--- title: | \begin{center} \vspace*{-2cm} % Adjust the negative value to bring the title closer to the top \large \textbf{Injury and Illness Surveillance} \\ \textit{ORGANIZATION NAME} \end{center} \vspace{-0.5cm} date: "`r format(Sys.Date(), '%B %d, %Y')`" # Dynamic date with the same format header-includes: - \usepackage{fancyhdr} - \usepackage{enumitem} geometry: - top=0.5in # Adjust the top margin size as desired - margin=0.5in # Adjust the other margin sizes as desired output: pdf_document: latex_engine: pdflatex keep_tex: FALSE mainfont: ArialMT --- \pagestyle{fancyplain} \lhead{\raisebox{-1\height} {\includegraphics[height=3cm]{/filepathToHeaderImage1.png}}} \rhead{ \raisebox{-1\height} {\includegraphics[height=3cm]{/filepathToHeaderImage2.png}}} \renewcommand{\headrulewidth}{0pt} ```{r setup, echo = FALSE, include = FALSE, warning = FALSE, message = FALSE} #load libraries knitr::opts_chunk$set(echo = TRUE) options(dplyr.summarise.inform = FALSE) library(tidyverse) library(htmltools) library(knitr) library(extrafont) library(png) library(cowplot) library(magick) library(treemapify) library(ggalluvial) library(ggpubr) library(readxl) library(scales) ``` ```{r colors, echo = FALSE, include = FALSE, warning = FALSE, message = FALSE} #load colors color_scheme <- c("red", "blue", "green") organization_font <- "ArialMT" ``` ```{r load, echo = FALSE, include = FALSE, warning = FALSE, message = FALSE} # Get a list of files in the directory and find the most recent date #files <- list.files("filepathToFiles", pattern = "InjuryData_cumulative_\\d{8}\\.csv", full.names = TRUE) #file_dates <- as.Date(sub(".*_(\\d{8})\\.csv", "\\1", files), format = "%Y%m%d") #latest_date <- max(file_dates) #latest_injury <- format(latest_date, '%Y%m%d') #latest_illness <- format(latest_date, '%Y%m%d') #load data #df.injuries <- read.csv(paste0("filepath_", latest_injury, ".csv")) #df.illnesses <- read.csv(paste0("filepath_", latest_illness, ".csv")) df.injuries <- data.frame() df.illnesses <- data.frame() ``` ```{r clean, echo = FALSE, include = FALSE, warning = FALSE, message = FALSE} #clean data ``` ```{r filterTime1Month, echo = FALSE, include = FALSE, warning = FALSE, message = FALSE} #filter data on the previous 1 month # df.injuries.acute <- df.injuries %>% # filter(RecordedDate >= as.Date(Sys.Date()) - 28) #Filter for last month # df.illnesses.acute <- df.illnesses %>% # filter(RecordedDate >= as.Date(Sys.Date()) - 28) #Filter for last month ``` ```{r filterTime6MonthsExclusive, echo = FALSE, include = FALSE, warning = FALSE, message = FALSE} #filter data on the previous 168 days # df.injuries.chronic <- df.injuries %>% # filter(RecordedDate >= as.Date(Sys.Date()) - 168) %>% #chronic is last 6 months # filter(RecordedDate < as.Date(Sys.Date()) - 28) #exclude the acute timeframe # df.illnesses.chronic <- df.illnesses %>% # filter(RecordedDate >= as.Date(Sys.Date()) - 168) %>% #chronic is last 6 months # filter(RecordedDate < as.Date(Sys.Date()) - 28) #exclude the acute timeframe ``` \begin{center} \LARGE \textbf{Executive Summary} \end{center} \begin{itemize} \item \textbf{Injury Trends:} EXECUTIVE SUMMARY POINT 1 \item \textbf{Illness Trends:} EXECUTIVE SUMMARY POINT 2 \item \textbf{Time Loss:} EXECUTIVE SUMMARY POINT 3 \end{itemize} \rule{\textwidth}{2pt} \begin{center} \LARGE \textbf{Injuries} \end{center} ```{r filterTime6MonthsInclusive, echo = FALSE, include = TRUE, warning = FALSE, message = FALSE, fig.width=12, fig.height=4} #Display injuries across time with cumulative line data.frame(Blank_Injuries = "PLOT INJURIES OVER TIME HERE") %>% ggplot(aes(x = Blank_Injuries, y = 0, fill = Blank_Injuries, label = Blank_Injuries)) %>% + geom_line(stat = "identity", color = "black", position = position_dodge2(width = 0.9, preserve = "single")) + geom_text(aes(label = Blank_Injuries), family = organization_font, vjust = -1, color = color_scheme[2], size = 8, position = position_dodge(width = 0.9)) + scale_y_continuous(limits = c(0, 1), breaks = seq(0, 1, by = 1)) + ylab("Injury Count (#)") + labs(fill = "Time Loss?") + scale_fill_manual(values = c(color_scheme[1], color_scheme[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5, family = organization_font), legend.title = element_text(size = 16), legend.text = element_text(size = 16), legend.position = "none", text = element_text(family = organization_font), axis.title.x = element_blank(), axis.title.y = element_text(size = 16), axis.text.y = element_text(size = 16), axis.text.x = element_blank(), panel.grid = element_blank()) ``` ```{r dataVisualizationCountBySport, echo = FALSE, include = TRUE, warning = FALSE, message = FALSE, fig.width=12, fig.height=3} #Display injuries by sport data.frame(Blank_Injuries = "PLOT INJURIES BY SPORT HERE") %>% ggplot(aes(x = Blank_Injuries, y = 0, fill = Blank_Injuries, label = Blank_Injuries)) %>% + geom_line(stat = "identity", color = "black", position = position_dodge2(width = 0.9, preserve = "single")) + geom_text(aes(label = Blank_Injuries), family = organization_font, vjust = -1, color = color_scheme[2], size = 8, position = position_dodge(width = 0.9)) + scale_y_continuous(limits = c(0, 1), breaks = seq(0, 1, by = 1)) + ylab("Injury Count (#)") + labs(fill = "Time Loss?") + scale_fill_manual(values = c(color_scheme[1], color_scheme[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5, family = organization_font), legend.title = element_text(size = 16), legend.text = element_text(size = 16), legend.position = "none", text = element_text(family = organization_font), axis.title.x = element_blank(), axis.title.y = element_text(size = 16), axis.text.y = element_text(size = 16), axis.text.x = element_blank(), panel.grid = element_blank()) ``` ```{r dataVisualizationPieCharts, echo = FALSE, include = TRUE, warning = FALSE, message = FALSE, fig.width=12, fig.height=3} #Plot binary variable injury counts #Sex pie chart plot1 <- data.frame(Blank_Injuries = "GAMES COMPARISON\nPIE CHART") %>% ggplot(aes(x = Blank_Injuries, y = 100, fill = Blank_Injuries)) + geom_bar(stat = "identity", color = "black", linewidth = 1) + ggtitle("Olympic vs. Paralympic") + geom_text(aes(label = Blank_Injuries), position = position_stack(vjust = 0.5), size = 5, family = organization_font, color = "white") + coord_polar(theta = "y") + scale_fill_manual(values = color_scheme) + theme_void() + theme(legend.position = "none", # Hide the legend legend.title = element_blank(), text = element_text(family = organization_font), legend.text = element_text(family = organization_font, size = 16), plot.title = element_text(hjust = 0.5)) #Olympic Paralympic Pie Chart plot2 <- data.frame(Blank_Injuries = "SEX COMPARISON\nPIE CHART") %>% ggplot(aes(x = Blank_Injuries, y = 100, fill = Blank_Injuries)) + geom_bar(stat = "identity", color = "black", linewidth = 1) + ggtitle("Male vs. Female") + geom_text(aes(label = Blank_Injuries), position = position_stack(vjust = 0.5), size = 5, family = organization_font, color = "white") + coord_polar(theta = "y") + scale_fill_manual(values = color_scheme) + theme_void() + theme(legend.position = "none", # Hide the legend legend.title = element_blank(), text = element_text(family = organization_font), legend.text = element_text(family = organization_font, size = 16), plot.title = element_text(hjust = 0.5)) #Time loss pie chart plot3 <- data.frame(Blank_Injuries = "TIME LOSS\nCOMPARISON\nPIE CHART") %>% ggplot(aes(x = Blank_Injuries, y = 100, fill = Blank_Injuries)) + geom_bar(stat = "identity", color = "black", linewidth = 1) + ggtitle("Times Loss vs. Non-Time Loss") + geom_text(aes(label = Blank_Injuries), position = position_stack(vjust = 0.5), size = 5, family = organization_font, color = "white") + coord_polar(theta = "y") + scale_fill_manual(values = color_scheme) + theme_void() + theme(legend.position = "none", # Hide the legend legend.title = element_blank(), text = element_text(family = organization_font), legend.text = element_text(family = organization_font, size = 16), plot.title = element_text(hjust = 0.5)) #plot on same row plotlist <- list(plot1, plot2, plot3) ggarrange(plotlist = plotlist, nrow = 1) ```