Setup
Loading the R libraries and data set.
# Loading libraries
library(geomtextpath) # For adding text to ggplot2 curves
library(tidytuesdayR) # For loading data set
library(ggbeeswarm) # For creating a beeswarm plot
library(tidyverse) # For the ggplot2, dplyr libraries
library(gganimate) # For plot animation
library(ggthemes) # For more ggplot2 themes
library(viridis) # For plot themes
# Loading data set
tt <- tt_load("2022-01-11")
Downloading file 1 of 2: `colony.csv`
Downloading file 2 of 2: `stressor.csv`Data wrangling
In this section, the Bee Colony data is wrangled into two tidy sets:
tidied_colony_counts_overallcontains quarterly colony counts for the USAtidied_colony_counts_per_statecontains quarterly colony counts for various states within the USA
To create these sets, the original data is filtered to select for the appropriate states, and the “tidy_colony_data()” function is applied. These sets are tidy as each column is a variable, each row is an observation, and every cell has a single value. The types of observations in these data sets are:
Total colonies: Bee colonies countedLost: Bee colonies lostAdded: Bee colonies addedRenovated: Bee colonies renovated
# Creating subsets of the original bee colony data
colony_counts_overall <- tt$colony %>%
filter(state == "United States")
colony_counts_per_state <- tt$colony %>%
filter(state != "United States" & state != "Other states")
# Defining a function to tidy bee colony count data, which takes
# "messy_colony_data" as an argument
tidy_colony_data <- function(messy_colony_data){
# Writing the result of the following piped steps to "tidied_colony_data"
tidied_colony_data <- messy_colony_data %>%
# Selecting variables
select(year, colony_n, colony_lost, colony_added, colony_reno) %>%
# Dropping rows with missing values
drop_na() %>%
# Changing columns to rows
pivot_longer(!year, names_to = "type", values_to = "count") %>%
# Setting "type" as a factor variable
mutate(type = factor(type)) %>%
# Recoding the levels of the "type" factor
mutate(type = fct_recode(type,
"Total colonies" = "colony_n",
"Lost" = "colony_lost",
"Added" = "colony_added",
"Renovated" = "colony_reno")) %>%
# Reordering "type" factor levels
mutate(type = fct_relevel(type,
"Total colonies", "Lost", "Added", "Renovated"))
# Returning "tidied_colony_data"
return(tidied_colony_data)
}
# Using this function to tidy the subsets
tidied_colony_counts_overall <- tidy_colony_data(colony_counts_overall)
tidied_colony_counts_per_state <- tidy_colony_data(colony_counts_per_state)
# Printing a summary of the subsets before tidying...
colony_counts_overall
# A tibble: 26 x 10
year months state colony_n colony_max colony_lost colony_lost_pct
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 2015 Januar~ Unit~ 2824610 NA 500020 18
2 2015 April-~ Unit~ 2849500 NA 352860 12
3 2015 July-S~ Unit~ 3132880 NA 457100 15
4 2015 Octobe~ Unit~ 2874760 NA 412380 14
5 2016 Januar~ Unit~ 2594590 NA 428800 17
6 2016 April-~ Unit~ 2801470 NA 329820 12
7 2016 July-S~ Unit~ 3181180 NA 397290 12
8 2016 Octobe~ Unit~ 3032060 NA 502350 17
9 2017 Januar~ Unit~ 2615590 NA 361850 14
10 2017 April-~ Unit~ 2886030 NA 225680 8
# ... with 16 more rows, and 3 more variables: colony_added <dbl>,
# colony_reno <dbl>, colony_reno_pct <dbl>colony_counts_per_state
# A tibble: 1,196 x 10
year months state colony_n colony_max colony_lost colony_lost_pct
<dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 2015 Januar~ Alab~ 7000 7000 1800 26
2 2015 Januar~ Ariz~ 35000 35000 4600 13
3 2015 Januar~ Arka~ 13000 14000 1500 11
4 2015 Januar~ Cali~ 1440000 1690000 255000 15
5 2015 Januar~ Colo~ 3500 12500 1500 12
6 2015 Januar~ Conn~ 3900 3900 870 22
7 2015 Januar~ Flor~ 305000 315000 42000 13
8 2015 Januar~ Geor~ 104000 105000 14500 14
9 2015 Januar~ Hawa~ 10500 10500 380 4
10 2015 Januar~ Idaho 81000 88000 3700 4
# ... with 1,186 more rows, and 3 more variables: colony_added <dbl>,
# colony_reno <dbl>, colony_reno_pct <dbl># ...and after tidying
tidied_colony_counts_overall
# A tibble: 100 x 3
year type count
<dbl> <fct> <dbl>
1 2015 Total colonies 2824610
2 2015 Lost 500020
3 2015 Added 546980
4 2015 Renovated 270530
5 2015 Total colonies 2849500
6 2015 Lost 352860
7 2015 Added 661860
8 2015 Renovated 692850
9 2015 Total colonies 3132880
10 2015 Lost 457100
# ... with 90 more rowstidied_colony_counts_per_state
# A tibble: 4,208 x 3
year type count
<dbl> <fct> <dbl>
1 2015 Total colonies 7000
2 2015 Lost 1800
3 2015 Added 2800
4 2015 Renovated 250
5 2015 Total colonies 35000
6 2015 Lost 4600
7 2015 Added 3400
8 2015 Renovated 2100
9 2015 Total colonies 13000
10 2015 Lost 1500
# ... with 4,198 more rowsPlotting Bee Colony observations using {ggbeeswarm}
The first graph plots a point for each type of observation using geom_beeswarm().
# Plotting Bee Colony observations using geom_beeswarm() from {ggbeeswarm}
tidied_colony_counts_per_state %>%
ggplot(aes(x = type, y = count)) +
geom_beeswarm(cex = 4, colour = "yellow") +
scale_y_log10() +
theme_solarized_2(light = FALSE) +
facet_wrap(~type, scales = "free") +
theme(legend.position="none", axis.text.x = element_blank()) +
labs(title = "Bee Colonies Counted, Lost, Added, Renovated",
subtitle = "Created using {ggbeeswarm}",
x = NULL, y = "Number of bee colonies (log10)",
fill = NULL)
Figure 1: Scatter plots of bee colony observations. This plot has a point for each observation. Points are jittered to reduce overplotting.
Animating Bee Colony observations over time
While the previous plot is thematically appropriate, it could be better. This graph plots the same points over time in an animation, with the year plotted given in the subtitle. This graph uses standard {ggplot2} jittered points, as well as a box plot to illustrate the distribution of the points. These box plots have notches, showing 95% confidence intervals for the median. Distributions with notches that do not overlap differ significantly.
# Defining an animation showing bee colony counts over time
p <- tidied_colony_counts_per_state %>%
ggplot(aes(x = count, y = fct_reorder(type, count))) +
geom_jitter(color = "yellow", alpha = 0.8) +
geom_boxplot(width = 0.2, alpha = 0.8, notch = TRUE, colour = "cyan") +
scale_x_log10() +
theme_solarized_2(light = FALSE) +
theme(legend.position="none", axis.ticks.y = element_blank(),
axis.line.y = element_blank()) +
transition_time(as.integer(year)) +
labs(title = "Bee Colonies Counted, Lost, Added, Renovated, per year",
subtitle = "Year: {frame_time}",
x = "Number of bee colonies (log10)", y = NULL)
# Rendering the animation as a .gif
animate(p, nframes = 180, start_pause = 20, end_pause = 20,
renderer = magick_renderer())
Figure 2: Animation showing bee colony counts from 2015 to 2021.
Plotting the distribution of different Bee Colony observation types
From the previous plot, we can see that the Added and Renovated variables have similar distributions based on their box plots. Distributions can also be visualised using density plots. In this graph, the distribution of different types of observation in the data set are plotted.
# Creating a density plot for different observation types
tidied_colony_counts_overall %>%
filter(type != "Total colonies") %>%
ggplot(aes(x = count, colour = type, label = type)) +
geom_textdensity(size = 7, fontface = 2, hjust = 0.89, vjust = 0.3,
linewidth = 1.2) +
theme_solarized_2(light = FALSE) +
theme(legend.position = "none") +
labs(title = "Distribution of Bee Colony Counts",
subtitle = "Distributions of Bee Colonies Addded, Renovated, Lost",
x = "Number of bee colonies")
Figure 3: A density plot, giving the distribution of various observations. Of the three types of observation plotted, Added and Renovated are the most similar.