Matthew Peterson’s Code Blog

Code Seen in Videos

Welcome to my running code blog where I will provide code seen in the videos below!

I will try to adapt the script to universally available data such as the iris package in R, but if that cannot be accomplished I will provide the code I used but will omit specific data names or file paths while still maintaining the integrity of the code that you can use and adapt to your data.

This will be a living, breathing document that I intend to update as needed. If you have questions or comments feel free to ask them below!

Pairwise Comparisons

(As seen in the video “Make a grouped boxplot with pairwise comparisons (ggplot2)!”

• You will need to use the ggplot2 package

library(ggplot2)

• Step 1, clear your global environment

rm(list = ls())

• Step 2, load the data set you intend to analyze

Data Name <- read.csv("Direct/Path/To/Your/Data/Data_name.csv")

• First let’s form our grouped boxplot

ggplot(data = Data, aes(x=________, y=_________, fill = _________))+

geom_boxplot(aes(x=_______, y=___________, fill=____________))+

scale_fill_viridis_d()+

theme_classic()

• Now let’s add the anova

ggplot(data = Data, aes(x=________, y=_________, fill = _________))+

geom_boxplot(aes(x=_______, y=___________, fill=____________))+

scale_fill_viridis_d()+

theme_classic()+

stat_compare_means(method = "anova"

• Now we create the comparisons we want

my_comparisons<- list(c("Afghan", "China"), c("Afghan", "India"), c("Afghan", "Viridis"))

• NOTE: You should list your comparisons for your specific data as shown above

• Finally, let’s add some comparisons

ggplot(data = Dry, aes(x=___________, y=_____________, fill = _________))+

geom_boxplot(aes(x=Accession, y=AVG_Dry_Wt., fill=Treatment))+

scale_fill_viridis_d()+

theme_classic()+

stat_compare_means(method = "anova", label.y = 750)+

stat_compare_means(comparisons = my_comparisons, method = "t.test")

• For clean figures it may be best to simply create an anova table to exhibit statistical comparisons

anova(lm(Y-variable ~X-Variable*Treatment, data = Dry))

Principal Component Analysis in R

(As seen in the video “Principal Component Analysis in R”)

• In this tutorial we will follow step by step how to make a PCA plot based on the R-bloggers webpage that will be linked below.

view(iris)

• PCA in R

• PCA plots are based on independent variables so any dependent variable must be removed

pc<-prcomp(iris[-5], center = TRUE, scale = TRUE)

• The scale function is used for normalization

pc$scale

• Here we will print the results which will get standard deviations and loadings

print(pc)

summary(pc)

• The first principal components explain the variability

  Making The Bi-Plot

• you will need the devtools package

library(devtools)

install_github("vqv/ggbiplot")

library(ggbiplot)

g <- ggbiplot(pc, obs.scale = 1, var.scale = 1, groups = iris$Species, ellipse = TRUE, circle = TRUE, ellipse.prob = 0.68)

• pc is the dataset we made which contains our principal components

• obs.scale and var.scale are set to 1 so no scale is applied

• we group by species so we can observe the differences within the iris dataset

• ellipse is true so it draws and ellipse around the group of points in the plot, which represent the probability density

• circle is true for plotting a unity circle in the plot which will represent the correlation structure of the variables

• ellipse.prob is a set parameter to make an ellipse that encompasses around 68% of the observations for each group

g <- g + scale_color_discrete(name = '')

g <- g + theme(legend.direction = 'horizontal',

               legend.position = 'top')

print(g)

• As we can see, PC1 explains about 73.7% and PC2 explains about 22,1%

• Arrows that are closer to each other indicates higher amounts of correlation

• A biplot is an important tool in PCA to understand what is going on in the dataset

Making a Box and Whisker Plot

(As seen in the video “RStudio Tutorial (Geom Boxplot with Kruskal Wallis)”)

• Using ggplot and the existing iris dataset within RStudio choose which 2 values you wish to compare. In this example we are looking to compare species and petal length.

• The first thing we need to do is to ensure we can plot a box and whisker plot before jumping into our comparisons

  View(iris)
  
  ggplot(iris, aes(x=Species, y=Petal.Length, fill=Species))+
    geom_boxplot()+
    theme_classic()+
    labs(y="Petal Length")

• Next we need to create our comparison list

  my_comparisons_iris<- list(c("setosa", "versicolor"), c("setosa", "virginica"))

• Next we need to integrate our comparisons into our box and whisker plot and tell ggplot to compare the mean values within our comparison list.

  ggplot(iris, aes(x=Species, y=Petal.Length, fill=Species))+
    geom_boxplot()+
    theme_classic()+
    labs(y="Petal Length")+
    stat_compare_means(comparisons = my_comparisons_iris, method = "wilcox.test")+
    stat_compare_means()

• Note: set your comparison method to whichever statistical test you need, in this example we use a Wilcox test.

Maxing a Boxplot with an ANOVA

(As seen in the video “RStudio ggplot tutorial (Geom Boxplot with ANOVA)”)

• Using ggplot and the existing dataset iris use the following code to create a boxplot with an ANOVA comparison

  ggplot(data = iris, aes(x=Species, y=Petal.Length, fill =Species))+
    geom_boxplot()+
    theme_classic()+
    scale_color_viridis_d()+
    labs(x = "Species", y= "Petal.Length")+ 
    theme(axis.title.x = element_text(size = 16), axis.title.y = element_text(size = 16))+
    stat_compare_means(method = "anova")

• Adjust the text size using the theme line and choose your comparison test with stat_compare_means

Making an Animated Plot

(As seen in the video “Animated Plot (make a .gif in R!)”)

• You will need to install the following packages

  install.packages("ggplot2") 
  install.packages("gganimate")
  install.packages("gapminder")
  devtools::install_github("dgrtwo/gganimate")
  install.packages("animation")
  install.packages("gifski")
  install.packages("magick")
  
  
  library(ggplot2)
  library(gganimate)
  library(gapminder)
  library(magick)
  library(animation)
  library(gifski)

• This tutorial uses the preexisting data set “gapminder” which looks at countries, life expectancy, population, and GDP every 5 years between 1952 and 2007.

  p <- ggplot(INSERTDATASET HERE, aes(X variable, Y variable, frame = motion capture variable, color = continent, size = pop)) +
    geom_point(alpha = 0.7) +  # Add scatter plot with transparency
    transition_time(year) + 
    scale_x_log10()+# Animate over time
    scale_color_viridis_d() +  # Color scale by continent
    scale_size_continuous(range = c(1, 10)) +  # Adjust size range for population
    labs(title = "Year: {frame_time}") +  # Title with frame time
    theme_minimal() +  # Minimal theme for better visibility
    guides(size = "none")  # Remove legend for size

• The plot was put into variable “p” to condense the code. The code above will require you to select your own x and y variables as well as the frame variable. If you need to refer to my video on this click the link at the start of this section.

• Now we can move onto animating the plot and save it.

  animate(p, renderer = gifski_renderer(), width = 800, height = 600, res = 100)
  
  
  anim_save("Creat your own file path here/animation3.gif", animate(p, renderer = gifski_renderer(), width = 800, height = 600, res = 100))

• This is how you can successfully create a .gif using RStudio!