Which chart would you use to show the change in a continuous variable over time?
For a line graph, the variable on the x-axis defines time. Most software tools store this variable as a continuous variable. Show
Continuous data: appropriate for a line graphLine graphs make sense for continuous data on the y-axis, since continuous data are measured on a scale with many possible values. Some examples of continuous data are:
For all of these examples, a line graph is an appropriate graphical tool to visualize changes in a variable over time. Categorical or nominal data: choose another chart typeLine graphs do not make sense for categorical or nominal data on the y-axis, since these types of data are measured on a scale with specific values. With categorical data, the sample is divided into groups and the responses might have a defined order. For example, in a survey where you are asked to give your opinion on a scale from “Strongly Disagree” to “Strongly Agree,” your responses are categorical. For nominal data, the sample is also divided into groups but there is no particular order. Country of residence is an example of a nominal variable. You can use the country abbreviation, or you can use numbers to code the country name. Either way, you are simply naming the different groups of data. You can use categorical or nominal variables as a grouping variable to add multiple groups using multiple lines to a line graph as shown in Figure 7. When working on any data science project, one of the essential steps to explore and interpret your results is to visualize your data. At the beginning of the project, visualizing your data helps you understand it better, find patterns and trends. At the end of the project, after you’ve done your analysis and applied different machine learning models, data visualization will help you communicate your results more efficiently. Humans are visual creatures by nature; things make sense to us when it’s represented in an easy to understand visualization. It’s way easier to interpret a bar chart than it is to look at massive amounts of numbers in a spreadsheet. Efficient data visualization can make or break your project. If you put tons of effort into analyzing and modeling your data, but you ended up using the wrong chart type to present your results, your audience will not grasp the effort you put in or how to use these results. There are many chart types, so many, the process of choosing the correct one can be overwhelming and confusing. This article will — hopefully — give you a simple and straightforward approach to selecting the best chart type that represents your data perfectly and communicate it most efficiently. How to start?Before you start looking at chart types, you need to ask yourself 5 critical questions about your data. These questions will help you understand your data better and hence, choose the perfect chart type to represent it. №1. What’s the story your data is trying to deliver?
So, the first thing you need to know about your data is, what story is it trying to deliver? Why was this data collected, and how? Is your data collected to find trends? To compare different options? Is it showing some distribution? Or is used to observe the relationship between different value sets? Understanding the origin story of your data and knowing what it’s trying to deliver will make choosing a chart type a much easier task for you. №2. Who will you present your results to?Once you figured out the story behind your data, next, you need to know who you will be presenting your results for. If you’re analyzing stock market trends and you will present your findings to some businessmen, you might use a different chart type than if you were representing your finding for people getting started with the stock market.
For that reason, you need to know your audience so you can choose the best chart type to use when representing your data to them. №3. How big is your data?The size of your data will significantly affect the type of chart you will use. Some types of charts are not meant to be used with massive datasets, while others are perfect for big data. For example, piecharts work best with a small number of datasets; however, if you’re using a significant amount of datasets, using a scatter plot will make more sense. You need to select a chart type that fits the size of your data best and represents it clearly without cluttering. №4. What is your data type?There are several types of data, describe, continuous, qualitative, or categorial. You can use the kind of data to eliminate some chart types. For example, if you have continuous data, a bar chart may not be the best choice; you may need to go with a line chart instead. Similarly, if you have categorical data, then using a bar chart or a pie chart may be a good idea. You probably will not want to use a line chart with categorical data, because by definition, you can’t have continuous categories. The has to be a discrete finite amount of categories. №5. How do the different elements of your data relate to each other?Finally, you need to ask yourself how do the different elements of your data relate. Is your data order based on some factor — time, size, type? Doesn’t represent a ranking based on some variable? Or a correlation between different variables? Is your data a time-series — data that changes over time? Or is it more of a distribution? The relationship between the values within your dataset may decide on what chart type to use a bit more straightforward. The top 7 used chart typesThere are more than 40 types of charts out there; some are more commonly used than others because they are easier to build and interpret. Let’s talk about the top 7 used charts type and when to use each of them. Bar ChartWhen to use:
When to avoid:
Pie ChartWhen to use:
When to avoid:
Line ChartWhen to use:
When to avoid:
Scatter PlotWhen to use:
When to avoid:
Area ChartWhen to use:
When to avoid:
Bubble ChartWhen to use:
When to avoid:
Combined ChartWhen to use:
When to avoid:
Whenever you decide to create some data visualization, use these best practices to make it more straightforward and effective.
Before you choose what chart type to use, you need to get to know your data better, the story behind it, and your target audience/media. Whenever you try to create a visualization, chose simple colors and fonts. Always aim for simple visualization than complex ones. The goal of visualizing data is to make it easier to understand and read. So, avoid overloading and cluttering your graphs. Having multiple simple graphs is always better than one elaborate graph. This article is the first of three-part series on visualization 101. The next articles will address tips for effective data visualization and the different visualization libraries in Python and how to choose the best one based on your data and graph type. What type of graph is best to show continuous change over time?. . . a Line graph.
Line graphs are used to track changes over short and long periods of time. When smaller changes exist, line graphs are better to use than bar graphs.
Which chart is used for continuous variable?Histograms are a standard way to graph continuous variables because they show the distribution of the values.
Which chart would you use to show a relationship between two continuous variables?Scatter plots are used to display the relationship between two continuous variables x and y.
Which chart is useful for showing change in variables over time?Line chart
Line charts show changes in value across continuous measurements, such as those made over time.
|