With every new set of data, you need to summarize it in a compact and easy-to-understand manner. Accordingly, this is the primary concept of descriptive statistics. In this article, our Statistics Homework Help experts share valuable techniques on computing descriptive statistics with R. They also help students with their statistics homework in every programming language. But before looking at these techniques, what is the meaning of descriptive statistics?

**What is Descriptive Statistics?**

Descriptive statistics entails summarizing and organizing the characteristics of a data set. In this case, the data set refers to a collection of responses or observations from a select sample group or population. Descriptive statistics also entails summarizing the sample under study without making any inferences based on the probability theory. Even when the study’s primary aim requires applying inferential statistics, you will still need to use descriptive statistics to provide a general summary.

Descriptive statistics is usually the first step and the most vital part of any statistical analysis. The technique allows the programmer or statistician time to check and verify data quality. Accordingly, this process assists in understanding data by developing an in-depth and straightforward overview of it. When presented properly, descriptive statistics can be an excellent starting point for extra analysis. Apply the following techniques to compute descriptive statistics with R.

**How to Calculate Descriptive Statistics with R**

**1. ****Use summary() Function**

summary (my data) |

The summary () function is useful when calculating specific values for each variable. The values include:

- The minimum value
- The first quartile
- The mean value
- The third quartile
- The median
- The maximum value

**2. ****Use sapply () Function**

**Example of How to Calculate Descriptive Statistics in R**

Assume the following information is available in your R data frame containing three variables:

Further, the **sapply()** function can be useful in calculating the particular descriptive statistics for each variable. The function is illustrated below:

**Tips on Studying Descriptive Statistics**

**1. ****Understand R Basics**

The best way to study and become an expert in descriptive statistics is to understand the R basics. Learn the R programming language at your own time. For example, use online resources and tutorials to master R. Besides, familiarize yourself with R’s syntax and functions. Accordingly, you will experience easier working and performing descriptive statistics with R. You can alternatively learn a programming language that processes and visualizes descriptive statistics.

**2. ****Learn Statistics Packages**

R utilizes various packages in statistical analysis. The commonly used packages in descriptive statistics are ‘**base**’ and ‘**summarytools**.’ Allocate more time to study these packages and their functions. Eventually, you will learn the easiest methods for calculating mean, median, standard deviation, and quartiles.

**3. ****Use Real Data to Practice**

There is no shortcut to becoming a descriptive statistics expert without real-time practice. You should master how to handle different datasets through regular practice. There are various options to access real data sets. These include online sources and R’s in-build datasets. When practicing, begin by loading datasets into R to help explore their structure. Apply descriptive statistics functions to analyze and summarize the data.

**4. ****Practice Data Visualization**

Again, data visualization is another viable strategy for becoming a descriptive statistics expert. For example, the ggplot2 package in R is an essential tool for plotting and creating informative graphs. The tool is also helpful in creating histograms, boxplots, scatterplots, and other visualizations. Indeed, improving your data visualization skills will help you understand the distribution and data patterns.

**5. ****Explore Online Resources and Courses**

Learning is a dynamic process that requires inputs from various sources. You cannot become a descriptive statistics expert by relying only on your lecture materials. Instead, use multiple online learning resources and courses to your advantage. For example, Coursera and Udemy offer valuable lessons on data analysis with R. Some of these online resources have video lectures, assignments, and quizzes that enhance the learning experience.

## Need Help with Descriptive Statistics with R

Understanding descriptive statistics with R is essential for anyone dealing with data analysis. Gaining such knowledge is the first step in summarizing and organizing data. Whether you plan to calculate minimums, medians, means, or other statistics, R provides powerful tools to perform these calculations. These include **summary()** and **sapply()** functions. Moreover, specialized packages like Hmisc, pastecs, and psych offer even more extensive options. Do you still need help with statistics? Get help from our statistics experts in every programming language. We also help students do their projects and exams.