Independent Study to consolidate this week

Types of variable, the normal distribution and summarising data

Set up

If you have just opened RStudio you will want to load the packages and import the data.

library(tidyverse)
library(readxl)

fly_bristles_means <- read_excel("data-raw/bristles-mean.xlsx")
cats <- read_csv("data-raw/cat-coats.csv")

Exercises

1. 💻 Summarise the fly_bristles_means dataframe by calculating the mean, median, sample size, standard deviation and standard error of the mean_count variable.

fly_bristles_means_summary <- fly_bristles_means |> 
  summarise(mean = mean(mean_count),
            median = median(mean_count),
            n = length(mean_count),
            standard_dev = sd(mean_count),
            standard_error = standard_dev / sqrt(n))

2. 💻 Create an appropriate plot to show the distribution of mean_count in fly_bristles_means

ggplot(fly_bristles_means, aes(x = mean_count)) +
  geom_histogram(bins = 10)

3. 💻 Can you format the plot 2. by removing the grey background, giving the bars a black outline and the fill colour of your choice and improving the axis format and labelling? You may want to refer to last week’s workshop.

ggplot(fly_bristles_means, aes(x = mean_count)) +
  geom_histogram(bins = 10, 
                 colour = "black",
                 fill = "skyblue") +
  scale_x_continuous(name = "Number of bristles",
                     expand = c(0, 0)) +
  scale_y_continuous(name = "Frequency",
                     expand = c(0, 0),
                     limits = c(0, 35)) +
  theme_classic()

4. 💻 Amend this code to change the order of the bars by the average mass of each coat colour? Changing the order of bars was covered last week. You may also want to practice formatting the graph nicely.

ggplot(cats, aes(x = coat, y = mass)) +
  geom_boxplot()

ggplot(cats, 
       aes(x = reorder(coat, mass), y = mass)) +
  geom_boxplot(fill = "darkcyan") +
  scale_x_discrete(name = "Coat colour") +
  scale_y_continuous(name = "Mass (kg)", 
                     expand = c(0, 0),
                     limits = c(0, 8)) +
  theme_classic()

5. 📖 Read Understanding the pipe |>