# Unpaired Two-Samples Wilcoxon Test in R

The unpaired two-samples Wilcoxon test (also known as Wilcoxon rank sum test or Mann-Whitney test) is a non-parametric alternative to the unpaired two-samples t-test, which can be used to compare two independent groups of samples. It’s used when your data are not normally distributed. This article describes how to compute two samples Wilcoxon test in R.

# Visualize your data and compute Wilcoxon test in R

## R function to compute Wilcoxon test

To perform two-samples Wilcoxon test comparing the means of two independent samples (x & y), the R function wilcox.test() can be used as follow:

``wilcox.test(x, y, alternative = "two.sided")``

• x,y: numeric vectors
• alternative: the alternative hypothesis. Allowed value is one of “two.sided” (default), “greater” or “less”.

## Import your data into R

1. Prepare your data as specified here: Best practices for preparing your data set for R

2. Save your data in an external .txt tab or .csv files

3. Import your data into R as follow:

``````# If .txt tab file, use this
# Or, if .csv file, use this

Here, we’ll use an example data set, which contains the weight of 18 individuals (9 women and 9 men):

``````# Data in two numeric vectors
women_weight <- c(38.9, 61.2, 73.3, 21.8, 63.4, 64.6, 48.4, 48.8, 48.5)
men_weight <- c(67.8, 60, 63.4, 76, 89.4, 73.3, 67.3, 61.3, 62.4)
# Create a data frame
my_data <- data.frame(
group = rep(c("Woman", "Man"), each = 9),
weight = c(women_weight,  men_weight)
)``````

We want to know, if the median women’s weight differs from the median men’s weight?

## Check your data

``print(my_data)``
``````   group weight
1  Woman   38.9
2  Woman   61.2
3  Woman   73.3
4  Woman   21.8
5  Woman   63.4
6  Woman   64.6
7  Woman   48.4
8  Woman   48.8
9  Woman   48.5
10   Man   67.8
11   Man   60.0
12   Man   63.4
13   Man   76.0
14   Man   89.4
15   Man   73.3
16   Man   67.3
17   Man   61.3
18   Man   62.4``````

It’s possible to compute summary statistics (median and interquartile range (IQR)) by groups. The dplyr package can be used.

• To install dplyr package, type this:
``install.packages("dplyr")``
• Compute summary statistics by groups:
``````library(dplyr)
group_by(my_data, group) %>%
summarise(
count = n(),
median = median(weight, na.rm = TRUE),
IQR = IQR(weight, na.rm = TRUE)
)``````
``````Source: local data frame [2 x 4]
group count median   IQR
(fctr) (int)  (dbl) (dbl)
1    Man     9   67.3  10.9
2  Woman     9   48.8  15.0``````

## Visualize your data using box plots

You can draw R base graphs as described at this link: R base graphs. Here, we’ll use the ggpubr R package for an easy ggplot2-based data visualization

• Install the latest version of ggpubr from GitHub as follow (recommended):
``````# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")``````
• Or, install from CRAN as follow:
``install.packages("ggpubr")``
• Visualize your data:
``````# Plot weight by group and color by group
library("ggpubr")
ggboxplot(my_data, x = "group", y = "weight",
color = "group", palette = c("#00AFBB", "#E7B800"),
ylab = "Weight", xlab = "Groups")`````` Unpaired Two-Samples Wilcoxon Test in R

## Compute unpaired two-samples Wilcoxon test

Question : Is there any significant difference between women and men weights?

1) Compute two-samples Wilcoxon test - Method 1: The data are saved in two different numeric vectors.

``````res <- wilcox.test(women_weight, men_weight)
res``````
``````
Wilcoxon rank sum test with continuity correction
data:  women_weight and men_weight
W = 15, p-value = 0.02712
alternative hypothesis: true location shift is not equal to 0``````

It will give a warning message, saying that “cannot compute exact p-value with tie”. It comes from the assumption of a Wilcoxon test that the responses are continuous. You can suppress this message by adding another argument exact = FALSE, but the result will be the same.

2) Compute two-samples Wilcoxon test - Method 2: The data are saved in a data frame.

``````res <- wilcox.test(weight ~ group, data = my_data,
exact = FALSE)
res``````
``````
Wilcoxon rank sum test with continuity correction
data:  weight by group
W = 66, p-value = 0.02712
alternative hypothesis: true location shift is not equal to 0``````
``````# Print the p-value only
res\$p.value``````
`` 0.02711657``

As you can see, the two methods give the same results.

The p-value of the test is 0.02712, which is less than the significance level alpha = 0.05. We can conclude that men’s median weight is significantly different from women’s median weight with a p-value = 0.02712.

Note that:

• if you want to test whether the median men’s weight is less than the median women’s weight, type this:
``````wilcox.test(weight ~ group, data = my_data,
exact = FALSE, alternative = "less")``````
• Or, if you want to test whether the median men’s weight is greater than the median women’s weight, type this
``````wilcox.test(weight ~ group, data = my_data,
exact = FALSE, alternative = "greater")``````

# Online unpaired two-samples Wilcoxon test calculator

You can perform unpaired two-samples Wilcoxon test, online, without any installation by clicking the following link:

• Compare one-sample mean to a standard known mean
• Compare the means of two independent groups

# Infos

This analysis has been performed using R software (ver. 3.2.4).

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