##### Question

In: Statistics and Probability

# R studio programing . can someone do an example of when to perform a one-sample wilcox...

R studio programing . can someone do an example of when to perform a one-sample wilcox signed-rank test. after, show modification to the previous function to perform a hypothesis comparing 2 population center with independent samples (mann-whitney test).

## Solutions

##### Expert Solution

1. whether the median (mm) of the sample is equal to the theoretical value (m0m0)?
2. whether the median (mm) of the sample is less than to the theoretical value (m0m0)?
3. wh

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

5. # Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
ether the median (mm) of the sample is greater than to the theoretical value(m0m0)
6. wilcox.test(x, mu = 0, alternative = "two.sided")
7. Import your data into R

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

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

10. Import your data into R as follow:

11. # If .txt tab file, use this
# Or, if .csv file, use this
my_data <- read.csv(file.choose())
12. set.seed(1234)
my_data <- data.frame(
name = paste0(rep("M_", 10), 1:10),
weight = round(rnorm(10, 20, 2), 1)
)

# Print the first 10 rows of the data
head(my_data, 10)
   name weight
1   M_1   17.6
2   M_2   20.6
3   M_3   22.2
4   M_4   15.3
5   M_5   20.9
6   M_6   21.0
7   M_7   18.9
8   M_8   18.9
9   M_9   18.9
10 M_10   18.2.
13.    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
15.30   18.38   18.90   19.25   20.82   22.20 
14. Min.: the minimum value
15. 1st Qu.: The first quartile. 25% of values are lower than this.
16. Median: the median value. Half the values are lower; half are higher.
17. 3rd Qu.: the third quartile. 75% of values are higher than this.
18. Max.: the maximum value
19. Visualize your data using box plots

library(ggpubr)
ggboxplot(my_data\$weight,
ylab = "Weight (g)", xlab = FALSE,
ggtheme = theme_minimal

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