11.1 Load packages and import data

Load the tidyverse, skimr, naniar, knitr, and janitor packages:

library(tidyverse)
library(skimr)
library(naniar)
library(knitr)
library(janitor)

We will also need a new package called binom, so install that package using the procedure you previously learned, then load it:

library(binom)

Import the damselfly.csv data set we used in the preceding tutorial, and also the birds.csv dataset we used in an earlier tutorial.

damselfly <- read_csv("https://raw.githubusercontent.com/ubco-biology/BIOL202/main/data/damselfly.csv")

## Rows: 20 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): direction
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

birds <- read_csv("https://raw.githubusercontent.com/ubco-biology/BIOL202/main/data/birds.csv")

## Rows: 86 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): type
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Recall what the damselfly dataset looks like:

damselfly

## # A tibble: 20 × 1
##    direction        
##    <chr>            
##  1 clockwise        
##  2 counter_clockwise
##  3 counter_clockwise
##  4 clockwise        
##  5 counter_clockwise
##  6 counter_clockwise
##  7 counter_clockwise
##  8 counter_clockwise
##  9 counter_clockwise
## 10 counter_clockwise
## 11 counter_clockwise
## 12 clockwise        
## 13 counter_clockwise
## 14 counter_clockwise
## 15 counter_clockwise
## 16 counter_clockwise
## 17 counter_clockwise
## 18 counter_clockwise
## 19 counter_clockwise
## 20 counter_clockwise

The data show the predominant direction (either clockwise or counter-clockwise) of 20 circular battles between male damseflies.

And remind yourself what the birds dataset looks like:

birds

## # A tibble: 86 × 1
##    type     
##    <chr>    
##  1 Waterfowl
##  2 Predatory
##  3 Predatory
##  4 Waterfowl
##  5 Shorebird
##  6 Waterfowl
##  7 Waterfowl
##  8 Songbird 
##  9 Predatory
## 10 Waterfowl
## # ℹ 76 more rows

These data describe the category of bird (variable “type” that has 4 different categories) for a random sample of 86 birds sampled at a marsh habitat.