Hwo to Read in Information and Make a Histagram in R
Statisticians and researchers often demand a histogram to study a dataset that holds continuous values. Information technology shows you the distribution of the frequency of the data and helps you understand elements such as the skew and outliers present in a dataset.
You lot can hands create a histogram in R using the hist() function in base R. This has a many options that requite y'all command of bin sizes, range, etc. Yous tin as well utilise ggplot.
In this tutorial, I volition explain what histograms are and what you can do with them along with some basic methods for plotting histograms in R.
What is a Histogram?
A histogram shows the distribution of information in terms of frequency count. Although some may observe a shut similarity between bar charts and histograms, in that location is i subtle but very important difference. While a bar chart shows the frequency of discrete variables, a histogram shows data for continuous data. Therefore, you may find gaps between the bars of a bar chart, but a histogram represents a continuous distribution with no gaps.
In order to effectively explain the usage of a histogram, I will commencement with an example. Down beneath you tin can see a histogram for a built-in dataset of R, "AirPassengers". It shows information for how many passengers travelled by air each month for ten years.
Histogram of Frequency in R
The x-axis shows you the number of passengers travelling by air and the y-axis shows you how ofttimes a figure in a given range on the x-axis appeared in the data. The 10-axis has been divided into intervals of x values; these intervals are chosen bins.
In the plot you can meet that 100 to 200 passengers travelled by air more than 20 times whereas 500 to 550 passengers travelled a little less than 5 times. Something you should accept noticed here is that the chart doesn't evidence data for precisely 100 passengers or 550 passengers. Instead, it gives you a range of continuous values in which the x-centrality has been categorized into. This is precisely why a histogram does not have gaps similar a bar chart.
Moreover, you tin can also place the outliers on the extreme right, showing that instances where there were more than than 200 passengers travelling past air occurred around 2 to 3 times in 10 years.
Why Do You Need a Histogram?
Now you may still be wondering why exactly nosotros needed the histogram when there are other ways to obtain like data. I take listed some of the most frequent uses of histograms down beneath.
Find Unremarkably Occurring Events
A researcher may have spent a while collecting data and now, he or she may be wondering what is the nigh ofttimes occurring event in the data. A histogram shows the relative frequency in continuous terms, hence helping us understand the range where the densest observations lie.
Understand the Pattern of Your Data
Your data may sometimes show a normal distribution and sometimes it may not. Moreover, if the information is symmetric, i.e., information technology is normal, you may be interested in learning how symmetric it is using a visual tool.
A histogram neatly displays the distribution of the data hence helping you identify whether your data follows a pattern and, if and so, the kind of design that it follows.
Identify Deviations
Someone working with information won't always see everything aligned perfectly. When studying trends in a information, a histogram can easily tell you if your data deviates from expected values in any range.
Suppose you had expected a specific event from an experiment but when conducted, it gave you a different distribution. This immediately tells you something is wrong, and you lot demand to become back and re-bank check things.
Plotting a Histogram in R
Now that you lot have some working knowledge of a histogram and what you tin can practise with information technology, I can proceed to show how you tin obtain one in R. I'll keep working on "AirPassengers", a built-in dataset of R. First, we'll load the data.
# r histogram example - load dataset > information(AirPassengers) You lot can at present plot a histogram using the "hist()" function. The function uses a vector of values as an input and returns a histogram for those values.
# r histogram case - hist function in r > hist(AirPassengers) 
[You tin can get some more than detail with the "hist()" function by adding boosted parameters to specify x and y labels and irresolute the bin width. In the code below, I have inverse the bin width past specifying that my histogram uses v intervals. Moreover, I have likewise express the x values (number of passengers) between 100 and 500.
# Frequency histogram in r (Formatting Options) > hist(AirPassengers, main="My hist() Plot ", xlab="# of Passengers", xlim=c(100,500), breaks=5) 
Something yous may have noticed here is that although I specified bin count to be 5, the plot uses 4 bins. The parameter "breaks" in the"hist()" function but takes a suggestion from the user and produces intervals either shut to or equal to the user defined value. In R, the "hist()" function uses a predefined algorithm to calculate bins and it notwithstanding uses the same algorithm just staying shut to the user specification.
Another very interesting tweak you can make is by choosing unequal bin width for dissimilar intervals. In the code beneath, I have divided the bins into a width that depends on the quantile of each range. You can try out other methods by specifying a vector that holds values for the width for each interval.
# how to generate a histogram in r - unequal bins > hist(AirPassengers, breaks = quantile(AirPassengers, 0:10 / 10)) 
Other Methods for Plotting Histograms in R
R gives a number of methods to perform any basic function and each has its pros and cons. An additional method that I detect very interesting is through the use of the "qplot()" function in the "ggplot2" package. You can start by installing the package if yous haven't done that already.
# histogram in R ggplot2 case > install.packages("ggplot2") > qplot(AirPassengers, geom="histogram") 
Conclusion
Histograms are very commonly used for analysis in data science considering of the amount of data they pack between the confined. This tutorial aimed at giving you some insight on how histograms are created using R. Withal, if you are interested in going a few steps alee, I encourage y'all to read the R documentation on the "hist()" function and try out a couple of more than tweaks. This should help yous get some more clarity on how the office really works and what y'all can utilize it for.
Going Deeper…
Interested in Learning More About Categorical Information Analysis in R? Cheque Out
Graphics
- How to Plot Categorical Data in R (Basic)
- How to Plot Categorical Data in R (Advanced)
Tutorials
- How To Create a Contingency Table in R
- How To Generate Descriptive Statistics in R
- How To Create a Histogram in R
- How To Run A Chi Square Test in R (before article)
The Writer:
Syed Abdul Hadi is an aspiring undergrad with a keen involvement in data analytics using mathematical models and data processing software. His expertise lies in predictive analysis and interactive visualization techniques. Reading, travelling and equus caballus back riding are amid his downtime activities. Visit him on LinkedIn for updates on his work.
Source: https://www.programmingr.com/statistics/histogram-in-r/
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