-1

I want to draw the same exact graph in R. However, I want to consider two options:
(1) with one x axis for each of the genders &
(2) two different xaxes for each of the gender. Here is also the link for where I found the image: https://rpubs.com/WhataBurger/Anovatype3

Thanks for sharing the knowledge.

enter image description here

Here is a randomly generated one. Please feel free to share your random data in the responses (if you have any).

Show in New Window
structure(list(gender = c("Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female", "Female", "Female", 
"Female", "Female", "Female", "Female", "Female"), education = c("Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "Education", 
"Education", "Education", "Education", "Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education", "No Education", "No Education", "No Education", 
"No Education"), salary = c(54395.2435344779, 57698.2251051672, 
75587.0831414912, 60705.0839142458, 61292.8773516095, 77150.6498688328, 
64609.162059892, 47349.3876539347, 53131.4714810647, 55543.3802990004, 
72240.8179743946, 63598.1382705736, 64007.7145059405, 61106.8271594512, 
54441.5886524592, 77869.1313680308, 64978.5047822924, 40333.8284337036, 
67013.5590156369, 55272.0859227207, 49321.7629401315, 57820.250853417, 
49739.9555169276, 52711.0877070886, 53749.6073215074, 54395.2435344779, 
57698.2251051672, 75587.0831414912, 60705.0839142458, 61292.8773516095, 
77150.6498688328, 64609.162059892, 47349.3876539347, 53131.4714810647, 
55543.3802990004, 72240.8179743946, 63598.1382705736, 64007.7145059405, 
61106.8271594512, 54441.5886524592, 77869.1313680308, 64978.5047822924, 
40333.8284337036, 67013.5590156369, 55272.0859227207, 49321.7629401315, 
57820.250853417, 49739.9555169276, 52711.0877070886, 53749.6073215074, 
23253.2267570303, 33351.1481779781, 30613.4924713461, 25447.4522519522, 
35015.2596842797, 31705.8568859073, 28819.7140680309, 33580.5026441801, 
33512.5339501322, 33286.3243265499, 32754.5610164004, 32215.6706141504, 
29752.3531576931, 28776.1493450403, 28478.1159959505, 27221.172084318, 
29168.3308879216, 24938.4145937269, 38675.8238613541, 34831.84799322, 
25507.5656671866, 28388.4606588037, 28133.3785855071, 33119.8604733453, 
29666.5237341127, 23253.2267570303, 33351.1481779781, 30613.4924713461, 
25447.4522519522, 35015.2596842797, 31705.8568859073, 28819.7140680309, 
33580.5026441801, 33512.5339501322, 33286.3243265499, 32754.5610164004, 
32215.6706141504, 29752.3531576931, 28776.1493450403, 28478.1159959505, 
27221.172084318, 29168.3308879216, 24938.4145937269, 38675.8238613541, 
34831.84799322, 25507.5656671866, 28388.4606588037, 28133.3785855071, 
33119.8604733453, 29666.5237341127)), class = "data.frame", row.names = c(NA, 
-100L))
MK25
  • 11
  • 4

1 Answers1

0

Look at this code, it may help you to start. Your data it's not complete as all Education are male and all No Education are female, so you can't get a facet_wrap() with all categories. Anyway, I think this may be of help.

Once your variables charged, make a dataframe and analyse with ggplot:

library (ggplot2)
df <- data. Frame(education, gender, salary)

# plot 1
ggplot(df, aes(x = education, y = salary, fill=gender)) +
 geom_boxplot() +
 facet_wrap(.~gender) +
 theme_bw()

# plot 2
ggplot(df, aes(x = education, y = salary, fill = gender)) +
 geom_boxplot() +
 theme_bw()

Plot 1

Plot 2

Juan Riera
  • 127
  • 8