In brouwern/wildlifeR: Wildlife biology and animal ecology datasets with example analyses.

knitr::opts_chunk$set(echo = TRUE)

Preliminaries

Load packagtes

library(wildlifeR)
library(ggplot2)
library(cowplot)
library(ggpubr)
library(dplyr)

Load data

data(frogarms)

Subset your data

The function make_my_data2L() will extact out a random subset of the data. Change "my.code" to your school email address, minus the "@pitt.edu" or whatever your affiliation is.

my.frogs <- make_my_data2L(dat = frogarms, 
                           my.code = "nlb24", # <=  change this!
                           cat.var = "sex",
                           n.sample = 20, 
                           with.rep = FALSE)

Boxplots

Basic boxplot

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex")

Notched boxplot.

We'll use the original frogarms dataframe first for this THese aren't commonly used; the notches work kind of like confidence intervals to determine if medians are different.

ggboxplot(data = frogarms,
          y = "mass",
          x = "sex",
          notch  = TRUE)

Now try your own subset of the data. The Notch calculations likely get messed up with small samples sizes)

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
          notch  = TRUE)

Add colored fill; note that it is "fill" not "color". Color changes the color of the lines

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
          notch  = TRUE,
          fill = "sex")

We can turn off the notchign by adding a "#" character before it. This is called "commenting out"

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
          #notch  = TRUE,
          fill = "sex")

Add raw data. This works best with small datasts

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
          #notch  = TRUE,
          fill = "sex",
          add = "point")

Jiter raw data This can be helpfu, though ggpubr::ggboxplot doesn't allow much control over the "jittering". Jittering helpful when you have large datsets and want to avoid overlap in the points.

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
          #notch  = TRUE,
          fill = "sex",
          add = "jitter")

Label axes

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
          notch  = TRUE,
          fill = "sex",
          add = "jitter",
          xlab = "Sex",      #x axis (horizontal)
          ylab = "Mass (g)") #y axis (vertical)

Add title not usually done for publication but useful for keeping track of things and for presentations

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
          notch  = TRUE,
          fill = "sex",
          add = "jitter",
          xlab = "Sex",
          ylab = "Mass (g)",
          main = "Mass of Australian frogs by sex") #Main title

Move legend to bottom

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
          notch  = TRUE,
          fill = "sex",
          add = "jitter",
          xlab = "Sex",
          ylab = "Mass (g)",
          main = "Mass of frogs by sex",
          legend = "bottom")

Change color pallete

ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex",
         # notch  = TRUE,
          fill = "sex",
          add = "jitter",
          xlab = "Sex",
          ylab = "Mass (g)",
          main = "Mass of frogs by sex",
          legend = "bottom",
          palette = c("green","blue"))

Plotting multple plots with cowplot::plot_grid

We can save a plot to an R object

gg.my.frogs <- ggboxplot(data = my.frogs,
          y = "mass",
          x = "sex")

Call just the object (eg, just type it into the console. or highlight jsut the word)

gg.my.frogs

Make an object using the frogarms data

gg.frogarms <- ggboxplot(data = frogarms, #use original data
          y = "mass",
          x = "sex")

Now plot both

plot_grid(gg.my.frogs,
          gg.frogarms)

Add labels. Note that alignment is off sometimes.

plot_grid(gg.my.frogs, 
          gg.frogarms,
          labels = c("a)My fogs","b)All the frogs"))

Plot means with error bars

Super hand function ggerrorplot() Default is mean +/- 1 standard error

ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex")

Mean and se; note sd is not often used in publications

ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex",
          desc_stat = "mean_sd")

Mean and 95% confidence interval

ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex",
          desc_stat = "mean_ci")

Plot your data and original data

#your data
gg.my.frogs <- ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex",
          desc_stat = "mean_ci")

#all of the data
gg.all.frogs <- ggerrorplot(data = frogarms, #change data
          y = "mass",
          x = "sex",
          desc_stat = "mean_ci")

plot_grid(gg.my.frogs, gg.all.frogs)

Set colors

ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex",
          desc_stat = "mean_ci",
          color = "sex")

Add raw data. kinda crazy

ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex",
          desc_stat = "mean_ci",
          color = "sex",
          shape = "sex",
          add = "point")

Jitter raw data. even crazier

ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex",
          desc_stat = "mean_ci",
          color = "sex",
          add = "jitter")

Back to just the means Increase size

ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex",
          desc_stat = "mean_ci",
          color = "sex",
          size = 1.5) #

Move legend to the bottom

?set all of this stuff as eval = F and have students figur eout how to add it? m aybe not - goal is just to do "1st encounter"

ggerrorplot(data = my.frogs,
          y = "mass",
          x = "sex",
          desc_stat = "mean_ci",
          color = "sex",
          size = 1.5,
          xlab = "Sex",
          ylab = "Mass (g)",
          legend = "bottom") #

arms

ggerrorplot(data = frogarms,
          y = "arm",
          x = "sex",
          desc_stat = "mean_ci",
          color = "sex",
          size = 1.5,
          xlab = "Sex",
          ylab = "Mass (g)",
          legend = "bottom") #

brouwern/wildlifeR documentation built on May 28, 2019, 7:13 p.m.