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CAPABEESURV_Rmarkdown.Rmd
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CAPABEESURV_Rmarkdown.Rmd
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---
title: CAPA Annual Colony Loss Survey Data Analysis
subtitle: CAPABEESURV
author: Brendan Daisley
date: June 14, 2024
output:
html_document:
code_folding: show
#theme: spacelab
theme: default
#highlight: monochrome
#fig_width: 11
#fig_height: 8.5
toc: true
toc_float: true
#toc-expand: false
collapse: false
toc_depth: 4
toc-expand: 3
smooth_scroll: true
number_sections: false
#css: doc.css
---
```{js, echo=FALSE}
// JS width changes
// R Markdown is using Bootstrap 3.3.5, see legacy documentation here
// https://getbootstrap.com/docs/3.3/css/#grid
// TOC column
const TOC_col = document.querySelector('div.row>div:not(div+div)'); // First div element under div.row
TOC_col.classList.replace('col-sm-4', 'col-sm-2'); // sm: Change TOC column width from 33% to 17% of total
TOC_col.classList.replace('col-md-3', 'col-md-2'); // md: Change TOC column width from 25% to 17% of total
// Main column
const main_col = document.querySelector('div.row>div+div'); // Second (and last) div element under div.row
main_col.classList.replace('col-sm-8', 'col-sm-10'); // sm: Change main column width from 67% to 83% of total
main_col.classList.replace('col-md-9', 'col-md-10'); // md: Change main column width from 75% to 83% of total
main_col.classList.add('col-sm-offset-2', 'col-md-offset-2') // sm+md: offset main column by width of TOC (for CSS styling below)
```
```{css, echo=FALSE}
/* CSS width changes */
/* Entire page - make wider, default setting is much too narrow */
/* https://stackoverflow.com/questions/38367392/override-rmarkdown-theme-in-order-to-change-html-page-width/54348996#54348996 */
div.main-container {
width: 1280px !important;
}
/* TOC column - move to left edge */
/* https://medium.com/@TheAtlasTeam/fixed-width-sidebars-in-bootstrap-6bd83db75669 */
div.row>div:not(div+div) {
position: fixed;
left: 0px;
}
/* TOC container within the TOC column - make narrower */
#TOC {
max-width: 200px !important;
width: 200px !important;
}
/*-- scss:rules --*/
.sidebar nav[role="doc-toc"] ul {
display: block;
}
```
<style type="text/css">
.main-container {
max-width: 100% !important;
margin: auto;
}
</style>
<br>
<b>Related article:</b> Daisley et al. (2024).Antibiotic use, air pollution, and seasonal temperature shifts are strong predictors of honey bee mortality in Canada .<i> In draft</i> <br><br>
<b>Related GitHub repository:</b> [https://github.com/bdaisley/CAPABEESURV](https://github.com/bdaisley/CAPABEESURV) <br>
The R code provided below was used to generate the following figures and table presented in related article:
* Figure 1
* Figure 2
* Figure 3
* Table 1
# 1. Load required packages
```{r, message=F, warning=F}
library(sf)
library(tidyr)
library(dplyr)
library(purrr)
library(readxl)
library(tibble)
library(future)
library(eoffice)
library(ggplot2)
library(stringr)
library(cowplot)
library(ggrepel)
library(ggridges)
library(pdftools)
library(reshape2)
library(tabulapdf)
library(packcircles)
library(rnaturalearth)
theme_set(theme_bw())
sessionInfo()
```
# Acquire datasets
Run the R scripts below to automatically download CAPA surveys, climate datasets, and air quality datasets
```{r, echo=TRUE, eval=TRUE}
#source("https://bdaisley.github.io/CAPABEESURV/scripts/get_capa_survey_data.R")
#source("https://bdaisley.github.io/CAPABEESURV/scripts/get_air_quality_data.R")
#source("https://bdaisley.github.io/CAPABEESURV/scripts/get_climate_data.R")
```
# Import datasets
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.align="center", fig.width=7, fig.height=7, eval=TRUE}
capa.df <- readxl::read_xlsx("tables/CAPA_survey_data.xlsx",
sheet="01 - CAPA Survey Data")
capa.df.filt <- capa.df %>%
mutate_at(vars(respondent_colonies_percent,
bac_spring_oxytet, bac_fall_oxytet,
nos_spring_fum,
nos_fall_fum,
var_spring_other,
var_fall_other), as.numeric) %>%
mutate(respondent_colonies_percent = ifelse(
respondent_colonies_percent > 100,
100,
respondent_colonies_percent)) %>%
mutate(oxytet_spring_fall = (bac_spring_oxytet +
bac_fall_oxytet)/2) %>%
mutate(fum_spring_fall = (nos_spring_fum +
nos_fall_fum)/2) %>%
mutate(var_spring_fall = (var_spring_other +
var_fall_other)/2) %>%
mutate(winterloss= winterloss_percent)
capa.melt <- melt(capa.df.filt , id.var=c("year",
"province",
"winterloss",
"raw_total",
"respondent_colonies")) %>%
mutate_at(vars("raw_total",
"respondent_colonies"),
as.numeric)
capa.melt.correlations <- melt(capa.df.filt,
id.var=c("year",
"province",
"winterloss_percent",
"raw_total",
"respondent_colonies")) %>%
filter(!is.na(winterloss_percent)) %>%
mutate_at(vars(winterloss_percent,
"raw_total",
"respondent_colonies"),
as.numeric) %>%
dplyr::rename("winterloss" = winterloss_percent)
```
# Figure 1
## Figure 1A
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.align="center", fig.width=9, fig.height=5.5, eval=TRUE}
capa.melt.g <- capa.melt %>%
filter(grepl("respondent_colonies_percent",
variable)) %>%
filter(!is.na(value)) %>%
filter(!grepl("TOTAL",
province)) %>%
mutate_at(vars(value),
as.numeric) %>%
arrange(province) %>%
group_by(province) %>%
mutate_at(vars(value),
funs(mean(.))) %>%
ungroup() %>%
droplevels() %>%
distinct(province,
.keep_all=TRUE)
#Get province polygons
provinces <- rnaturalearth::ne_states(country = "canada",
returnclass = "sf") %>%
filter(!grepl("Yukon|Nunavut|North",
name)) %>%
st_simplify(.,
dTolerance = 5000) %>% #simplify edges for plotting purposes
st_transform(., crs=3348) %>% #for 3348 info, see: https://epsg.io/3348
mutate(colonies_represented = dplyr::recode(!!!setNames(capa.melt.g$value,
paste0("CA-",
capa.melt.g$province)),
iso_3166_2))
#Get central points for each province
provinces.points <- provinces %>%
as.data.frame() %>%
st_as_sf(coords = c("longitude",
"latitude"),
crs=4326) %>%
st_transform(crs=3348)
#Plot
fig1.top.1 <- ggplot() +
geom_sf(data = provinces,
aes(fill = colonies_represented),
linewidth=0.05,
alpha=1,
colour="white") +
geom_sf(data = provinces.points,
colour="black",
size=2) +
ggtitle("CAPA Survey Overview (2015-2023)") +
theme_void() +
ggsflabel::geom_sf_label_repel(data= provinces.points,
aes(label=name),
nudge_x = c(-20, #BC
20, #AB
-20, #SK
15, #MB
-25, #ON
-50, #QC
-1.5, #NB
50, #NL
20, #NS
20)*10000,
nudge_y = c(-90, #BC
70, #AB
-75, #SK
75, #MB
-60, #ON
40, #QC
-70, #NB
50, #NL
-40, #NS
20)*10000) +
theme(panel.grid = element_blank(),
plot.title=element_text(size=24,
face="bold",
hjust = 0.5,
vjust=-2),
legend.position = "none")
fig1.top.2 <- ggplot() +
geom_sf(data = provinces,
aes(fill = colonies_represented),
linewidth=0.05,
alpha=1,
colour="white") +
geom_sf(data = provinces.points,
colour="black",
size=2) +
ggtitle("CAPA Survey Participation") +
scale_fill_gradientn(colours = c("#132B43",
"#56B1F7"),
limits = c(40, 100),
breaks = c(40, 60, 80, 100),
labels = c("40", "60", "80", "100"),
guide = guide_colourbar(direction = "horizontal",
title = "% participation",
title.position = "top")) +
theme_void() +
theme(panel.grid = element_blank(),
plot.title=element_text(size=24,
face="bold",
hjust = 0.5,
vjust=-2),
legend.position="bottom")
fig1.top.legend <- cowplot::plot_grid(NULL,
ggplotGrob(
fig1.top.2
)$grobs[[
which(sapply(ggplotGrob(fig1.top.2)$grobs,
function(x) x$name) == "guide-box")
]],
ncol=2,
rel_widths=c(0.95, 0.05))
fig1.a <- cowplot::plot_grid(fig1.top.legend +
theme(plot.margin = margin(4,3.25,-5,-3, "cm")),
fig1.top.1,
ncol=1,
rel_heights=c(0.2,0.8))
print(fig1.a)
```
## Figure 1B
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=7, fig.height=4, eval=TRUE}
capa.melt.g <- capa.melt %>%
filter(grepl("respondent_colonies_percent",
variable)) %>%
mutate_at(vars(value),
as.numeric) %>%
filter(grepl("TOTAL",
province)) %>%
droplevels() %>%
mutate(rel_respondent = (respondent_colonies/raw_total)*100)
capa.melt.g <- rbind(capa.melt.g %>%
mutate(new_respondents = respondent_colonies) %>%
mutate(rel_group="a_Colonies surveyed"),
capa.melt.g %>%
mutate(new_respondents = raw_total-respondent_colonies) %>%
mutate(rel_group="b_Colonies not surveyed"))
#All of Canada
bar.full <- ggplot(capa.melt.g,
aes(x=year,
y=new_respondents)) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
stat="identity",
position=position_dodge()) +
ylab("Total no. colonies") +
scale_x_continuous(breaks=unique(capa.melt.g$year)) +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(axis.text.x = element_text(size = 8,
angle=45,
hjust=1),
axis.title = element_text(size=10),
axis.title.x = element_blank(),
axis.text.y = element_text(size=8),
legend.position = "none")
#---Pie graph 2015---
pie.2015 <- ggplot(capa.melt.g %>%
filter(year==2015),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie graph 2016---
pie.2016 <- ggplot(capa.melt.g %>%
filter(year==2016),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie graph 2017---
pie.2017 <- ggplot(capa.melt.g %>%
filter(year==2017),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie graph 2018---
pie.2018 <- ggplot(capa.melt.g %>%
filter(year==2018),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie graph 2019---
pie.2019 <- ggplot(capa.melt.g %>%
filter(year==2019),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie graph 2020---
pie.2020 <- ggplot(capa.melt.g %>%
filter(year==2020),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie graph 2021---
pie.2021 <- ggplot(capa.melt.g %>%
filter(year==2021),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie graph 2022---
pie.2022 <- ggplot(capa.melt.g %>%
filter(year==2022),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie graph 2023---
pie.2023 <- ggplot(capa.melt.g %>%
filter(year==2023),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
linewidth=0.7,
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50")) +
theme(legend.position = "none")
#---Pie legend---
pie.legend <- ggplot(capa.melt.g %>%
filter(year==2015),
aes(x=year,
y=(new_respondents))) +
geom_bar(aes(group=rel_group,
fill= rel_group),
colour="white",
stat="identity") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values= c("a_Colonies surveyed" = "#3875A7",
"b_Colonies not surveyed" = "grey50"),
labels = c("Colonies surveyed",
"Colonies not surveyed")) +
theme(legend.position = "bottom",
legend.title=element_blank())
pie.g <- cowplot::plot_grid(pie.2015,
pie.2016,
pie.2017,
pie.2018,
pie.2019,
pie.2020,
pie.2021,
pie.2022,
pie.2023,
ncol=9)
pie.g.legend <- cowplot::plot_grid(ggplotGrob(
pie.legend
)$grobs[[
which(sapply(ggplotGrob(pie.legend)$grobs,
function(x) x$name) == "guide-box")
]])
pie.full <- cowplot::plot_grid(pie.g.legend +
theme(plot.margin = margin(0,0,0,0, "cm")),
pie.g,
ncol=1,
rel_heights=c(0.1,0.9))
fig1.b <- cowplot::plot_grid(cowplot::plot_grid(NULL,
pie.full +
theme(plot.margin = margin(0,0,0,0, "cm")),
NULL,
ncol=3,
rel_widths=c(0.15, 0.8, 0.05)) +
theme(plot.margin = margin(0.2,0,-0.5,0, "cm")),
bar.full,
ncol=1,
rel_heights=c(0.25,0.75))
print(fig1.b)
```
## Figure 1C
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=10, fig.height=10, eval=TRUE}
fig1.c <- readRDS("figures/fig1c.RDS")
print(fig1.c)
```
## Figure 1D
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=7, fig.height=5, eval=TRUE}
capa.melt.g <- capa.melt %>%
filter(grepl("^winterloss",
variable)) %>%
mutate_at(vars(value),
as.numeric) %>%
mutate(value = ifelse(is.na(value),
0,
value)) %>%
mutate_at(vars(value),
as.numeric) %>%
droplevels() %>%
mutate(line_width = ifelse(grepl("TOTAL",
province),
2, 0.1)) %>%
mutate(line_cols = ifelse(grepl("TOTAL",
province),
"#640000",
"grey50")) %>%
arrange(year) %>% arrange(province)
#Plot
fig1.d <- ggplot(capa.melt.g,
aes(x=year,
y=value,
group=province,
colour=province)) +
geom_line(aes(linewidth=province)) +
scale_colour_manual(values=setNames(capa.melt.g$line_cols,
capa.melt.g$province)) +
scale_linewidth_manual(values = setNames(capa.melt.g$line_width,
capa.melt.g$province)) +
directlabels::geom_dl(aes(label=province),
method=list("last.points",
cex=0.75,
rot =0,
hjust = -.5)) +
scale_x_continuous(breaks=unique(capa.melt.g$year)) +
ylab(unique(capa.melt.g$variable)) +
xlab("Year") +
ylab("Overwintering loss (%)") +
theme_classic() +
theme(axis.text.x = element_text(size = 8,
angle=45,
hjust=1),
axis.title = element_text(size=10),
axis.title.x = element_blank(),
axis.text.y = element_text(size=8),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x.bottom = element_text(size=10),
legend.position = "none")
fig1.d
```
## Figure 1E
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=7, fig.height=5, eval=TRUE}
capa.melt.g <- capa.melt %>%
filter(grepl("^winterloss",
variable)) %>%
mutate_at(vars(value),
as.numeric) %>%
filter(!grepl("TOTAL",
province)) %>%
droplevels() %>%
mutate(province= factor(province,
levels=c("BC",
"AB",
"SK",
"MB",
"ON",
"QC",
"NB",
"PE",
"NS",
"NL")))
#Per province
fig1.e <- ggplot(capa.melt.g,
aes(x=value,
y=province,
fill=stat(x))) +
geom_density_ridges_gradient(scale = 1.7,
panel_scaling = TRUE,
rel_min_height = 0.0005,
colour="black",
linewidth = 0.3) +
scale_fill_gradientn(colours=colorRampPalette(c("white",
"darkred"))(100)) +
geom_vline(xintercept=15,
linewidth = 0.8,
linetype = "dotted",
colour="red") +
annotate(geom="text",
x = 15,
y = 12.9,
label="Limit of \nsustainability",
colour="red") +
coord_cartesian(ylim = c(1,10.5),
clip = 'off') +
xlab("Overwintering loss (%)") +
ylab("Province") +
theme(plot.margin = unit(c(2.5,1,1,1),
"lines")) +
theme(panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(size = 8),
axis.title = element_text(size=10),
axis.title.x = element_blank(),
axis.text.y = element_text(size=8),
legend.position = "none")
fig1.e
```
## Figure 1 (Full panel)
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=9.1, fig.height=11.7, eval=TRUE}
fig1.bottom.left <- cowplot::plot_grid(fig1.b,
fig1.c,
ncol=1,
labels=c('B', "C"),
rel_heights=c(0.45, 0.55))
fig1.bottom.right <- cowplot::plot_grid(fig1.e,
cowplot::plot_grid(fig1.d,
NULL,
ncol=2,
rel_widths=c(0.9,0.1)),
ncol=1,
labels=c('D', "E"),
rel_heights=c(0.55, 0.45))
fig1.bottom <- cowplot::plot_grid(fig1.bottom.left,
fig1.bottom.right, ncol=2)
fig1.a <- cowplot::plot_grid(fig1.top.legend +
theme(plot.margin = margin(3,3,-5,-3, "cm")),
fig1.top.1,
ncol=1,
rel_heights=c(0.2,0.8))
fig1.full <- cowplot::plot_grid(fig1.a +
theme(plot.margin = unit(c(-0.04, -0.04, -0.04, -0.04),
"null")),
fig1.bottom,
ncol=1,
rel_heights=c(0.5, 0.5))
print(fig1.full)
```
# Figure 2
## Setup
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=7, fig.height=7, eval=TRUE}
capa.df.filt <- capa.df %>%
mutate_at(vars(respondent_colonies_percent,
bac_spring_oxytet,
bac_fall_oxytet,
nos_spring_fum,
nos_fall_fum,
var_spring_other,
var_fall_other),
as.numeric) %>%
mutate(respondent_colonies_percent = ifelse(
respondent_colonies_percent > 100, 100,
respondent_colonies_percent)) %>%
mutate(oxytet_spring_fall = (bac_spring_oxytet +
bac_fall_oxytet)/2) %>%
mutate(fum_spring_fall = (nos_spring_fum +
nos_fall_fum)/2) %>%
mutate(var_spring_fall = (var_spring_other +
var_fall_other)/2) %>%
mutate(winterloss= winterloss_percent)
capa.melt <- melt(capa.df.filt ,
id.var=c("year",
"province",
"winterloss",
"raw_total",
"respondent_colonies")) %>%
mutate_at(vars("raw_total",
"respondent_colonies"),
as.numeric)
capa.melt.correlations <- melt(capa.df.filt,
id.var=c("year",
"province",
"winterloss_percent",
"raw_total",
"respondent_colonies")) %>%
filter(!is.na(winterloss_percent)) %>%
mutate_at(vars(winterloss_percent, "raw_total",
"respondent_colonies"),
as.numeric) %>%
dplyr::rename("winterloss" = winterloss_percent)
```
## Figure 2A
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=7, fig.height=4, eval=TRUE}
capa.melt.g <- capa.melt.correlations %>%
filter(grepl("oxytet_spring_fall",
variable)) %>%
mutate_at(vars(value),
as.numeric) %>%
droplevels() %>%
mutate(province= factor(province,
levels=c("TOTAL",
"BC",
"AB",
"SK",
"MB",
"ON",
"QC",
"NB",
"PE",
"NS",
"NL"))) %>%
mutate(line_width = ifelse(grepl("TOTAL",
province),
1.5,
0.1)) %>%
mutate(line_cols = ifelse(grepl("TOTAL",
province),
"#2980B9",
"grey50")) %>%
arrange(year) %>%
arrange(province)
fig2.a <- ggplot(capa.melt.g,
aes(x=year,
y=value,
group=province,
colour=province)) +
geom_line(aes(linewidth=province)) +
ylim(-2,86) +
scale_y_continuous(limits=c(-2,110),
breaks=c(0,25,50,75,100)) +
scale_colour_manual(values=setNames(capa.melt.g$line_cols,
capa.melt.g$province)) +
scale_linewidth_manual(values = setNames(capa.melt.g$line_width,
capa.melt.g$province)) +
directlabels::geom_dl(aes(label=province),
method=list("last.points",
cex=0.5,
rot =0,
hjust = 0)) +
ylab(unique(capa.melt.g$variable)[1]) +
xlab("Year") +
theme(legend.position = "none") +
ylab("Oxytetracycline use (%)") +
scale_x_continuous(breaks = seq(2015, 2023, 1)) +
theme(axis.text.x = element_text(angle=45,
hjust=1),
axis.text = element_text(size=8),
axis.title = element_text(size=10),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
fig2.a
```
## Figure 2B
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=7, fig.height=4, eval=TRUE}
capa.melt.g <- capa.melt.correlations %>%
filter(grepl("fum_spring_fall",
variable)) %>%
mutate_at(vars(value),
as.numeric) %>%
droplevels() %>%
mutate(province= factor(province,
levels=c("TOTAL",
"BC",
"AB",
"SK",
"MB",
"ON",
"QC",
"NB",
"PE",
"NS",
"NL"))) %>%
mutate(line_width = ifelse(grepl("TOTAL",
province),
1.5, 0.1)) %>%
mutate(line_cols = ifelse(grepl("TOTAL",
province),
"green4",
"grey50")) %>%
arrange(year) %>%
arrange(province)
fig2.b <-ggplot(capa.melt.g,
aes(x=year,
y=value,
group=province,
colour=province)) +
geom_line(aes(linewidth=province)) +
scale_y_continuous(limits=c(-2,110),
breaks=c(0,25,50,75,100)) +
scale_colour_manual(values=setNames(capa.melt.g$line_cols,
capa.melt.g$province)) +
scale_linewidth_manual(values = setNames(capa.melt.g$line_width,
capa.melt.g$province)) +
directlabels::geom_dl(aes(label=province),
method=list("last.points",
cex=0.5,
rot =0,
hjust = 0)) +
ylab(unique(capa.melt.g$variable)[1]) +
xlab("Year") + theme(legend.position = "none") +
ylab("Fumagillin use (%)") +
scale_x_continuous(breaks = seq(2015, 2023, 1)) +
theme(axis.text.x = element_text(angle=45,
hjust=1),
axis.text = element_text(size=8), axis.title = element_text(size=10),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
fig2.b
```
## Figure 2C
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=7, fig.height=4, eval=TRUE}
capa.melt.g <- capa.melt.correlations %>%
filter(grepl("var_spring_fall",
variable)) %>%
mutate_at(vars(value),
as.numeric) %>%
droplevels() %>%
mutate(province= factor(province,
levels=c("TOTAL",
"BC",
"AB",
"SK",
"MB",
"ON",
"QC",
"NB",
"PE",
"NS",
"NL"))) %>%
mutate(line_width = ifelse(grepl("TOTAL",
province),
1.5, 0.1)) %>%
mutate(line_cols = ifelse(grepl("TOTAL",
province),
"#AC8300",
"grey50")) %>%
arrange(year) %>%
arrange(province)
fig2.c <- ggplot(capa.melt.g,
aes(x=year,
y=value,
group=province,
colour=province)) +
geom_line(aes(linewidth=province)) +
scale_y_continuous(limits=c(-2,110),
breaks=c(0,25,50,75,100)) +
scale_colour_manual(values=setNames(capa.melt.g$line_cols,
capa.melt.g$province)) +
scale_linewidth_manual(values = setNames(capa.melt.g$line_width,
capa.melt.g$province)) +
directlabels::geom_dl(aes(label=province),
method=list("last.points",
cex=0.5,
rot =0,
hjust = 0)) +
ylab(unique(capa.melt.g$variable)[1]) +
xlab("Year") +
theme(legend.position = "none") +
ylab("Miticide use (%)") +
scale_x_continuous(breaks = seq(2015, 2023, 1)) +
theme(axis.text.x = element_text(angle=45, hjust=1),
axis.text = element_text(size=8),
axis.title = element_text(size=10),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
fig2.c
```
## Figure 2D
```{r, echo=TRUE, warning=FALSE, message=FALSE, fig.width=4, fig.height=4, eval=TRUE}
capa.melt.g <- capa.melt.correlations %>%
filter(grepl("oxytet_spring_fall",
variable)) %>%
mutate_at(vars(value),
as.numeric) %>%
droplevels() %>%
mutate(province= factor(province,
levels=c("TOTAL",
"BC",
"AB",
"SK",
"MB",
"ON",
"QC",
"NB",
"PE",
"NS",
"NL"))) %>%
mutate(before_after = ifelse(year<=2018,
"Before",
"After")) %>%
mutate(before_after = factor(before_after,
levels=c("Before",
"After"))) %>%
mutate(line_width = ifelse(grepl("TOTAL",
province),
2, 0.1)) %>%
mutate(line_cols = ifelse(grepl("TOTAL",