This is a series of five posts for this data:

Part 1: data downloading and cleaning
Part 2a: more cleaning
Part 2b: visualizing
Part 3: a Shiny app
Part 4: a gganimate plot

Introduction

About two years ago, I was taking my dog for a walk through a park and I began to notice the birds and how fascinating they were! 🐦 I began regularly going out birding (aka “bird-watching”) and reading up on these cool little flying dinosaurs.

It turns out there’s a lot of data in the birding world as well. Birding attracts the sort of detail-oriented person who likes to count and record stuff.

So there are opportunities to get involved in citizen science projects, including a long-running project called the Christmas Bird Count (CBC). It started in 1900, when Frank Chapman, an ornithologist, came up with the idea of counting birds as an alternative to hunting them at Christmas (hunting them being the previous tradition).¹

Birders have been going out every year around Christmas, to spend the day walking, biking, or driving through a census area to count all the birds they see or hear.

For the past two years, I have gone out with Hamilton’s Christmas Bird Count. I learn a lot while I’m out there and it feels like we are contributing to a larger purpose because of the data we are collecting.

So I thought I would look at the data and see what it could tell me!

Specifically, I’ve noticed birders will say things like, “the House Sparrows are getting worse every year” or, “the number of Bald Eagles has increased”, and I was wondering if the Christmas Bird Count data would agree or disagree with those statements.

To access the data, I went on the Bird Studies Canada website, clicked on Citizen Science, then Christmas Bird Count, then CBC Audubon Database, and then Historical Results by Count. I downloaded all years of data that existed for the Hamilton count.

If you would like to directly access the csv file that I used from my Github page, here it is!

Data import

I started by loading all of the packages I will be using and reading in the data using the readr and here packages.

library(dplyr)
library(janitor)
library(readr)
library(naniar)
library(lubridate)
library(stringr)
library(tidyr)
library(here)

hamilton_cbc <- read_csv(here::here(
  "posts",
  "2019-01-07-hamilton-cbc-part-1",
  "hamilton-cbc-all-years-csv.csv"))

Data cleaning

As shown below, it turns out that the first row just gives information about the count name and latitude/longitude, so I extracted those two pieces of information as current_circle_name and lat_long and then sliced the file so that the first two lines were excluded from the dataset. I then used clean_names from the janitor package.

hamilton_cbc %>%
  head()

# A tibble: 6 × 9
  CircleName Abbrev        LatLong           ...4  ...5  ...6  ...7  ...8  ...9 
  <chr>      <chr>         <chr>             <chr> <chr> <chr> <chr> <chr> <chr>
1 Hamilton   ONHA          43.2678790000/-7… <NA>  <NA>  <NA>   <NA> <NA>  <NA> 
2 <NA>       <NA>          <NA>              <NA>  <NA>  <NA>   <NA> <NA>  <NA> 
3 CountYear3 LowTemp       HighTemp          AMCl… PMCl… AMRa… "PMR… AMSn… PMSn…
4 118        -18.0 Celsius -13.0 Celsius     Clear Clear None  "Non… None  None 
5 117        -2.0 Celsius  11.0 Celsius      Clou… Clou… Light "Hea… None  None 
6 116        -2.0 Celsius  5.0 Celsius       Part… Part… None  "Lig… None  None

current_circle_name <- hamilton_cbc[1, 1]
lat_long <- hamilton_cbc[1, 3]

hamilton_cbc <- hamilton_cbc %>%
  slice(3 : n())

hamilton_cbc <- hamilton_cbc %>%
  clean_names()

Since I played around with the data before writing this, I know that there are actually six tables in this dataset.

The first three tables contain count day weather data. A lot of the weather data is missing and inconsistent. I will remove these three tables from hamilton_cbc.

Here is the end of the first table and the start of the second table. Notice the line of NAs between the two tables:

hamilton_cbc %>%
  slice(47:54)

# A tibble: 8 × 9
  circle_name abbrev     lat_long  x4       x5        x6      x7     x8    x9   
  <chr>       <chr>      <chr>     <chr>    <chr>     <chr>   <chr>  <chr> <chr>
1 66          10         25        Clear    Clear     Unknown Unkno… Unkn… Unkn…
2 65          18         31        Cloudy   Cloudy    Unknown Unkno… Unkn… Unkn…
3 64          26         34        Cloudy   Cloudy    Unknown Unkno… Unkn… Unkn…
4 63          21         36        Cloudy   Cloudy    Unknown Unkno… Unkn… Unkn…
5 <NA>        <NA>       <NA>      <NA>     <NA>      <NA>    <NA>   <NA>  <NA> 
6 CountYear5  LowTemp3   HighTemp2 AMCloud2 PMClouds2 <NA>    <NA>   <NA>  <NA> 
7 118         12/26/2017 85        198.75   100       <NA>    <NA>   <NA>  <NA> 
8 117         12/26/2016 95        216.65   97        <NA>    <NA>   <NA>  <NA>

Here is the end of the second table and the start of the third table. Notice the line of NAs between the two tables:

hamilton_cbc %>% 
  slice(143:150)

# A tibble: 8 × 9
  circle_name abbrev                lat_long x4    x5    x6    x7    x8    x9   
  <chr>       <chr>                 <chr>    <chr> <chr> <chr> <chr> <chr> <chr>
1 26          12/26/1925            10       <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
2 25          12/27/1924            8        <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
3 23          12/26/1922            9        <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
4 22          12/23/1921            2        8     <NA>  <NA>  <NA>  <NA>  <NA> 
5 <NA>        <NA>                  <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
6 CountYear4  LowTemp2              <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
7 118         Hamilton Naturalists… <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
8 117         Hamilton Naturalists… <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA>

Here is the end of the third table and the start of the fourth table. Notice that there is a line of NAs between the two tables. The fourth table is where the bird count data actually starts!

hamilton_cbc %>%
  slice(239:246)

# A tibble: 8 × 9
  circle_name                abbrev lat_long x4    x5    x6    x7    x8    x9   
  <chr>                      <chr>  <chr>    <chr> <chr> <chr> <chr> <chr> <chr>
1 "26"                        <NA>  <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
2 "25"                        <NA>  <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
3 "23"                        <NA>  <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
4 "22"                        <NA>  <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
5  <NA>                       <NA>  <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
6 "COM_NAME"                 "Coun… how_man… Numb… Flags <NA>  <NA>  <NA>  <NA> 
7 "Snow Goose\r\n[Chen caer… "1921… <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
8 "Snow Goose\r\n[Chen caer… "1922… <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA>

The last two tables of the six tables contain the names of the people who went out counting each year. I will also remove these two tables.

Since the tables are separated by having a line of NA’s in between each table, I will first figure out which rows are a line of NAs. Then I will only keep the rows of the fourth table.

blank_lines <- hamilton_cbc %>%
  mutate(row_num = row_number()) %>%
  filter(is.na(circle_name))

blank_lines

# A tibble: 5 × 10
  circle_name abbrev lat_long x4    x5    x6    x7    x8    x9    row_num
  <chr>       <chr>  <chr>    <chr> <chr> <chr> <chr> <chr> <chr>   <int>
1 <NA>        <NA>   <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA>       51
2 <NA>        <NA>   <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA>      147
3 <NA>        <NA>   <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA>      243
4 <NA>        <NA>   <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA>    23463
5 <NA>        <NA>   <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA>    23473

starting_line <- blank_lines %>%
  filter(row_number() == 3) %>%
  pull(row_num)

ending_line <- blank_lines %>%
  filter(row_number() == 4) %>%
  pull(row_num)

So, with those values of starting_line and ending_line, we can slice our dataset to only have the rows between those two values. Here’s what it looks like:

hamilton_cbc <- hamilton_cbc %>%
  # Only keep the rows within the fourth table
  slice((starting_line + 1):(ending_line - 1))

hamilton_cbc %>%
  head(n = 3)

# A tibble: 3 × 9
  circle_name                abbrev lat_long x4    x5    x6    x7    x8    x9   
  <chr>                      <chr>  <chr>    <chr> <chr> <chr> <chr> <chr> <chr>
1 "COM_NAME"                 "Coun… how_man… Numb… Flags <NA>  <NA>  <NA>  <NA> 
2 "Snow Goose\r\n[Chen caer… "1921… <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
3 "Snow Goose\r\n[Chen caer… "1922… <NA>     <NA>  <NA>  <NA>  <NA>  <NA>  <NA>

hamilton_cbc %>%
  tail(n = 3)

# A tibble: 3 × 9
  circle_name                abbrev lat_long x4    x5    x6    x7    x8    x9   
  <chr>                      <chr>  <chr>    <chr> <chr> <chr> <chr> <chr> <chr>
1 "House Sparrow\r\n[Passer… "2015… 2326     10.5… <NA>  <NA>  <NA>  <NA>  <NA> 
2 "House Sparrow\r\n[Passer… "2016… 2565     11.8… <NA>  <NA>  <NA>  <NA>  <NA> 
3 "House Sparrow\r\n[Passer… "2017… 2731     13.7… <NA>  <NA>  <NA>  <NA>  <NA>

You can see that the table starts with Snow Goose data from 1921 and goes until House Sparrow data in 2017.

Now we can clean this dataset up a bit more using the janitor package ❤️! This package will remove any empty columns, convert the top row to the column names of the dataset and it will clean the names.

# Janitor package to the rescue!
hamilton_cbc <- hamilton_cbc %>%
  janitor::remove_empty(which = "cols") %>%  
  janitor::row_to_names(row_number = 1) %>%
  janitor::clean_names() %>%
  rename(species = com_name)

hamilton_cbc %>%
  head()

# A tibble: 6 × 5
  species                     count_year how_many_cw number_by_party_hours flags
  <chr>                       <chr>      <chr>       <chr>                 <chr>
1 "Snow Goose\r\n[Chen caeru… "1921 [22… <NA>        <NA>                  <NA> 
2 "Snow Goose\r\n[Chen caeru… "1922 [23… <NA>        <NA>                  <NA> 
3 "Snow Goose\r\n[Chen caeru… "1924 [25… <NA>        <NA>                  <NA> 
4 "Snow Goose\r\n[Chen caeru… "1925 [26… <NA>        <NA>                  <NA> 
5 "Snow Goose\r\n[Chen caeru… "1926 [27… <NA>        <NA>                  <NA> 
6 "Snow Goose\r\n[Chen caeru… "1928 [29… <NA>        <NA>                  <NA>

species gives the species name in English and the scientific name, in parentheses
count_year data has a lot of information that we will parse out in a moment
how_many_cw provides the actual bird count
number_by_party_hours is how many birds were counted divided by the number of person-hours that year
flags contains values like US for “unusual” bird (as per the Christmas Bird Count documentation)

Now we do some regex!

First, I want to split up the species variable into the common species name and the scientific species_latin name.

For the first mutate: I will use @kohske’s regex I found on StackOverflow, which, as Nettle writes:

I like @kohske’s regex, which looks behind for an open parenthesis ?<=\(, looks ahead for a closing parenthesis ?=\), and grabs everything in the middle (lazily) .+?, in other words (?<=\().+?(?=\)) s

For the second mutate: As you can see in the code below, there is a line break (\n) between every English name and every scientific name in species. We will use that to parse out the scientific name:

hamilton_cbc %>% 
  filter(row_number() == 1) %>% 
  pull(species)

[1] "Snow Goose\r\n[Chen caerulescens]"

Here are the two mutates together:

# Putting it together: Mutating the two variables
hamilton_cbc <- hamilton_cbc %>%
  mutate(species_latin = str_extract(species, "(?<=\\[).+?(?=\\])"),
         species = word(species, start = 1, sep = fixed('\n[')))

Now we will look at the count_year variable. Let’s get a sense of what the variable looks like, using the White-Breasted Nuthatch count in 2016:

hamilton_cbc %>% 
  filter(row_number() == 15133) %>% 
  pull(count_year)

[1] "2016 [117]\r\nCount Date: 12/26/2016\r\n# Participants: 1\r\n# Species Reported: 97\r\nTotal Hrs.: 216.65"

The count_year variable is actually several variables in one:

calendar year
[CBC count number]
calendar count date
number of participants
number of species reported
total hours spent that year on the count

This is all metadata and we can take most of it out of this dataset. The only variable we will keep in the hamilton_cbc dataset is the calendar year.

And where are we at with the hamilton_cbc dataset?

hamilton_cbc %>%
  tail()

# A tibble: 6 × 6
  species       count_year how_many_cw number_by_party_hours flags species_latin
  <chr>         <chr>      <chr>       <chr>                 <chr> <chr>        
1 "House Sparr… "2012 [11… 1473        7.5713                <NA>  Passer domes…
2 "House Sparr… "2013 [11… 1802        9.8902                <NA>  Passer domes…
3 "House Sparr… "2014 [11… 1318        7.3529                <NA>  Passer domes…
4 "House Sparr… "2015 [11… 2326        10.5249               <NA>  Passer domes…
5 "House Sparr… "2016 [11… 2565        11.8394               <NA>  Passer domes…
6 "House Sparr… "2017 [11… 2731        13.7409               <NA>  Passer domes…

Let’s clean up the variables a bit more:

hamilton_cbc <- hamilton_cbc %>%
  rename(participant_info = count_year,
         how_many_counted = how_many_cw) %>%
  mutate(year = as.integer(word(participant_info)),  # We will keep year and total_hours
         total_hours = as.double(
           str_extract(
             participant_info, "(?<=Hrs\\.:\\s).*$")))

We almost have a clean dataset! ✨

I am going to remove the flags variable. I am also going to remove number_by_party_hours and derive it myself instead.

hamilton_cbc <- hamilton_cbc %>%
  select(year, species, species_latin, how_many_counted, total_hours)

It turns out that how_many_counted also has a cw value, which means the bird was not seen on count day itself, but was seen on a day close to the count. I am going to set these bird counts to be NA, as they don’t have a specified value.

hamilton_cbc <- hamilton_cbc %>%
  mutate(how_many_counted = ifelse(how_many_counted == "cw", NA, how_many_counted),
         how_many_counted = as.integer(how_many_counted))

In the species variable, there are some rows that are identified only to the genus level (and not to the species level). I will exclude these records, as I believe eBird excludes them too.

hamilton_cbc %>%
filter(str_detect(species, "sp\\.")) %>%
distinct(species)

# A tibble: 25 × 1
   species            
   <chr>              
 1 "scoter sp.\r"     
 2 "duck sp.\r"       
 3 "loon sp.\r"       
 4 "Accipiter sp.\r"  
 5 "hawk sp.\r"       
 6 "eagle sp.\r"      
 7 "jaeger sp.\r"     
 8 "gull sp.\r"       
 9 "screech-owl sp.\r"
10 "owl sp.\r"        
# ℹ 15 more rows

hamilton_cbc <- hamilton_cbc %>%
  filter(!(str_detect(species, "sp\\.")))

Two final mutates:

Using tidyr’s replace_na function, let’s make all of the NAs equal to 0 for how_many_counted. That means we are assuming that all birds in the area were successfully counted on count day.
Let’s also calculate the number of birds counted (within each species) divided by the total number of count hours that happened that year.

hamilton_cbc <- hamilton_cbc %>%
  mutate(how_many_counted = replace_na(how_many_counted, 0),
         how_many_counted_by_hour = as.double(how_many_counted) / total_hours)

And that’s it! 😄 🎉 We have cleaned the dataset and are ready to do some visualizing 👀 in Part 2!

Final dataset

Here is a glimpse of our final dataset:

hamilton_cbc %>%
  tail()

# A tibble: 6 × 6
   year species           species_latin     how_many_counted total_hours
  <int> <chr>             <chr>                        <int>       <dbl>
1  2012 "House Sparrow\r" Passer domesticus             1473        195.
2  2013 "House Sparrow\r" Passer domesticus             1802        182.
3  2014 "House Sparrow\r" Passer domesticus             1318        179.
4  2015 "House Sparrow\r" Passer domesticus             2326        221 
5  2016 "House Sparrow\r" Passer domesticus             2565        217.
6  2017 "House Sparrow\r" Passer domesticus             2731        199.
# ℹ 1 more variable: how_many_counted_by_hour <dbl>

And thank you to the CBC! The CBC Data was provided by National Audubon Society and through the generous efforts of Bird Studies Canada and countless volunteers across the western hemisphere.

Session info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.0 (2023-04-21 ucrt)
 os       Windows 11 x64 (build 22000)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  English_Canada.utf8
 ctype    English_Canada.utf8
 tz       Pacific/Honolulu
 date     2023-09-21
 pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
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 xfun          0.39       2023-04-20 [1] CRAN (R 4.3.0)
 xtable        1.8-4      2019-04-21 [1] CRAN (R 4.3.0)
 yaml          2.3.7      2023-01-23 [1] CRAN (R 4.3.0)

 [1] C:/Users/sharl/AppData/Local/R/win-library/4.3
 [2] C:/Program Files/R/R-4.3.0/library

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Footnotes

https://news.nationalgeographic.com/news/2014/12/141227-christmas-bird-count-anniversary-audubon-animals-science/↩︎