# Types of graphs: the various ways of representing data visually

**All research of a scientific nature is supported and based on a set of data** duly analyzed and interpreted. To reach a point where we can extract relationships of causality or correlation, it is necessary to observe multiple observations in a way that can falsify and prove the existence of the same relationship in different cases or in the same subject over time. And once these observations are made, it is necessary to take into account aspects such as frequency, average, fashion or dispersion of the data obtained.

In order to facilitate understanding and analysis both by the researchers themselves and in order to show the variability of the data and where the conclusions go to the rest of the world, it is very useful to use visual elements of easy interpretation: graphics or graphics.

Depending on what we want to show, we can use different types of graphics. In this article **we will see different types of graphs** that are used in research based on the use of statistics.

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## The graphic

At a statistical and mathematical level, called graphic a **that visual representation from which they can be represented and interpreted** generally numerical values. Among the many extractable information from the observation of the graph we can find the existence of a relationship between variables and the degree to which they occur, the frequencies or the proportion of appearance of certain values.

This visual representation serves as support when it comes to showing and comprehending in a synthesized way the data collected during the investigation, so that both the researchers who carry out the analysis and others can **can understand the results and is easy to use as a reference** , as information to be taken into account or as a point of contrast when conducting new research and meta-analysis.

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## Types of graphics

There are many types of graphics, generally applying one or the other depending on what is intended to represent or simply the author's preferences. Below we indicate some of the most known and common.

### 1. Bar chart

The most known and used of all the types of graphs is the graph or bar chart. In this, the data are presented in the form of bars contained in two Cartesian axes (coordinate and abscissa) that indicate the different values. **The visual aspect that tells us the data is the length of said bars** , thickness is not important.

It is usually used to represent the frequency of different conditions or discrete variables (for example the frequency of the different colors of the iris in a given sample, which can only be specific values). Only one variable is observed in the abscissa, and the frequencies in the coordinates.

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### 2. Pie chart or by sectors

The also very usual graphic in the form of "quesito", in this case the representation of the data is carried out by dividing a circle into as many parts as the values of the variable investigated and having each part **a size proportional to its frequency within the total data** . Each sector will represent a value of the variable with which one works.

This type of graph or diagram is common when the proportion of cases within the total is being shown, using to represent percent values (the percentage of each value).

### 3. Histogram

Although at first glance very similar to the bar graph, the histogram is one of the types of graph that statistically is more important and reliable. On this occasion, bars are also used to indicate the frequency of certain values through Cartesian axes, but instead of limiting the frequency of a specific value of the evaluated variable, it reflects an entire interval. Thus, a range of values is observed, which also **they could reflect intervals of different lengths** .

This allows observing not only the frequency but also the dispersion of a continuum of values, which in turn can help infer the probability. It is generally used against continuous variables, such as time.

### 4. Line chart

In this type of graph lines are used to **delimit the value of a dependent variable with respect to another independent** . It can also be used to compare the values of the same variable or different investigations using the same graph (using different lines).It is usual to use it to observe the evolution of a variable over time.

A clear example of this type of graphics are the frequency polygons. Its operation is practically identical to that of the histograms although using points instead of bars, with the exception that it allows to establish the slope between two of these points and the comparison between different variables related to the independent or between the results of different experiments with the same variables, as for example the measures of an investigation regarding the effects of a treatment, **observing the data of a pretreatment and post-treatment variable** .

### 8. Scatter chart

The scatter plot or graph xy is a type of graph in which all the data obtained through observation is represented by points using the Cartesian axes. **The x and y axes each show the values of a dependent variable and an independent variable** or two variables that are being observed if they have some kind of relationship.

The points represent the value reflected in each observation, which at a visual level will show a cloud of points through which we can observe the level of dispersion of the data.

You can see if there is a relationship between the variables or not by calculation. It is the procedure that is usually used, for example, to establish the existence of linear regression lines that allow determining if there is a relationship between variables and even the type of existing relationship.

### 9. Cash and mustache chart

The cash charts are one of the types of graphs that tend to be used in order to observe the dispersion of the data and how they group their values. It is based on the calculation of the quartiles, which are the values that p**ermiten divide the data into four equal parts** . Thus, we can find a total of three quartiles (the second of which correspond to the median of the data) that will configure the "box" in question. The so-called whiskers would be the graphic representation of extreme values.

This graph **It is useful when evaluating intervals** , as well as to observe the level of dispersion of the data from the values of the quartiles and the extreme values.

### 10. Area chart

In this type of graph, the relationship between dependent and independent variable is observed in a similar way with line graphs. Initially **a line is made that unites the points that mark the different values of the variable** measure, but everything below is also included: this type of graph allows us to see the accumulation (a certain point includes those located below).

Through it you can measure and compare the values of different samples (for example, compare the results obtained by two people, companies, countries, by two records of the same value ....). The different results can be stacked, easily observing the differences between the various samples.

### 11. Pictogram

A pictogram is a graphic in which, instead of representing the data from abstract elements such as bars or circles, **elements of the subject that is being investigated are used** . In this way it becomes more visual. However, its operation is similar to that of the bar graph, representing frequencies in the same way

### 12. Cartogram

This graph is useful in the field of epidemiology, indicating the geographical areas or areas in which a certain value of a variable appears more or less frequently. Frequencies or frequency ranges are indicated by the use of color (requiring a legend to be understood) or size.

#### Bibliographic references:

- Martínez-González, M.A .; Faulin, F.J. and Sánchez, A. (2006). Friendly bio-statistics, 2nd ed. Diaz de Santos, Madrid.