## Columns, sectors, and an illustrious Italian

When you read the title of this post, you can ask yourself with what stupid occurrence am I going to crush the suffered concurrence today, but do not fear, all we are going to do is to put in prospective value that famous aphorism that says that a picture is worth a thousand words. Have I clarified something? I suppose not.

As we all know, descriptive statistics is that branch of statistics that we usually use to obtain a first approximation to the results of our study, once we have finished it.

The first thing we do is to describe the data, for which we make frequency tables and use various measures of tendency and dispersion. The problem with these parameters is that, although they truly represent the essence of the data, it is sometimes difficult to provide a synthetic and comprehensive view with them. It is in these cases that we can resort to another resource, which is none other than the graphic representation of the study results. You know, a picture is worth a thousand words, or so they say.

There are many types of graphs to help us better understand the data, but today we are only going to talk about those that have to do with qualitative or categorical variables.

Remember that qualitative variables represent attributes or categories of the variable. When the variable does not include any sense of order, it is said to be a nominal categorical variable, while if a certain order can be established between the categories, we would say that it is an ordinal categorical variable. For example, the variable “smoker” would be nominal if it has two possibilities: “yes” or “no”. However, if we define it as “occasional”, “little smoker”, “moderate” or “heavy smoker”, there is already a certain hierarchy and we speak of ordinal qualitative variable.

The first type of chart that we are going to consider when representing a qualitative variable is the pie chart. This consists of a circle whose area represents the total data. Thus, an area that will be directly proportional to its frequency is assigned to each category. In this way, the most frequent categories will have larger areas, so that we can get an idea of how the frequencies are distributed in the categories at a glance.

There are three ways to calculate the area of each sector. The simplest is to multiply the relative frequency of each category by 360 °, obtaining the degrees of that sector.

The second is to use the absolute frequency of the category, according to the following rule of three:

Absolute frequency / Total data frequency = Degrees of the sector / 360 °

Finally, the third way is to use the proportions or percentages of the categories:

% of the category / 100% = Degrees of the sector / 360 °

The formulas are very simple, but, in any case, there will be no need to resort to them because the program with which we make the graph will do it for us. The instruction in R is pie(), as you can see in the first figure, in which I show you a distribution of children with exanthematic diseases and how the pie chart would be represented.The pie chart is designed to represent nominal categorical variables, although it is not uncommon to see pies representing variables of other types. However, and in my humble opinion, this is not entirely correct.

For example, if we make a pie chart for an ordinal qualitative variable, we will be losing information about the hierarchy of the variables, so it would be more correct to use a chart that allows to sort the categories from less to more. And this chart is none other than the bar chart, which we’ll talk about next.

The pie chart will be especially useful when there are few categories of the variable. If there are many, the interpretation is no longer so intuitive, although we can always complete the graph with a frequency table that helps us to better interpret the data. Another tip is to be very careful with 3D effects when drawing cakes. If we go from elaborate, the graphic will lose clarity and will be more difficult to read.

The second graph that we are going to see is, as we have already mentioned, the bar chart, the optimum to represent ordinal qualitative variables. On the horizontal axis, the different categories are represented, and on it some columns or bars are raised whose height is proportional to the frequency of each category. We could also use this type of graph to represent discrete quantitative variables, but what is not very correct to do is use it for the qualitative nominal variables.

The bar chart is able to express the magnitude of the differences between the categories of the variable, but it is precisely its weak point, since it is easily manipulated if we modify the axes’ scales. That is why we must be careful when analyzing this type of graphics to avoid being deceived by the message that the author of the study may want to convey.

This chart is also easy to do with most statistical programs and spreadsheets. The function in R is barplot(), as you can see in the second figure, which represents a sample of asthmatic children classified by severity.

With what has been seen so far, some will think that the title of this post is a bit misleading. Actually, the thing is not about columns and sectors, but about bars and pies. Also, who is the illustrious Italian? Well, here I do not fool anyone, because the character was both Italian and illustrious, and I am referring to Vilfredo Federico Pareto.

Pareto was an Italian who was born in the mid-19th century in Paris. This small contradiction is due to the fact that his father was then exiled in France for being one of the followers of Giuseppe Mazzini, who was then committed to Italian unification. Anyway, Pareto lived in Italy from he was 10 years old on, becoming an engineer with extensive mathematical and humanistic knowledge and who contributed decisively to the development of microeconomics. He spoke and wrote fluently in French, English, Italian, Latin and Greek, and became famous for a multitude of contributions such as the Pareto’s distribution, Pareto’s efficiency, Pareto’s index and Pareto’s principle. To represent the latter, he invented the Pareto’s diagram, which is what brings him here today among us.

Pareto chart (also known in economics as a closed curve or A-B-C distribution) organizes the data in descending order from left to right, represented by bars, thus assigning an order of priorities. In addition, the diagram incorporates a curved line that represents the cumulative frequency of the categories of the variable. This initially allowed the Pareto’s principle to be explained, which goes on to say that there are many minor problems compared to a few that are important, which was very useful for decision-making.

As it is easy to understand, this prioritization makes the Pareto diagram especially useful for representing ordinal qualitative variables, surpassing the bar chart by giving information on the percentage accumulated by adding the categories of the distribution of the variable. The change in slope of this curve also informs us of the change in the concentration of data, which depends on the variability in which the subjects of the sample are divided between the different categories.

Unfortunately, R does not have a simple function to represent Pareto diagrams, but we can easily obtain it with the script that I attached in the third figure, obtaining the graph of the fourth.

And here we are going to leave it for today. Before saying goodbye, I want to warn you that you should not confuse the bars of the bar chart with those of the histogram since, although they can be similar from the graphic point of view, both represent very different things. In a bar chart only the values of the variables we have observed when doing the study are represented. However, the histogram goes much further since, in reality, it contains the frequency distribution of the variable, so it represents all possible values that exist within the intervals, although we have not observed any directly. It allows us to calculate the probability that any distribution value will be represented, which is of great importance if we want to make inference and estimate population values based on the results of our sample. But that is another story…

## Do not eat too many pies

Pies, how good they are! The problem is that, as you know, what is not frowned upon is fattening or causes cancer. And pies could not be the exception, so be careful to avoid eating too much so that they don’t end in your spare tyre or in worse places.

But there is a type of pie that is not fattening at all (nor causes cancer), and this is the pie chart, which is frequently used in statistics. Did I say just frequently? I am probably short. Because it is not fattening nor has detrimental health effects there is a tendency to abuse their use.

The pie chart, or circle chart, is easy to draw. It consists of a circle whose area represents the total of data. Thus, an area proportional to its frequency is assigned to each category so the much frequent categories have larger areas and you can get an idea of how frequencies are distributed among categories at a glance.

There are three ways to calculate the area of each sector. The simplest is to multiply the relative frequency of each category by 360 °, obtaining the degrees corresponding to each sector.

The second is using the absolute frequency of the category, according to the following rule of thirds:

Finally, the third way is to use the proportions or percentages of the categories:

These formulas are very simple but, anyway, there will be no need for them because the program we use to draw the graph will do it for us.

The pie chart is designed to represent nominal categorical variables, although it is not uncommon to see pies representing other variables. However, in my humble opinion, this is not entirely correct.

For example, if we make a pie chart for an ordinal qualitative variable we will lose the information on the hierarchy of variables, and it would be more correct to use a graphic that allows sort categories from less to more. And this figure is none other than the bar chart.

The pie chart is especially useful when there are few variables. If you have many variables interpretation ceases to be so intuitive, although we can always complete the chart with a frequency table to help us better interpret the data. Another tip is to be very careful with 3D effects when drawing the pie: too artistic pies could be difficult to understand.

Finally, just say that it makes no sense to use a pie to represent a quantitative variable. For that there is another more appropriate procedure, which is to use a histogram that best represents the frequency distribution of a continuous quantitative variable. But that is another story…

## As an egg to a chestnut

What an egg and a chestnut look alike?. If we fired our imagination we can give some answers as absurd as stilted. Both are more or less rounded, the two can serve as food and both have a hard shell that encloses the part that is eaten. But in fact, and egg and a chestnut don’t resemble each other at all, even though we want to look for similarities.

The same thing happens to two graphic tools widely used in descriptive statistics: the bar chart and the histogram. At first glance they may look very similar, but if you look closely there are clear differences between the two types of graphs, which enclose totally different concepts.

We know that there are different types of variables. On the one hand there’re quantitative variables, which may be continuous or discrete. Continuous are those that can take any value within a range, as with the weight or blood pressure (in practice, possible values may be limited ​​due to the precision of the measuring devices, but in theory we can find any weight value between the minimum and maximum of the distribution). Discrete variables are those that can only take certain values ​​within a set, for example, the number of children or the number of episodes of myocardial ischemia.

Furthermore, there are qualitative variables that represent attributes or categories of the variable. When the variable does not include any sense of order, it is said to be a qualitative nominal variable, whereas if you can establish some order among the categories you will say that it is a qualitative ordinal variable. For example, smoking will be a qualitative nominal variable if it has two possibilities: yes or no. However, if we define the variable into categories like casual, slightly smoker, moderate or heavy smoker, there will be a hierarchy among the categories and it will be an ordinal qualitative variable.

Well, the bar graph is used to represent ordinal qualitative variables. The horizontal axis represents the different categories and over it are drawn a series of columns or bars whose heights are proportional to the frequency of each category. We could also use this type of graph to represent discrete quantitative variables, but what is not right to do is to use it to plot nominal qualitative variables.

The great merit of the bar chart is expressing the magnitude of the differences between the categories of the variable. But that is precisely its weakness because they are easily manipulated by modifying its axes. As you can see in the first figure, the difference between short and occasional smokers seems much higher in the second graph, in which we have miss out part of the vertical axis. So be careful when analyzing this type of graph to avoid being deceived with the message that the author of the study may want to convey.

Moving on, the histogram is a graph with a much deeper meaning. A histogram is a frequency distribution that is used (or should) to represent the frequency of continuous quantitative variables. This is not the height, but the area of the bar which is proportional to the frequency of that interval, and is related to the probability with which each interval may occur. As you can see in the second figure, columns, unlike in the bar chart, are side-by-side and the midpoint gives the name to the interval. The intervals need not to be all of the same width (although it is the most common situation), but they will always have a larger area the more frequent those intervals are.

In addition, there’s another very important difference between the bar graph and the histogram. In the first graph there’re represented only those values of the variable than have been observed in the study. Meanwhile, the histogram goes much further, since its represents all the possible values that exist within the range, although we haven’t seen some of them in a direct way. So, it allows calculating the probability of any value of the represented distribution, which is very important if we want to make inference and to estimate population’s values from the result of our sample.

And here we leave these graphs that may look the same but, as we’ve shown, seem like an egg to a chestnut.

Just one last comment. We’ve said at the beginning that it was a mistake to use a bar chart (or, of course, histograms) to represent nominal qualitative variables. And what can we use for that?. Well, a sectors’ chart, the famous and ubiquitous pie that is used on more occasions than the proper and that has its own idiosyncrasies. But that’s another story…