked end to end over one another. In our case we can stack the bars corresponding to confirmed, recovered and deceased cases in one graph.
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In order to convert multiple columns into columns of key-value pairs, the`gather()` function from the [tidyverse](https://www.tidyverse.org/) and [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) packages will help us summarize the 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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df_covid %>% group_by(updated) %>%
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summarise(n=n(),
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Deceased = mean(deaths),
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Recovered = mean(recovered),
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Confirmed = mean(confirmed)) %>%
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gather("key", "value", - c(updated, n)) %>%
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ggplot(aes(x = updated, y = value, group = key, fill = key)) +
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geom_bar(stat = "identity") +
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scale_fill_manual(values = c("#0c94ed", "#e34a0d", "#3bed0c")) +
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xlab("Month") +
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ylab("Number of cases") +
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ggtitle("COVID-19 cases from Jan 2020 to Nov 2021") +
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scale_y_continuous(labels=comma) +
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labs(fill="Type of case")
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```
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## Scatter plot
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You can also plot a scatter plot using the`geom_point()`function to have a look at the variation in the data.
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```r
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df%>% group_by(updated) %>%
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summarise(n=n(),
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Deceased = mean(deaths),
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Recovered = mean(recovered),
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Confirmed = mean(confirmed)) %>%
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gather("key", "value", - c(updated, n)) %>%
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ggplot(aes(x = updated, y = value, group = key, fill = key)) +
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geom_point(stat = "identity") +
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scale_color_manual(values = c("#0c94ed", "#e34a0d", "#3bed0c")) +
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xlab("Month") +
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ylab("Number of cases") +
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ggtitle("COVID-19 cases from Jan 2020 to Nov 2021") +
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scale_y_continuous(labels=comma) +aes(color=key)
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```
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All these graphs help in clear interpretation and analysis of the COVID-19 data
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## 🚀 Challenge
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The dataset used in this lesson visualizes the data worldwide. Practice building plots and visualizing quantities around the data for countries that you like. The dataset can be found [here](https://docs.microsoft.com/en-in/azure/open-datasets/dataset-bing-covid-19?tabs=azure-storage)
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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. 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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[Line, Bar and Scatter plot]() yet to be linked
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# Visualizing Distributions
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<em> Sketchnote comes here </em>
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In the previous lesson, you learned about visualizing quantities. This lesson will focus on distributions and their visualizations.
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## [Pre-lecture quiz]() Yet to be linked
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## Explore the COVID-19 dataset
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We can now look at the distribution of this data along an axis. This would help us understand how a variable is distributed, thereby helping us in analysis.
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Let's get started on this lesson by importing the dataset and then parsing the dates to the date-time format using the ```lubridate``` package.
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```r
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library(lubridate)
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df_covid <- read.csv("../../data/covid.csv")
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df_covid$updated<-as.Date(parse_date_time(df_covid$updated, c('ymd', 'ymd')))
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```
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## Working with histograms
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Histograms gives a true image of the distribution of a variable. In our case, let us visualize the distribution of the "change in confirmed, recovered and deceased COVID-19 cases". Let us now load `ggplot2` and use the `geom_histogram()` function for making a histogram.
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```r
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library("ggplot2")
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ggplot(data=df_covid, aes(x=confirmed_change)) +
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geom_histogram()
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```
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This produces an output like this
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Let's make the histogram more presentable for easy interpretation. Using the `scales` package, we would alter the number of axes ticks/points. Along with this a change in the axes titles and addition of the plot title would help in easy interpretation.
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```r
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library("scales")
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ggplot(data=df_covid, aes(x=confirmed_change)) +
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geom_histogram( bins = 16,fill="orange",color="red") +
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scale_x_continuous(breaks=seq(0,2000000,100000)) +
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scale_y_continuous(breaks=seq(0,200,20)) +
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xlab("Change in confirmed cases") +
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ggtitle("Distribution of change in confirmed COVID-19 cases")
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```
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You have successfully added ticks on the x-axis ranging from 0 to 2000000 with a gap of 100000 between two consecutive ticks. Similarly added ticks on the y-axis ranging from 0 to 200 with a gap of 20 between two consecutive ticks. Along with this you have now given an appropriate x-axis label and chart title. The `bins` tells `ggplot2` how many bars or bins do we want in our histogram. In this chart, the number of bins are 16 but you can customize them to as many bins as you like. This plot shows us that the change of about 300000 was the most frequent for the confirmed COVID-19 cases
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Similarly, try plotting the histograms for change in the number of recovered and deceased cases.
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## Density plots
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Density plots are another charts that help analyse distributions for variables. However, unlike the box-structure of histograms, density plots have a smooth line structure. In other words, they can be thought of as smoothened histograms. Use the` geom_density()` function to make a density plot.
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```r
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ggplot(data = df_covid, aes(x = confirmed_change)) +
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geom_density(color = "red") +
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scale_y_continuous(labels = comma) +
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xlab("Change in confirmed cases") +
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ggtitle("Distribution of change in confirmed COVID-19 cases")
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```
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The density plot is clearly a smoothened form of the histogram.
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Let's also plot density plots for change in recovered and deceased cases.
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## 🚀 Challenge
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Research about overlaying ggplots and try to make visualizations for multiple variables in one graph. This will help you look at the distribution of multiple variables in one chart.
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## [Post-lecture quiz]() yet to be linked
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## Review & Self Study
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In this lesson we visualized distributions using Histograms and density plots. You can explore other types of graphs used for visualizing distributions (like box plot,etc.) and try drawing them using packages you like.
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## Assignment
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[Histogram and density plots]() yet to be linked
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