To determine if there is a significant difference between the means or medians of two or more groups in R, you should compare groups.

By comparing groups, you can identify patterns or trends in the data and see how different factors affect the outcome of interest.

Now, let’s see how to compare groups in R and the different ways to do that.

**Compare groups in R**

4 easy ways to compare groups in R.

**Use t-tests**- Using the
**aov()****function** - Use
**wilcox.test()** - Use
**kruskal.test()**

**Method 1: Use t-tests**

A **t-test** is a statistical test used to compare the mean of a continuous variable between two groups.

There are several different types of t-tests.

- One-sample t-test.
- Two-sample t-test.
- Paired t-test.

If you have a data frame with two columns, **“group”** and **“value”**, compare the mean values of the **“value”** column for different levels of the** “group”** column. Use the **t.test()** function to perform a **t-test**.

```
data <- read.csv("data.csv")
t.test(value ~ group, data = data)
```

In this code, you can feed your own **data.csv** file, and it will perform the **t-test**.

**Method 2: Use the aov() function**

**ANOVA (Analysis of Variance)** is a statistical method used to compare the means of multiple groups.

The **aov()** function performs a one-way **ANOVA** to compare the means of the **“value”** column for multiple levels of the **“group”** column.

```
data <- read.csv("data.csv")
aov_output <- aov(value ~ group, data = data)
summary(aov_output)
```

The **summary()** function returns the **ANOVA** **table**, which includes the **F-statistic** and the **p-value** for the test.

If the **p-value** is less than the significance level (e.g. 0.05), it suggests that at least one group means is significantly different from the others.

**Method 3: Use the wilcox.test()**

The **wilcox.test()** function to perform a **Wilcoxon rank-sum test**.

```
data <- read.csv("data.csv")
wilcox.test(value ~ group, data = data)
```

The **wilcox.test()** function returns the test statistic (U) and the p-value for the test. If the p-value is less than the significance level (e.g. 0.05), it suggests that the medians of the two groups are quiet different.

**Method 4: Use the kruskal.test()**

The **Kruskal-Wallis** test is a non-parametric statistical test used to compare the medians of multiple groups.

```
data <- read.csv("data.csv")
kruskal.test(value ~ group, data = data)
```

The **kruskal.test()** method returns the test statistic (H) and the p-value for the test. If the p-value is less than the significance level (e.g. 0.05), it suggests that at least one of the group medians is significantly different from the others.

That’s it for this post.

Krunal Lathiya is a Software Engineer with over eight years of experience. He has developed a strong foundation in computer science principles and a passion for problem-solving. In addition, Krunal has excellent knowledge of Data Science and Machine Learning, and he is an expert in R Language.