Understanding the Problem with ggplot() and the ‘color’ Aesthetic

Introduction

The problem at hand revolves around creating a plot using ggplot(), specifically dealing with the color aesthetic. The user is trying to create a plot of statistic data (y) for each type of MNS (color) on different areas (x), but they are facing issues with displaying the colors as expected.

Background

The problem involves manipulating and plotting data from a dataframe called statMNS. To begin, we will review how to use ggplot() effectively in R. The code snippet below shows an example of creating a simple plot using ggplot():

library(ggplot2)

ggplot(mtcars, aes(x=wt, y=mpg))+
  geom_point()

This code generates a scatter plot with the weight (wt) on the x-axis and miles per gallon (mpg) on the y-axis.

Understanding the ‘color’ Aesthetic

In ggplot(), we can use the color aesthetic to specify the color of points in our plots. To do this, we need to map the data values to colors using a scale function (usually a factor or a vector of colors). Here is an updated version of the previous example with a color aesthetic:

library(ggplot2)

ggplot(mtcars, aes(x=wt, y=mpg, color=factor(cyl)))+
  geom_point()

In this case, we map the number of cylinders (cyl) to colors using a factor scale. This results in different shapes and sizes for each type of car.

Solving the Problem

Now that we have an understanding of the color aesthetic, let’s address the problem at hand. The user wants to plot statistic data (y) for each type of MNS (color) on different areas (x). We can achieve this using a similar approach:

library(ggplot2)

ggplot(statMNS2, aes(x=readerName, y=VAL, color=TYPE))+
  geom_point(size=2)

Here we map the readerName to x and the value of interest (VAL) to y. We also use the color aesthetic to map the TYPE column to colors.

Short Version of the Problem Data

However, when looking at the code provided by the user, it seems like there might be an issue with their data or their ggplot() call. The original R session output shows:

dput(statMNS)
structure(list(readerName = structure(list(X1 = "LBO_1", X2 = "LBO_11", 
X3 = "LBO_12", X4 = "LBO_13", X5 = "LBO_2", X6 = "LBO_3", 
X7 = "LBO_4", X8 = "LBO_5", X9 = "LBO_6", X10 = "LBO_7"), .Names = c("X1", 
"X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10")), 
vmax_lidar = structure(list(X1 = 167.4, X2 = 159.1, X3 = 163.5, X4 = 163.4, 
X5 = 160, X6 = 158.9, X7 = 153.8, X8 = 151, X9 = 157.3, X10 = 155.8), .Names 
= c("X1","X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10")), 
vmax_photo = structure(list(X1 = 148.8, X2 = 157.5, X3 = 161.1, X4 = 161.7, 
X5 = 158.1, X6 = 150.8, X7 = 141.6, X8 = 147.1, X9 = 155.1, X10 = 151.4), 
.Names = c("X1","X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10")), 
TYPE = c("vmax_lidar", "vmax_photo", "v3qu_lidar", "v3qu_photo", "vmean_lidar", 
"vmean_photo", "vmean_photo","vmedian_lidar", "vmedian_photo","v1qu_lidar",
 "v1qu_photo", "vmin_lidar", "vmin_photo", "vmin_photo", "vmin_photo", "vmin_photo",
 "vmin_photo", "vmin_photo", "vmin_photo", "vmin_photo"), VAL = c(167.4, 159.1, 
163.5, 163.4, 160, 158.9, 153.8, 151, 157.3, 155.8, 148.8, 157.5, 161.1,
 161.7, 158.1, 150.8, 141.6, 147.1, 155.1, 151.4, 120.5, 159.3, 162.2,
 163.1, 160.2, 151.8, 157.9, 152.4, 157.7, 158.3, 156.2, 143.2, 153.1,
 153.6, 155.2, 148.5, 147.4, 159.4, 151.8, 154.9, 149.5, 150.7, 152.3),
   .Dim = c(17L, 4L)))

The user also provided an edited version of the ggplot() call that they were using:

statMNS2$VAL <- as.numeric(statMNS2$VAL)

library(ggplot2)
ggplot(statMNS2, aes(x=readerName, y=VAL, color=TYPE))+
  geom_point(size=2)

It seems like this might be the correct ggplot() call for their data.

Conclusion

In conclusion, we have discussed how to use ggplot() with the color aesthetic and created a scatter plot of statistic data (y) for each type of MNS (color) on different areas (x). The user’s problem was likely due to a misunderstanding about where to map the colors or an issue with their data.

Last modified on 2023-05-26