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Country_report.Rmd
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---
output:
pdf_document:
keep_tex: true
latex_engine: xelatex
header-includes:
- |
```{=latex}
\usepackage{microtype}
\usepackage{fontspec}
\setmainfont{Times.otf}[BoldFont = TimesBold.otf]
\usepackage{ragged2e}
\renewcommand{\footnotesize}{\scriptsize \justify}
\usepackage{setspace}
\usepackage{xcolor}
\definecolor{very-light-gray}{gray}{0.95}
\pagenumbering{gobble}
\makeatletter
\def\MT@is@composite#1#2\relax{%
\ifx\\#2\\\else
\expandafter\def\expandafter\MT@char\expandafter{\csname\expandafter
\string\csname\MT@encoding\endcsname
\MT@detokenize@n{#1}-\MT@detokenize@n{#2}\endcsname}%
% 3 lines added:
\ifx\UnicodeEncodingName\@undefined\else
\expandafter\expandafter\expandafter\MT@is@uni@comp\MT@char\iffontchar\else\fi\relax
\fi
\expandafter\expandafter\expandafter\MT@is@letter\MT@char\relax\relax
\ifnum\MT@char@ < \z@
\ifMT@xunicode
\edef\MT@char{\MT@exp@two@c\MT@strip@prefix\meaning\MT@char>\relax}%
\expandafter\MT@exp@two@c\expandafter\MT@is@charx\expandafter
\MT@char\MT@charxstring\relax\relax\relax\relax\relax
\fi
\fi
\fi
}
% new:
\def\MT@is@uni@comp#1\iffontchar#2\else#3\fi\relax{%
\ifx\\#2\\\else\edef\MT@char{\iffontchar#2\fi}\fi
}
\makeatother
```
classoption:
- landscape
- a4paper
fontsize: 11pt
geometry: top=1cm, right=1.7cm, bottom=0.6cm, left=1.7cm
urlcolor: blue
params:
ISO3: NA
Country: NA
Country_ISO3: NA
Continent: NA
Population: NA
Density: NA
GDP: NA
Antibiotics: NA
PAG_2030: NA
Cattle: NA
Chickens: NA
Pigs: NA
resistancebank_surveys: NA
AMR_Exposure_data_rm: NA
resistancebank_amr: NA
---
```{r setup, include=FALSE}
# - \setmainfont[Path = \string~/Desktop/, BoldFont = TimesBold, Extension = .otf]{Times}
# - \setmainfont[ExternalLocation=\string~/, BoldFont = TimesBold, Extension = .otf]{Times}
knitr::opts_chunk$set(echo = FALSE,
warning = FALSE,
message = FALSE,
fig.pos = "h",
dev = "cairo_pdf")
### Adding minipages in Rmarkdown only seems to work when returned from function
fig_table_verysmall <- function() {
return("\\begin{minipage}{0.30\\textwidth}")
}
fig_table_small <- function() {
return("\\begin{minipage}{0.33\\textwidth}")
}
fig_table_large <- function() {
return("\\begin{minipage}{0.67\\textwidth}")
}
fig_table_verylarge <- function() {
return("\\begin{minipage}{0.70\\textwidth}")
}
fig_table_end <- function() {
return("\\end{minipage}")
}
```
\setstretch{1}
\begin{center}
\LARGE\textbf{Antimicrobial resistance in animals in `r params[["Country"]]`}\\
\normalsize\href{https://resistancebank.org}{resistancebank.org}
\end{center}
`r fig_table_small()`
```{r P50 map, out.width="248pt", out.height="186pt"}
selected_country <- params[["Country"]]
download.file(url = paste0(paste0("https://amr-hegep-bucket.s3.eu-central-1.amazonaws.com/downloads/",
version,
"/country_reports/other_plots/P50_rasters/"),
gsub(pattern = " ",
replacement = "+",
x = selected_country),
".png"),
destfile = paste0(tempdir(),
gsub(pattern = " ",
replacement = "+",
x = selected_country),
".png"),
mode = 'wb')
knitr::include_graphics(path = paste0(tempdir(),
gsub(x = selected_country,
pattern = " ",
replacement = "+"),
".png"))
```
\vspace{2pt}
\setstretch{0.3}
\begingroup\scriptsize
\textbf{Figure 1}. Hotspots. Percentage of antimicrobial drugs with resistance higher than 50\%, \href{https://science.sciencemag.org/content/365/6459/eaaw1944?fbclid=IwAR28EBh4FvZXyrDmaIFCFcLdK_8yyku0AFfjUUbDvkn-S9OwEisEXA7S1zo}{Van Boeckel \& Pires, Global Trends in Antimicrobial Resistance in Animals in Low- and Middle-Income Countries, Science 365, (2019)}.
\endgroup
\setstretch{1}
\vspace{4pt}
```{r piecharts, fig.align='center', fig.height=3}
resistancebank_surveys <- params[["resistancebank_surveys"]]
resistancebank_surveys$Study <- 1
years <- data.frame("DOI" = "nostudy",
"PubDate" = as.character(seq(from = 2000,
to = max(x = as.integer(unique(resistancebank_surveys$PubDate)),
na.rm = TRUE),
by = 1)),
"Study" = 0)
for (i in names(resistancebank_surveys)[!names(resistancebank_surveys) %in% c("DOI", "PubDate", "Study")]) {
years[, i] <- NA
}
resistancebank_surveys <- rbind(resistancebank_surveys,
years[!years$PubDate %in% resistancebank_surveys$PubDate, ])
resistancebank_surveys <- resistancebank_surveys[order(resistancebank_surveys$PubDate,
decreasing = FALSE), ]
my_breaks <- seq(from = "2000",
to = max(as.character(years$PubDate)),
by = 1)
resistancebank_surveys$Type <- sapply(X = 1:nrow(resistancebank_surveys),
FUN = function(i) {
if (is.na(resistancebank_surveys$Journal[i])) {
return(NA)
} else if (resistancebank_surveys$Journal[i] ==
"Report could not be linked to a peer-reviewed publication.") {
as.character("Gray literature")
} else {
as.character("Peer-reviewed")
}
})
if (length(unique(na.omit(resistancebank_surveys$Type))) == 2) {
resistancebank_surveys$Type <- factor(x = resistancebank_surveys$Type,
levels = c("Gray literature",
"Peer-reviewed"))
}
country_data <- data.frame("Variable" = c("Population",
"GDP",
"Peer-reviewed",
"Gray literature",
"Cattle",
"Chickens",
"Pigs",
"Antibiotics",
"PAG_2030"),
"Value" = c(params[["Population"]],
params[["GDP"]],
sum(subset(x = resistancebank_surveys,
subset = !is.element(el = DOI,
set = "nostudy"))$Type == "Peer-reviewed"),
sum(subset(x = resistancebank_surveys,
subset = !is.element(el = DOI,
set = "nostudy"))$Type == "Gray literature"), # No Gray literature = sum will be 0, fine for plotting
params[["Cattle"]],
params[["Chickens"]],
params[["Pigs"]],
params[["Antibiotics"]],
params[["PAG_2030"]]))
###= Antimicrobial usage =#####
au_piechart <- ggplot(data = country_data[1, ], # I've used the "Population" data because it's never 0 and hence the one-colour pie chart is displayed
aes(x = "",
y = Value)) +
geom_bar(stat = "identity",
width = 1,
fill = "lightblue") +
coord_polar(theta = "y",
start = 0) +
labs(title = expression(paste(bold(Antimicrobial),
" ",
bold(use^1)))) +
geom_text(label = paste(ifelse(test = is.na(country_data$Value[8]),
yes = "---",
no = paste0(format(x = country_data$Value[8],
big.mark = ","),
" tonnes/y")),
ifelse(test = is.na(country_data$Value[9]),
yes = "---",
no = paste0(ifelse(test = country_data$Value[9] >= 0, yes = "+", no = ""), country_data$Value[9], "% in 2030")),
" ",
" ",
sep = "\n"),
size = 4.7,
family = "Times",
position = position_stack(vjust = 0.5)) +
theme_void() +
theme(text = element_text(family = "Times"),
plot.title = element_text(size = 14,
face = "bold",
hjust = 0.5),
legend.position = "none")
###= Livestock =#####
livestock_data <- na.omit(country_data[5:7, ])
livestock_data$Rounded <- sapply(X = 1:nrow(livestock_data),
FUN = function(i) {
if (livestock_data$Value[i] >= 1e9) {
paste(format(round(x = livestock_data$Value[i]/1e9,
digits = 2),
big.mark = ",",
trim = TRUE),
"B",
sep = "")
} else if (livestock_data$Value[i] >= 1e6) {
paste(format(round(x = livestock_data$Value[i]/1e6,
digits = 2),
big.mark = ",",
trim = TRUE),
"M",
sep = "")
} else if (livestock_data$Value[i] >= 1e3 &
livestock_data$Value[i] < 1e6) {
paste(format(round(x = livestock_data$Value[i]/1e3,
digits = 2),
big.mark = ",",
trim = TRUE),
"K",
sep = "")
} else {
format(round(x = livestock_data$Value[i],
digits = 2),
trim = TRUE)
}
})
livestock_piechart <- ggplot(data = livestock_data,
aes(x = "",
y = Value,
fill = Variable)) +
geom_bar(stat = "identity",
width = 1) +
coord_polar(theta = "y",
start = 0) +
scale_fill_manual(values = c("Cattle" = "#83C77A",
"Chickens" = "#F9A600",
"Pigs" = "#F5C0CD")[c(unique(livestock_data$Variable))],
labels = c(unique(livestock_data$Variable))) +
labs(title = expression(paste(bold(Livestock), " ", bold(heads^2)))) +
theme_void() +
ggrepel::geom_text_repel(aes(label = Rounded),
size = 4.7,
family = "Times",
position = position_stack(vjust = 0.5)) +
theme_void() +
theme(text = element_text(family = "Times"),
plot.title = element_text(size = 14,
face = "bold",
hjust = 0.5),
legend.position = c(0.5, -0.05),
legend.box = "vertical",
legend.direction = "vertical",
legend.title = element_blank(),
legend.text = element_text(size = 13),
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(0, 0, 0, 0),
legend.key.size = unit(0.6, "cm")) +
guides(fill = guide_legend(ncol = 2))
###= Population & GDP =#####
population_piechart <- ggplot(data = country_data[1, ],
aes(x = "",
y = Value)) +
geom_bar(stat = "identity",
width = 1,
fill = "grey85") +
coord_polar(theta = "y",
start = 0) +
labs(title = expression(paste(bold(Population^3), ", ", bold(GDP^4)))) +
geom_text(label = paste(paste(ifelse(test = country_data$Value[1] >= 1e9,
yes = paste(format(round(x = country_data$Value[1]/1e9,
digits = 1),
trim = TRUE),
"B",
sep = ""),
no = ifelse(test = country_data$Value[1] < 1e9 & country_data$Value[1] >= 1e6,
yes = paste(format(round(x = country_data$Value[1]/1e6,
digits = 1),
trim = TRUE),
"M",
sep = ""),
no = ifelse(test = country_data$Value[1] < 1e6 & country_data$Value[1] >= 1e3,
yes = paste(format(round(x = country_data$Value[1]/1e3,
digits = 1),
trim = TRUE),
"K",
sep = ""),
no = country_data$Value[1]))),
" inhab.",
sep = ""),
ifelse(test = is.na(country_data$Value[2]),
yes = paste0("---"),
no = ifelse(test = country_data$Value[2] >= 1e3,
yes = paste(format(round(x = country_data$Value[2]/1e3,
digits = 1),
trim = TRUE),
"K USD/in.",
sep = ""),
no = paste(country_data$Value[2],
"USD/in."))),
" ",
" ",
sep = "\n"),
size = 4.7,
family = "Times",
position = position_stack(vjust = 0.5)) +
theme_void() +
theme(text = element_text(family = "Times"),
plot.title = element_text(size = 14,
face = "bold",
hjust = 0.5),
legend.position = "none")
###= Studies and gray literature =#####
sgl_piechart <- ggplot(data = {if (sum(subset(x = resistancebank_surveys,
subset = !is.element(el = DOI,
set = "nostudy"))$Type == "Gray literature") != 0) {country_data[3:4, ]} else {country_data[3, ]}},
aes(x = "",
y = Value,
fill = Variable)) +
geom_bar(stat = "identity",
width = 1) +
geom_text(aes(label = Value),
size = 4.7,
family = "Times",
position = position_stack(vjust = 0.5)) +
scale_fill_manual(values = c("Gray literature" = "#FF6C67",
"Peer-reviewed" = "#00C2C6")) +
coord_polar(theta = "y",
start = 0) +
labs(title = expression(bold("Surveys"))) +
theme_void() +
theme(text = element_text(family = "Times"),
plot.title = element_text(size = 14,
face = "bold",
hjust = 0.5),
legend.position = c(0.5, -0.05),
legend.box = "vertical",
legend.direction = "vertical",
legend.title = element_blank(),
legend.text = element_text(size = 13),
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(0, 0, 0, 0),
legend.key.size = unit(0.6, "cm"))
###= 4 piecharts combination
gridExtra::grid.arrange(au_piechart,
livestock_piechart,
population_piechart,
sgl_piechart,
nrow = 1)
```
\vspace{4pt}
```{r, fig.align='center', fig.height=2.9}
# Barplot of the studies per year
ggplot(data = resistancebank_surveys,
aes(x = as.factor(PubDate),
y = Study,
fill = Type)) +
stat_summary(fun = sum,
geom = "bar",
position = "stack") +
geom_hline(yintercept = sum(resistancebank_surveys$Study)/length(my_breaks),
linetype = "dashed",
colour = "red",
size = 0.8) +
scale_fill_manual(values = c("Gray literature" = "#FF6C67",
"Peer-reviewed" = "#00C2C6")[c(unique(na.omit(resistancebank_surveys$Type)))]) +
scale_x_discrete(breaks = my_breaks,
labels = label_fill(orig = my_breaks,
.mod = 6)) +
scale_y_continuous(breaks = function(x) unique(floor(pretty(x)))) +
labs(x = "Year",
y = "Surveys") +
theme(text = element_text(family = "Times"),
axis.title = element_text(size = 14,
face = "bold"),
axis.text = element_text(colour = "black",
size = 14),
plot.margin = margin(t = 0.3,
r = 0.3,
b = 0.3,
l = 0.3,
unit = "cm"),
panel.background = element_rect(fill = "white"),
panel.grid = element_line(colour = "gray93"),
legend.position = "none")
```
\vspace{2pt}
\setstretch{0.3}
\begingroup\scriptsize
\textbf{Figure 2}. Number of peer-reviewed surveys{`r if (sum(subset(x = resistancebank_surveys, subset = !is.element(el = DOI, set = "nostudy"))$Type == "Gray literature") != 0) {" and gray literature reports,"} else {""}`} and average number of point-prevalence surveys per year (red line).
\endgroup
`r fig_table_end()`
`r fig_table_large()`
\setstretch{0.3}
\vspace{2pt}
```{r, fig.align='right', fig.height=3.6, fig.width=6.7}
country_exposure_images_report <- country_exposure_images
country_exposure_images_report$Country <- c("Senegal",
"Rwanda",
"Burkina Faso")
country_exposure_images_report[1, 2] <- 0.50
country_exposure_images_report[3, 2] <- 0.41
country_exposure_images_report[3, 5] <- 0.10
if (params[["ISO3"]] %in% params[["AMR_Exposure_data_rm"]]$ISO3) {
ggplot(data = params[["AMR_Exposure_data_rm"]],
aes(x = reorder_within(Country, desc(Exposure), Animal_species),
y = Exposure)) +
geom_col(aes(fill = Continent),
colour = "black",
size = 0.1) +
ggimage::geom_image(data = country_exposure_images_report,
image = country_exposure_images_report$image,
size = country_exposure_images_report$Size - 0.043,
asp = country_exposure_images_report$Aspect_ratio + 5.4) +
lemon::facet_rep_wrap(~Animal_species,
scales = "free",
repeat.tick.labels = "all",
nrow = 3) +
scale_fill_manual(values = c("orange",
"red",
"black")) +
scale_y_continuous(breaks = seq(from = 0,
to = 0.5,
by = 0.2)) +
scale_x_reordered() +
labs(x = NULL,
y = expression(paste(bold("National P50 level "), "[", bold("%"), "]")),
fill = NULL) +
theme(text = element_text(family = "Times"),
axis.text.x = element_text(colour = "black",
size = 4.1,
angle = 90,
hjust = 1),
axis.title.y = element_text(size = 7.5),
axis.text.y = element_text(colour = "black",
size = 7.5),
axis.ticks.length = unit(0.05, "cm"),
axis.ticks.x = element_blank(),
axis.ticks.y = element_line(size = 0.2),
panel.background = element_blank(),
panel.spacing = unit(0, "lines"),
strip.text.x = element_blank(),
strip.background = element_blank(),
legend.text = element_text(size = 7.5),
legend.box = "horizontal",
legend.direction = "horizontal",
legend.position = c(0.5, 0.97),
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(0, 0, 0, 0),
legend.key.size = unit(0.3, "cm"),
legend.spacing.x = unit(0.1, "cm")) +
geom_segment(aes(x = reorder_within(Country, desc(Exposure), Animal_species),
xend = reorder_within(Country, desc(Exposure), Animal_species),
y = Exposure + 0.14,
yend = Exposure + 0.015,
color = Selection,
alpha = Selection),
size = 0.6,
arrow = arrow(length = unit(1, "mm"),
type = "closed"),
show.legend = FALSE) +
scale_color_manual(values = c("TRUE" = "black",
"FALSE" = "white")) +
scale_alpha_manual(values = c("TRUE" = 1,
"FALSE" = 0))
}
# ggsave(filename = paste0(tempdir(), "/plot_nico.png"),
# plot = a)
#
# put_object(file = paste0(tempdir(), "/plot_nico.png"),
# object = "plot_nico.png",
# bucket = "amr-hegep-bucket",
# acl = c("public-read"))
```
```{r, fig.align='right', out.height="297pt", out.width="475.2pt"}
if (!(params[["ISO3"]] %in% params[["AMR_Exposure_data_rm"]]$ISO3)) {
download.file(url = paste0("https://amr-hegep-bucket.s3.eu-central-1.amazonaws.com/downloads/",
version,
"/country_reports/other_plots/amr_exposure_plot_report_transparent.png"),
destfile = paste0(tempdir(),
"amr_exposure_plot_report_transparent.png"),
mode = 'wb')
knitr::include_graphics(path = paste0(tempdir(),
"amr_exposure_plot_report_transparent.png"))
}
```
\vspace{2pt}
\setlength{\leftskip}{1cm}
\begingroup\scriptsize
\textbf{Figure 3}. Relative country-level exposure to antimicrobial resistance in chickens, pigs and cattle. This metric quantifies the exposure levels of the animal populations. The analysis was restricted to countries with at least 10 million chicken, 250,000 pigs, and 500,000 cattle heads.
\endgroup
\vspace{18pt}
```{r, fig.align='right', fig.height=2.3, fig.width=6.7}
resistancebank_amr <- params[["resistancebank_amr"]]
if (nrow(resistancebank_amr) > 0) {
resistancebank_amr_list <- split(x = resistancebank_amr,
f = list(resistancebank_amr$Author,
resistancebank_amr$DOI,
resistancebank_amr$YCoord,
resistancebank_amr$XCoord,
resistancebank_amr$PubDate),
drop = TRUE,
lex.order = TRUE)
resistancebank_amr_pathogens <- NULL
for (i in 1:length(resistancebank_amr_list)) {
resistancebank_amr_pathogens <- c(resistancebank_amr_pathogens,
as.character(unique(resistancebank_amr_list[[i]][["Pathogens"]])))
}
# Number of surveys per pathogen
named_resistancebank_amr_pathogens <- c(table(resistancebank_amr_pathogens))
# Number of isolates per pathogen
resistancebank_amr_isolates <- resistancebank_amr %>%
dplyr::distinct(Author, DOI, YCoord, XCoord, PubDate, ISO3, Species, Pathogens, NIsolates) %>%
dplyr::group_by(Pathogens) %>%
dplyr::summarise(Isolates = sum(NIsolates, na.rm = T))
facets_resistancebank_amr_names <- NULL
# Facets text
for (i in names(named_resistancebank_amr_pathogens)) {
facets_resistancebank_amr_names <- c(facets_resistancebank_amr_names,
bquote(bold(.(names(named_resistancebank_amr_pathogens)[which(names(named_resistancebank_amr_pathogens) == i)])) ~
bold("("*n[s]*" =" ~ bold(.(format(named_resistancebank_amr_pathogens[[i]],
big.mark = ",")))*"," ~
bold(n[i]*" =" ~ bold(.(format(resistancebank_amr_isolates$Isolates[resistancebank_amr_isolates$Pathogens == i],
big.mark = ",")))*")"))))
}
resistancebank_amr$Pathogens <- factor(resistancebank_amr$Pathogens,
labels = facets_resistancebank_amr_names)
# Barplot
resistancebank_amr_plot <- ggplot(data = resistancebank_amr,
aes(x = reorder_within(Compound,
desc(Rescom),
Pathogens),
y = Rescom,
fill = Species)) +
stat_summary(geom = "bar",
fun = mean,
position = position_dodge(preserve = "single"),
colour = "black",
size = 0.1,
width = {if (length(unique(resistancebank_amr$Compound)) <= 9 &
length(unique(resistancebank_amr$Pathogens)) == 1) {0.2}}) +
stat_summary(geom = "errorbar",
fun.data = mean_se,
position = position_dodge(preserve = "single",
width = 0.9),
width = ifelse(test = length(unique(resistancebank_amr$Compound)) <= 9 &
length(unique(resistancebank_amr$Pathogens)) == 1,
yes = 0.1,
no = 0.4),
size = 0.1) +
scale_y_continuous(breaks = c(0, 50, 100),
limits = c(0, 100)) +
scale_fill_manual(values = c("Buffalo" = "#F9766D",
"Cattle" = "#83C77A",
"Camel" = "#B8860B",
"Chicken" = "#F9A600",
"Duck" = "#00BFC4",
"Goat" = "#016400",
"Horse" = "#C77CFF",
"Pig" = "#F5C0CD",
"Rabbit" = "#FFDEAD",
"Sheep" = "#BFBFBF",
"Turkey" = "#1A74CD")[c(unique(as.character(resistancebank_amr$Species)))]) +
scale_x_reordered() +
facet_wrap(facets = ~Pathogens,
scales = "free_x",
labeller = label_parsed) +
labs(x = NULL,
y = expression(paste(bold("Resistance "), "[", bold("%"), "]"))) +
theme(text = element_text(family = "Times"),
axis.title = element_text(size = 7.5),
axis.title.x = element_text(face = "bold"),
axis.text.x = element_text(size = 5,
colour = "black",
angle = 90,
hjust = 1),
axis.text.y = element_text(colour = "black",
size = 7.5),
axis.ticks = element_line(size = 0.2),
axis.ticks.length = unit(0.05, "cm"),
strip.text.x = element_text(size = 7.5,
face = "bold"),
strip.background.x = element_rect(fill = "white"),
panel.background = element_rect(fill = "white"),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(colour = "lightgray",
size = 0.1),
legend.text = element_text(size = 6.4),
legend.title = element_blank(),
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(0, 0, 0, 0),
legend.key.size = unit(0.20, "cm"),
legend.spacing.x = unit(0.1, 'cm'),
legend.background = element_rect(color = NA))
if (length(unique(resistancebank_amr$Pathogens)) == 1 |
length(unique(resistancebank_amr$Pathogens)) == 2) {
resistancebank_amr_plot <- resistancebank_amr_plot +
theme(legend.box = "horizontal",
legend.direction = "horizontal",
legend.position = c(ifelse(test = length(unique(resistancebank_amr$Pathogens)) == 1 &
length(unique(resistancebank_amr$Species)) == 4,
yes = 0.1,
no = ifelse(test = length(unique(resistancebank_amr$Pathogens)) == 1 &
length(unique(resistancebank_amr$Species)) == 3,
yes = 0.07,
no = ifelse(test = length(unique(resistancebank_amr$Pathogens)) == 1 &
length(unique(resistancebank_amr$Species)) == 2,
yes = 0.05,
no = ifelse(test = length(unique(resistancebank_amr$Pathogens)) == 1 &
length(unique(resistancebank_amr$Species)) == 1,
yes = 0.032,
no = 0.5)))),
1.08))
} else if (length(unique(resistancebank_amr$Pathogens)) == 3) {
resistancebank_amr_plot <- resistancebank_amr_plot +
theme(legend.key = element_rect(fill = NA,
colour = "black",
size = 0.1),
legend.position = c(0.95, 0.87))
} else if (length(unique(resistancebank_amr$Pathogens)) == 4) {
resistancebank_amr_plot <- resistancebank_amr_plot +
theme(legend.key = element_rect(fill = NA,
colour = "black",
size = 0.1),
legend.position = c(0.97, 0.99))
}
resistancebank_amr_plot
}
```
```{r, fig.align='right', out.height="294pt", out.width="470.4pt"}
if (nrow(resistancebank_amr) == 0) {
download.file(url = paste0("https://amr-hegep-bucket.s3.eu-central-1.amazonaws.com/downloads/",
version,
"/country_reports/other_plots/plot_AGISAR_transparent.png"),
destfile = paste0(tempdir(),
"plot_AGISAR_transparent.png"),
mode = 'wb')
knitr::include_graphics(path = paste0(tempdir(),
"plot_AGISAR_transparent.png"))
}
```
```{r, results="asis"}
# Function to add link
myFun <- function(text, link, inline=TRUE) {
if(inline) {
paste("[", text, "](", link, ")", sep="")
} else {
cat("[", text, "](", link, ")", sep="")
}
}
```
\vspace{2pt}
\begingroup\scriptsize
\textbf{Figure 4}. Resistance rates, number of surveys (n$_{s}$) and total number of isolates tested (n$_{i}$) by pathogen listed by the \href{https://www.who.int/foodsafety/areas_work/antimicrobial-resistance/agisar/en/}{AGISAR consortium}. See protocol S1 in \href{https://science.sciencemag.org/content/sci/suppl/2019/09/18/365.6459.eaaw1944.DC1/aaw1944-Van-Boeckel-SM.pdf}{Van Boeckel \& Pires, Science 365, (2019)}.
\endgroup
`r fig_table_end()`
\setstretch{0.3}
`r fig_table_verylarge()`
\begingroup\scriptsize
\rule[2pt]{7cm}{0.3pt}\\
$^1$Data updated to 2020. Source: Mulchandani et al., Global trends in antimicrobial use in food-producing animals: 2020 to 2030 (Accepted).\\
$^2$Data updated to 2022. Source: \href{http://www.fao.org/faostat/en/\#data/QA}{FAOSTAT}.\\
$^3$Data updated to 2018. Source: \href{http://wdi.worldbank.org}{World Development Indicators}.\\
$^4$Data updated to 2016. Source: \href{https://data.worldbank.org/indicator/ny.gdp.mktp.cd}{World Bank Group}.\\
\endgroup
`r fig_table_end()`
`r fig_table_verysmall()`
\fcolorbox{black}{very-light-gray}{\begin{minipage}{21em}
\begingroup\footnotesize
\begin{center}
\textbf{Disclaimer}\\
\end{center}
\endgroup
\rule[2pt]{7.8cm}{0.3pt}\\
\begingroup\tiny
The information in this country-report result from the aggregation, and extrapolation of data from surveys conducted by independent scientists. It does not reflect the official positions of {`r if (params[["Continent"]] == "Europe") {paste("the ", "\\href{https://www.efsa.europa.eu/en/efsajournal/pub/7209}{European Food Safety Authority}", " and")} else {""}`} the country’s veterinary authorities on antimicrobial resistance levels in animals in {`r params[["Country"]]`}. The detailed methodology and data source for this country-report are presented in \href{https://science.sciencemag.org/content/365/6459/eaaw1944?fbclid=IwAR28EBh4FvZXyrDmaIFCFcLdK_8yyku0AFfjUUbDvkn-S9OwEisEXA7S1zo}{Van Boeckel \& Pires, 2019}.
\endgroup
\end{minipage}}
`r fig_table_end()`