We explain different ways to visualize complex data.
The different current sciences work with very large volumes of data. In general, it is difficult for people to imagine the “raw” data correctly .
For this reason, graphic aids are used to facilitate the understanding of the data and results obtained from an investigation, although they are also used for the same reason in business environments.
There are also various types of visual forms with which we can choose to represent this data. Each of them must be used both for the data that has been studied and for the type of information that is expected to be extracted from the graph.
To make it easier for you to read the different types of graphs, in this article we will introduce you to some types while explaining what they can be used for.
6 types of graphs to represent data and statistics
The number of ways in which we can represent data are practically inexhaustible . Part of a good analysis and preparation for viewing a group of data is choosing the appropriate way to represent them, depending on the capacity and needs of the project.
In this article we present some basic types of graphs so that you become familiar with the amount of variation that exists when it comes to representing data.
1. Bar Charts
A bar chart allows you to separate the sample into specific groups, which correspond to the different vertical bars on the graph. In addition, these are measured against a variable on the vertical axis (the ordinate axis), which gives information about the different groups that we are studying.
The thickness of the bars is irrelevant in this type of chart. The only thing that is observed is its height with respect to the ordinate axis and to the other bars , which represent the frequency in the variable that we are indicating.
2. Pie Charts
Pie charts are also known as pie charts, pie charts , or cheese charts, reminiscent of triangular packages of cheese inside a circular container. This type of graph is especially useful when we look at qualitative values instead of quantitative ones, also allowing the relative frequencies of the different groups to be compared very quickly.
In the pie chart, the different sectors (triangles) represent different groups separated by qualitative elements. This means that their separation would not correspond to numerical values, but rather to characteristics that are difficult to translate into numbers, such as different colors or sectors of the population.
In addition, pie charts have the advantage of representing the entire sample. Observing the graph, we know that we are seeing 100% of the events represented in it, with the different percentage frequencies belonging to the different sectors in which we have separated the study sample.
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3. Line Charts
Line graphs are especially useful when we want to observe the evolution of an event over time. On the horizontal axis (abscissas) the time scale is usually represented, be it days, hours or years. On the vertical axis, we will see the frequency of the values we are studying.
Each line corresponds to a studied group. If we observe several lines, each of these corresponds to a study group, whose values evolve over time . This allows us to easily compare the changes in frequencies of different groups, while observing how these changes gradually change over time.
4. Scatter plots
Scatter plots, known as “scatter plots” in English, are very interesting graphical representations, where a cloud of points is shown on a 2D axis with Cartesian coordinates.
This type of graph allows the use of different variables on the axes to check the correlation that exists between them . The more linear the dispersion of the points is (that is, the data increases following a linear distribution) the more correlated these variables will be.
Scatter plots also allow analysis of different population groups, as long as we use different types of points to represent the different groups being observed.
Scatter plots can also be made in 3D by adding a third variable that influences the analysis and the scatter of the points.
5. Histograms
Commonly confused with bar charts, histograms are a statistical representation commonly used in scientific fields. The confusion comes from the fact that like the bar graph, histograms also use bars of irrelevant thickness for the representation of their data.
The main difference of histogram vs. bar chart is that the histograms represent different groups separated by continuous quantitative values . This means that the bars represent continuous numerical intervals, separated as decided by the analyst making the representation.
Thanks to the histograms we can easily observe the relative frequency of the different groups, being able to easily establish which are the most common groups and which are the least common.
6. Box and Whisker Plots
Box and whiskers plots are graphs that represent different groups, illustrated by the different boxes. Within each of these groups, a certain number of percentage grouped data is represented. The vertical axis represents values studied, and the horizontal axis does not contain values beyond marking the different groups to study.
Within each of the boxes with whiskers, the different quartiles are represented (from 0 to 24%, from 25% to 49%, etc…) with which the group studied has. The lines shown inside the boxes represent the second quartile (half of the sample). Depending on the length of the box and whiskers, there will be more or less spread of data.
The whiskers allow to represent additional data, such as the expected normal dispersion, certain extreme percentiles or the maximum and minimum values within each group
Bibliographic references
- Martinez-Gonzalez, MA; Faulin, F.J. & Sanchez, A. (2006). Friendly Biostatistics, 2nd ed. Diaz de Santos, Madrid.
To the classic question “what do you do?” I always answer “basically I am a psychologist”. In fact, my academic training has revolved around the psychology of development, education and community, a field of study influenced my volunteer activities, as well as my first work experiences in personal services.