├── .gitignore ├── README.md ├── app ├── .gitignore ├── app.R ├── data │ ├── IND_remittances.csv │ ├── Katrina_Migration.csv │ └── us-states.geojson ├── rsconnect │ └── shinyapps.io │ │ └── willbpayne │ │ ├── relational_reprojection_platform.dcf │ │ └── rrp_beta.dcf └── www │ └── RRP_style.css ├── docs └── relationalreprojection.jpeg ├── relational_reprojection_platform.Rproj └── rsconnect └── shinyapps.io └── willbpayne └── relational_reprojection_platform.dcf /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | .DS_Store 6 | PCP_functions 7 | testPlot3.svg 8 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Relational Reprojection Platform 2 | 3 | While quantitative geography has long acknowledged that non-Cartesian spaces and distances are often more appropriate for analyzing and visualizing real-world data and complex spatial phenomena, commonly available GIS software solutions make working with non-linear distances extremely difficult. Current prevailing GIS logics force specific kinds of data standards not relevant to many exploratory spatial analyses. 4 | 5 | Our **Relational Reprojection Platform (RRP)** fills this gap with a simple stereographic projection engine centering any given data point to the rest of the set, and transforming great circle distances from this point to the other locations using a variety of non-linear functions as options with particular use cases in mind. This method of re-projecting data allows users to quickly and easily explore spatial relationships within complex spatial data sets. 6 | 7 | Our initial release is a [Shiny app](https://willbpayne.shinyapps.io/rrp_beta/) that allows users to upload simple CSV files with geographic coordinates and data columns and minimal cleaning and explore a variety of spatial transformations of their data. We hope this heuristic tool will enhance the exploratory stages of social research using spatial data. 8 | 9 | ![RRP](docs/relationalreprojection.jpeg) 10 | -------------------------------------------------------------------------------- /app/.gitignore: -------------------------------------------------------------------------------- 1 | test.svg 2 | -------------------------------------------------------------------------------- /app/app.R: -------------------------------------------------------------------------------- 1 | # This is a Shiny web application. You can run the application by clicking 2 | # the 'Run App' button above. Find out more about Shiny here: http://shiny.rstudio.com/ 3 | 4 | library(base) 5 | library(datasets) 6 | library(dplyr) 7 | library(forcats) 8 | library(geojsonsf) # read in GeoJSON for background 9 | library(geosphere) # where we get bearing 10 | library(ggforce) # to plot circles 11 | library(ggplot2) 12 | library(ggrepel) 13 | library(gmt) # actually for geodist 14 | library(graphics) 15 | library(grDevices) 16 | library(httpuv) # ditto 17 | library(lubridate) 18 | library(methods) 19 | library(pracma) 20 | library(purrr) 21 | library(readr) 22 | library(rsconnect) # for web hosting 23 | library(scico) # for newer graph colors (colorblind friendly, better for continuous) 24 | library(sf) # for geometry transformations 25 | #library(shiny) 26 | #library(shinylive) # to host this on GitHub Pages 27 | library(stats) 28 | library(stringr) 29 | library(svglite) 30 | library(tibble) 31 | library(tidyr) 32 | library(tidyverse) 33 | library(useful) # for cartesian conversions 34 | library(utils) 35 | 36 | options(ggrepel.max.overlaps = Inf) 37 | 38 | # libraries we thought we needed but don't use yet 39 | 40 | #library(sp) # might not need 41 | #library(tools) #to learn file extension 42 | #library(jsonlite) # for loading in json files 43 | #library(geojson) # for loading geojson (need this for geojsonio to work) 44 | #library(geojsonio) # for loading geojson 45 | 46 | dataFile <- "data/Katrina_Migration.csv" # for testing 47 | 48 | ######################################## 49 | ## UI PARTY TONIGHT! ## 50 | ######################################## 51 | 52 | ui <- fluidPage(theme = "RRP_style.css", 53 | div(style = "padding: 10px", h1("Relational Reprojection Platform (BETA)"), 54 | "Created by ", tags$a(href="https://willbpayne.com", "Dr. Will B. Payne, ", target="blank"), 55 | "Rutgers University ", tags$a(href="mailto:will.b.payne@rutgers.edu", "(Contact)"), 56 | "and ", tags$a(href="https://cmes.fas.harvard.edu/people/eve-mcglynn", "Dr. Evangeline McGlynn, ", target="blank"), 57 | "Harvard University ", tags$a(href="mailto:emcglynn@fas.harvard.edu", "(Contact)."), 58 | h5("RRP reprojects geographic point datasets to focus on a central point (azimuth) with a variety of non-linear distance transformations for visualization (including via the 'Export SVG' button below)."), 59 | h5("Upload a CSV with latitude and longitude columns (and an 'isCTR' column with a single value of 1 for the center) and at least one numeric variable to assign to point area, as well as a GeoJSON polygon background layer."), 60 | h5("Sample dataset shows ACS 2006 1-year estimates of people who moved from Louisiana to other U.S. states (the year after Hurricane Katrina). Polygon data from", 61 | tags$a(href="https://www.naturalearthdata.com", "Natural Earth.", target="blank"))), # Application title and credit 62 | sidebarLayout(sidebarPanel(width=5, 63 | div(class = "panel", 64 | div(style = "font-size: 14px; padding: 0px; margin-top: -5px;", 65 | fluidRow( 66 | column(5,fileInput("uploadFile"," 1. Select Data File (CSV)", multiple = FALSE, accept = NULL)), 67 | column(2,checkboxInput("basemap","Show Polygons?", value = TRUE, width = NULL)), 68 | column(5,fileInput("uploadPolygon"," 2. Select Polygons (GeoJSON)", multiple = FALSE, accept = NULL)) 69 | )), 70 | div(style = "font-size: 14px; padding: 10px 0px; margin-top: -35px", 71 | fluidRow( 72 | column(12,radioButtons("interpMeth", "3. Choose Distance Interpolation Method", choices = c("Great Circle","Square Root","Cube Root","Log","Custom"), inline = TRUE, width = "100%", selected = "Square Root")) 73 | )), 74 | div(style = "font-size: 14px; padding: 10px 0px; margin-top: -20px", 75 | fluidRow( 76 | column(6, uiOutput("ValChoicesFromServer")), 77 | column(6,sliderInput("SymbolSizeRange", "Point Size Range", 0, 50, c(1, 25), ticks = TRUE) 78 | ))), 79 | div(style = "font-size: 14px; padding: 10px 0px; margin-top: -30px", 80 | fluidRow( 81 | column(6, uiOutput("NameChoicesFromServer")), 82 | column(6, sliderInput("label_size", "Label Size", 1, 10, 3, ticks = TRUE) 83 | ))), 84 | div(style = "font-size: 14px; padding: 10px 0px; margin-top: -40px", 85 | fluidRow( 86 | column(4,checkboxInput("labelsOn", "Show Labels?", value = TRUE, width = NULL)), 87 | column(4,checkboxInput("removeZeroes","Remove Zero Values?", value = TRUE, width = NULL)), 88 | column(4,checkboxInput("latlon","Show Lat/Lon Plot", value = FALSE, width = NULL)) 89 | )), 90 | div(style = "font-size: 14px; padding: 10px 0px; margin-top: -25px", 91 | fluidRow( 92 | column(6,selectInput("plotTheme", 93 | label = NULL, c("Light Theme", "Dark Theme", "Mono Theme"), 94 | selected = "Light", multiple = FALSE, selectize = TRUE, width = "100%", size = NULL)), 95 | column(6, 96 | div(style = "margin-bottom:15px", 97 | downloadButton('downloadPlot','Export SVG'))), 98 | )), 99 | conditionalPanel(condition = "input.interpMeth == 'Custom'", 100 | uiOutput("CustomDistanceSlider")), 101 | conditionalPanel(condition = "input.latlon == 1", 102 | div(style = "font-size: 14px; padding: 10px 0px; margin-top: -35p", 103 | fluidRow( 104 | column(12, plotOutput("distPlot", height = "250px")), #plot size 105 | ))) 106 | )), 107 | #end of sidebar panel, end of class panel div 108 | mainPanel(width=7,div(class = "mainP", htmlOutput("newdfparser"), plotOutput("geoPlot", height = "800px") 109 | )) 110 | ) 111 | ) 112 | 113 | #div(style = "font-size: 14px; padding: 10px 0px; margin-top: -50px", 114 | # fluidRow( 115 | # column(6,checkboxInput("centerOn", "Show Center?", value = FALSE, width = NULL)), 116 | # column(6, checkboxInput("HideOverlappingLabels","Hide Overlapping Labels?", value = TRUE, width = NULL)) 117 | # )), 118 | 119 | 120 | #div(style = "font-size: 14px; padding: 10px 0px; margin:3%; margin-top: -35px", 121 | # fluidRow( 122 | # column(6,sliderInput("SymbolSizeRange", "Point Size Range", 0, 50, c(1, 25), ticks = TRUE) 123 | # ))), 124 | # Radio buttons for interpolation method 125 | #div(style = "font-size: 14px; padding: 10px 0px; margin:3%; margin-top: -25px", 126 | # fluidRow( 127 | # column(12,radioButtons("valTransMeth","Value (Symbol Size) Interpolation", choices = c("None", "Square Root", "Log"), inline = TRUE, width = "100%", selected = "Square Root")) 128 | # )), 129 | 130 | #div(style = "font-size: 14px; padding: 10px 0px; margin:3%; margin-top: -40px", 131 | # fluidRow( 132 | # column(12,radioButtons("interpMeth", "Distance Interpolation", choices = c("Great Circle","Square Root","Cube Root","Log","Custom"), inline = TRUE, width = "100%", selected = "Square Root")) 133 | # )), 134 | 135 | ## If distance transformation radio button is on "Custom", show cut point slider 136 | 137 | 138 | 139 | 140 | ######################################## 141 | ## STOP, SERVER TIME! ## 142 | ######################################## 143 | 144 | server <- function(input, output) { 145 | 146 | output$distPlot <- renderPlot({ # the basic dot plot for sidebar 147 | 148 | if (is.null(input$uploadFile) == TRUE){ 149 | df <- read.csv(file = "data/Katrina_Migration.csv") 150 | } 151 | else{ 152 | uploadFileData <- input$uploadFile 153 | df <- read.csv(file = uploadFileData$datapath) 154 | } 155 | 156 | # if (is.null(input$uploadPolygon) == TRUE){ 157 | # polygon <- geojson_sf("data/us-states.geojson") 158 | # } 159 | # else{ 160 | # uploadPolygonData <- input$uploadPolygon 161 | # polygon <- geojson_sf(file = uploadPolygonData$datapath) 162 | # } 163 | 164 | par(bg = "#F0F0F0", #default color is #f5f5f5 165 | mgp=c(1.75,0.5,0), 166 | mar=c(4,4,4,4), 167 | col.lab="#404040", 168 | col.axis="#404040", 169 | fg="#404040") 170 | 171 | df3 <- df # cloning for non-destructive editing and with a different name than in dataframefinder() below 172 | latNames2 <- c("lat","Lat","LAT", "latitude", "Latitude", "LATITUDE", "y","Y", "coords.x2") # add as they come up 173 | lonNames2 <- c("lon","Lon","LON","long","Long","LONG","longitude", "Longitude", "LONGITUDE", "x","X", "coords.x1") 174 | 175 | for (col in 1:ncol(df)) { 176 | if (max(as.numeric(df[[col]]), na.rm = T) <= 90.0 177 | && min(as.numeric(df[[col]]), na.rm = T) >= -90.0 178 | && names(df)[[col]] %in% latNames2) # lat 179 | { 180 | df3$latitude <- as.numeric(df[[col]]) 181 | print("I found latitude!") 182 | } 183 | else{ 184 | if (max(as.numeric(df[[col]]), na.rm = T) <= 180.0 185 | && min(as.numeric(df[[col]]), na.rm = T) >= -180.0 186 | && names(df)[col] %in% lonNames2) # lon 187 | { 188 | df3$longitude <- as.numeric(df[[col]]) 189 | } 190 | } 191 | } 192 | 193 | plot(df3$longitude, df3$latitude, 194 | col = "#404040", 195 | xlab = "Longitude", 196 | ylab = "Latitude", 197 | tck = -.04, 198 | cex.axis = 0.7 199 | ) 200 | 201 | }) 202 | 203 | output$downloadPlot <- downloadHandler( 204 | filename = function(){paste("testPlot",'.svg',sep='')}, 205 | content = function(file){ 206 | ggsave(file, plot = last_plot(), 207 | height = 4000, 208 | width = 4000, 209 | units = "px") #distPlot the right thing to call here or p2? 210 | }) 211 | 212 | dataframefinder <- function() { # First reactive function! 213 | if(is.null(input$uploadFile) == TRUE){ 214 | found_df <- read.csv(file = dataFile) 215 | } else { 216 | n <- input$uploadFile 217 | found_df <- read.csv(file = n$datapath) 218 | } 219 | return(found_df) 220 | } 221 | 222 | polygonfinder <- function() { # Second reactive function! 223 | if(is.null(input$uploadPolygon) == TRUE){ 224 | polygon <- geojson_sf("data/us-states.geojson") 225 | } else { 226 | n <- input$uploadPolygon 227 | polygon <- geojson_sf(n$datapath) 228 | } 229 | return(polygon) 230 | } 231 | 232 | output$newdfparser <- renderText({ # New place to store reactive output 233 | parserOutputs <- dfparser(dataframefinder()) # run it once! 234 | if (parserOutputs[[2]] > 20037.5){ 235 | distanceWarning <- " (over half Earth's circumference; plot may be unreliable!)" 236 | } 237 | else { 238 | distanceWarning <- "" 239 | } 240 | paste(" Maximum distance in dataset: ", round(parserOutputs[[2]],0), "km",distanceWarning,"
Concentric circle spacing: ", round((parserOutputs[[2]] / 10),2), "km", "
", 241 | " Center point: ", parserOutputs[[5]], " ", 242 | " (Latitude: ", round(parserOutputs[[3]], 5), ", Longitude: ", round(parserOutputs[[4]], 5), ")
", sep='',collapse = "") 243 | }) 244 | 245 | dfparser <- function(selected_dataframe) { # First non-reactive function! We copied a bunch o code for this 246 | # ^^ works in concert with newdfparser 247 | df <- selected_dataframe 248 | df_ext <- ".csv" 249 | 250 | colListOrig <- colnames(df) # store column names for later 251 | latNames <- list("lat","Lat","LAT", "latitude", "Latitude", "LATITUDE", "y","Y", "coords.x2") # add as they come up 252 | lonNames <- list("lon","Lon","LON","long","Long","LONG","longitude", "Longitude", "LONGITUDE", "x","X", "coords.x1") 253 | nameChoices <- c() # changed from a list to a vector 254 | valChoices <- c() # ditto 255 | 256 | df2 <- df # cloning df for non-destructive editing 257 | 258 | latflag <- 0 # need these here for the column detection 259 | lonflag <- 0 260 | ctrBinflag <- 0 261 | nameFlag <- 0 262 | valflag <- 0 263 | 264 | for (col in 1:ncol(df)) { 265 | if (typeof(df[[col]]) == "double" 266 | && latflag == 0 267 | && max(as.numeric(df[[col]]), na.rm = T) <= 90.0 268 | && min(as.numeric(df[[col]]), na.rm = T) >= -90.0 269 | && names(df)[[col]] %in% latNames) # lat 270 | { 271 | df2$lat <- df[[col]] 272 | latflag <- 1} 273 | else{ 274 | if (typeof(df[[col]]) == "double" 275 | && lonflag == 0 276 | && max(as.numeric(df[[col]]), na.rm = T) <= 180.0 277 | && min(as.numeric(df[[col]]), na.rm = T) >= -180.0 278 | && names(df)[col] %in% lonNames) # lon 279 | { 280 | df2$lon <- df[[col]] 281 | lonflag <- 1} 282 | else{ 283 | if (typeof(df[[col]]) != "character" 284 | && typeof(df[[col]]) != "list" 285 | && min(as.numeric(df[[col]]), na.rm = T) == 0 286 | && max(as.numeric(df[[col]]), na.rm = T) == 1 287 | && sum(as.numeric(df[[col]]), na.rm = T) == 1) # ctrBin 288 | { df2$ctrBin <- as.logical(df[[col]]) 289 | ctrBinflag <- 1} 290 | else{ 291 | if (typeof(df[[col]]) == "character" # catches name and name_long 292 | || is.factor(df[[col]]) == T) # valName 293 | { if (nameFlag == 0) 294 | {df2$valName <- as.character(df[[col]]) 295 | nameChoices <- c(nameChoices, names(df)[[col]]) 296 | nameFlag <- 1} 297 | else{ 298 | nameChoices <- c(nameChoices, names(df)[[col]])} 299 | } 300 | else{ 301 | if (typeof(df[[col]]) == "integer" || typeof(df[[col]]) == "double" # val 302 | && !(names(df)[col] %in% lonNames) 303 | && !(names(df)[col] %in% latNames) 304 | && names(df[col]) != "geometry") 305 | { if (valflag == 0) 306 | {df2$val <- as.double(df[[col]]) 307 | ValColNametoprint <- names(df)[[col]] 308 | valChoices <- c(valChoices, names(df)[[col]]) 309 | valflag <- 1} 310 | else{ 311 | valChoices <- c(valChoices, names(df)[[col]])} 312 | } 313 | } 314 | } 315 | } 316 | } 317 | } 318 | if("ctrBin" %in% colnames(df2)) { 319 | ctrPtName <- df2$valName[df2$ctrBin == TRUE] 320 | ctrPt <- c(df2$lat[df2$ctrBin == TRUE],df2$lon[df2$ctrBin == TRUE]) # get lat/lon 321 | 322 | } else { 323 | ctrPtName <- "Default Center" 324 | ctrPt <- c(median(df2$lat), median(df2$lon)) # 325 | } 326 | 327 | valChoicesList <- as.list(valChoices) 328 | 329 | nameChoicesList <- as.list(nameChoices) 330 | 331 | # if(df_ext == "csv"){ 332 | # df2 <- dplyr::select(df2, valName, val, lat, lon) # just the fields we want 333 | # } else { 334 | # if(df_ext == "geojson"){ 335 | # df2 <- dplyr::select(df2, valName, val, lat, lon) #something else 336 | # } else { 337 | # # print(paste("What even is this?")) 338 | # } 339 | # } 340 | 341 | df2$distance <- geodist(ctrPt[1], ctrPt[2], df2$lat, df2$lon, units = "km") 342 | maxdist <- max(df2$distance) # max great circle distance in kilometers 343 | 344 | maxvaltoprint <- max(df2$val) 345 | minvaltoprint <- min(df2$val) 346 | 347 | my_list <- list(df2, maxdist, ctrPt[1], ctrPt[2], ctrPtName, ValColNametoprint, maxvaltoprint, minvaltoprint, valChoicesList, nameChoicesList) 348 | return(my_list) 349 | } 350 | 351 | output$CustomValueSlider <- renderUI({ 352 | valuesfromparser <- dfparser(dataframefinder()) 353 | maxvalue_forslider <- valuesfromparser[[7]] 354 | minvalue_forslider <- valuesfromparser[[8]] 355 | sliderInput("manualValueCutPoints", "Value Cut Points", min = round(minvalue_forslider), max = round(maxvalue_forslider), value = c((maxvalue_forslider/3),(maxvalue_forslider*2)/3), step = NULL) 356 | }) 357 | 358 | output$CustomDistanceSlider <- renderUI({ 359 | maxdist_forslider <- dfparser(dataframefinder())[[2]] 360 | default_dist_low <- maxdist_forslider/3 361 | default_dist_high <- (maxdist_forslider*2)/3 362 | if (maxdist_forslider < 10) # keep round slider numbers only for big distances 363 | { 364 | sliderInput("manualCutPoints", "Custom Distance Cut Points", min = 0, max = round(maxdist_forslider,2), value = c(round(default_dist_low,2),round(default_dist_high,2)), step = NULL, animate = FALSE, post = ' km', round = FALSE) 365 | } 366 | else 367 | { 368 | sliderInput("manualCutPoints", "Custom Distance Cut Points", min = 0, max = round(maxdist_forslider,-1), value = c(round(default_dist_low,-1),round(default_dist_high,0)), step = NULL, animate = FALSE, post = ' km', round = TRUE) 369 | } 370 | }) 371 | 372 | output$ValChoicesFromServer <- renderUI({ # serve up a list of value columns 373 | choicesForDropdown <- dfparser(dataframefinder())[[9]] 374 | selectInput("valSelection", "4. Select Point Size Column", 375 | multiple = FALSE, 376 | choices = choicesForDropdown, 377 | selected = choicesForDropdown[[1]], 378 | selectize = TRUE, 379 | width = "100%", size = NULL) 380 | }) 381 | 382 | output$NameChoicesFromServer <- renderUI({ # serve up a list of value columns 383 | namechoicesForDropdown <- dfparser(dataframefinder())[[10]] 384 | selectInput("nameSelection", "5. Select Label Column", 385 | multiple = FALSE, 386 | choices = namechoicesForDropdown, 387 | selected = namechoicesForDropdown[1], 388 | selectize = TRUE, 389 | width = "100%", size = NULL) 390 | }) 391 | 392 | output$geoPlot <- renderPlot({ 393 | 394 | ################################### 395 | # PARSE COLUMNS # 396 | ################################### 397 | 398 | df <- dataframefinder() 399 | df_ext <- ".csv" 400 | 401 | colListOrig <- colnames(df) # store column names for later 402 | latNames <- list("lat","Lat","LAT", "latitude", "Latitude", "LATITUDE", "y","Y", "coords.x2") # add as they come up 403 | lonNames <- list("lon","Lon","LON","long","Long","LONG","longitude", "Longitude", "LONGITUDE", "x","X", "coords.x1") 404 | valNameChoices <- list() 405 | valChoices <- list() 406 | 407 | df2 <- df # cloning df for non-destructive editing 408 | 409 | latflag <- 0 # need these here for the column detection 410 | lonflag <- 0 411 | ctrBinflag <- 0 412 | nameFlag <- 0 413 | valFlag <- 0 414 | 415 | for (col in 1:ncol(df)) { 416 | if (typeof(df[[col]]) == "double" 417 | && latflag == 0 418 | && max(as.numeric(df[[col]]), na.rm = T) <= 90.0 419 | && min(as.numeric(df[[col]]), na.rm = T) >= -90.0 420 | && names(df)[[col]] %in% latNames) # lat 421 | { 422 | df2$lat <- df[[col]] 423 | latflag <- 1} 424 | else{ 425 | if (typeof(df[[col]]) == "double" 426 | && lonflag == 0 427 | && max(as.numeric(df[[col]]), na.rm = T) <= 180.0 428 | && min(as.numeric(df[[col]]), na.rm = T) >= -180.0 429 | && names(df)[col] %in% lonNames) # lon 430 | { 431 | df2$lon <- df[[col]] 432 | lonflag <- 1} 433 | else{ 434 | if (typeof(df[[col]]) != "character" 435 | && typeof(df[[col]]) != "list" 436 | && min(as.numeric(df[[col]]), na.rm = T) == 0 437 | && max(as.numeric(df[[col]]), na.rm = T) == 1 438 | && sum(as.numeric(df[[col]]), na.rm = T) == 1) # ctrBin 439 | { df2$ctrBin <- as.logical(df[[col]]) 440 | ctrBinflag <- 1} 441 | else{ 442 | if (typeof(df[[col]]) == "character" # catches name and name_long 443 | || is.factor(df[[col]]) == T) # valName 444 | { if (nameFlag == 0) 445 | {df2$valName <- as.character(df[[col]]) 446 | nameFlag <- 1} 447 | else{ 448 | valNameChoices <- c(valNameChoices, names(df)[[col]])} 449 | } 450 | else{ 451 | if (typeof(df[[col]]) == "integer" || typeof(df[[col]]) == "double" # val 452 | && !(names(df)[col] %in% lonNames) 453 | && !(names(df)[col] %in% latNames) 454 | && names(df[col]) != "geometry") 455 | { if (valFlag == 0) 456 | {df2$val <- as.double(df[[col]]) 457 | LegendValName <- names(df)[[col]] 458 | valFlag <- 1} 459 | else{ 460 | valChoices <- c(valChoices, names(df)[[col]])} 461 | } 462 | } 463 | } 464 | } 465 | } 466 | } 467 | 468 | valColumn <- input$valSelection # override with UI selection 469 | LegendValName <- input$valSelection # update in the legend too 470 | df2$val <- df[[valColumn]] 471 | 472 | nameColumn <- input$nameSelection # override with UI selection 473 | df2$valName <- df[[nameColumn]] 474 | 475 | ################################### 476 | # SET CENTER POINT # 477 | ################################### 478 | 479 | if("ctrBin" %in% colnames(df2)) { 480 | ctrPtName <- df2$valName[df2$ctrBin == TRUE] 481 | ctrPt <- c(df2$lat[df2$ctrBin == TRUE],df2$lon[df2$ctrBin == TRUE]) # get lat/lon 482 | 483 | } else { 484 | ctrPtName <- "Default Center" 485 | ctrPt <- c(median(df2$lat), median(df2$lon)) # 486 | } 487 | 488 | ################################### 489 | # CREATE DF2 # 490 | ################################### 491 | 492 | if(df_ext == "csv"){ 493 | df2 <- dplyr::select(df2, valName, val, lat, lon) # just the fields we want 494 | } else { 495 | if(df_ext == "geojson"){ 496 | df2 <- dplyr::select(df2, valName, val, lat, lon) #something else 497 | } else { 498 | # print(paste("What even is this?")) 499 | } 500 | } 501 | df2$distance <- geodist(ctrPt[1], ctrPt[2], df2$lat, df2$lon, units = "km")*1000 502 | maxdist <- max(df2$distance) # max great circle distance in meters 503 | 504 | df2 <- df2 %>% mutate(ctrPtGeobearing = geosphere::bearing(c(ctrPt[2],ctrPt[1]), cbind(lon, lat), a=6378137, f=1/298.257223563)) # get bearing of all points to center 505 | 506 | for (row in 1:nrow(df2)) # convert from geographic bearings to polar coordinates 507 | {if(df2$ctrPtGeobearing[row] <= 0) 508 | # if geobearing is 0 or negative, mathbearing is 90 plus bearing 509 | df2$ctrPtMathbearing[row] <- abs(df2$ctrPtGeobearing[row]) + 90 510 | if(df2$ctrPtGeobearing[row] > 0 & df2$ctrPtGeobearing[row] < 90) 511 | # if geobearing is positive and equal to or under 90, mathbearing is 90 - bearing 512 | df2$ctrPtMathbearing[row] <- (90 - df2$ctrPtGeobearing[row]) 513 | if(df2$ctrPtGeobearing[row] >= 90 & df2$ctrPtGeobearing[row] <= 180) 514 | # if geobearing is positive and between 90 and 180 (inclusive), mathbearing is 360 - (geobearing - 90) 515 | df2$ctrPtMathbearing[row] <- 360 - (df2$ctrPtGeobearing[row] - 90)} 516 | rm(row) 517 | 518 | df2 <- dplyr::select(df2,-starts_with("ctrPtGeo")) 519 | 520 | maxdist <- max(df2$distance) # max great circle distance 521 | 522 | ################################### 523 | # POLYGON BACKGROUND # 524 | ################################### 525 | 526 | polygon <- polygonfinder() 527 | 528 | # fix multipolygon issue 529 | polygon_cast <- st_cast(st_cast(polygon,"MULTIPOLYGON"),"POLYGON") 530 | print("CAST THE BACKGROUND TO POLYGONS!") 531 | 532 | # this converts it to a dataframe of just points 533 | polygon_dataframe = data.frame(st_coordinates(st_cast(polygon_cast$geometry,"MULTIPOINT"))) 534 | 535 | # fix data type issue 536 | polygon_dataframe$L1_Chr <- as.character(polygon_dataframe$L1) 537 | 538 | # add geographic distance to center point 539 | polygon_dataframe$distance <- geodist(ctrPt[1], ctrPt[2], polygon_dataframe$Y, polygon_dataframe$X, units = "km")*1000 540 | 541 | # add bearing 542 | polygon_dataframe_bearing <- polygon_dataframe %>% mutate(ctrPtGeobearing = geosphere::bearing(c(ctrPt[2],ctrPt[1]), cbind(X, Y), a=6378137, f=1/298.257223563)) # get bearing of all points to center 543 | 544 | # convert to polar coordinates 545 | for (row in 1:nrow(polygon_dataframe_bearing)) # convert from geographic bearings to polar coordinates 546 | {if(polygon_dataframe_bearing$ctrPtGeobearing[row] <= 0) 547 | # if geobearing is 0 or negative, mathbearing is 90 plus bearing 548 | polygon_dataframe_bearing$ctrPtMathbearing[row] <- abs(polygon_dataframe_bearing$ctrPtGeobearing[row]) + 90 549 | if(polygon_dataframe_bearing$ctrPtGeobearing[row] > 0 & polygon_dataframe_bearing$ctrPtGeobearing[row] < 90) 550 | # if geobearing is positive and equal to or under 90, mathbearing is 90 - bearing 551 | polygon_dataframe_bearing$ctrPtMathbearing[row] <- (90 - polygon_dataframe_bearing$ctrPtGeobearing[row]) 552 | if(polygon_dataframe_bearing$ctrPtGeobearing[row] >= 90 & polygon_dataframe_bearing$ctrPtGeobearing[row] <= 180) 553 | # if geobearing is positive and between 90 and 180 (inclusive), mathbearing is 360 - (geobearing - 90) 554 | polygon_dataframe_bearing$ctrPtMathbearing[row] <- 360 - (polygon_dataframe_bearing$ctrPtGeobearing[row] - 90)} 555 | rm(row) # remove counter 556 | polygon_dataframe_polar <- dplyr::select(polygon_dataframe_bearing,-starts_with("ctrPtGeo")) 557 | 558 | ################################### 559 | # DATA VALUE TRANSLATION # 560 | ################################### 561 | 562 | valMin <- min(df2$val[df2$val != 0], na.rm = TRUE) # find lowest non-zero value (that will be radius 1) 563 | valMax <- max(df2$val, na.rm = T) # find highest non-zero value (that will be radius X) 564 | valMed <- median(df2$val, na.rm = T) # find median value (that could be radius 1 + X / 2) 565 | 566 | minRadius <- input$SymbolSizeRange[1] # change this with the UI later? 567 | maxRadius <- input$SymbolSizeRange[2] # change this with the UI later? 568 | 569 | # if(input$valTransMeth == "None"){ 570 | # df2$valTrans <- (df2$val/valMax) * maxRadius + (minRadius - 1) 571 | # # proportion of maximum value without scaling 572 | # } else if(input$valTransMeth == "Square Root"){ 573 | # df2$valTrans <- (sqrt(df2$val/valMax)) * maxRadius + (minRadius - 1) 574 | # } else if(input$valTransMeth == "Log"){ 575 | # df2$valTrans <- (log(df2$val)/log(valMax) * maxRadius) + (minRadius - 1) 576 | # df2$valTrans[is.infinite(df2$valTrans)]<-0 577 | # } else if(input$valTransMeth == "Custom"){ 578 | # df2$valTrans <- df2$val 579 | # } 580 | 581 | df2$valTrans <- (sqrt(df2$val/valMax)) * maxRadius + (minRadius - 1) 582 | 583 | if(input$removeZeroes == TRUE){ 584 | df2$val[df2$val == 0] <- NaN 585 | df2<-subset(df2, val >= 0) 586 | } 587 | if(input$removeZeroes == FALSE){ 588 | df2$val[df2$val == 0] <- NA 589 | } 590 | 591 | df2$labelNames <- df2$valName # initialize empty strings for labels 592 | df2$labelNames[df2$val == 0] <- " " # add label text for non-zero values 593 | df2$labelNames[df2$isCTR == FALSE & is.na(df2$val) == TRUE] <- " " # add label text for non-zero values 594 | 595 | ################################### 596 | # GREAT CIRCLE # 597 | ################################### 598 | 599 | # Run polar to cartesian conversion on x and y axes 600 | df2 <- df2 %>% mutate(circdistancex = (useful::pol2cart(distance,ctrPtMathbearing,degrees = TRUE)[[1]])) 601 | df2 <- df2 %>% mutate(circdistancey = (useful::pol2cart(distance,ctrPtMathbearing,degrees = TRUE)[[2]])) 602 | 603 | # Combine x and y into a matrix, add as a column, remove x and y columns 604 | df2$circcoords <- cbind(df2$circdistancex,df2$circdistancey) 605 | df2 <- dplyr::select(df2,-starts_with("circdistance")) 606 | 607 | # Lets make some circles starting at the center point going to maxdist 608 | circles <- data.frame( 609 | x0 = 0, 610 | y0 = 0, 611 | r = seq(0, maxdist,length.out = 11) 612 | ) 613 | circles <- circles[-1,] # Remove zero-radius circle 614 | 615 | # set up great circle basemap coords 616 | polygon_dataframe_polar <- polygon_dataframe_polar %>% mutate( 617 | circdistancex = (useful::pol2cart(distance,ctrPtMathbearing,degrees = TRUE)[[1]]), 618 | circdistancey = (useful::pol2cart(distance,ctrPtMathbearing,degrees = TRUE)[[2]]) 619 | ) 620 | polygon_dataframe_polar$circcoords <- cbind(polygon_dataframe_polar$circdistancex,polygon_dataframe_polar$circdistancey) 621 | polygon_dataframe_polar <- select(polygon_dataframe_polar,-starts_with("circdistancex")) 622 | 623 | ################################### 624 | # LOGARITHMIC SCALE # 625 | ################################### 626 | 627 | # Replot coordinates on log distance scale 628 | df2 <- df2 %>% mutate( 629 | logdistancex = (useful::pol2cart(log(distance + 1),ctrPtMathbearing,degrees = TRUE)[[1]]), 630 | logdistancey = (useful::pol2cart(log(distance + 1),ctrPtMathbearing,degrees = TRUE)[[2]]) 631 | ) 632 | 633 | # Overrides infinite values 634 | df2$logdistancex[is.nan(df2$logdistancex)] <- 0 635 | df2$logdistancey[is.nan(df2$logdistancey)] <- 0 636 | 637 | # Combine x and y into a matrix, add as a column, remove x and y columns 638 | df2$logcoords <- cbind(df2$logdistancex,df2$logdistancey) 639 | df2 <- dplyr::select(df2,-starts_with("logdistance")) 640 | 641 | # Plot it 642 | df2 <- dplyr::arrange(df2, -val) # sorting for draw order below 643 | df2$num <- ave(df2$val, FUN = seq_along) # also sorting? 644 | 645 | # set up logarithmic basemap values 646 | polygon_dataframe_polar <- polygon_dataframe_polar %>% mutate( 647 | logdistancex = (useful::pol2cart(log(distance + 1),ctrPtMathbearing,degrees = TRUE)[[1]]), 648 | logdistancey = (useful::pol2cart(log(distance + 1),ctrPtMathbearing,degrees = TRUE)[[2]]) 649 | ) 650 | 651 | # Overrides infinite values 652 | polygon_dataframe_polar$logdistancex[is.nan(polygon_dataframe_polar$logdistancex)] <- 0 653 | polygon_dataframe_polar$logdistancey[is.nan(polygon_dataframe_polar$logdistancey)] <- 0 654 | 655 | # Combine x and y into a matrix, add as a column, remove x and y columns 656 | polygon_dataframe_polar$logcoords <- cbind(polygon_dataframe_polar$logdistancex,polygon_dataframe_polar$logdistancey) 657 | polygon_dataframe_polar <- dplyr::select(polygon_dataframe_polar,-starts_with("logdistance")) 658 | 659 | ################################### 660 | # SQUARE ROUTE # 661 | ################################### 662 | 663 | # Replot coordinates on square root scale, combine x and y into a matrix, 664 | # add as a column, remove x and y columns 665 | df2 <- df2 %>% mutate( 666 | sqrtdistancex = (useful::pol2cart(sqrt(distance),ctrPtMathbearing,degrees = TRUE)[[1]]), 667 | sqrtdistancey = (useful::pol2cart(sqrt(distance),ctrPtMathbearing,degrees = TRUE)[[2]]) 668 | ) 669 | df2$sqrtcoords <- cbind(df2$sqrtdistancex,df2$sqrtdistancey) 670 | df2 <- select(df2,-starts_with("sqrtdistance")) 671 | 672 | # Set up square root basemap coords 673 | polygon_dataframe_polar <- polygon_dataframe_polar %>% mutate( 674 | sqrtdistancex = (useful::pol2cart(sqrt(distance),ctrPtMathbearing,degrees = TRUE)[[1]]), 675 | sqrtdistancey = (useful::pol2cart(sqrt(distance),ctrPtMathbearing,degrees = TRUE)[[2]]) 676 | ) 677 | polygon_dataframe_polar$sqrtcoords <- cbind(polygon_dataframe_polar$sqrtdistancex,polygon_dataframe_polar$sqrtdistancey) 678 | polygon_dataframe_polar <- select(polygon_dataframe_polar,-starts_with("sqrtdistance")) 679 | 680 | ################################### 681 | # CUBE ROOT # 682 | ################################### 683 | 684 | # Replot coordinates on cube root distance scale 685 | # Combine x and y into a matrix, add as a column, remove x and y columns 686 | df2 <- df2 %>% mutate( 687 | cuberootdistancex = (useful::pol2cart(pracma::nthroot(distance, 3),ctrPtMathbearing,degrees = TRUE)[[1]]), 688 | cuberootdistancey = (useful::pol2cart(pracma::nthroot(distance, 3),ctrPtMathbearing,degrees = TRUE)[[2]]) 689 | ) 690 | df2$cuberootcoords <- cbind(df2$cuberootdistancex,df2$cuberootdistancey) 691 | df2 <- select(df2,-starts_with("cuberootdistance")) 692 | 693 | # Set up cube root basemap coords 694 | polygon_dataframe_polar <- polygon_dataframe_polar %>% mutate( 695 | cuberootdistancex = (useful::pol2cart(pracma::nthroot(distance, 3),ctrPtMathbearing,degrees = TRUE)[[1]]), 696 | cuberootdistancey = (useful::pol2cart(pracma::nthroot(distance, 3),ctrPtMathbearing,degrees = TRUE)[[2]]) 697 | ) 698 | polygon_dataframe_polar$cuberootcoords <- cbind(polygon_dataframe_polar$cuberootdistancex,polygon_dataframe_polar$cuberootdistancey) 699 | polygon_dataframe_polar <- select(polygon_dataframe_polar,-starts_with("cuberootdistancex")) 700 | 701 | ################################### 702 | # CUSTOM DIST # 703 | ################################### 704 | # atm, more just a piecewise linear function 705 | neardist <- input$manualCutPoints[1]*1000 706 | fardist <- input$manualCutPoints[2]*1000 707 | # 708 | chartdist <- c(0, 800, 1200) # this just gets us equal intervals on the graph for the different segments of the lines 709 | # 710 | # 711 | a <- function(x){ # here is some example code for the piecewise funciton 712 | ifelse((x >= 0 & x < neardist), 713 | (x * 400/neardist), 714 | ifelse((neardist <= x & x < fardist), 715 | ((x * 400)/(fardist - neardist) + (400-((400)/(fardist - neardist)*neardist))), 716 | ifelse((fardist <= x & x <= maxdist),((x * 400)/(maxdist - fardist) + (800-((400)/(maxdist - fardist)*fardist))), NA))) 717 | } 718 | 719 | # function to make new circles with any stepwise function set above 720 | custom_predictstep <- function(dataframe) { 721 | customcirclesdataframe <- dataframe # duplicate dataframe 722 | for (row in 1:nrow(dataframe)){ 723 | customcirclesdataframe$r[row] <- a(dataframe$r[row])} # applying function a to every row in dataframe 724 | return(customcirclesdataframe) 725 | } 726 | customcircles <- custom_predictstep(circles) # this projects the circles 727 | 728 | # Replot coordinates on custom distance scale using piecewise function 729 | df2 <- df2 %>% mutate( 730 | customdistancex = (useful::pol2cart(a(distance),ctrPtMathbearing,degrees = TRUE)[[1]]), 731 | customdistancey = (useful::pol2cart(a(distance),ctrPtMathbearing,degrees = TRUE)[[2]]) 732 | ) 733 | df2$customcoords <- cbind(df2$customdistancex,df2$customdistancey) 734 | df2 <- select(df2,-starts_with("customdistance")) 735 | 736 | # custom distance coordinates 737 | polygon_dataframe_polar <- polygon_dataframe_polar %>% mutate( 738 | customdistancex = (useful::pol2cart(a(distance),ctrPtMathbearing,degrees = TRUE)[[1]]), 739 | customdistancey = (useful::pol2cart(a(distance),ctrPtMathbearing,degrees = TRUE)[[2]]) 740 | ) 741 | polygon_dataframe_polar$customcoords <- cbind(polygon_dataframe_polar$customdistancex,polygon_dataframe_polar$customdistancey) 742 | polygon_dataframe_polar <- select(polygon_dataframe_polar,-starts_with("customdistance")) 743 | 744 | ################################### 745 | # PLOT CALL # 746 | ################################### 747 | 748 | #initating variables 749 | circleColor = "red" #default value for circle color. If they are red, something is broken 750 | ctrPtColor = "red" #default value for center point color. If it is red, something is broken 751 | themeText = "red" 752 | 753 | # PLOT STYLE 754 | darkPlot <- list( 755 | scale_color_scico(palette = "tokyo", begin = 0.1, end = 0.95), 756 | #scale_color_viridis_c(option = "plasma"), 757 | theme(panel.background = element_rect(fill = "grey80", linetype = "blank"), 758 | plot.background = element_rect(fill= "grey80"), 759 | axis.ticks = element_blank(), 760 | axis.text.x = element_blank(), 761 | axis.text.y = element_blank(), 762 | panel.grid = element_blank(), 763 | legend.background = element_rect(fill = "grey80"), 764 | legend.text = element_text(color = "white"), 765 | legend.title = element_text(color = "white") 766 | ), 767 | coord_fixed(), 768 | labs(color = paste0("Total ",tolower(LegendValName), " :: ", '\n',ctrPtName), x = NULL, y = NULL), 769 | geom_point(na.rm = TRUE, stroke = 1, alpha = 0.7, size = df2$valTrans), 770 | guides(colour = "colorbar",size = "legend")#, 771 | ) 772 | scale_fill_scico() 773 | lightPlot <- list( 774 | scale_color_scico(palette = "imola", begin = 0.2, end = 0.95), 775 | theme(panel.background = element_blank(), 776 | axis.ticks = element_blank(), 777 | axis.text.x = element_blank(), 778 | axis.text.y = element_blank()), 779 | coord_fixed(), 780 | geom_point(stroke = 1, alpha = 0.78, size = df2$valTrans), 781 | labs(color = paste0("Total ",tolower(LegendValName), " :: ", '\n',ctrPtName), x = NULL, y = NULL), 782 | guides(colour = "colorbar", size = "legend")#, 783 | ) 784 | 785 | monoPlot <- list( 786 | scale_color_gradient(low = "gray80", high = "#000000"), 787 | theme(panel.background = element_blank(), 788 | axis.ticks = element_blank(), 789 | axis.text.x = element_blank(), 790 | axis.text.y = element_blank()), 791 | coord_fixed(), 792 | geom_point(stroke = 1, alpha = 0.7, size = df2$valTrans), 793 | labs(color = paste0("Total ",tolower(LegendValName), " :: ", '\n',ctrPtName), x = NULL, y = NULL), 794 | guides(size = guide_legend())#, 795 | ) 796 | 797 | #this is where all the themes go 798 | if(input$plotTheme == "Light Theme"){ 799 | selectedPlotTheme <- lightPlot 800 | circleColor <- "ivory3" 801 | ctrPtColor <- "red" 802 | themeText = "black" #change for nacis 803 | # themeText = "gray60" 804 | } else if (input$plotTheme == "Dark Theme"){ 805 | selectedPlotTheme <-darkPlot 806 | circleColor <- "gray50" 807 | ctrPtColor <- "red" 808 | themeText = "black" 809 | } else if (input$plotTheme == "Mono Theme"){ 810 | selectedPlotTheme <-monoPlot 811 | circleColor <- "gray75" 812 | ctrPtColor <- "red" 813 | themeText = "black" 814 | } 815 | 816 | # Figure out which plot to show 817 | plot_circles <- circles # set default for great circle/logarithmic 818 | if(input$interpMeth == "Great Circle"){ 819 | plot_coordinates <- df2$circcoords 820 | basemapcoords <- polygon_dataframe_polar$circcoords 821 | } else if(input$interpMeth == "Square Root"){ 822 | plot_coordinates <- df2$sqrtcoords 823 | plot_circles <- sqrt(circles) 824 | basemapcoords <- polygon_dataframe_polar$sqrtcoords 825 | } else if(input$interpMeth == "Cube Root"){ 826 | plot_coordinates <- df2$cuberootcoords 827 | basemapcoords <- polygon_dataframe_polar$cuberootcoords 828 | } else if(input$interpMeth == "Log"){ 829 | plot_coordinates <- df2$logcoords 830 | basemapcoords <- polygon_dataframe_polar$logcoords 831 | } else if(input$interpMeth == "Decimal Log"){ 832 | plot_coordinates <- df2$declogcoords 833 | } else if(input$interpMeth == "Custom"){ 834 | plot_coordinates <- df2$customcoords 835 | basemapcoords <- polygon_dataframe_polar$customcoords 836 | plot_circles <- customcircles 837 | } 838 | 839 | polygon_dataframe_polar_list<-split(polygon_dataframe_polar, polygon_dataframe_polar$L1_Chr) # this splits into list of polygons, but some are still multipolygons (and would need to be split previously) 840 | 841 | # For all the circular plots, plug in variables 842 | if(input$interpMeth != "Lat & Long"){ 843 | plot <- ggplot(df2 %>% arrange(desc(val)), aes( 844 | plot_coordinates[,1], 845 | plot_coordinates[,2], 846 | color = df2$val, 847 | stroke = 'black')) + 848 | selectedPlotTheme 849 | 850 | if(input$basemap == TRUE){ 851 | 852 | if(input$plotTheme == "Light Theme"){ 853 | plot$layers <- c(geom_polygon(colour = 'black', data = polygon_dataframe_polar, 854 | aes(x=basemapcoords[,1], y=basemapcoords[,2], group = L1_Chr), rule = 'winding', fill = 'gray', colour = "black", size = .2), plot$layers) 855 | # themeText = "gray60" 856 | } else if (input$plotTheme == "Dark Theme"){ 857 | plot$layers <- c(geom_polygon(colour = 'black', data = polygon_dataframe_polar, 858 | aes(x=basemapcoords[,1], y=basemapcoords[,2], group = L1_Chr), rule = 'winding', fill = 'white', colour = "black", size = .2), plot$layers) 859 | } else if (input$plotTheme == "Mono Theme"){ 860 | plot$layers <- c(geom_polygon(colour = 'black', data = polygon_dataframe_polar, 861 | aes(x=basemapcoords[,1], y=basemapcoords[,2], group = L1_Chr), rule = 'winding', fill = 'gray95', colour = "black", size = .2), plot$layers) 862 | } 863 | } 864 | 865 | if(input$interpMeth == "Log"){ # sneaky way to add circles below 866 | plot$layers <- c(geom_circle(aes(x0 = x0, y0 = y0, r = log(r)), 867 | colour = "black", 868 | data = plot_circles, 869 | show.legend = NA, 870 | inherit.aes = FALSE), plot$layers) 871 | } 872 | else if(input$interpMeth == "Cube Root"){ # sneaky way to add circles below 873 | plot$layers <- c(geom_circle(aes(x0 = x0, y0 = y0, r = pracma::nthroot(r, 3)), 874 | colour = circleColor, 875 | data = plot_circles, 876 | show.legend = NA, 877 | inherit.aes = FALSE), plot$layers) 878 | } 879 | else if(input$interpMeth == "Decimal Log"){ # sneaky way to add circles below 880 | plot$layers <- c(geom_circle(aes(x0 = x0, y0 = y0, r = log10(r)), 881 | colour = circleColor, 882 | data = plot_circles, 883 | show.legend = NA, 884 | inherit.aes = FALSE), plot$layers) 885 | } 886 | else{ 887 | plot$layers <- c(geom_circle(aes(x0 = x0, y0 = y0, r = r), 888 | colour = circleColor, 889 | data = plot_circles, 890 | show.legend = NA, 891 | inherit.aes = FALSE), plot$layers) 892 | } 893 | 894 | if(input$labelsOn == TRUE){ 895 | plot <- plot + geom_text(data = df2, 896 | aes(plot_coordinates[,1], 897 | plot_coordinates[,2], 898 | label = df2$labelName), 899 | size = 0, 900 | check_overlap = input$showAllLabels, # tried to do the text outline thing and failed. could we do a conditional or expression for text color, where depending where it is on the scale it gets a different text color? 901 | color = "white", fontface = "bold") + geom_label_repel(data = df2, 902 | aes(plot_coordinates[,1], 903 | plot_coordinates[,2], 904 | label = df2$labelName), 905 | size = input$label_size, 906 | min.segment.length = 0.75, 907 | check_overlap = input$HideOverlappingLabels, 908 | color = themeText, 909 | segment.color = 'gray20', 910 | segment.size = 0.25, 911 | alpha = 0.85, seed = 1234) 912 | # + geom_label_repel(data = df2, 913 | # aes(plot_coordinates[,1], 914 | # plot_coordinates[,2], 915 | # label = df2$labelName), 916 | # size = input$label_size, 917 | # min.segment.length = 0.75, 918 | # check_overlap = input$HideOverlappingLabels, 919 | # color = themeText, 920 | # segment.color = 'gray20', 921 | # segment.size = 0.25, 922 | # fill = NA, 923 | # alpha = 1, seed = 1234) 924 | } 925 | 926 | #if(input$centerOn == TRUE){ 927 | # plot <- plot + geom_point(data = (as.data.frame(ctrPt)), aes(0, 0), colour = ctrPtColor, shape = 10, size = 3) 928 | #} 929 | 930 | plot 931 | 932 | } 933 | # Use simpler plot for Lat-Long 934 | else{ 935 | plot_latLon <- ggplot(df2, aes(df2$lon, df2$lat, color = df2$val)) + 936 | geom_point() + 937 | selectedPlotTheme + 938 | geom_point(data = (as.data.frame(ctrPt)), aes(ctrPt[2], ctrPt[1]), color = ctrPtColor, shape = 10, size = 3) + 939 | scale_color_viridis_c(option = "plasma") 940 | plot_latLon 941 | } 942 | 943 | }) 944 | } 945 | 946 | shinyApp(ui = ui, server = server) # Run the application 947 | 948 | #shinylive::export(appdir = "app", destdir = "docs") 949 | 950 | #httpuv::runStaticServer("docs") 951 | #install.packages('rsconnect') 952 | 953 | #rsconnect::deployApp("app",appName="rrp_beta") 954 | -------------------------------------------------------------------------------- /app/data/IND_remittances.csv: -------------------------------------------------------------------------------- 1 | long,lat,name,IND_remit,name_long,isCTR 2 | 54.2067147616,23.8686336533,UAE,13823,United Arab Emirates,0 3 | -98.583333,39.833333,USA,11715,United States,0 4 | 44.5163637683,24.1232898391,SAU,11239,Saudi Arabia,0 5 | 47.6000988876,29.307266634,KWT,4587,Kuwait,0 6 | 51.1835025789,25.3218509742,QAT,4143,Qatar,0 7 | -2.8531353952,53.9147734805,GBR,3941,United Kingdom,0 8 | 56.09867282,20.6111743742,OMN,3250,Oman,0 9 | 84.0131736769,28.239440019,NPL,3016,Nepal,0 10 | -98.1423813721,61.4690761453,CAN,2877,Canada,0 11 | 134.5027754754,-25.7306547797,AUS,1944,Australia,0 12 | 50.583333,26.216667,BHR,1336,Bahrain,0 13 | 80.6672357493,7.7005344172,LKA,1265,Sri Lanka,0 14 | 103.833333,1.283333,SGP,886,Singapore,0 15 | 12.1407883722,42.751183053,ITA,612,Italy,0 16 | 172.7019259441,-41.6625787572,NZL,341,New Zealand,0 17 | 10.2884850927,51.1337226904,DEU,332,Germany,0 18 | 109.6981484486,3.7255884258,MYS,287,Malaysia,0 19 | -2.8805991914,42.4577181048,FRA,251,France,0 20 | 101.0061335463,15.0169749914,THA,197,Thailand,0 21 | 25.0480138799,-28.94703326,ZAF,188,South Africa,0 22 | 90.4724248062,27.4279686491,BTN,186,Bhutan,0 23 | -3.6170206024,40.3486561062,ESP,183,Spain,0 24 | 34.7529881315,-6.2577327208,TZA,153,Tanzania,0 25 | 96.5058409421,21.0169998738,MMR,138,Myanmar,0 26 | 90.2679282772,23.8394617953,BGD,126,Bangladesh,0 27 | 138.0649621327,37.6631108117,JPN,116,Japan,0 28 | 16.5962658468,62.8114849681,SWE,112,Sweden,0 29 | 8.1183006134,46.7917376837,CHE,110,Switzerland,0 30 | 5.512217101,52.2987003744,NLD,110,Netherlands,0 31 | -8.0102365449,53.18059121,IRL,100,Ireland,0 32 | 35.0038500487,31.484917815,ISR,98,Israel,0 33 | 37.7915552867,0.5959662522,KEN,86,Kenya,0 34 | 73.508889,4.175278,MDV,81,Maldives,0 35 | 4.5808315907,50.6524426065,BEL,77,Belgium,0 36 | 14.0761588843,47.6139487927,AUT,72,Austria,0 37 | 103.8836162892,36.5550685352,CHN,64,China,0 38 | 15.4681261227,69.1568563971,NOR,64,Norway,0 39 | 114.9151087739,4.6902505425,BRN,59,Brunei Darussalam,0 40 | 9.8763729377,56.0639344618,DNK,51,Denmark,0 41 | 22.7198134471,39.0667158997,GRC,43,Greece,0 42 | 57.516667,-20.166667,MUS,43,Mauritius,0 43 | 117.4234075623,-2.2217379365,IDN,42,Indonesia,0 44 | 122.9026723699,11.7637993623,PHL,42,Philippines,0 45 | -8.0557655883,39.634049775,PRT,39,Portugal,0 46 | 23.7730814658,-22.0997113788,BWA,37,Botswana,0 47 | 127.8213171283,36.4275986042,KOR,30,"Korea, Rep.",0 48 | 55.45,-4.616667,SYC,29,Seychelles,0 49 | 26.2117646974,64.5040939186,FIN,26,Finland,0 50 | 96.875223256,61.9808407507,RUS,25,Russian Federation,0 51 | 36.7794549063,31.2454905847,JOR,20,Jordan,0 52 | 17.9743527792,26.997460407,LBY,20,Libya,0 53 | -80.1091648355,8.5300193889,PAN,17,Panama,0 54 | 33.039553803,34.9070608509,CYP,16,Cyprus,0 55 | 46.6911709147,-19.3561140778,MDG,16,Madagascar,0 56 | 32.3575481537,1.2954869631,UGA,16,Uganda,0 57 | 15.3345581024,49.7752452944,CZE,12,Czech Republic,0 58 | 163.8531646446,-17.3163094264,FJI,9,Fiji,0 59 | 35.4726171541,-17.2304465978,MOZ,9,Mozambique,0 60 | 34.1936053256,-13.1728327167,MWI,9,Malawi,0 61 | -65.1753607711,-35.4468214895,ARG,8,Argentina,0 62 | 29.8444615131,26.5066199997,EGY,8,"Egypt, Arab Rep.",0 63 | 35.87098632,33.9118272078,LBN,8,Lebanon,0 64 | -77.3242548016,18.1376361279,JAM,7,Jamaica,0 65 | 27.72759194,-13.3950675201,ZMB,7,Zambia,0 66 | 19.3576286277,47.199951172,HUN,6,Hungary,0 67 | 106.2858407971,16.6579377533,VNM,6,Vietnam,0 68 | 2.5980477916,28.1854812787,DZA,5,Algeria,0 69 | 29.8626040123,15.9905850031,SDN,5,Sudan,0 70 | 30.1986175823,7.2928897148,SDS,5,South Sudan,0 71 | 31.3695330804,48.9730179682,UKR,5,Ukraine,0 72 | -53.0543400358,-10.8067736435,BRA,4,Brazil,0 73 | -1.2369685557,7.9286518131,GHA,4,Ghana,0 74 | -102.576349524,23.9353719022,MEX,4,Mexico,0 75 | 19.3110143084,52.1482602193,POL,4,Poland,0 76 | 120.9748007375,23.7409649798,TWN,4,Turkey,0 77 | -71.5206439452,-39.0470143099,CHL,3,Chile,0 78 | -9.4108361544,6.4316198623,LBR,3,Liberia,0 79 | 29.9139008931,-3.3773918124,BDI,2,"Bahamas, The",0 80 | -88.703421253,17.1970899115,BLZ,2,Belize,0 81 | -84.175423096,9.9656711275,CRI,2,Costa Rica,0 82 | -70.462358457,18.884487088,DOM,2,Dominican Republic,0 83 | -18.7610287708,65.0742763353,ISL,2,Iceland,0 84 | 71.034435049,38.5830814642,TJK,2,Tajikistan,0 85 | 9.5347161207,34.1729390369,TUN,2,Trinidad and Tobago,0 86 | 25.1951109533,42.753118762,BGR,1,Bulgaria,0 87 | -73.077732087,3.9272138627,COL,1,Colombia,0 88 | -78.3841667461,-1.4547717055,ECU,1,Ecuador,0 89 | 43.7569114187,33.036822341,IRQ,1,Iraq,0 90 | 5.9652234323,49.7657050742,LUX,1,Luxembourg,0 91 | 24.8332961498,56.8071751343,LVA,1,Latvia,0 92 | -74.3918058168,-9.1915629051,PER,1,Peru,0 93 | 145.3175746278,-6.4516445146,PNG,1,Papua New Guinea,0 94 | 127.1650167628,40.1430208608,PRK,1,"Korea, Dem. Rep.",0 95 | 29.9189658371,-2.0135155259,RWA,1,Rwanda,0 96 | 159.9666154474,-8.8524974708,SLB,1,Solomon Islands,0 97 | -11.7952574286,8.5303537262,SLE,1,Sierra Leone,0 98 | 19.5076571474,48.7267113517,SVK,1,Slovak Republic,0 99 | 14.9381523208,46.125422059,SVN,1,Slovenia,0 100 | 63.2036395282,41.7486026647,UZB,1,Uzbekistan,0 101 | -66.1638272783,7.1621322676,VEN,1,"Venezuela, RB",0 102 | 66.0866902219,33.8563992817,AFG,0,Afghanistan,0 103 | 17.4705725523,-12.2458690361,AGO,0,Angola,0 104 | 20.0324264314,41.141353306,ALB,0,Albania,0 105 | 45.000290011,40.2166076123,ARM,0,Armenia,0 106 | 47.55390959,40.2206906036,AZE,0,Azerbaijan,0 107 | 2.3373775535,9.6474307807,BEN,0,Benin,0 108 | -1.7765374521,12.3116504941,BFA,0,Burkina Faso,0 109 | 17.8168834213,44.1807678414,BIH,0,Bosnia and Herzegovina,0 110 | 27.9813539869,53.5063444048,BLR,0,Belarus,0 111 | -64.641405606,-16.7289870153,BOL,0,Bolivia,0 112 | 20.3743472912,6.5427787059,CAF,0,Central African Republic,0 113 | -5.6120436452,7.553755007,CIV,0,Cote d'Ivoire,0 114 | 12.6115525151,5.6630981303,CMR,0,Cameroon,0 115 | 23.5829558315,-2.8502757111,COD,0,"Congo, Dem. Rep.",0 116 | 15.1344617674,-0.8378010872,COG,0,"Congo, Rep.",0 117 | -78.9606849097,21.631751541,CUB,0,Cuba,0 118 | 42.498019736,11.7730443955,DJI,0,Djibouti,0 119 | 38.6781773422,15.4272756579,ERI,0,Eritrea,0 120 | 25.8247283753,58.6436952407,EST,0,Estonia,0 121 | 39.5512557929,8.6539991881,ETH,0,Ethiopia,0 122 | 11.6877511749,-0.6470481398,GAB,0,Gabon,0 123 | 43.4815404198,42.1620186998,GEO,0,Georgia,0 124 | -11.0608537412,10.4482728773,GIN,0,Guinea,0 125 | -15.4318728077,13.4753343587,GMB,0,"Gambia, The",0 126 | -15.1106237517,12.0227043823,GNB,0,Guinea-Bissau,0 127 | 10.3660313251,1.64586432,GNQ,0,Equatorial Guinea,0 128 | -41.5001811149,74.770487694,GRL,0,Greenland,0 129 | -90.3694583605,15.699360612,GTM,0,Guatemala,0 130 | -58.9712031086,4.7902253752,GUY,0,Guyana,0 131 | -86.589963838,14.8229470817,HND,0,Honduras,0 132 | 16.5661911067,45.016234368,HRV,0,Croatia,0 133 | -72.6580133054,18.9007006918,HTI,0,Haiti,0 134 | 79.5937037633,22.9250064074,IND,0,India,1 135 | 54.2854514969,32.5189173176,IRN,0,"Iran, Islamic Rep.",0 136 | 67.2846086075,48.1916620925,KAZ,0,Kazakhstan,0 137 | 74.6204048109,41.5068937132,KGZ,0,Kyrgyz Republic,0 138 | 104.8760853253,12.6847286294,KHM,0,Cambodia,0 139 | 103.750259895,18.444978089,LAO,0,Lao PDR,0 140 | 28.1701052952,-29.6252904937,LSO,0,Lesotho,0 141 | 23.8806402758,55.2843194848,LTU,0,Lithuania,0 142 | -8.4204795445,29.8853946983,MAR,0,Morocco,0 143 | 28.4104827908,47.2036764261,MDA,0,Moldova,0 144 | 21.6978968161,41.6059289308,MKD,0,"Macedonia, FYR",0 145 | -3.5432943395,17.2677720617,MLI,0,Mali,0 146 | 19.2861809672,42.7890383314,MNE,0,Montenegro,0 147 | 102.9464051863,46.8236825035,MNG,0,Mongolia,0 148 | -10.3263969252,20.2092672064,MRT,0,Mauritania,0 149 | 17.1561681262,-22.0997769317,NAM,0,Namibia,0 150 | 165.5344746009,-21.2613576125,NCL,0,New Caledonia,0 151 | 9.3244293382,17.3455523533,NER,0,Niger,0 152 | 7.9951277541,9.5483184182,NGA,0,Nigeria,0 153 | -85.0203185008,12.848190428,NIC,0,Nicaragua,0 154 | 69.4139980632,29.9734600255,PAK,0,Pakistan,0 155 | -58.3873878335,-23.2480419463,PRY,0,Paraguay,0 156 | 24.9432524946,45.8571010357,ROU,0,Romania,0 157 | -14.5098027859,14.3541399885,SEN,0,Senegal,0 158 | -88.8729031703,13.7260916258,SLV,0,El Salvador,0 159 | 45.7267007672,4.7523477565,SOM,0,Somalia,0 160 | 20.8196512674,44.2330372036,SRB,0,Serbia,0 161 | -55.9114562995,4.1200080318,SUR,0,Suriname,0 162 | 31.3952559021,-26.4898552885,SWZ,0,Swaziland,0 163 | 38.5442394196,35.0126142811,SYR,0,Syrian Arab Republic,0 164 | 18.5813295253,15.3288673998,TCD,0,Chad,0 165 | 0.9964039437,8.4395419547,TGO,0,Togo,0 166 | 59.2754302624,39.0912401802,TKM,0,Turkmenistan,0 167 | -61.3303669144,10.4282370892,TTO,0,Timor-Leste,0 168 | 35.116901308,39.0683717414,TUR,0,Tunisia,0 169 | -56.0032786665,-32.7809043652,URY,0,Uruguay,0 170 | 167.0737512682,-15.5426770576,VUT,0,Vanuatu,0 171 | 47.5350447585,15.9132319501,YEM,0,"Yemen, Rep.",0 172 | 29.7885483719,-18.9069879479,ZWE,0,Zimbabwe,0 173 | -------------------------------------------------------------------------------- /app/data/Katrina_Migration.csv: -------------------------------------------------------------------------------- 1 | State_Abbrev,Lat,Lon,State_Name,LA_Migration_2006_ACS,Census_Pop_2005,Migration_per_100K,Is_CTR 2 | AL,32.318231,-86.902298,Alabama,10034.00,4557808.00,220,0 3 | AK,63.588753,-154.493062,Alaska,332.00,663661.00,50,0 4 | AZ,34.048928,-111.093731,Arizona,2609.00,5939292.00,44,0 5 | AR,35.20105,-91.831833,Arkansas,8216.00,2779154.00,296,0 6 | CA,36.778261,-119.417932,California,10561.00,36132147.00,29,0 7 | CO,39.550051,-105.782067,Colorado,2246.00,4665177.00,48,0 8 | CT,41.603221,-73.087749,Connecticut,269.00,3510297.00,8,0 9 | DE,38.910832,-75.52767,Delaware,136.00,843524.00,16,0 10 | DC,38.905985,-77.033418,District of Columbia,274.00,550521.00,50,0 11 | FL,27.664827,-81.515754,Florida,15995.00,17789864.00,90,0 12 | GA,32.157435,-82.907123,Georgia,19472.00,9072576.00,215,0 13 | HI,19.898682,-155.665857,Hawaii,238.00,1275194.00,19,0 14 | ID,44.068202,-114.742041,Idaho,93.00,1429096.00,7,0 15 | IL,40.633125,-89.398528,Illinois,2956.00,12763371.00,23,0 16 | IN,40.551217,-85.602364,Indiana,2506.00,6271973.00,40,0 17 | IA,41.878003,-93.097702,Iowa,1299.00,2966334.00,44,0 18 | KS,39.011902,-98.484246,Kansas,1425.00,2744687.00,52,0 19 | KY,37.839333,-84.270018,Kentucky,982.00,4173405.00,24,0 20 | LA,31.244823,-92.145024,Louisiana,0.00,4523628.00,0,1 21 | ME,45.253783,-69.445469,Maine,314.00,1321505.00,24,0 22 | MD,39.045755,-76.641271,Maryland,3975.00,5600388.00,71,0 23 | MA,42.407211,-71.382437,Massachusetts,1564.00,6398743.00,24,0 24 | MI,44.314844,-85.602364,Michigan,1784.00,10120860.00,18,0 25 | MN,46.729553,-94.6859,Minnesota,2087.00,5132799.00,41,0 26 | MS,32.354668,-89.398528,Mississippi,19184.00,2921088.00,657,0 27 | MO,37.964253,-91.831833,Missouri,4589.00,5800310.00,79,0 28 | MT,46.879682,-110.362566,Montana,203.00,935670.00,22,0 29 | NE,41.492537,-99.901813,Nebraska,547.00,1758787.00,31,0 30 | NV,38.80261,-116.419389,Nevada,1628.00,2414807.00,67,0 31 | NH,43.193852,-71.572395,New Hampshire,689.00,1309940.00,53,0 32 | NJ,40.058324,-74.405661,New Jersey,1044.00,8717925.00,12,0 33 | NM,34.97273,-105.032363,New Mexico,870.00,1928384.00,45,0 34 | NY,43.299428,-74.217933,New York,2555.00,19254630.00,13,0 35 | NC,35.759573,-79.0193,North Carolina,5627.00,8683242.00,65,0 36 | ND,47.551493,-101.002012,North Dakota,96.00,636677.00,15,0 37 | OH,40.417287,-82.907123,Ohio,3807.00,11464042.00,33,0 38 | OK,35.007752,-97.092877,Oklahoma,3122.00,3547884.00,88,0 39 | OR,43.804133,-120.554201,Oregon,1697.00,3641056.00,47,0 40 | PA,41.203322,-77.194525,Pennsylvania,1212.00,12429616.00,10,0 41 | RI,41.580095,-71.477429,Rhode Island,327.00,1076189.00,30,0 42 | SC,33.836081,-81.163725,South Carolina,2417.00,4255083.00,57,0 43 | SD,43.969515,-99.901813,South Dakota,448.00,775933.00,58,0 44 | TN,35.517491,-86.580447,Tennessee,7087.00,5962959.00,119,0 45 | TX,31.968599,-99.901813,Texas,118552.00,22859968.00,519,0 46 | UT,39.32098,-111.093731,Utah,1226.00,2469585.00,50,0 47 | VT,44.558803,-72.577841,Vermont,0.00,623050.00,0,0 48 | VA,37.431573,-78.656894,Virginia,6189.00,7567465.00,82,0 49 | WA,47.751074,-120.740139,Washington,2785.00,6287759.00,44,0 50 | WV,38.597626,-80.454903,West Virginia,429.00,1816856.00,24,0 51 | WI,43.78444,-88.787868,Wisconsin,1301.00,5536201.00,23,0 52 | WY,43.075968,-107.290284,Wyoming,432.00,509294.00,85,0 -------------------------------------------------------------------------------- /app/data/us-states.geojson: -------------------------------------------------------------------------------- 1 | {"type":"FeatureCollection","features":[ 2 | 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