appPublic/app.R
In aqli-epic/epi.meta.analysis: An Amazing Shiny App
## shiny app script for epi.meta.analysis--------------------------------------
# load app.R helper file
# source("./appPublic/app_helper.R")
# libraries
library(shiny)
library(shinydashboard)
library(plotly)
library(magrittr)
library(DT)
library(forcats)
library(rnaturalearth)
library(rnaturalearthdata)
library(shinycssloaders)
library(waiter)
library(colorspace)
# setting global options
options(shiny.maxRequestSize = 900*1024^2)
# loading the .RData file that contain all required data objects
# save(list = ls(all = TRUE), file= "./appPublic/all.RData")
load("all.RData", .GlobalEnv)
#----------------------------------------------------------------------------------
ui <- shinydashboard::dashboardPage(
skin = "black",
shinydashboard::dashboardHeader(
title = "AQ Epi Studies"
),
shinydashboard::dashboardSidebar(
collapsed = TRUE,
shinydashboard::sidebarMenu(
shinydashboard::menuItem(tabName = "blog_post_graphs_1", text = "Blog Post Graphs", icon = icon("chart-bar")),
shinydashboard::menuItem(tabName = "underlying_data_table_app_1", text = "Underlying Data", icon = icon("database")),
shinydashboard::menuItem(tabName = "other_content_1", text = "Other Content", icon = icon("pencil"))
)
),
shinydashboard::dashboardBody(
shinydashboard::tabItems(
shinydashboard::tabItem("blog_post_graphs_1",
shiny::fluidRow(
shinydashboard::valueBoxOutput("num_papers", width = 3),
shinydashboard::valueBoxOutput("cohort_size_range_1", width = 3),
shinydashboard::valueBoxOutput("study_publishing_year_range", width = 3),
shinydashboard::valueBoxOutput("num_of_countries_covered", width = 3)
),
shiny::fluidRow(
shiny::tags$hr(),
shinydashboard::box(width = 3,
shiny::sliderInput("pm2.5_bucket_2_buckets_ll", "PM₂.₅ Range (LL)", 0, 120, value = 0)
),
shinydashboard::box(width = 3,
shiny::sliderInput("pm2.5_bucket_2_buckets_ul", "PM₂.₅ Range (UL)", 0, 120, value = 15)
),
shinydashboard::box(
width = 6, status = "info", solidHeader = TRUE,
title = "PM₂.₅ exposure Range and Global Population Distribution with 2 buckets",
shinycssloaders::withSpinner(shiny::plotOutput("pm2.5_expo_glob_pop_graph_2_buckets"))
)
),
shiny::fluidRow(
shinydashboard::box(
width = 3,
shiny::selectInput("countries_fig2", "Countries", choices = epi %>% dplyr::filter(!is.na(country)) %>% dplyr::select(country) %>% unlist() %>% unique(), multiple = TRUE, selected = "USA")
),
shinydashboard::box(
width = 9, status = "info", solidHeader = TRUE,
title = expression("Country Wise Mean PM₂.₅ Distribution"),
shinycssloaders::withSpinner(shiny::plotOutput("mean_pm2.5_dist_country_wise_graph"))
)
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 2,
shiny::selectInput("countries_fig3", "Countries", choices = c("all", "United States", epi %>% dplyr::filter(!is.na(country)) %>% dplyr::select(country) %>% dplyr::filter(country != "USA") %>% unlist() %>% unique()), multiple = TRUE, selected = "all"),
#shiny::dataTableOutput("geographic_dist_studies_table")
),
shinydashboard::box(
status = "info", solidHeader = TRUE,
title = "Geographic Distribution of Studies and Annual Average PM₂.₅ pollution",
width = 10,
shinycssloaders::withSpinner(shiny::plotOutput("geographic_dist_studies"))
)
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 3,
shiny::sliderInput("year_range", "Year Range", min(epi$publishing_year, na.rm = TRUE), max(epi$publishing_year, na.rm = TRUE), value = c(min(epi$publishing_year, na.rm = TRUE), max(epi$publishing_year, na.rm = TRUE)), sep = ""),
#DT::dataTableOutput("aq_epi_studies_over_time")
),
shinydashboard::box(
width = 9, status = "info", solidHeader = TRUE,
title = "AQ Epi Studies Over Time",
shinycssloaders::withSpinner(shiny::plotOutput("epi_studies_over_time_graph"))
)
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 2,
shiny::selectInput("continent_list", "Continents", choices = c("North America (589.2 million people)",
"Europe (740.8 million people)",
"Asia - China (3161.5 million people)",
"Africa (1373.8 million people)",
"South America (429.2 million people)",
"China (1402.5 million people)"), multiple = TRUE, selected = "North America (589.2 million people)"),
hr(),
shiny::selectInput("plot_type_fig5", "Plot Type", choices = c("Histogram (Frequency)", "Histogram (Density)"), selected = "Histogram (Frequency)")
),
shinydashboard::box(status = "info", solidHeader = TRUE,
title = "Epi Studies Duration Distribution (Continent Wise): Histogram",
width = 10,
shinycssloaders::withSpinner(shiny::plotOutput("continent_wise_dist_duration_study_graph"))
)
),
shiny::tags$hr(),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(width = 12,
status = "info", solidHeader = TRUE,
title = "Other Graphs")
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 2,
status = "info",
solidHeader = TRUE,
shiny::selectInput("plot_type_fig6", "Plot Type", choices = c("Histogram", "Density"), selected = "Histogram")
),
shinydashboard::box(title = "Lower and Upper Limit Distributions of PM₂.₅ exposure range",
width = 5,
status = "info",
solidHeader = TRUE,
shinycssloaders::withSpinner(shiny::plotOutput("pm2.5_ll_ul_dist_graph"))
),
shinydashboard::box(title = "Lower and Upper Limit Distributions of Age Range covered in the analysis",
width = 5,
status = "info",
solidHeader = TRUE,
shinycssloaders::withSpinner(shiny::plotOutput("age_ll_ul_graph")))
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 2,
status = "info",
solidHeader = TRUE,
shiny::selectInput("countries_list_fig7", "Countries", choices = c("all", epi %>% dplyr::filter(!is.na(country)) %>% dplyr::select(country) %>% unlist() %>% unique()), multiple = TRUE, selected = "USA")
),
shinydashboard::box(title = "Country Wise Distribution of Cohort Size",
width = 5,
status = "info",
solidHeader = TRUE,
shinycssloaders::withSpinner(plotly::plotlyOutput("country_wise_dist_log_cohort_size_graph_1"))),
shinydashboard::box(title = "Country Wise Distribution of Study Duration",
width = 5,
status = "info",
solidHeader = TRUE,
shinycssloaders::withSpinner(plotly::plotlyOutput("country_wise_dist_study_duration_1")))
),
shiny::tags$br()
# shiny::fluidRow(
# shiny::tags$hr(),
# shinydashboard::box(width = 4,
# shiny::sliderInput("pm2.5_bucket", "PM₂.₅ Range", 0, 120, value = c(0, 15))
# ),
# shinydashboard::box(
# width = 8, status = "info", solidHeader = TRUE,
# title = "PM₂.₅ exposure Range and Global Population Distribution",
# shinycssloaders::withSpinner(shiny::plotOutput("pm2.5_expo_glob_pop_graph"))
# )
# )
),
shinydashboard::tabItem("underlying_data_table_app_1",
# shiny::fluidRow(
# shinycssloaders::withSpinner(shiny::tableOutput("underlying_data_table_3"))
# )
),
shinydashboard::tabItem("other_content_1")
)
)
)
# server------------------------------------------------------------------------
server <- function(input, output, session) {
#> Summary Stats
# number of papers value box
output$num_papers <- shinydashboard::renderValueBox({
# num_papers (not counting different countries in a pooled study separately, but including all pollutants)
num_papers <- as.numeric(length(unique(epi$paper_uid)))
shinydashboard::valueBox(
value = shiny::tags$p(stringr::str_c(num_papers, "", sep = ""), style = "font-size: 75%;"),
subtitle = "Number of Papers Analyzed",
icon = shiny::icon("file-circle-check fa-2xs")
)
})
# cohort size range value box
output$cohort_size_range_1 <- shinydashboard::renderValueBox({
# cohort size range (counting different studies in a pooled study separately, and including all pollutants)
cohort_size_ll <- epi %>%
dplyr::filter(!is.na(cohort_size), cohort_size != "NA") %>%
dplyr::select(cohort_size) %>%
min(na.rm = TRUE) %>%
unlist()
cohort_size_ul <- epi %>%
dplyr::filter(!is.na(cohort_size), cohort_size != "NA") %>%
dplyr::select(cohort_size) %>%
max(na.rm = TRUE) %>%
unlist()
cohort_size_ul_billions <- round((cohort_size_ul/1000000000), 1)
final_cohort_range_string <- stringr::str_c(cohort_size_ll, "-", cohort_size_ul_billions, "billion", sep = " ")
shinydashboard::valueBox(
value = shiny::tags$p(stringr::str_c(final_cohort_range_string, "", sep = ""), style = "font-size: 75%;"),
subtitle = "Cohort Size Range",
icon = shiny::icon("people-line fa-2xs")
)
})
# study publishing value box
output$study_publishing_year_range <- shinydashboard::renderValueBox({
# study duration range (count multiple countries in a pooled study to be a single country, include all pollutants)
study_publishing_min <- min(epi$publishing_year, na.rm = TRUE)
study_publishing_max <- ifelse(max(epi$publishing_year, na.rm = TRUE) < 2022, "Present", max(epi$publishing_year, na.rm = TRUE))
final_study_duration_string <- stringr::str_c(study_publishing_min, "-", study_publishing_max, sep = " ")
shinydashboard::valueBox(
value = tags$p(stringr::str_c(final_study_duration_string, "", sep = ""), style = "font-size: 75%;"),
subtitle = "Study Publishing Year Range",
icon = shiny::icon("calendar-days fa-2xs")
)
})
# Number of countries covered value box
output$num_of_countries_covered <- shinydashboard::renderValueBox({
num_countries_covered <- epi %>% filter(!is.na(country), country != "NA") %>% select(country) %>% unlist() %>% unique() %>% length()
shinydashboard::valueBox(
value = tags$p(stringr::str_c(num_countries_covered, "", sep = ""), style = "font-size: 75%;"),
subtitle = "Number of Countries Covered",
icon = shiny::icon("earth-americas fa-2xs")
)
})
#> PM2.5 exposure and Global Population distribution with 2 buckets
output$pm2.5_expo_glob_pop_graph_2_buckets <- shiny::renderPlot({
return(pop_pol_graph_2_buckets(aqli_color, epi, input$pm2.5_bucket_2_buckets_ll, input$pm2.5_bucket_2_buckets_ul, "pm2021"))
})
#> Country wise PM2.5 distribution Graph
output$mean_pm2.5_dist_country_wise_graph <- shiny::renderPlot({
# sanity checks
if(length(input$countries_fig2) == 0){
stop("Please select atleast one country to proceed.")
}
mean_pm2.5_country_wise_graph <- epi %>%
dplyr::filter(!is.na(mean_pm2.5), mean_pm2.5 != "NA", non_pm2.5 == 0, country %in% input$countries_fig2) %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = mean_pm2.5, fill = country, group = country), alpha = 0.5, color = "black", position = "identity") +
ggplot2::labs(x = expression("Mean PM₂.₅ concentration (in µg/m³)"),
y = "Density",
fill = "Country",
caption = stringr::str_wrap("*This graph 'only' takes into account the PM₂.₅ specific studies. For multi-country (pooled) studies, it averages the mean PM₂.₅ values, across all countries."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.position = "bottom") +
viridis::scale_fill_viridis(discrete = TRUE)
return(mean_pm2.5_country_wise_graph)
})
#> Geographic Distribution of Studies Graph
output$geographic_dist_studies <- shiny::renderPlot({
# generate summary dataset for total number of studies conducted in a given country
epi_country_count <- epi %>%
dplyr::select(country) %>%
dplyr::count(country) %>%
dplyr::filter(country != "NA", !is.na(country), !is.na(n))
# making country names match before joining epi file with color file
epi_country_count$country <- stringr::str_replace(epi_country_count$country, "USA", "United States")
#> plot choropleth map
# get data ready for the choropleth map
world <- rnaturalearth::ne_countries(scale = "medium", returnclass = "sf")
# making country names match before joining epi file with color file
world$name_long <- stringr::str_replace(world$name_long, "Russian Federation", "Russia")
world$name_long <- stringr::str_replace(world$name_long, "Lao PDR", "Laos")
world$name_long <- stringr::str_replace(world$name_long, "Dem. Rep. Korea", "North Korea")
world$name_long <- stringr::str_replace(world$name_long, "Republic of Korea", "South Korea")
world$name_long <- stringr::str_replace(world$name_long, "Vatican", "Vatican City")
world$name_long <- stringr::str_replace(world$name_long, "Brunei Darussalam", "Brunei")
world$name_long <- stringr::str_replace(world$name_long, "Somaliland", "Somalia")
world$name_long <- stringr::str_replace(world$name_long, "Mexico", "México")
world$name_long <- stringr::str_replace(world$name_long, "Republic of Congo", "Republic of the Congo")
world$name_long <- stringr::str_replace(world$name_long, "Czech Republic", "Czechia")
# joining world shape file with the epi conutry wise count dataset
world_shp_epi <- world %>%
dplyr::left_join(epi_country_count, by = c("name_long" = "country")) %>%
dplyr::select(name_long, n, geometry) %>%
dplyr::rename(num_studies = n, country = name_long)
# color 2020 collapse to country level to get the PM2.5 pollution layer (first layer of the map)
aqli_color_country <- aqli_color %>%
dplyr::group_by(country) %>%
dplyr::mutate(pop_weights = population/sum(population, na.rm = TRUE),
pm2021_pop_weighted = pm2021*pop_weights) %>%
dplyr::summarise(avg_pm2.5_2021 = sum(pm2021_pop_weighted, na.rm = TRUE))
# joining the world shape + epi joined file with the aqli color file
world_shp_epi_color <- world_shp_epi %>%
dplyr::left_join(aqli_color_country, by = "country") %>%
dplyr::filter(country != "Antarctica") %>%
dplyr::select(country, num_studies, avg_pm2.5_2021, geometry)
# In the total number of studies column, replacing NAs with 0s
world_shp_epi_color <- world_shp_epi_color %>%
dplyr::mutate(num_studies = ifelse(is.na(num_studies) == TRUE, 0, num_studies))
# getting centroids for countries after correcting for problematic polygons
world_shp_epi_color_for_centroids <<- sf::st_make_valid(world_shp_epi_color)
country_wise_centroids <<- sf::st_centroid(world_shp_epi_color_for_centroids, of_largest_polygon = TRUE)
# adding an identifier column for countries where no studies have happened
country_wise_centroids$color_circle = ifelse(country_wise_centroids$num_studies == 0, "No Studies", "Atleast 1 study")
# creating a dataframe out of the latitude, longitude coordinates of the country centroids, so that we can plot it
# using geom_point.
coord_df <- dplyr::as_tibble(sf::st_coordinates(country_wise_centroids))
# reassigning the X and Y coordinates to x_coord and y_coord
country_wise_centroids$x_coord <- coord_df$X
country_wise_centroids$y_coord <- coord_df$Y
# moving the geometry column to the end and making sure that the entire dataset is a sf object
country_wise_centroids <- country_wise_centroids %>%
dplyr::select(-geometry, geometry) %>%
sf::st_as_sf()
# without geometry version of country wise centroids dataset and adding a label_plt column
country_wise_centroids_sans_geom <- country_wise_centroids %>%
sf::st_drop_geometry() %>%
dplyr::as_tibble() %>%
dplyr::mutate(label_plt = ifelse(num_studies == 0, "", num_studies))
# plotting the choropleth
if(length(input$countries_fig3) == 0){
stop("Please select atleast one country to proceed.")
} else if(("all" %in% input$countries_fig3) & (length(input$countries_fig3)) > 1){
stop("Please choose either multiple countries, or 'all', but not both")
}
if("all" %in% input$countries_fig3){
# options(repr.plot.width = 50, repr.plot.height = 50)
geographic_dist_graph <- world_shp_epi_color %>%
ggplot2::ggplot() +
ggplot2::geom_sf(mapping = ggplot2::aes(fill = avg_pm2.5_2021, group = avg_pm2.5_2021), color = "darkgrey") +
ggplot2::geom_sf(data = world_shp_epi_color %>% dplyr::filter(num_studies > 0) %>% sf::st_as_sf(), color = "black", size = 6, fill = "transparent") +
colorspace::scale_fill_continuous_sequential(palette = "YlOrRd", name = expression("Annual Average" ~ PM[2.5] ~ "in 2021 (in µg/m³)")) +
ggplot2::geom_jitter(data = country_wise_centroids_sans_geom, mapping = aes(x = x_coord, y = y_coord, size = num_studies, color = color_circle)) +
ggplot2::geom_text(data = country_wise_centroids_sans_geom, mapping = aes(x = x_coord, y = y_coord, label = label_plt), size = 3, color = "white", size = 2) +
ggplot2::theme(legend.position = "bottom") +
ggplot2::scale_size(breaks = c(1, 5, 10, 20, 30), range = c(1, 12),
guide = ggplot2::guide_legend(override.aes = list(color = "darkgrey"))) +
ggplot2::labs(caption = stringr::str_wrap("*The size of the circles is directly proportional to the total number of times a given country has been included
in a study (any study, not necessarily PM2.5 specific - mentioned as the number in the circle). Countries with atleast 1 study are highlighted (black borders)."), width = 30,
size = "Number of Studies") +
ggplot2::scale_color_manual(values = c("No Studies" = "cadetblue3", "Atleast 1 studys" = "darkolivegreen"), name = "") +
ggthemes::theme_map() +
ggplot2::ggtitle(expression("Country wise distribution of epi studies and Annual Average" ~PM[2.5] ~ "pollution (in µg/m³)")) +
ggplot2::theme(plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.position = "bottom",
legend.background = ggplot2::element_rect(color = "black"),
legend.justification = c(0.5, 3),
plot.title = ggplot2::element_text(hjust = 0.5, size = 16))
return(geographic_dist_graph)
} else if ((("all" %in% input$countries_fig3) == FALSE) & (length(input$countries_fig3) >= 1)) {
geographic_dist_graph <- world_shp_epi_color %>%
dplyr::filter(country %in% input$countries_fig3) %>%
ggplot2::ggplot() +
ggplot2::geom_sf(mapping = ggplot2::aes(fill = avg_pm2.5_2021, group = avg_pm2.5_2021), color = "darkgrey") +
ggplot2::geom_sf(data = world_shp_epi_color %>% dplyr::filter(num_studies > 0, country %in% input$countries_fig3) %>% sf::st_as_sf(), color = "black", size = 6, fill = "transparent") +
colorspace::scale_fill_continuous_sequential(palette = "YlOrRd", name = expression("Annual Average" ~ PM[2.5] ~ "in 2021 (in µg/m³)")) +
ggplot2::geom_jitter(data = country_wise_centroids_sans_geom %>% dplyr::filter(country %in% input$countries_fig3), mapping = aes(x = x_coord, y = y_coord, size = num_studies, color = color_circle)) +
ggplot2::geom_text(data = country_wise_centroids_sans_geom %>% dplyr::filter(country %in% input$countries_fig3), mapping = aes(x = x_coord, y = y_coord, label = label_plt), size = 3) +
ggplot2::theme(legend.position = "bottom") +
ggplot2::scale_size(breaks = c(1, 5, 10, 20, 30), range = c(1, 12),
guide = ggplot2::guide_legend(override.aes = list(color = "darkgrey"))) +
ggplot2::labs(caption = stringr::str_wrap("*The size of the circles is directly proportional to the total number of times a given country has been included
in a study (any study, not necessarily PM2.5 specific - mentioned as the number in the circle). Countries with atleast 1 study are highlighted (black borders)."), width = 30,
size = "Number of Studies") +
ggplot2::scale_color_manual(values = c("No Studies" = "cadetblue3", "Atleast 1 studys" = "darkolivegreen"), name = "") +
ggthemes::theme_map() +
ggplot2::ggtitle(expression("Country wise distribution of epi studies and Annual Average" ~PM[2.5] ~ "pollution (in µg/m³)")) +
ggplot2::theme(plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.position = "bottom",
legend.background = ggplot2::element_rect(color = "black"),
legend.justification = c(0.5, 3),
plot.title = ggplot2::element_text(hjust = 0.5, size = 16))
return(geographic_dist_graph)
}
})
#> Geographic Distribution of Studies table
# output$geographic_dist_studies_table <- shiny::renderDataTable({
# if(length(input$countries_fig3) == 0){
# stop("Please select atleast one country to proceed.")
# } else if(("all" %in% input$countries_fig3) & (length(input$countries_fig3)) > 1){
# stop("Please either choose one/multiple countries, or 'all', but not both")
# }
#
# if("all" %in% input$countries_fig3){
# dt_all <- country_wise_centroids %>%
# sf::st_drop_geometry() %>%
# dplyr::filter(num_studies != 0)
# DT::datatable(dt_all, options = list(pageLength = 6))
# } else if ((("all" %in% input$countries_fig3) == FALSE) & (length(input$countries_fig3) >= 1)){
# dt_filter <- country_wise_centroids %>%
# sf::st_drop_geometry() %>%
# dplyr::filter(num_studies !=0, country %in% input$countries_fig3)
# DT::datatable(dt_filter, options = list(pageLength = 6))
# }
# })
#> Epi studies over time graph
output$epi_studies_over_time_graph <- shiny::renderPlot({
epi %>%
dplyr::distinct(paper_uid, .keep_all = TRUE) %>%
dplyr::group_by(publishing_year) %>%
dplyr::summarise(total_studies = dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::filter(publishing_year >= input$year_range[1], publishing_year <= input$year_range[2], total_studies != "NA", !is.na(total_studies)) %>%
ggplot2::ggplot() +
ggplot2::geom_line(mapping = ggplot2::aes(x = publishing_year, y = total_studies), lwd = 1.2, color = "cornflowerblue") +
ggplot2::labs(x = "Year", y = "Total Number of Studies",
caption = stringr::str_wrap("*This graph displays the total number of AQ epi studies (all studies, not PM₂.₅ specific) published over time."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7)) %>%
return()
})
#> Epi studies over time table
# output$aq_epi_studies_over_time <- shiny::renderDataTable({
# dt_filter_epi_over_time <- epi %>%
# dplyr::distinct(paper_uid, .keep_all = TRUE) %>%
# dplyr::group_by(publishing_year) %>%
# dplyr::summarise(total_studies = dplyr::n()) %>%
# dplyr::ungroup() %>%
# dplyr::filter(publishing_year >= input$year_range[1], publishing_year <= input$year_range[2], total_studies != "NA", !is.na(total_studies))
# DT::datatable(dt_filter_epi_over_time, options = list(pageLength = 6))
#
# })
#> Continent Wise Distribution of Duration of Study graph
output$continent_wise_dist_duration_study_graph <- shiny::renderPlot({
# creating a copy of epi database, with China listed as a separate continent, so that we can plot the China panel and China - Asia panel
# adding logic to check for missing continents and automatically adding missing panels for that
if(sum(missing_continents_logical) == 0){
continent_wise_study_duration_graph_summary_table <- epi_tmp %>%
dplyr::filter(continent != "NA", !is.na(continent), study_duration != "NA", !is.na(study_duration)) %>%
dplyr::group_by(paper_uid, continent) %>%
dplyr::summarise(average_study_duration = mean(study_duration, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::filter(continent != "Oceania") %>%
#dplyr::mutate(continent = as.factor(continent)) %>%
dplyr::mutate(order_continent = ifelse(continent == "China", 2, 0),
order_continent = ifelse(continent == "Asia - China", 1, order_continent),
order_continent = ifelse(continent == "Europe", 5, order_continent),
order_continent = ifelse(continent == "North America", 6, order_continent),
order_continent = ifelse(continent == "South America", 4, order_continent),
order_continent = ifelse(continent == "Africa", 3, order_continent)) %>%
dplyr::mutate(continent = ifelse(continent == "China", "China (1402.5 million people)", continent),
continent = ifelse(continent == "Africa", "Africa (1373.8 million people)", continent),
continent = ifelse(continent == "North America", "North America (589.2 million people)", continent),
continent = ifelse(continent == "South America", "South America (429.2 million people)", continent),
continent = ifelse(continent == "Europe", "Europe (740.8 million people)", continent),
continent = ifelse(continent == "Asia - China", "Asia - China (3161.5 million people)", continent))
} else{
for(i in 1:length(missing_continents)){
if(i == 1){
continent_wise_study_duration_graph_summary_table <- epi_tmp %>%
dplyr::filter(continent != "NA", !is.na(continent), study_duration != "NA", !is.na(study_duration)) %>%
dplyr::group_by(paper_uid, continent) %>%
dplyr::summarise(average_study_duration = mean(study_duration, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::add_row(continent = missing_continents[i])
} else{
continent_wise_study_duration_graph_summary_table <- continent_wise_study_duration_graph_summary_table %>%
dplyr::add_row(continent = missing_continents[i])
}
}
continent_wise_study_duration_graph_summary_table <- continent_wise_study_duration_graph_summary_table %>%
dplyr::filter(continent != "Oceania") %>%
# dplyr::mutate(continent = as.factor(continent)) %>%
dplyr::mutate(order_continent = ifelse(continent == "China", 2, 0),
order_continent = ifelse(continent == "Asia - China", 1, order_continent),
order_continent = ifelse(continent == "Europe", 5, order_continent),
order_continent = ifelse(continent == "North America", 6, order_continent),
order_continent = ifelse(continent == "South America", 4, order_continent),
order_continent = ifelse(continent == "Africa", 3, order_continent)) %>%
dplyr::mutate(continent = ifelse(continent == "China", "China (1402.5 million people)", continent),
continent = ifelse(continent == "Africa", "Africa (1373.8 million people)", continent),
continent = ifelse(continent == "North America", "North America (589.2 million people)", continent),
continent = ifelse(continent == "South America", "South America (429.2 million people)", continent),
continent = ifelse(continent == "Europe", "Europe (740.8 million people)", continent),
continent = ifelse(continent == "Asia - China", "Asia - China (3161.5 million people)", continent))
}
#----------------
# plot the figure with empty Africa panel
if(length(input$continent_list) == 0){
stop("Please select atleast one continent to proceed.")
}
if(input$plot_type_fig5 == "Histogram (Frequency)"){
continent_wise_study_duration_graph <- continent_wise_study_duration_graph_summary_table %>%
dplyr::filter(continent %in% input$continent_list) %>%
ggplot2::ggplot() +
ggplot2::geom_histogram(mapping = ggplot2::aes(x = average_study_duration, fill = continent, group = continent), color = "white", position = "identity", binwidth = 2) +
ggplot2::labs(x = "Study Duration", y = "Count",
caption = stringr::str_wrap("*This graph displays distribution by continent (and for China, Asia - China) for all studies (not PM2.5 specific)."), width = 10,
title = expression("Continent (and China) wise Study Duration Distribution and 2021 Annual Average" ~ PM[2.5] ~ "(in µg/m³)"),
subtitle = "(Ordered from most polluted to least polluted)") +
ggplot2::scale_x_continuous(breaks = seq(0, 40, 10)) +
ggthemes::theme_hc() +
ggplot2::theme(legend.title = element_blank()) +
ggplot2::facet_wrap(~forcats::fct_reorder(continent, order_continent), nrow = 1) +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
plot.title = element_text(size = 14, hjust = 0.5),
plot.subtitle = element_text(size = 8, hjust = 0.5),
strip.background = element_rect(fill = "floralwhite"),
strip.text = element_text(size = 8),
legend.background = element_rect(color = "black")) +
ggplot2::scale_fill_manual(values = c("North America (589.2 million people)" = "#FFBA00", "Europe (740.8 million people)" = "#FF9600",
"South America (429.2 million people)" = "#FF6908", "Africa (1373.8 million people)" = "#E63D23", "China (1402.5 million people)" = "#BD251C",
"Asia - China (3161.5 million people)" = "#8C130E")) +
ggplot2::scale_y_continuous(breaks = seq(0, 10, 2))
} else if (input$plot_type_fig5 == "Histogram (Density)"){
continent_wise_study_duration_graph <- continent_wise_study_duration_graph_summary_table %>%
dplyr::filter(continent %in% input$continent_list) %>%
ggplot2::ggplot() +
ggplot2::geom_histogram(mapping = ggplot2::aes(x = average_study_duration, y = ..density.., fill = continent, group = continent), color = "white", binwidth = 2) +
ggplot2::labs(x = "Study Duration", y = "Density",
caption = stringr::str_wrap("*This graph displays distribution by continent (and for China, Asia - China) for all studies (not PM2.5 specific)."), width = 10,
title = expression("Continent (and China) wise Study Duration Distribution and 2021 Annual Average" ~ PM[2.5] ~ "(in µg/m³)"),
subtitle = "(Ordered from most polluted to least polluted)") +
ggplot2::scale_x_continuous(breaks = seq(0, 40, 10)) +
ggthemes::theme_hc() +
ggplot2::theme(legend.title = element_blank()) +
ggplot2::facet_wrap(~forcats::fct_reorder(continent, order_continent), nrow = 1) +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
plot.title = element_text(size = 14, hjust = 0.5),
plot.subtitle = element_text(size = 8, hjust = 0.5),
strip.background = element_rect(fill = "floralwhite"),
strip.text = element_text(size = 8),
legend.background = element_rect(color = "black")) +
ggplot2::scale_fill_manual(values = c("North America (589.2 million people)" = "#FFBA00", "Europe (740.8 million people)" = "#FF9600",
"South America (429.2 million people)" = "#FF6908", "Africa (1373.8 million people)" = "#E63D23", "China (1402.5 million people)" = "#BD251C",
"Asia - China (3161.5 million people)" = "#8C130E"))
}
return(continent_wise_study_duration_graph)
})
#> Distribution of lower and upper limits of PM2.5 exposure range
# Note: This is PM2.5 specific and different countries in a pooled study are counted separately.
output$pm2.5_ll_ul_dist_graph <- shiny::renderPlot({
# create long dataset
epi_long <- epi %>%
dplyr::filter(!is.na(mean_pm2.5), mean_pm2.5 != "NA", non_pm2.5 == 0) %>%
tidyr::pivot_longer(cols = contains("pm2.5_exposure"), names_to = "exposure_type", values_to = "exposure_value") %>%
dplyr::select(paper_uid, exposure_type, exposure_value) %>%
dplyr::filter(!is.na(exposure_value)) %>%
dplyr::mutate(exposure_type = ifelse(exposure_type == "pm2.5_exposure_ll", "PM₂.₅ exposure in µg/m³ (Lower Limit)", "PM₂.₅ exposure in µg/m³ (Upper Limit)"))
if(input$plot_type_fig6 == "Histogram"){
epi_long %>%
dplyr::group_by(paper_uid, exposure_type) %>%
dplyr::summarise(exposure_value = ifelse(exposure_type == "pm2.5_exposure_ll", min(exposure_value, na.rm = TRUE), max(exposure_value, na.rm = TRUE))) %>%
ggplot2::ggplot() +
ggplot2::geom_histogram(mapping = ggplot2::aes(x = exposure_value, fill = exposure_type, group = exposure_type), position = "identity", alpha = 0.4, color = "white") +
ggplot2::labs(x = "PM₂.₅ concentration (in µg/m³)",
y = "Count",
fill = "Exposure Type",
caption = stringr::str_wrap("*This graph takes into account only those PM₂.₅ studies that have atleast one of 'pm2.5 lower limit value', 'pm2.5 upper limit value'."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.key = element_rect(color = "black"),
legend.text = element_text(size = 8)) +
viridis::scale_fill_viridis(discrete = TRUE)
} else if(input$plot_type_fig6 == "Density"){
epi_long %>%
dplyr::group_by(paper_uid, exposure_type) %>%
dplyr::summarise(exposure_value = ifelse(exposure_type == "pm2.5_exposure_ll", min(exposure_value, na.rm = TRUE), max(exposure_value, na.rm = TRUE))) %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = exposure_value, fill = exposure_type, group = exposure_type), alpha = 0.6, position = "identity") +
ggplot2::labs(x = "PM₂.₅ concentration (in µg/m³)",
y = "Density",
fill = "Exposure Type",
caption = stringr::str_wrap("*This graph takes into account only those PM₂.₅ studies that have atleast one of 'pm2.5 lower limit value', 'pm2.5 upper limit value'."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.text = element_text(size = 8)) +
viridis::scale_fill_viridis(discrete = TRUE)
}
})
#> Country Wise Distribution of Cohort Size Graph
# Different countries in a pooled study are counted separately, but this is not a PM2.5 specific graph.
output$country_wise_dist_log_cohort_size_graph_1 <- plotly::renderPlotly({
if(length(input$countries_list_fig7) == 0){
stop("Please select atleast one country to proceed.")
} else if(("all" %in% input$countries_list_fig7) & (length(input$countries_list_fig7)) > 1){
stop("Please either choose one/multiple countries, or 'all', but not both")
}
if("all" %in% input$countries_list_fig7){
epi %>%
dplyr::filter(!is.na(cohort_size), cohort_size != "NA") %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = cohort_size, fill = country, group = country), alpha = 0.5) +
ggplot2::scale_x_log10() +
ggthemes::theme_hc() +
ggplot2::labs(caption = stringr::str_wrap("*This graph takes into account only those studies (can include non-PM₂.₅ studies) in which a cohort/sample size is available."), width = 10,
x = "Cohort Size",
y = "Density",
fill = "Country") +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.background = element_rect(),
legend.title = element_text(hjust = 0.5)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
} else if ((("all" %in% input$countries_list_fig7) == FALSE) & (length(input$countries_list_fig7) >= 1)){
epi %>%
dplyr::filter(!is.na(cohort_size), cohort_size != "NA") %>%
dplyr::filter(country %in% input$countries_list_fig7) %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = cohort_size, fill = country, group = country), alpha = 0.5) +
ggplot2::scale_x_log10() +
ggplot2::labs(caption = stringr::str_wrap("*This graph takes into account only those studies (can include non-PM₂.₅ studies) in which a cohort/sample size is available."), width = 10,
x = "Cohort Size",
y = "Density",
fill = "Country") +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.background = element_rect(),
legend.title = element_text(hjust = 0.5)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
}
})
#> Distribution of lower limit and upper limit of ages covered in this analysis
# This is not a PM2.5 specific graph and different countries in a cohort study are counted separately.
output$age_ll_ul_graph <- shiny::renderPlot({
epi_age_dist <- epi %>%
dplyr::filter(((!is.na(cohort_age_ll)) | (cohort_age_ll != "NA")) & ((!is.na(cohort_age_ul)) | (cohort_age_ul != "NA"))) %>%
tidyr::pivot_longer(cols = contains("cohort_age"), names_to = "age_dist_type", values_to = "age_value") %>%
dplyr::select(age_dist_type, age_value) %>%
dplyr::filter(!is.na(age_value)) %>%
dplyr::mutate(age_dist_type = ifelse(age_dist_type == "cohort_age_ll", "Cohort Age (Lower Limit)", "Cohort Age (Upper Limit)"))
if(input$plot_type_fig6 == "Histogram"){
epi_age_dist %>%
ggplot2::ggplot() +
ggplot2::geom_histogram(mapping = ggplot2::aes(x = age_value, fill = age_dist_type, group = age_dist_type), alpha = 0.5, position = "identity", color = "white") +
ggplot2::scale_x_continuous(breaks = seq(0, 130, 20)) +
ggplot2::scale_y_continuous(breaks = seq(0, 15, 2)) +
ggplot2::labs(x = "Age",
fill = "Age Distribution Type",
caption = stringr::str_wrap("*This graph takes into account those studies (can include non-PM₂.₅ studies) that have atleast one of 'age lower limit', 'age upper limit'."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.text = element_text(size = 8)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
} else if(input$plot_type_fig6 == "Density"){
epi_age_dist %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = age_value, fill = age_dist_type, group = age_dist_type), alpha = 0.6, color = "black") +
ggplot2::scale_x_continuous(breaks = seq(0, 130, 20)) +
ggplot2::labs(x = "Age",
caption = stringr::str_wrap("*This graph takes into account those studies (can include non-PM₂.₅ studies) that have atleast one of 'age lower limit', 'age upper limit'."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.text = element_text(size = 8)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
}
})
#> Country wise distribution of study duration
# This is not a PM2.5 specific graph and in this all countries in a given pooled study are counted separately.
# Country wise distribution
output$country_wise_dist_study_duration_1 <- plotly::renderPlotly({
if(length(input$countries_list_fig7) == 0){
stop("Please select atleast one country to proceed.")
}
if(("all" %in% input$countries_list_fig7) & (length(input$countries_list_fig7)) > 1){
stop("Please either choose one/multiple countries, or 'all', but not both")
}
if("all" %in% input$countries_list_fig7){
epi %>%
dplyr::filter(!is.na(study_duration), study_duration != "NA") %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = study_duration, fill = country, group = country), alpha = 0.5) +
ggplot2::scale_x_continuous(breaks = seq(0, 40, 5)) +
ggthemes::theme_hc() +
ggplot2::labs(caption = stringr::str_wrap("*This graph takes into account only those studies (can include non-PM₂.₅ studies) in which study duration is available."), width = 10,
fill = "Country",
x = "Study Duration",
y = "Density") +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.background = element_rect(),
legend.title = element_text(hjust = 0.5)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
} else if ((("all" %in% input$countries_list_fig7) == FALSE) & (length(input$countries_list_fig7) >= 1)){
epi %>%
dplyr::filter(!is.na(study_duration), study_duration != "NA") %>%
dplyr::filter(country %in% input$countries_list_fig7) %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = study_duration, fill = country, group = country), alpha = 0.5) +
ggplot2::labs(caption = stringr::str_wrap("*This graph takes into account only those studies (can include non-PM₂.₅ studies) in which study duration is available."), width = 10,
x = "Study Duration",
y = "Density",
fill = "Country") +
ggplot2::scale_x_continuous(breaks = seq(0, 40, 5)) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.background = element_rect(),
legend.title = element_text(hjust = 0.5)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
}
})
#> pm2.5 and global population distribution graph
# output$pm2.5_expo_glob_pop_graph <- shiny::renderPlot({
#
# # percent population in pollution buckets
# tot_pop_in_bucket <- aqli_color %>%
# dplyr::filter(pm2020 >= input$pm2.5_bucket[1], pm2020 <= input$pm2.5_bucket[2]) %>%
# dplyr::summarise(tot_pop = sum(population, na.rm = TRUE)) %>%
# unlist()
#
# world_pop <- aqli_color %>%
# dplyr::summarise(tot_pop = sum(population, na.rm = TRUE)) %>%
# unlist()
#
# percent_pop_in_bucket <- round((tot_pop_in_bucket/world_pop)*100, 1)
#
# # percent studies in the given pollution bucket
# tot_studies_in_bucket <- epi %>%
# dplyr::filter(!is.na(mean_pm2.5), mean_pm2.5 != "NA", non_pm2.5 == 0) %>%
# dplyr::group_by(paper_uid) %>%
# dplyr::summarise(mean_pm2.5 = mean(mean_pm2.5, na.rm = TRUE)) %>%
# dplyr::filter(mean_pm2.5 >= input$pm2.5_bucket[1], mean_pm2.5 <= input$pm2.5_bucket[2]) %>%
# nrow()
#
# tot_studies_overall <- nrow(epi %>%
# dplyr::distinct(paper_uid, .keep_all = TRUE) %>%
# dplyr::filter(!is.na(mean_pm2.5), mean_pm2.5 != "NA", non_pm2.5 == 0))
#
# percent_studies_in_bucket <- round((tot_studies_in_bucket/tot_studies_overall)*100, 1)
#
# # creating a dataframe for plotting
# pm2.5_expo_glob_pop_df <- tibble::tibble(pm2.5_bucket = c(stringr::str_c(input$pm2.5_bucket[1], "-", input$pm2.5_bucket[2], "µg/m³"),
# stringr::str_c(input$pm2.5_bucket[1], "-", input$pm2.5_bucket[2], "µg/m³")),
# prop_in_bucket = c(percent_pop_in_bucket, percent_studies_in_bucket),
# type_of_prop = c("Percent Population in PM2.5 bucket", "Percent Studies in PM2.5 bucket"))
#
# pm2.5_expo_glob_pop_df$type_of_prop <- as.factor(pm2.5_expo_glob_pop_df$type_of_prop)
#
# pm2.5_expo_glob_pop_df %>%
# ggplot2::ggplot() +
# ggplot2::geom_col(mapping = ggplot2::aes(x = pm2.5_bucket, y = prop_in_bucket, fill = type_of_prop, group = type_of_prop), position = position_dodge(), width = 0.4) +
# ggplot2::scale_fill_manual(values = c("Percent Population in PM2.5 bucket" = "grey", "Percent Studies in PM2.5 bucket" = "cornflowerblue"), labels = c("Proportion of World Population in Bucket", "Proportion of Studies Completed in Bucket")) +
# ggplot2::labs(x = expression("Mean" ~ PM[2.5] ~ "bucket (in µg/m³)"), y = "Percentage", fill = "",
# caption = stringr::str_wrap("*This graph 'only' takes into account the PM₂.₅ specific studies. For multi-country (pooled) studies, it averages the mean PM2.5 values, across all countries."), width = 17) +
# ggthemes::theme_hc() +
# ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
# axis.line.x = ggplot2::element_line(color = "black"),
# plot.caption = ggplot2::element_text(hjust = 0, size = 7),
# legend.position = "bottom",
# axis.title.x = element_text(size = 11),
# axis.title.y = element_text(size = 11),
# legend.text = element_text(size = 9))
# viridis::scale_fill_viridis(discrete = TRUE) %>%
# return()
#
# })
#> Render the underlying data as the Data Table
# output$underlying_data_table_3 <- shiny::renderTable({
# # epi
# })
}
shiny::shinyApp(ui, server)
aqli-epic/epi.meta.analysis documentation built on July 2, 2023, 4:18 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## shiny app script for epi.meta.analysis--------------------------------------
# load app.R helper file
# source("./appPublic/app_helper.R")
# libraries
library(shiny)
library(shinydashboard)
library(plotly)
library(magrittr)
library(DT)
library(forcats)
library(rnaturalearth)
library(rnaturalearthdata)
library(shinycssloaders)
library(waiter)
library(colorspace)
# setting global options
options(shiny.maxRequestSize = 900*1024^2)
# loading the .RData file that contain all required data objects
# save(list = ls(all = TRUE), file= "./appPublic/all.RData")
load("all.RData", .GlobalEnv)
#----------------------------------------------------------------------------------
ui <- shinydashboard::dashboardPage(
skin = "black",
shinydashboard::dashboardHeader(
title = "AQ Epi Studies"
),
shinydashboard::dashboardSidebar(
collapsed = TRUE,
shinydashboard::sidebarMenu(
shinydashboard::menuItem(tabName = "blog_post_graphs_1", text = "Blog Post Graphs", icon = icon("chart-bar")),
shinydashboard::menuItem(tabName = "underlying_data_table_app_1", text = "Underlying Data", icon = icon("database")),
shinydashboard::menuItem(tabName = "other_content_1", text = "Other Content", icon = icon("pencil"))
)
),
shinydashboard::dashboardBody(
shinydashboard::tabItems(
shinydashboard::tabItem("blog_post_graphs_1",
shiny::fluidRow(
shinydashboard::valueBoxOutput("num_papers", width = 3),
shinydashboard::valueBoxOutput("cohort_size_range_1", width = 3),
shinydashboard::valueBoxOutput("study_publishing_year_range", width = 3),
shinydashboard::valueBoxOutput("num_of_countries_covered", width = 3)
),
shiny::fluidRow(
shiny::tags$hr(),
shinydashboard::box(width = 3,
shiny::sliderInput("pm2.5_bucket_2_buckets_ll", "PM₂.₅ Range (LL)", 0, 120, value = 0)
),
shinydashboard::box(width = 3,
shiny::sliderInput("pm2.5_bucket_2_buckets_ul", "PM₂.₅ Range (UL)", 0, 120, value = 15)
),
shinydashboard::box(
width = 6, status = "info", solidHeader = TRUE,
title = "PM₂.₅ exposure Range and Global Population Distribution with 2 buckets",
shinycssloaders::withSpinner(shiny::plotOutput("pm2.5_expo_glob_pop_graph_2_buckets"))
)
),
shiny::fluidRow(
shinydashboard::box(
width = 3,
shiny::selectInput("countries_fig2", "Countries", choices = epi %>% dplyr::filter(!is.na(country)) %>% dplyr::select(country) %>% unlist() %>% unique(), multiple = TRUE, selected = "USA")
),
shinydashboard::box(
width = 9, status = "info", solidHeader = TRUE,
title = expression("Country Wise Mean PM₂.₅ Distribution"),
shinycssloaders::withSpinner(shiny::plotOutput("mean_pm2.5_dist_country_wise_graph"))
)
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 2,
shiny::selectInput("countries_fig3", "Countries", choices = c("all", "United States", epi %>% dplyr::filter(!is.na(country)) %>% dplyr::select(country) %>% dplyr::filter(country != "USA") %>% unlist() %>% unique()), multiple = TRUE, selected = "all"),
#shiny::dataTableOutput("geographic_dist_studies_table")
),
shinydashboard::box(
status = "info", solidHeader = TRUE,
title = "Geographic Distribution of Studies and Annual Average PM₂.₅ pollution",
width = 10,
shinycssloaders::withSpinner(shiny::plotOutput("geographic_dist_studies"))
)
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 3,
shiny::sliderInput("year_range", "Year Range", min(epi$publishing_year, na.rm = TRUE), max(epi$publishing_year, na.rm = TRUE), value = c(min(epi$publishing_year, na.rm = TRUE), max(epi$publishing_year, na.rm = TRUE)), sep = ""),
#DT::dataTableOutput("aq_epi_studies_over_time")
),
shinydashboard::box(
width = 9, status = "info", solidHeader = TRUE,
title = "AQ Epi Studies Over Time",
shinycssloaders::withSpinner(shiny::plotOutput("epi_studies_over_time_graph"))
)
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 2,
shiny::selectInput("continent_list", "Continents", choices = c("North America (589.2 million people)",
"Europe (740.8 million people)",
"Asia - China (3161.5 million people)",
"Africa (1373.8 million people)",
"South America (429.2 million people)",
"China (1402.5 million people)"), multiple = TRUE, selected = "North America (589.2 million people)"),
hr(),
shiny::selectInput("plot_type_fig5", "Plot Type", choices = c("Histogram (Frequency)", "Histogram (Density)"), selected = "Histogram (Frequency)")
),
shinydashboard::box(status = "info", solidHeader = TRUE,
title = "Epi Studies Duration Distribution (Continent Wise): Histogram",
width = 10,
shinycssloaders::withSpinner(shiny::plotOutput("continent_wise_dist_duration_study_graph"))
)
),
shiny::tags$hr(),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(width = 12,
status = "info", solidHeader = TRUE,
title = "Other Graphs")
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 2,
status = "info",
solidHeader = TRUE,
shiny::selectInput("plot_type_fig6", "Plot Type", choices = c("Histogram", "Density"), selected = "Histogram")
),
shinydashboard::box(title = "Lower and Upper Limit Distributions of PM₂.₅ exposure range",
width = 5,
status = "info",
solidHeader = TRUE,
shinycssloaders::withSpinner(shiny::plotOutput("pm2.5_ll_ul_dist_graph"))
),
shinydashboard::box(title = "Lower and Upper Limit Distributions of Age Range covered in the analysis",
width = 5,
status = "info",
solidHeader = TRUE,
shinycssloaders::withSpinner(shiny::plotOutput("age_ll_ul_graph")))
),
shiny::tags$hr(),
shiny::fluidRow(
shinydashboard::box(
width = 2,
status = "info",
solidHeader = TRUE,
shiny::selectInput("countries_list_fig7", "Countries", choices = c("all", epi %>% dplyr::filter(!is.na(country)) %>% dplyr::select(country) %>% unlist() %>% unique()), multiple = TRUE, selected = "USA")
),
shinydashboard::box(title = "Country Wise Distribution of Cohort Size",
width = 5,
status = "info",
solidHeader = TRUE,
shinycssloaders::withSpinner(plotly::plotlyOutput("country_wise_dist_log_cohort_size_graph_1"))),
shinydashboard::box(title = "Country Wise Distribution of Study Duration",
width = 5,
status = "info",
solidHeader = TRUE,
shinycssloaders::withSpinner(plotly::plotlyOutput("country_wise_dist_study_duration_1")))
),
shiny::tags$br()
# shiny::fluidRow(
# shiny::tags$hr(),
# shinydashboard::box(width = 4,
# shiny::sliderInput("pm2.5_bucket", "PM₂.₅ Range", 0, 120, value = c(0, 15))
# ),
# shinydashboard::box(
# width = 8, status = "info", solidHeader = TRUE,
# title = "PM₂.₅ exposure Range and Global Population Distribution",
# shinycssloaders::withSpinner(shiny::plotOutput("pm2.5_expo_glob_pop_graph"))
# )
# )
),
shinydashboard::tabItem("underlying_data_table_app_1",
# shiny::fluidRow(
# shinycssloaders::withSpinner(shiny::tableOutput("underlying_data_table_3"))
# )
),
shinydashboard::tabItem("other_content_1")
)
)
)
# server------------------------------------------------------------------------
server <- function(input, output, session) {
#> Summary Stats
# number of papers value box
output$num_papers <- shinydashboard::renderValueBox({
# num_papers (not counting different countries in a pooled study separately, but including all pollutants)
num_papers <- as.numeric(length(unique(epi$paper_uid)))
shinydashboard::valueBox(
value = shiny::tags$p(stringr::str_c(num_papers, "", sep = ""), style = "font-size: 75%;"),
subtitle = "Number of Papers Analyzed",
icon = shiny::icon("file-circle-check fa-2xs")
)
})
# cohort size range value box
output$cohort_size_range_1 <- shinydashboard::renderValueBox({
# cohort size range (counting different studies in a pooled study separately, and including all pollutants)
cohort_size_ll <- epi %>%
dplyr::filter(!is.na(cohort_size), cohort_size != "NA") %>%
dplyr::select(cohort_size) %>%
min(na.rm = TRUE) %>%
unlist()
cohort_size_ul <- epi %>%
dplyr::filter(!is.na(cohort_size), cohort_size != "NA") %>%
dplyr::select(cohort_size) %>%
max(na.rm = TRUE) %>%
unlist()
cohort_size_ul_billions <- round((cohort_size_ul/1000000000), 1)
final_cohort_range_string <- stringr::str_c(cohort_size_ll, "-", cohort_size_ul_billions, "billion", sep = " ")
shinydashboard::valueBox(
value = shiny::tags$p(stringr::str_c(final_cohort_range_string, "", sep = ""), style = "font-size: 75%;"),
subtitle = "Cohort Size Range",
icon = shiny::icon("people-line fa-2xs")
)
})
# study publishing value box
output$study_publishing_year_range <- shinydashboard::renderValueBox({
# study duration range (count multiple countries in a pooled study to be a single country, include all pollutants)
study_publishing_min <- min(epi$publishing_year, na.rm = TRUE)
study_publishing_max <- ifelse(max(epi$publishing_year, na.rm = TRUE) < 2022, "Present", max(epi$publishing_year, na.rm = TRUE))
final_study_duration_string <- stringr::str_c(study_publishing_min, "-", study_publishing_max, sep = " ")
shinydashboard::valueBox(
value = tags$p(stringr::str_c(final_study_duration_string, "", sep = ""), style = "font-size: 75%;"),
subtitle = "Study Publishing Year Range",
icon = shiny::icon("calendar-days fa-2xs")
)
})
# Number of countries covered value box
output$num_of_countries_covered <- shinydashboard::renderValueBox({
num_countries_covered <- epi %>% filter(!is.na(country), country != "NA") %>% select(country) %>% unlist() %>% unique() %>% length()
shinydashboard::valueBox(
value = tags$p(stringr::str_c(num_countries_covered, "", sep = ""), style = "font-size: 75%;"),
subtitle = "Number of Countries Covered",
icon = shiny::icon("earth-americas fa-2xs")
)
})
#> PM2.5 exposure and Global Population distribution with 2 buckets
output$pm2.5_expo_glob_pop_graph_2_buckets <- shiny::renderPlot({
return(pop_pol_graph_2_buckets(aqli_color, epi, input$pm2.5_bucket_2_buckets_ll, input$pm2.5_bucket_2_buckets_ul, "pm2021"))
})
#> Country wise PM2.5 distribution Graph
output$mean_pm2.5_dist_country_wise_graph <- shiny::renderPlot({
# sanity checks
if(length(input$countries_fig2) == 0){
stop("Please select atleast one country to proceed.")
}
mean_pm2.5_country_wise_graph <- epi %>%
dplyr::filter(!is.na(mean_pm2.5), mean_pm2.5 != "NA", non_pm2.5 == 0, country %in% input$countries_fig2) %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = mean_pm2.5, fill = country, group = country), alpha = 0.5, color = "black", position = "identity") +
ggplot2::labs(x = expression("Mean PM₂.₅ concentration (in µg/m³)"),
y = "Density",
fill = "Country",
caption = stringr::str_wrap("*This graph 'only' takes into account the PM₂.₅ specific studies. For multi-country (pooled) studies, it averages the mean PM₂.₅ values, across all countries."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.position = "bottom") +
viridis::scale_fill_viridis(discrete = TRUE)
return(mean_pm2.5_country_wise_graph)
})
#> Geographic Distribution of Studies Graph
output$geographic_dist_studies <- shiny::renderPlot({
# generate summary dataset for total number of studies conducted in a given country
epi_country_count <- epi %>%
dplyr::select(country) %>%
dplyr::count(country) %>%
dplyr::filter(country != "NA", !is.na(country), !is.na(n))
# making country names match before joining epi file with color file
epi_country_count$country <- stringr::str_replace(epi_country_count$country, "USA", "United States")
#> plot choropleth map
# get data ready for the choropleth map
world <- rnaturalearth::ne_countries(scale = "medium", returnclass = "sf")
# making country names match before joining epi file with color file
world$name_long <- stringr::str_replace(world$name_long, "Russian Federation", "Russia")
world$name_long <- stringr::str_replace(world$name_long, "Lao PDR", "Laos")
world$name_long <- stringr::str_replace(world$name_long, "Dem. Rep. Korea", "North Korea")
world$name_long <- stringr::str_replace(world$name_long, "Republic of Korea", "South Korea")
world$name_long <- stringr::str_replace(world$name_long, "Vatican", "Vatican City")
world$name_long <- stringr::str_replace(world$name_long, "Brunei Darussalam", "Brunei")
world$name_long <- stringr::str_replace(world$name_long, "Somaliland", "Somalia")
world$name_long <- stringr::str_replace(world$name_long, "Mexico", "México")
world$name_long <- stringr::str_replace(world$name_long, "Republic of Congo", "Republic of the Congo")
world$name_long <- stringr::str_replace(world$name_long, "Czech Republic", "Czechia")
# joining world shape file with the epi conutry wise count dataset
world_shp_epi <- world %>%
dplyr::left_join(epi_country_count, by = c("name_long" = "country")) %>%
dplyr::select(name_long, n, geometry) %>%
dplyr::rename(num_studies = n, country = name_long)
# color 2020 collapse to country level to get the PM2.5 pollution layer (first layer of the map)
aqli_color_country <- aqli_color %>%
dplyr::group_by(country) %>%
dplyr::mutate(pop_weights = population/sum(population, na.rm = TRUE),
pm2021_pop_weighted = pm2021*pop_weights) %>%
dplyr::summarise(avg_pm2.5_2021 = sum(pm2021_pop_weighted, na.rm = TRUE))
# joining the world shape + epi joined file with the aqli color file
world_shp_epi_color <- world_shp_epi %>%
dplyr::left_join(aqli_color_country, by = "country") %>%
dplyr::filter(country != "Antarctica") %>%
dplyr::select(country, num_studies, avg_pm2.5_2021, geometry)
# In the total number of studies column, replacing NAs with 0s
world_shp_epi_color <- world_shp_epi_color %>%
dplyr::mutate(num_studies = ifelse(is.na(num_studies) == TRUE, 0, num_studies))
# getting centroids for countries after correcting for problematic polygons
world_shp_epi_color_for_centroids <<- sf::st_make_valid(world_shp_epi_color)
country_wise_centroids <<- sf::st_centroid(world_shp_epi_color_for_centroids, of_largest_polygon = TRUE)
# adding an identifier column for countries where no studies have happened
country_wise_centroids$color_circle = ifelse(country_wise_centroids$num_studies == 0, "No Studies", "Atleast 1 study")
# creating a dataframe out of the latitude, longitude coordinates of the country centroids, so that we can plot it
# using geom_point.
coord_df <- dplyr::as_tibble(sf::st_coordinates(country_wise_centroids))
# reassigning the X and Y coordinates to x_coord and y_coord
country_wise_centroids$x_coord <- coord_df$X
country_wise_centroids$y_coord <- coord_df$Y
# moving the geometry column to the end and making sure that the entire dataset is a sf object
country_wise_centroids <- country_wise_centroids %>%
dplyr::select(-geometry, geometry) %>%
sf::st_as_sf()
# without geometry version of country wise centroids dataset and adding a label_plt column
country_wise_centroids_sans_geom <- country_wise_centroids %>%
sf::st_drop_geometry() %>%
dplyr::as_tibble() %>%
dplyr::mutate(label_plt = ifelse(num_studies == 0, "", num_studies))
# plotting the choropleth
if(length(input$countries_fig3) == 0){
stop("Please select atleast one country to proceed.")
} else if(("all" %in% input$countries_fig3) & (length(input$countries_fig3)) > 1){
stop("Please choose either multiple countries, or 'all', but not both")
}
if("all" %in% input$countries_fig3){
# options(repr.plot.width = 50, repr.plot.height = 50)
geographic_dist_graph <- world_shp_epi_color %>%
ggplot2::ggplot() +
ggplot2::geom_sf(mapping = ggplot2::aes(fill = avg_pm2.5_2021, group = avg_pm2.5_2021), color = "darkgrey") +
ggplot2::geom_sf(data = world_shp_epi_color %>% dplyr::filter(num_studies > 0) %>% sf::st_as_sf(), color = "black", size = 6, fill = "transparent") +
colorspace::scale_fill_continuous_sequential(palette = "YlOrRd", name = expression("Annual Average" ~ PM[2.5] ~ "in 2021 (in µg/m³)")) +
ggplot2::geom_jitter(data = country_wise_centroids_sans_geom, mapping = aes(x = x_coord, y = y_coord, size = num_studies, color = color_circle)) +
ggplot2::geom_text(data = country_wise_centroids_sans_geom, mapping = aes(x = x_coord, y = y_coord, label = label_plt), size = 3, color = "white", size = 2) +
ggplot2::theme(legend.position = "bottom") +
ggplot2::scale_size(breaks = c(1, 5, 10, 20, 30), range = c(1, 12),
guide = ggplot2::guide_legend(override.aes = list(color = "darkgrey"))) +
ggplot2::labs(caption = stringr::str_wrap("*The size of the circles is directly proportional to the total number of times a given country has been included
in a study (any study, not necessarily PM2.5 specific - mentioned as the number in the circle). Countries with atleast 1 study are highlighted (black borders)."), width = 30,
size = "Number of Studies") +
ggplot2::scale_color_manual(values = c("No Studies" = "cadetblue3", "Atleast 1 studys" = "darkolivegreen"), name = "") +
ggthemes::theme_map() +
ggplot2::ggtitle(expression("Country wise distribution of epi studies and Annual Average" ~PM[2.5] ~ "pollution (in µg/m³)")) +
ggplot2::theme(plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.position = "bottom",
legend.background = ggplot2::element_rect(color = "black"),
legend.justification = c(0.5, 3),
plot.title = ggplot2::element_text(hjust = 0.5, size = 16))
return(geographic_dist_graph)
} else if ((("all" %in% input$countries_fig3) == FALSE) & (length(input$countries_fig3) >= 1)) {
geographic_dist_graph <- world_shp_epi_color %>%
dplyr::filter(country %in% input$countries_fig3) %>%
ggplot2::ggplot() +
ggplot2::geom_sf(mapping = ggplot2::aes(fill = avg_pm2.5_2021, group = avg_pm2.5_2021), color = "darkgrey") +
ggplot2::geom_sf(data = world_shp_epi_color %>% dplyr::filter(num_studies > 0, country %in% input$countries_fig3) %>% sf::st_as_sf(), color = "black", size = 6, fill = "transparent") +
colorspace::scale_fill_continuous_sequential(palette = "YlOrRd", name = expression("Annual Average" ~ PM[2.5] ~ "in 2021 (in µg/m³)")) +
ggplot2::geom_jitter(data = country_wise_centroids_sans_geom %>% dplyr::filter(country %in% input$countries_fig3), mapping = aes(x = x_coord, y = y_coord, size = num_studies, color = color_circle)) +
ggplot2::geom_text(data = country_wise_centroids_sans_geom %>% dplyr::filter(country %in% input$countries_fig3), mapping = aes(x = x_coord, y = y_coord, label = label_plt), size = 3) +
ggplot2::theme(legend.position = "bottom") +
ggplot2::scale_size(breaks = c(1, 5, 10, 20, 30), range = c(1, 12),
guide = ggplot2::guide_legend(override.aes = list(color = "darkgrey"))) +
ggplot2::labs(caption = stringr::str_wrap("*The size of the circles is directly proportional to the total number of times a given country has been included
in a study (any study, not necessarily PM2.5 specific - mentioned as the number in the circle). Countries with atleast 1 study are highlighted (black borders)."), width = 30,
size = "Number of Studies") +
ggplot2::scale_color_manual(values = c("No Studies" = "cadetblue3", "Atleast 1 studys" = "darkolivegreen"), name = "") +
ggthemes::theme_map() +
ggplot2::ggtitle(expression("Country wise distribution of epi studies and Annual Average" ~PM[2.5] ~ "pollution (in µg/m³)")) +
ggplot2::theme(plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.position = "bottom",
legend.background = ggplot2::element_rect(color = "black"),
legend.justification = c(0.5, 3),
plot.title = ggplot2::element_text(hjust = 0.5, size = 16))
return(geographic_dist_graph)
}
})
#> Geographic Distribution of Studies table
# output$geographic_dist_studies_table <- shiny::renderDataTable({
# if(length(input$countries_fig3) == 0){
# stop("Please select atleast one country to proceed.")
# } else if(("all" %in% input$countries_fig3) & (length(input$countries_fig3)) > 1){
# stop("Please either choose one/multiple countries, or 'all', but not both")
# }
#
# if("all" %in% input$countries_fig3){
# dt_all <- country_wise_centroids %>%
# sf::st_drop_geometry() %>%
# dplyr::filter(num_studies != 0)
# DT::datatable(dt_all, options = list(pageLength = 6))
# } else if ((("all" %in% input$countries_fig3) == FALSE) & (length(input$countries_fig3) >= 1)){
# dt_filter <- country_wise_centroids %>%
# sf::st_drop_geometry() %>%
# dplyr::filter(num_studies !=0, country %in% input$countries_fig3)
# DT::datatable(dt_filter, options = list(pageLength = 6))
# }
# })
#> Epi studies over time graph
output$epi_studies_over_time_graph <- shiny::renderPlot({
epi %>%
dplyr::distinct(paper_uid, .keep_all = TRUE) %>%
dplyr::group_by(publishing_year) %>%
dplyr::summarise(total_studies = dplyr::n()) %>%
dplyr::ungroup() %>%
dplyr::filter(publishing_year >= input$year_range[1], publishing_year <= input$year_range[2], total_studies != "NA", !is.na(total_studies)) %>%
ggplot2::ggplot() +
ggplot2::geom_line(mapping = ggplot2::aes(x = publishing_year, y = total_studies), lwd = 1.2, color = "cornflowerblue") +
ggplot2::labs(x = "Year", y = "Total Number of Studies",
caption = stringr::str_wrap("*This graph displays the total number of AQ epi studies (all studies, not PM₂.₅ specific) published over time."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7)) %>%
return()
})
#> Epi studies over time table
# output$aq_epi_studies_over_time <- shiny::renderDataTable({
# dt_filter_epi_over_time <- epi %>%
# dplyr::distinct(paper_uid, .keep_all = TRUE) %>%
# dplyr::group_by(publishing_year) %>%
# dplyr::summarise(total_studies = dplyr::n()) %>%
# dplyr::ungroup() %>%
# dplyr::filter(publishing_year >= input$year_range[1], publishing_year <= input$year_range[2], total_studies != "NA", !is.na(total_studies))
# DT::datatable(dt_filter_epi_over_time, options = list(pageLength = 6))
#
# })
#> Continent Wise Distribution of Duration of Study graph
output$continent_wise_dist_duration_study_graph <- shiny::renderPlot({
# creating a copy of epi database, with China listed as a separate continent, so that we can plot the China panel and China - Asia panel
# adding logic to check for missing continents and automatically adding missing panels for that
if(sum(missing_continents_logical) == 0){
continent_wise_study_duration_graph_summary_table <- epi_tmp %>%
dplyr::filter(continent != "NA", !is.na(continent), study_duration != "NA", !is.na(study_duration)) %>%
dplyr::group_by(paper_uid, continent) %>%
dplyr::summarise(average_study_duration = mean(study_duration, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::filter(continent != "Oceania") %>%
#dplyr::mutate(continent = as.factor(continent)) %>%
dplyr::mutate(order_continent = ifelse(continent == "China", 2, 0),
order_continent = ifelse(continent == "Asia - China", 1, order_continent),
order_continent = ifelse(continent == "Europe", 5, order_continent),
order_continent = ifelse(continent == "North America", 6, order_continent),
order_continent = ifelse(continent == "South America", 4, order_continent),
order_continent = ifelse(continent == "Africa", 3, order_continent)) %>%
dplyr::mutate(continent = ifelse(continent == "China", "China (1402.5 million people)", continent),
continent = ifelse(continent == "Africa", "Africa (1373.8 million people)", continent),
continent = ifelse(continent == "North America", "North America (589.2 million people)", continent),
continent = ifelse(continent == "South America", "South America (429.2 million people)", continent),
continent = ifelse(continent == "Europe", "Europe (740.8 million people)", continent),
continent = ifelse(continent == "Asia - China", "Asia - China (3161.5 million people)", continent))
} else{
for(i in 1:length(missing_continents)){
if(i == 1){
continent_wise_study_duration_graph_summary_table <- epi_tmp %>%
dplyr::filter(continent != "NA", !is.na(continent), study_duration != "NA", !is.na(study_duration)) %>%
dplyr::group_by(paper_uid, continent) %>%
dplyr::summarise(average_study_duration = mean(study_duration, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::add_row(continent = missing_continents[i])
} else{
continent_wise_study_duration_graph_summary_table <- continent_wise_study_duration_graph_summary_table %>%
dplyr::add_row(continent = missing_continents[i])
}
}
continent_wise_study_duration_graph_summary_table <- continent_wise_study_duration_graph_summary_table %>%
dplyr::filter(continent != "Oceania") %>%
# dplyr::mutate(continent = as.factor(continent)) %>%
dplyr::mutate(order_continent = ifelse(continent == "China", 2, 0),
order_continent = ifelse(continent == "Asia - China", 1, order_continent),
order_continent = ifelse(continent == "Europe", 5, order_continent),
order_continent = ifelse(continent == "North America", 6, order_continent),
order_continent = ifelse(continent == "South America", 4, order_continent),
order_continent = ifelse(continent == "Africa", 3, order_continent)) %>%
dplyr::mutate(continent = ifelse(continent == "China", "China (1402.5 million people)", continent),
continent = ifelse(continent == "Africa", "Africa (1373.8 million people)", continent),
continent = ifelse(continent == "North America", "North America (589.2 million people)", continent),
continent = ifelse(continent == "South America", "South America (429.2 million people)", continent),
continent = ifelse(continent == "Europe", "Europe (740.8 million people)", continent),
continent = ifelse(continent == "Asia - China", "Asia - China (3161.5 million people)", continent))
}
#----------------
# plot the figure with empty Africa panel
if(length(input$continent_list) == 0){
stop("Please select atleast one continent to proceed.")
}
if(input$plot_type_fig5 == "Histogram (Frequency)"){
continent_wise_study_duration_graph <- continent_wise_study_duration_graph_summary_table %>%
dplyr::filter(continent %in% input$continent_list) %>%
ggplot2::ggplot() +
ggplot2::geom_histogram(mapping = ggplot2::aes(x = average_study_duration, fill = continent, group = continent), color = "white", position = "identity", binwidth = 2) +
ggplot2::labs(x = "Study Duration", y = "Count",
caption = stringr::str_wrap("*This graph displays distribution by continent (and for China, Asia - China) for all studies (not PM2.5 specific)."), width = 10,
title = expression("Continent (and China) wise Study Duration Distribution and 2021 Annual Average" ~ PM[2.5] ~ "(in µg/m³)"),
subtitle = "(Ordered from most polluted to least polluted)") +
ggplot2::scale_x_continuous(breaks = seq(0, 40, 10)) +
ggthemes::theme_hc() +
ggplot2::theme(legend.title = element_blank()) +
ggplot2::facet_wrap(~forcats::fct_reorder(continent, order_continent), nrow = 1) +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
plot.title = element_text(size = 14, hjust = 0.5),
plot.subtitle = element_text(size = 8, hjust = 0.5),
strip.background = element_rect(fill = "floralwhite"),
strip.text = element_text(size = 8),
legend.background = element_rect(color = "black")) +
ggplot2::scale_fill_manual(values = c("North America (589.2 million people)" = "#FFBA00", "Europe (740.8 million people)" = "#FF9600",
"South America (429.2 million people)" = "#FF6908", "Africa (1373.8 million people)" = "#E63D23", "China (1402.5 million people)" = "#BD251C",
"Asia - China (3161.5 million people)" = "#8C130E")) +
ggplot2::scale_y_continuous(breaks = seq(0, 10, 2))
} else if (input$plot_type_fig5 == "Histogram (Density)"){
continent_wise_study_duration_graph <- continent_wise_study_duration_graph_summary_table %>%
dplyr::filter(continent %in% input$continent_list) %>%
ggplot2::ggplot() +
ggplot2::geom_histogram(mapping = ggplot2::aes(x = average_study_duration, y = ..density.., fill = continent, group = continent), color = "white", binwidth = 2) +
ggplot2::labs(x = "Study Duration", y = "Density",
caption = stringr::str_wrap("*This graph displays distribution by continent (and for China, Asia - China) for all studies (not PM2.5 specific)."), width = 10,
title = expression("Continent (and China) wise Study Duration Distribution and 2021 Annual Average" ~ PM[2.5] ~ "(in µg/m³)"),
subtitle = "(Ordered from most polluted to least polluted)") +
ggplot2::scale_x_continuous(breaks = seq(0, 40, 10)) +
ggthemes::theme_hc() +
ggplot2::theme(legend.title = element_blank()) +
ggplot2::facet_wrap(~forcats::fct_reorder(continent, order_continent), nrow = 1) +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
plot.title = element_text(size = 14, hjust = 0.5),
plot.subtitle = element_text(size = 8, hjust = 0.5),
strip.background = element_rect(fill = "floralwhite"),
strip.text = element_text(size = 8),
legend.background = element_rect(color = "black")) +
ggplot2::scale_fill_manual(values = c("North America (589.2 million people)" = "#FFBA00", "Europe (740.8 million people)" = "#FF9600",
"South America (429.2 million people)" = "#FF6908", "Africa (1373.8 million people)" = "#E63D23", "China (1402.5 million people)" = "#BD251C",
"Asia - China (3161.5 million people)" = "#8C130E"))
}
return(continent_wise_study_duration_graph)
})
#> Distribution of lower and upper limits of PM2.5 exposure range
# Note: This is PM2.5 specific and different countries in a pooled study are counted separately.
output$pm2.5_ll_ul_dist_graph <- shiny::renderPlot({
# create long dataset
epi_long <- epi %>%
dplyr::filter(!is.na(mean_pm2.5), mean_pm2.5 != "NA", non_pm2.5 == 0) %>%
tidyr::pivot_longer(cols = contains("pm2.5_exposure"), names_to = "exposure_type", values_to = "exposure_value") %>%
dplyr::select(paper_uid, exposure_type, exposure_value) %>%
dplyr::filter(!is.na(exposure_value)) %>%
dplyr::mutate(exposure_type = ifelse(exposure_type == "pm2.5_exposure_ll", "PM₂.₅ exposure in µg/m³ (Lower Limit)", "PM₂.₅ exposure in µg/m³ (Upper Limit)"))
if(input$plot_type_fig6 == "Histogram"){
epi_long %>%
dplyr::group_by(paper_uid, exposure_type) %>%
dplyr::summarise(exposure_value = ifelse(exposure_type == "pm2.5_exposure_ll", min(exposure_value, na.rm = TRUE), max(exposure_value, na.rm = TRUE))) %>%
ggplot2::ggplot() +
ggplot2::geom_histogram(mapping = ggplot2::aes(x = exposure_value, fill = exposure_type, group = exposure_type), position = "identity", alpha = 0.4, color = "white") +
ggplot2::labs(x = "PM₂.₅ concentration (in µg/m³)",
y = "Count",
fill = "Exposure Type",
caption = stringr::str_wrap("*This graph takes into account only those PM₂.₅ studies that have atleast one of 'pm2.5 lower limit value', 'pm2.5 upper limit value'."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.key = element_rect(color = "black"),
legend.text = element_text(size = 8)) +
viridis::scale_fill_viridis(discrete = TRUE)
} else if(input$plot_type_fig6 == "Density"){
epi_long %>%
dplyr::group_by(paper_uid, exposure_type) %>%
dplyr::summarise(exposure_value = ifelse(exposure_type == "pm2.5_exposure_ll", min(exposure_value, na.rm = TRUE), max(exposure_value, na.rm = TRUE))) %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = exposure_value, fill = exposure_type, group = exposure_type), alpha = 0.6, position = "identity") +
ggplot2::labs(x = "PM₂.₅ concentration (in µg/m³)",
y = "Density",
fill = "Exposure Type",
caption = stringr::str_wrap("*This graph takes into account only those PM₂.₅ studies that have atleast one of 'pm2.5 lower limit value', 'pm2.5 upper limit value'."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.text = element_text(size = 8)) +
viridis::scale_fill_viridis(discrete = TRUE)
}
})
#> Country Wise Distribution of Cohort Size Graph
# Different countries in a pooled study are counted separately, but this is not a PM2.5 specific graph.
output$country_wise_dist_log_cohort_size_graph_1 <- plotly::renderPlotly({
if(length(input$countries_list_fig7) == 0){
stop("Please select atleast one country to proceed.")
} else if(("all" %in% input$countries_list_fig7) & (length(input$countries_list_fig7)) > 1){
stop("Please either choose one/multiple countries, or 'all', but not both")
}
if("all" %in% input$countries_list_fig7){
epi %>%
dplyr::filter(!is.na(cohort_size), cohort_size != "NA") %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = cohort_size, fill = country, group = country), alpha = 0.5) +
ggplot2::scale_x_log10() +
ggthemes::theme_hc() +
ggplot2::labs(caption = stringr::str_wrap("*This graph takes into account only those studies (can include non-PM₂.₅ studies) in which a cohort/sample size is available."), width = 10,
x = "Cohort Size",
y = "Density",
fill = "Country") +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.background = element_rect(),
legend.title = element_text(hjust = 0.5)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
} else if ((("all" %in% input$countries_list_fig7) == FALSE) & (length(input$countries_list_fig7) >= 1)){
epi %>%
dplyr::filter(!is.na(cohort_size), cohort_size != "NA") %>%
dplyr::filter(country %in% input$countries_list_fig7) %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = cohort_size, fill = country, group = country), alpha = 0.5) +
ggplot2::scale_x_log10() +
ggplot2::labs(caption = stringr::str_wrap("*This graph takes into account only those studies (can include non-PM₂.₅ studies) in which a cohort/sample size is available."), width = 10,
x = "Cohort Size",
y = "Density",
fill = "Country") +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.background = element_rect(),
legend.title = element_text(hjust = 0.5)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
}
})
#> Distribution of lower limit and upper limit of ages covered in this analysis
# This is not a PM2.5 specific graph and different countries in a cohort study are counted separately.
output$age_ll_ul_graph <- shiny::renderPlot({
epi_age_dist <- epi %>%
dplyr::filter(((!is.na(cohort_age_ll)) | (cohort_age_ll != "NA")) & ((!is.na(cohort_age_ul)) | (cohort_age_ul != "NA"))) %>%
tidyr::pivot_longer(cols = contains("cohort_age"), names_to = "age_dist_type", values_to = "age_value") %>%
dplyr::select(age_dist_type, age_value) %>%
dplyr::filter(!is.na(age_value)) %>%
dplyr::mutate(age_dist_type = ifelse(age_dist_type == "cohort_age_ll", "Cohort Age (Lower Limit)", "Cohort Age (Upper Limit)"))
if(input$plot_type_fig6 == "Histogram"){
epi_age_dist %>%
ggplot2::ggplot() +
ggplot2::geom_histogram(mapping = ggplot2::aes(x = age_value, fill = age_dist_type, group = age_dist_type), alpha = 0.5, position = "identity", color = "white") +
ggplot2::scale_x_continuous(breaks = seq(0, 130, 20)) +
ggplot2::scale_y_continuous(breaks = seq(0, 15, 2)) +
ggplot2::labs(x = "Age",
fill = "Age Distribution Type",
caption = stringr::str_wrap("*This graph takes into account those studies (can include non-PM₂.₅ studies) that have atleast one of 'age lower limit', 'age upper limit'."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.text = element_text(size = 8)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
} else if(input$plot_type_fig6 == "Density"){
epi_age_dist %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = age_value, fill = age_dist_type, group = age_dist_type), alpha = 0.6, color = "black") +
ggplot2::scale_x_continuous(breaks = seq(0, 130, 20)) +
ggplot2::labs(x = "Age",
caption = stringr::str_wrap("*This graph takes into account those studies (can include non-PM₂.₅ studies) that have atleast one of 'age lower limit', 'age upper limit'."), width = 10) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.text = element_text(size = 8)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
}
})
#> Country wise distribution of study duration
# This is not a PM2.5 specific graph and in this all countries in a given pooled study are counted separately.
# Country wise distribution
output$country_wise_dist_study_duration_1 <- plotly::renderPlotly({
if(length(input$countries_list_fig7) == 0){
stop("Please select atleast one country to proceed.")
}
if(("all" %in% input$countries_list_fig7) & (length(input$countries_list_fig7)) > 1){
stop("Please either choose one/multiple countries, or 'all', but not both")
}
if("all" %in% input$countries_list_fig7){
epi %>%
dplyr::filter(!is.na(study_duration), study_duration != "NA") %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = study_duration, fill = country, group = country), alpha = 0.5) +
ggplot2::scale_x_continuous(breaks = seq(0, 40, 5)) +
ggthemes::theme_hc() +
ggplot2::labs(caption = stringr::str_wrap("*This graph takes into account only those studies (can include non-PM₂.₅ studies) in which study duration is available."), width = 10,
fill = "Country",
x = "Study Duration",
y = "Density") +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.background = element_rect(),
legend.title = element_text(hjust = 0.5)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
} else if ((("all" %in% input$countries_list_fig7) == FALSE) & (length(input$countries_list_fig7) >= 1)){
epi %>%
dplyr::filter(!is.na(study_duration), study_duration != "NA") %>%
dplyr::filter(country %in% input$countries_list_fig7) %>%
ggplot2::ggplot() +
ggplot2::geom_density(mapping = ggplot2::aes(x = study_duration, fill = country, group = country), alpha = 0.5) +
ggplot2::labs(caption = stringr::str_wrap("*This graph takes into account only those studies (can include non-PM₂.₅ studies) in which study duration is available."), width = 10,
x = "Study Duration",
y = "Density",
fill = "Country") +
ggplot2::scale_x_continuous(breaks = seq(0, 40, 5)) +
ggthemes::theme_hc() +
ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
axis.line.x = ggplot2::element_line(color = "black"),
plot.caption = ggplot2::element_text(hjust = 0, size = 7),
legend.background = element_rect(),
legend.title = element_text(hjust = 0.5)) +
viridis::scale_fill_viridis(discrete = TRUE) %>%
return()
}
})
#> pm2.5 and global population distribution graph
# output$pm2.5_expo_glob_pop_graph <- shiny::renderPlot({
#
# # percent population in pollution buckets
# tot_pop_in_bucket <- aqli_color %>%
# dplyr::filter(pm2020 >= input$pm2.5_bucket[1], pm2020 <= input$pm2.5_bucket[2]) %>%
# dplyr::summarise(tot_pop = sum(population, na.rm = TRUE)) %>%
# unlist()
#
# world_pop <- aqli_color %>%
# dplyr::summarise(tot_pop = sum(population, na.rm = TRUE)) %>%
# unlist()
#
# percent_pop_in_bucket <- round((tot_pop_in_bucket/world_pop)*100, 1)
#
# # percent studies in the given pollution bucket
# tot_studies_in_bucket <- epi %>%
# dplyr::filter(!is.na(mean_pm2.5), mean_pm2.5 != "NA", non_pm2.5 == 0) %>%
# dplyr::group_by(paper_uid) %>%
# dplyr::summarise(mean_pm2.5 = mean(mean_pm2.5, na.rm = TRUE)) %>%
# dplyr::filter(mean_pm2.5 >= input$pm2.5_bucket[1], mean_pm2.5 <= input$pm2.5_bucket[2]) %>%
# nrow()
#
# tot_studies_overall <- nrow(epi %>%
# dplyr::distinct(paper_uid, .keep_all = TRUE) %>%
# dplyr::filter(!is.na(mean_pm2.5), mean_pm2.5 != "NA", non_pm2.5 == 0))
#
# percent_studies_in_bucket <- round((tot_studies_in_bucket/tot_studies_overall)*100, 1)
#
# # creating a dataframe for plotting
# pm2.5_expo_glob_pop_df <- tibble::tibble(pm2.5_bucket = c(stringr::str_c(input$pm2.5_bucket[1], "-", input$pm2.5_bucket[2], "µg/m³"),
# stringr::str_c(input$pm2.5_bucket[1], "-", input$pm2.5_bucket[2], "µg/m³")),
# prop_in_bucket = c(percent_pop_in_bucket, percent_studies_in_bucket),
# type_of_prop = c("Percent Population in PM2.5 bucket", "Percent Studies in PM2.5 bucket"))
#
# pm2.5_expo_glob_pop_df$type_of_prop <- as.factor(pm2.5_expo_glob_pop_df$type_of_prop)
#
# pm2.5_expo_glob_pop_df %>%
# ggplot2::ggplot() +
# ggplot2::geom_col(mapping = ggplot2::aes(x = pm2.5_bucket, y = prop_in_bucket, fill = type_of_prop, group = type_of_prop), position = position_dodge(), width = 0.4) +
# ggplot2::scale_fill_manual(values = c("Percent Population in PM2.5 bucket" = "grey", "Percent Studies in PM2.5 bucket" = "cornflowerblue"), labels = c("Proportion of World Population in Bucket", "Proportion of Studies Completed in Bucket")) +
# ggplot2::labs(x = expression("Mean" ~ PM[2.5] ~ "bucket (in µg/m³)"), y = "Percentage", fill = "",
# caption = stringr::str_wrap("*This graph 'only' takes into account the PM₂.₅ specific studies. For multi-country (pooled) studies, it averages the mean PM2.5 values, across all countries."), width = 17) +
# ggthemes::theme_hc() +
# ggplot2::theme(axis.line.y = ggplot2::element_line(color = "black"),
# axis.line.x = ggplot2::element_line(color = "black"),
# plot.caption = ggplot2::element_text(hjust = 0, size = 7),
# legend.position = "bottom",
# axis.title.x = element_text(size = 11),
# axis.title.y = element_text(size = 11),
# legend.text = element_text(size = 9))
# viridis::scale_fill_viridis(discrete = TRUE) %>%
# return()
#
# })
#> Render the underlying data as the Data Table
# output$underlying_data_table_3 <- shiny::renderTable({
# # epi
# })
}
shiny::shinyApp(ui, server)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.