Data visualization: Basic charts

This article turns from Nanyi http://www.ruanyifeng.com/blog/2014/11/basic-charts.html

"Data visualization" helps users understand the data and is always in the hot direction.

Charts are a common means of "data visualization", with basic charts----histograms, line charts, pie charts, and so on----most commonly used.

Users are very familiar with these charts, but if asked, what are their characteristics and what are the most appropriate occasions (datasets)? I'm afraid there are not many people to answer.

This is a note from the first chapter of Data visualization with JavaScript, summarizing the features and applications of the six basic charts and answering the above questions very well.

0. Preface

Before you get to the point, correct a misunderstanding.

Some people feel that the basic chart is too simple, too primitive, not high-end, not atmospheric, so the pursuit of more complex charts. But, the simpler the diagram, the easier it is to understand, and the more quickly understandable the data, the most important purpose and the highest pursuit of "Data visualization"?

So please don't underestimate these basic graphs. Because users are most familiar with them, they should be considered as a priority when applicable.

Bar chart (bar chart)

Histograms are the most common charts and are the easiest to read.

It is used in a two-dimensional dataset (each data point includes two values x and y), but only one dimension needs to be compared. Annual sales are two-dimensional data, "year" and "sales" are its two dimensions, but only need to compare the "sales" dimension.

The histogram uses the height of the column to reflect the difference in the data. The human eye is sensitive to height differences and is very well identified. The limitation of a histogram is that only small and medium sized datasets are available.

Usually, the x-axis of a histogram is a time dimension, and the user habitually believes that there is a time trend. If the x-axis is not a time dimension, it is recommended to use color to differentiate each pillar, changing the user's focus on time trends.

Is the number of winning courses for each team in the English football League, the x-axis represents different teams, and the y-axis represents the number of wins.

Second, line chart (lines chart) data

Line charts are suitable for two-dimensional large datasets, especially where trends are more important than a single data point.

It is also suitable for comparison of multiple two-dimensional datasets.

is a line chart of two-dimensional datasets (atmospheric carbon dioxide concentrations, surface temperatures).

Three, pie chart (pie chart)

A pie chart is a chart that should be avoided because the naked eye is insensitive to area size.

, the size of the five color blocks on the left pie chart is not easy to see. It's a lot easier to switch to a bar chart.

In general, you should always use a histogram instead of a pie chart. One exception, however, is to reflect the proportion of a part of the overall population, such as the percentage of the poor who share population the total.

Four, scatter chart (scatter chart)

Scatter plots are suitable for three-dimensional datasets, but only two of them need to be compared.

is the medical expenditure and life expectancy of countries, three dimensions are national, medical expenditure, life expectancy, only the latter two dimensions need to be compared.

To identify the third dimension, you can add text to each point, or different colors.

Five, Bubble chart (Bubble chart)

A bubble chart is a variant of a scatter plot that reflects the third dimension by the area size of each point.

is the path to Hurricane Katrina, with three dimensions of longitude, latitude, and intensity, respectively. The larger the area of the point, the greater the intensity. Because the user is not good at judging the size of the area, so the bubble chart only for the application does not require accurate identification of the third dimension.

If you add a different color (or text label) to a bubble, the bubble chart can be used to express four-dimensional data. For example, the wind level of each point is represented by color.

Vi. Radar Charts (Radar Chart)

Radar charts apply to multidimensional data (more than four dimensions), and each dimension must be sortable (nationality cannot be sorted). However, it has a limitation, that is, data points of up to 6, otherwise can not be distinguished, so the application is limited.

Here is the data for the five basketball players from the Miami Heat. In addition to the name, each data point has five dimensions, namely score, rebounds, assists, steals, capping.

Draw a radar chart, that's the following.

The larger the data point, the more important it is. It is clear that LeBron James (the red zone) is the most important player in the Heat team.

When needing attention, the user is not familiar with the radar chart, the interpretation has difficulty. Use as much as possible to add a description, reduce the burden of interpretation.

Vii. Summary

Chart	Dimension of	watch out.
Bar chart	Two-dimensional	Just compare one dimension
Line chart	Two-dimensional	For larger datasets
Pie chart	Two-dimensional	Only applicable to reflect the relationship between the part and the whole
Scatter chart	Two-dimensional or three-dimensional	There are two dimensions that need to be compared
Bubble chart	Three-dimensional or four-dimensional	Only two dimensions can be accurately identified.
Radar	More than four	No more than 6 data points

Finish

Data visualization: Basic charts