Visualizing data using charts, graphs, and maps is one of the most impactful ways to communicate complex data. In this course, you’ll learn how to choose the best visualization for your dataset, and how to interpret common plot types like histograms, scatter plots, line plots and bar plots. You’ll also learn about best practices for using colors and shapes in your plots, and how to avoid common pitfalls. Through hands-on exercises, you’ll visually explore over 20 datasets including global life expectancies, Los Angeles home prices, ESPN’s 100 most famous athletes, and the greatest hip-hop songs of all time.
Course URL: https://www.datacamp.com/courses/understanding-data-visualization
1.1 Visualizing distributions
In this chapter you’ll learn the value of visualizations, using real-world data on British monarchs, Australian salaries, Panamanian animals, and US cigarette consumption, to graphically represent the spread of a variable using histograms and box plots.
1.1.1 A plot tells a thousand words
Motivating visualization
To get an insight from a dataset, you can calculate summary statistics or run statistical models, but often it’s easier to draw a plot.
In this exercise, you can see the price of the Bitcoin cryptocurrency from the start of 2016 to the start of 2020. Columns in the table are filterable and sortable.
Look at the Bitcoin prices on January the first each year. Which year began with the highest Bitcoin price?
Key: 2018
Continuous vs. categorical variables
In order to choose an appropriate type of plot to draw, you need to be able to distinguish between continuous variables (roughly: “things you can do arithmetic on”) and categorical variables (roughly: “things that can be classified”).
State which of these variables are continuous and which are categorical.
1.1.2. Histograms
Interpreting histograms
Here is a histogram of salaries for various jobs in Australia. Each row of the dataset is the average salary for that job, so the counts are counts of jobs.
Tip: This left-hand pane of the exercise containing text and instructions is resizable. If the plot is too small to see clearly, making the pane wider will increase the plot size. Move your mouse in-between the left-hand pane and the drag and drop portion of the exercise so a gray vertical bar appears. In Chrome, click and drag this bar to the right. In Firefox, click the bar, move your mouse right, then click again.
Data Source: Tidy Tuesday
Categorize these statements about the histogram as true or false.
Adjusting bin width
The appearance of a histogram is heavily influenced by the width of its bins: the intervals that determine where each bar lies on the x-axis. If the bins are too wide, you don’t see enough detail in the shape of the distribution. If the bins are too narrow, the distribution can be obscured by noise. It’s very difficult to know the “best” binwidth, until you physically look at the plot: draw lots of histograms with a range of binwidths until you find one that helps you answer the question.
Here you can see a histogram of agouti (a rodent) sightings from a camera trap on Barra Colorado Island in Panama. When an animal passed the camera, a photo was taken with a timestamp, so the histogram shows the distribution of the time of day when the agouti were most active.
Which of these statements about the agouti activity is true?
Data Source: Rowcliffle et al. 2014
- The agouti had a high level of activity from 4am to 12pm, then moderate activity from 12pm to 8pm.
- The agouti were most active for a couple of hours after sunrise (6:30am to 8:30am), and before sunset (4pm to 6m).
- The agouti showed a constant level of activity throughout sunlight hours.
- The agouti activity was highly variable, with over a dozen peaks in activity throughout the day
1.1.3. Box plots
Interpreting box plots
Here are box plots of cigarette consumption per person in the USA from 1985 to 1995 (Alaska and Hawaii are not included). Each observation in the dataset is the average number of packets of cigarette smoked per person in one state in one year. Thus each box plot represents the distribution of 48 data points (because there are 48 US states included in the dataset).
Data Source: Stock, James H. and Mark W. Watson (2003)
Categorize these statements about the box plots as true or false.
Ordering box plots
How you order the box plots affects the kinds of questions that are easy to answer.
Here you can see the US cigarette consumption dataset again. This time each box plot represents the distribution of cigarette consumption over time for a given US state. Thus each box plot is formed from 11 data points representing 1985 to 1995.
By default, the box plots are ordered alphabetically by state name. This makes it really easy to look up the details for a specific state, but difficult to answer questions about where the highest or lowest consumption can be found. Sorting the rows by median cigarette consumption makes those questions easier to answer.
Inter-quartile range (IQR) measures the variation in the “middle half” of the population (from the 25th percentile to the 75th percentile). That means that sorting by the IQR makes it easier to answer questions about how much variation there was among the “typical” population.
Which statement is false?
- The lower whisker for Alabama is completely above 100 packs/capita/year.
- North Carolina has the fourth highest median consumption.
- New Hampshire has the third widest inter-quartile range of consumption.
- Idaho has the fourth lowest median consumption.
1.2 Visualizing two variables
You’ll learn how to interpret data plots and understand core data visualization concepts such as correlation, linear relationships, and log scales. Through interactive exercises, you’ll also learn how to explore the relationship between two continuous variables using scatter plots and line plots. You’ll explore data on life expectancies, technology adoption, COVID-19 coronavirus cases, and Swiss juvenile offenders. Next you’ll be introduced to two other popular visualizations—bar plots and dot plots—often used to examine the relationship between categorical variables and continuous variables. Here, you’ll explore famous athletes, health survey data, and the price of a Big Mac around the world.
1.2.1. Scatter plots
Interpreting scatter plots
Scatter plots let you explore the relationship between two continuous variables.
Here you can see a scatter plot of average life expectancy (on the y-axis) versus average length of schooling (on the x-axis) for countries around the world. Each point in the plot represents one country. A straight trend line from a linear regression model is shown
Categorize these statements about the scatter plot as true or false.
Trends with scatter plots
Adding trend lines to a scatter plot can make it easier to articulate the relationship between the two variables.
Here you can see the life expectancy for each country again, this time plotted against the Gross National Income (GNI) per capita (a measure of how rich the country is). You have a choice between linear and logarithmic scales on the x-axis, and can add linear or curved trend lines.
Which statement best describes the trend?
- Life expectancy increases linearly with GNI when GNI is between $1k and $50k.
- Life expectancy increases linearly with the logarithm of GNI when GNI is between $1k and $50k.
- Life expectancy decreases when GNI increases above $50k.
- Life expectancy increases when GNI decreases below $500.
1.2.2. Line plots
Interpreting line plots
Line plots are excellent for comparing two continuous variables, where consecutive observations are connected somehow. A common type of line plot is to have dates or times on the x-axis, and a numeric quantity on the y-axis. In this case, “consecutive observations” means values on successive dates, like today and tomorrow. By drawing multiple lines on the same plot, you can compare values.
The following line plot shows the percentage of households in the United States that adopted each of four technologies (automobiles, refrigerators, stoves, and vacuums) from 1930 to 1970.
Data source: Hannah Ritchie and Max Roser (2019) – Technology Adoption
Categorize these statements about the line plot as true or false.
Logarithmic scales for line plots
If you have a dataset where the values span several orders of magnitude, it can be easier to view them on a logarithmic scale.
A subset of the COVID-19 coronavirus data is shown in the line plot. You saw in the video that most of the cases in early 2020 occurred in mainland China. You might wonder what is happening in the rest of the world. Here, the six countries with the most number of confirmed cases outside of mainland China are shown.
On the linear scale, notice that moving up one grid line in the plot adds 20000. On the logarithmic scale, moving up one grid line in the plot multiplies by 4.
Considering the six countries on the plot, which statement is true?
- On Feb 3, excluding mainland China, US had the most cumulative confirmed cases of COVID-19.
- On Feb 17, Germany had more cumulative confirmed cases of COVID-19 than France.
- On Mar 02, Iran had less than 1000 cumulative confirmed cases of COVID-19.
- On Mar 16, US had less than 4000 cumulative confirmed cases of COVID-19.
Line plots without dates on the x-axis
Although dates and times are the most common type of variable for the x-axis in line plot, other types of variable are possible.
In the video, you saw data on the ages of juvenile offenders in Switzerland. That data was presented with time on the x-axis and one line for each age. Since that plot wasn’t very satisfactory, we’ll try again. This time, age is on the x-axis and there is one line for each year. In the plot you can see two separate clusters of lines representing different age profiles for the offenders.
Which year did the change in age profile of juvenile offenders take place?
Data source: Senior Attorney of the Canton of Zurich
- 2009
- 2010
- 2011
- 2012
1.2.3. Bar plots
Interpreting bar plots
Bar plots are a great way to see counts of each category in a categorical variable.
The ESPN Top 100 famous athletes dataset has two categorical variables: country and sport.
Explore the plots and determine which statement is false.
- Germany had the third most famous athletes.
- Five sports had more than five famous athletes.
- Soccer players from the USA had more famous athletes than any other country/sport combination.
- There were more famous cricketers on the list than famous French athletes.
Interpreting stacked bar plots
If you care about percentages rather than counts, then stacked bar plots are often a good choice of plot.
The dataset for this exercise relates to another question from the Health Survey for England. Adults aged 65 or more were asked how many “activities of daily living” (day-to-day tasks) they needed assistance with.
Type show_plot in the DataCamp console and press ENTER to see the plot. It’s interactive – hover your mouse over the bars to see the percentage for that block.
Which statement is true?
- Less than half the women aged 80+ needed assistance for two or more activities.
- The group with the smallest percentage of people needing assistance for exactly one activity was men aged 75-79.
- The group with the largest percentage of people needing no assistance was men aged 70-74.
- More than half the men aged 80+ needed assistance for at least one activity.
1.2.4. Dot plots
Interpreting dot plots
Dot plots are similar to bar plots in that they show a numeric metric for each category of a categorical variable. They have two advantages over bar plots: you can use a log scale for the metric, and you can display more than one metric per category.
Here is a dot plot of the social media followings of the ESPN 2017 top 100 famous athletes, with one row per athlete. Three metrics are shown for each athlete: the number of followers on Facebook, Instagram, and Twitter. Only the athletes for Basketball, Cricket, Soccer, and Tennis who had accounts on each platform are shown. Rows are sorted alphabetically for each sport.
Based on the plot, which statement about the athlete’s social media following is false?
- Basketball: Russell Westbrook has more Instagram followers than Carmelo Anthony.
- Cricket: Virat Kohli has more followers on Facebook than the other platforms.
- Soccer: Christiano Ronaldo has more Twitter followers than Marcelo Viera.
- Tennis: Maria Sharapova has more Facebook followers than Roger Federer.
Sorting dot plots
As with box plots and bar plots, how you order the rows in a dot plot affects the kinds of questions that are easy to answer.
Here you can see the Big Mac Index: the price of a McDonalds Big Mac in various countries around the world (in Jan 2020). The “Actual price” is the price converted to US dollars. The “GDP adjusted price” has an additional correction for the gross domestic product of a country. Roughly, if people earn less in a country, it will cost more using the adjusted price.
By default, the rows in the dot plot are ordered alphabetically. This makes it really easy to look up the price for a specific country, but difficult to answer question about where the most expensive or least expensive Big Macs can be found. By sorting the rows by price, those questions are easier to answer.
Which statement is true?
Data source: The Economist
- Ukraine has the fifth most expensive Big Macs by actual price.
- Two countries have Big Macs that cost over 100 USD after adjusting for GDP.
- After adjusting for GDP, South Africa has the cheapest Big Macs.
- Azerbaijan has the fifth most expensive Big Macs by actual price.
1.3 The color and the shape
It’s time to make your insights even more impactful. Discover how you can add color and shape to make your data visualizations clearer and easier to understand, especially when you find yourself working with more than two variables at the same time. You’ll explore Los Angeles home prices, technology stock prices, math anxiety, the greatest hiphop songs, scotch whisky preferences, and fatty acids in olive oil.
1.3.1. Higher dimensions
Another dimension for scatter plots
If you have a scatter plot, but want to distinguish the points in some way based on another variable, then you have a few options. As discussed in the video, 3D plots are usually dreadful on a 2D screen, but there are other options to stick to x-y axes and still visualize the third dimension. You can change the color, size, transparency or shape of the points, or split the plot into multiple panels.
Here you can see the dataset of house prices in Los Angeles county, USA, that you first encountered in Chapter 2.
Explore different options for distinguishing points from the four cities, then determine which statement is false.
- Using different sizes or transparencies makes it hard to distinguish points that overlap.
- Using separate panels provides the best way to distinguish points from each city, but makes it harder to see if there is a single trend across the whole dataset.
- Using different shapes provides the best way to distinguish points from each city, but makes it harder to see if there is a single trend across the whole dataset.
- Using different color provides a good way to distinguish points from each city, but lighter colors can be hard to see against a white background.
Another dimension for line plots
As with points in a scatter plot, you may often want to be able to distinguish several lines in a line plot. Like points, you can change the colors or transparency level, or use multiple panels. The two differences to points are that you can change line widths rather than point sizes, and you can change linetype (solid, dashes, or dots) rather than point shape.
The line plot shows the stock price for five technology companies collectively known as “FAAMG”: Facebook (FB), Apple (AAPL), Amazon (AMZN), Microsoft (MSFT), and Google (GOOG). The prices have been adjusted for dividends and splits, then scaled to be relative to their highest price over the time period so they are more easily comparable.
Explore different options for distinguishing lines from the five companies, then determine which statement is false.
Data source: Yahoo Finance
- All five companies began 2018 with a higher price than they began 2017.
- In 2018, Facebook’s stock price decreased by a greater fraction than any of the other companies.
- From the start of 2019 to the start of 2020, Apple’s stock price more than doubled.
- All five companies began 2020 with a higher price than they had half way through 2019.
1.3.2. Using color
Eye-catching colors
Not all colors are as eye-catching as others. This can cause a problem for data visualization, because having some data point more obvious than others can bias the way you interpret a plot. Unless you specifically want to highlight some points, each data point should be as easy to look at as all the others.
Here you can see the dataset from the camera trap in Panama. This time, the speed of the animal as they passed the camera is plotted against the time of day that they were caught on camera, and the agouti have been joined by another rodent, the paca.
Each version of the plot contains purple and yellow points, but in one version, the purple points are easier to perceive than the yellow points.
Which statement is true?
- To ensure that all data points are equally perceivable, they should all have the same color.
- To ensure that all data points are equally perceivable, they should all have the same chroma.
- To ensure that all data points are equally perceivable, they should all have the same luminance.
- To ensure that all data points are equally perceivable, choose a qualitative, sequential, or diverging scale in hue-chroma-luminance colorspace.
- To ensure that all data points are equally perceivable, they should all have the same hue.
Qualitative, sequential, diverging
There are three types of color scale, each designed to highlight different things in a visualization.
| Type | Purpose |
| qualitative | Distinguish unordered categories |
| sequential | Show ordering |
| diverging | Show above or below a midpoint |
Here you can see the results of a survey about math anxiety from a class of students. Each question has its own row, and responses range from “Strongly Disagree” to “Neutral” to “Strongly Agree”.
Choose an appropriate color scale for the plot.
Data source: Bai et al. (2009)
- None
- Qualitative
- Sequential
- Diverging
Highlighting data
Up to now we’ve focused on using color and other plot aesthetics to make all data points stand out as much as others. Usually that’s a good idea, but occasionally you’ll want to highlight specific data points.
Here’s the dataset of the greatest hip-hop songs of all time, from Chapter 2. Songs by two rappers at the center of the 1990s East Coast-West Coast hip-hop Rivalry, The Notorious B.I.G. and 2Pac, are colored differently.
Use the sliders to control the highlighting of songs by these artists via point size, transparency, and chroma.
How many songs for each artist made it onto the critics’ list?
- The Notorious B.I.G. has 9 and 2Pac has 8.
- The Notorious B.I.G. has 7 and 2Pac has 7.
- The Notorious B.I.G. has 8 and 2Pac has 7.
- The Notorious B.I.G. has 7 and 2Pac has 8.
- The Notorious B.I.G. has 9 and 2Pac has 7.
1.3.3. Plotting many variables at once
Interpreting pair plots
To get a quick overview of a dataset, it’s really helpful to draw a plot of the distribution of each variable, and the relationship between each pair of variables. A pair plot displays all these plots together in a matrix of panels. It shows a lot of information at once, so to interpret it, try looking at one panel at a time.
Here you can see the Panamanian camera trap data for the agouti and paca, as well as three new species: coati, brocket, and peccary.
Categorize these statements about the pair plot as true or false. (true -false)
Interpreting correlation heatmaps
If you want to find the relationship between many pairs of numeric variables, you can use a close relative of the pair plot, namely the correlation heatmap. It takes the correlation scores you saw from the pair plot, but rather than giving you lots of numbers to look at, it displays them using colors. A great use case for this is finding related products.
Here’s a dataset from a survey on scotch whisky consumption. In the correlation heatmap, each row and column shows a brand of scotch, and cells show that correlation between drinking one brand and drinking another within the past twelve months.
True – False:
Interpreting parallel coordinates plots
Parallel coordinates plots are designed to help you view the relationship between many continuous variables at once.
Here is a dataset of fatty acid levels in olive oils samples from six regions in Italy. Each line in the plot represents one oil sample. Since the region is a categorical variable, you have six parallel coordinates plots, one in each panel.
Categorize these statements about the parallel coordinates plot as true or false.
Data source: Graphics of large datasets
1.4 problems but a plot ain’t one of them
In this final chapter, you’ll learn how to identify and avoid the most common plot problems. For example, how can you avoid creating misleading or hard to interpret plots, and will your audience understand what it is you’re trying to tell them? All will be revealed! You’ll explore wind directions, asthma incidence, and seats in the German Federal Council.
1.4.1. Polar coordinates
Pie plots
Pie plots (sometimes called pie charts) are extremely popular, but often difficult to interpret. They are just bar plots converted into polar coordinates, and humans are generally worse at perceiving angles accurately compared to lengths.
Following on from the scotch whisky dataset in the last chapter, here’s another dataset from the Health Survey for England, this time on alcohol consumption in English men aged 16 or more. Pie segments and bar heights represent percentages of responders.
Look at the pie plot and the bar plot and determine which statement is true.
- Only the 75+ age group had more non-drinkers than people drinking 14 to 35 units per week.
- Three age groups had more than 30% of people drinking 14 to 35 units per week.
- All age groups had less than 20% non-drinkers.
- All age groups had at least 50% of people drinking up to 14 units per week.
Rose plots
One good use case for polar coordinates is when the data is naturally circular, for example, when it is a compass direction. If you plot a histogram with polar coordinates, you get a rose plot.
Here you can see a plot of wind direction data from a meteorological mast. Knowing the predominant wind direction is important for weather modeling and for determining where to site wind turbines. Wind measurements were taken at 10 minute intervals over an eight month period.
Look at the histogram and rose plot, then determine which statement is true.
Data Source: bReeze
- The distribution of the wind directions has three peaks.
- The predominant wind directions were N and SW.
- The distribution of the wind directions has one peak.
- The predominant wind directions were E and NW.
1.4.2. Axes of evil
Bar plot axes
When we look at a bar plot, we use the relative lengths of each bar to help interpret what is happening. If you don’t include zero on the axis used for bar lengths, then the relative lengths of bars are distorted, and it is easy to be misled.
Here is a bar plot of another question from the Health Survey for England, this time about people with asthma. (“Not asthmatic” means no asthma symptoms were reported, and no medication was taken for asthma in the previous 12 months.)
Compare version of the plot with each y-axis, and determine which statement is true.
- The percentage of asthmatics is less than 15% for every age group.
- 16-24 years olds have more than twice the percentage of non-asthmatics than 45-54 year olds.
- The majority of people aged 35-74 are asthmatic.
- The percentage of asthmatics ranges from about 40% to about 80%, depending upon the age group.
Dual axes
One popular but terrible idea is to draw a scatter plot or line plot with two different y-axes. This typically happens when you have two metrics with different units, and different scales that you want to plot against a common x-axis. The problem is that by changing the relationship between the two axes, you can tell almost any story that you want with the data.
Here you can see the stock prices of Microsoft (MSFT) and Amazon (AMZN) from 2017 to 2020. When you saw these in Chapter 3, each price had been adjusted to relative to the maximum for that company. That way each line was comparable. Here, the prices have been adjusted for dividends and splits but they have not been scaled relative to their maxima.
Adjust the vertical position and steepness of the slope for the AMZN line, then determine which statement is true.
- MSFT and AMZN are strongly positively correlated.
- MSFT and AMZN are strongly negatively correlated.
- MSFT and AMZN have no correlation.
- You can’t make a conclusion about the correlation of MSFT and AMZN from this plot.
Delightful debunking of dual axes! It would have been better to draw each line in its
1.4.3. Sensory overload
Chartjunk
Chartjunk is anything in a plot that distracts from getting insight. That is, removing it would make the plot easier to understand.
Here’s the scatter plot of the greatest hip-hop songs from Chapter 2, this time with added bling.
Which element of the plot is not chartjunk?
- Bold, italic text
- Chunky grid lines
- Dollar signs for points
- Golden panel background
- Axis labels
Joyous junk detection! The font face may have been terrible, but having axis labels helps readers interpret the plot, so they aren’t chart junk. In general, anything that makes it harder to interpret a plot should be removed.
Multiple plots
Sometimes a dataset is so complex that it takes several plots to explore properly. Rather than trying to find a single, perfect plot that captures all the insight, you can combine several plots into a report or – if you want to have fun – a dashboard.
Here you can see the German Bundesrat (Federal Council) seats dataset in a dashboard of three plots. In the “by party” plots, values are colored according to each party’s marketing brand color. The level of transparency is based on power: the primary party in a coalition is fully opaque, secondary parties are slightly transparent, and tertiary parties are very transparent.
Explore the dashboard and determine which statement is false.
- The coalition with the most seats is `SPD+CDU`.
- The Grüne party have more seats as the secondary party in a coalition than any other party.
- The SPD have more seats as the tertiary party in a coalition than any other party.
- The FDP only have seats in the Western states.
- Bavaria (the large state in the South East) has different political parties to those found in power in other states.
Perfect political analysis! The Grune party had more seats as the tertiary party in a coalition. For complex datasets, it is often best to draw lots of simpler plots that each answer a couple of questions, rather than trying to draw a single plot that answers everything