Merge pull request #68 from ESHackathon/restructure

Restructure as package with ability to save custom app

Author: afeierman

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$

DESCRIPTION

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+Package: eviatlas
+Version: 0.4.0
+Title: Visualisation and mapping for datasets from systematic reviews and maps
+Authors@R: c(
+  person(
+    given = "Martin",
+    family = "Westgate",
+    role = c("aut", "cre"),
+    email = "martinjwestgate@gmail.com",
+    comment = c(ORCID = "0000-0003-0854-2034")),
+  person(
+    given = "Andrew",
+    family = "Feierman",
+    role = c("aut"),
+    email = "andrew.feierman@sei.org"))
+Description: Run a shiny app for visualising the datasets produced by systematic maps, or produce your own.
+Depends: R (>= 3.5.0), shiny
+License: GPL-3
+BugReports: https://github.com/rmetaverse/eviatlas
+LazyData: true
+RoxygenNote: 7.1.1
+Imports: 
+    dplyr, 
+    tidyr,
+    readr,
+    stringr,
+    magrittr,
+    ggplot2,
+    DT,
+    RColorBrewer,
+    viridis,
+    sf,
+    leaflet,
+    mapview,
+    leafem,
+    shinyBS,
+    shinyWidgets,
+    shinydashboard

LICENSE

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+# Generated by roxygen2: do not edit by hand
+
+S3method(GenTimeTrend,default)
+S3method(GenTimeTrend,numeric)
+export("%<>%")
+export("%>%")
+export(eviatlas)
+export(save_eviatlas_app)
+export(shiny_config)
+export(shiny_server)
+export(shiny_ui)
+import(shiny)
+importFrom(magrittr,"%<>%")
+importFrom(magrittr,"%>%")
+importFrom(utils,capture.output)
+importFrom(utils,data)
+importFrom(utils,read.csv)
+importFrom(utils,write.csv)

NAMESPACE

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+#' Create heatmap from dataset
+#'
+#' Feed in a dataset and get a heatmap in return
+#'
+#' @param idata Input dataframe
+#' @param selcols Numeric vector of column indices
+#' @param axis_txt_lim character limit for label text
+#'
+#' @return Returns a heatmap object showing number of literature under different categories in user specified \code{selcols}
+#' @keywords internal
+#' @author Ezgi Tanriver-Ayder and Sanita Dhaubanjar
+
+GenHeatMap <- function(idata, selcols, axis_txt_lim = 60) {
+
+  # if df is a shapefile, remove geometry column
+  if (any(class(idata) == "sf")) {
+    idata <- sf::st_drop_geometry(idata)
+  }
+
+  # Bind variables locally to function (helps avoid R CMD notes)
+  listone <- listtwo <- n <- NULL
+
+  # Convert columns to factors to allow for categorical classification for both numeric and character data -------
+  tmp <- as.data.frame(sapply(idata[selcols], function(x) as.factor(x)))
+
+
+  # Plot Heatmap ------
+  heatmp <- tmp %>%
+    dplyr::rename(listone = colnames(tmp[1]), listtwo = colnames(tmp[2])) %>%
+    dplyr::count(listone, listtwo) %>%
+    tidyr::complete(listone, listtwo, fill = list(n = 0)) %>%
+    ggplot2::ggplot(ggplot2::aes(x = listone, y = listtwo, fill = n, label = n)) +
+    ggplot2::geom_tile(alpha = 0.3, color = "grey60") +
+    ggplot2::geom_text() +
+    viridis::scale_fill_viridis() +
+    ggplot2::theme_minimal() +
+    ggplot2::theme(
+      axis.text.x = ggplot2::element_text(angle = 45, hjust = 1),
+      panel.grid = ggplot2::element_blank(),
+      text = ggplot2::element_text(size = 14),
+      axis.title = ggplot2::element_text(size = 16),
+      title = ggplot2::element_text(size = 18)
+    ) +
+    ggplot2::xlab(paste0(selcols[1])) +
+    ggplot2::ylab(paste0(selcols[2])) +
+    ggplot2::labs(fill = "Count") +
+    # Limit axis text to a certain number of characters, so that long text doesn't ruin the chart display
+    ggplot2::scale_x_discrete(labels = function(x) substr(x, 1, axis_txt_lim)) +
+    ggplot2::scale_y_discrete(labels = function(x) substr(x, 1, axis_txt_lim)) +
+    ggplot2::ggtitle("Study Heatmap", subtitle = paste(selcols[2], "by", selcols[1]))
+
+  heatmp
+}

R/GenHeatMap.R

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+#' Create bar plot with distribution of studies over the region from lat/long info
+#'
+#' Created for: ES Hackathon 2018
+#' @param df Input dataframe
+#' @param location_column Column with location information (preferably country-level or higher)
+#' @param axis_txt_lim character limit for label text
+#' @return Returns a bar plot object showing counts of literature in systematic review for each location
+#'
+#' @author Sanita Dhaubanjar
+#'
+#' @keywords SystematicReview
+#' @keywords internal
+
+GenLocationTrend <- function(df, location_column, axis_txt_lim = 60) {
+
+  # if df is a shapefile, remove geometry column
+  if (any(class(df) == "sf")) {
+    df <- sf::st_drop_geometry(df)
+  }
+
+
+  # Bind variables locally to function (helps avoid R CMD notes)
+  listone <- listtwo <- n <- NULL
+
+  # Count per locations --------
+  location_counts <- as.data.frame(table(df[location_column])) # table() tabulates frequency
+  colnames(location_counts) <- c(location_column, "n")
+
+  # Plot bar chart
+  locmp <- ggplot2::ggplot(location_counts, ggplot2::aes_string(
+    x = colnames(location_counts[1]),
+    y = colnames(location_counts[2]),
+    label = colnames(location_counts[2])
+  )) +
+    ggplot2::geom_bar(alpha = 0.9, stat = "identity", fill = "light green") +
+    ggplot2::scale_x_discrete(labels = function(x) substr(x, 1, axis_txt_lim)) +
+    ggplot2::geom_text(ggplot2::aes(), size = 3, nudge_y = 10) +
+    ggplot2::labs(y = "# Studies") +
+    ggplot2::ggtitle(paste(location_column, "frequency")) +
+    ggplot2::theme_bw() +
+    ggplot2::theme(
+      axis.line = ggplot2::element_line(colour = "black"),
+      panel.background = ggplot2::element_blank(),
+      plot.title = ggplot2::element_text(hjust = .5),
+      text = ggplot2::element_text(size = 14)
+    )
+
+  # Rotate xaxis label if too many
+  if (nrow(location_counts) > 15) {
+    locmp <- locmp + ggplot2::theme(
+      axis.text.x = ggplot2::element_text(angle = 45, hjust = 0.95, size = 11)
+    )
+  }
+
+  locmp
+}

R/GenLocationTrend.R

@@ -1,38 +1,57 @@
 #' Create Histogram from dataset
+#' @param idata dataset from eviatlas
+#' @param hist_col column used for histogram
+#' @param axis_txt_lim character limit for label text in axis
+#' @keywords internal
 
-GenTimeTrend = function(idata, hist_col, axis_txt_lim = 60){
+GenTimeTrend <- function(idata, hist_col, axis_txt_lim = 60) {
   UseMethod("GenTimeTrend", object = idata[hist_col][[1]])
 }
 
-GenTimeTrend.default <- function(idata, hist_col, axis_txt_lim = 60){
-
-  gttmp <- ggplot2::ggplot(idata, aes_string(x = hist_col)) +
+#' Create Histogram from dataset - default method
+#' @method GenTimeTrend default
+#' @export
+#' @keywords internal
+GenTimeTrend.default <- function(idata, hist_col, axis_txt_lim = 60) {
+  gttmp <- ggplot2::ggplot(idata, ggplot2::aes_string(x = hist_col)) +
     ggplot2::geom_bar(
       alpha = 0.9,
       stat = "count",
       fill = "dodger blue"
     ) +
     ggplot2::labs(y = "Studies") +
-    ggplot2::scale_x_discrete(name = paste(hist_col), labels = function(x) substr(x, 1, axis_txt_lim)) +
+    ggplot2::scale_x_discrete(
+      name = paste(hist_col),
+      labels = function(x) substr(x, 1, axis_txt_lim)
+    ) +
     ggplot2::theme_bw() +
     ggplot2::theme(
       axis.line = ggplot2::element_line(colour = "black"),
       panel.background = ggplot2::element_blank(),
       plot.title = ggplot2::element_text(hjust = .5),
       text = ggplot2::element_text(size = 13)
     )
-  
+
   # Rotate xaxis label if too many categories
-  if (dplyr::n_distinct(idata[hist_col]) > 15){
+  if (dplyr::n_distinct(idata[hist_col]) > 15) {
     gttmp <- gttmp + ggplot2::theme(
-      axis.text.x = element_text(angle = 40, hjust = 0.95, size = 12))
+      axis.text.x = ggplot2::element_text(
+        angle = 40,
+        hjust = 0.95,
+        size = 12
+      )
+    )
   }
   gttmp
 }
 
-GenTimeTrend.numeric <- function(idata, hist_col, axis_txt_lim = 60){
-  
-  ggplot2::ggplot(idata, aes_string(x = hist_col)) +
+#' Create Histogram from dataset - numeric column
+#' @method GenTimeTrend numeric
+#' @export
+#' @keywords internal
+
+GenTimeTrend.numeric <- function(idata, hist_col, axis_txt_lim = 60) {
+  ggplot2::ggplot(idata, ggplot2::aes_string(x = hist_col)) +
     ggplot2::geom_bar(
       alpha = 0.9,
       stat = "count",

src/GenTimeTrend.R R/GenTimeTrend.Rsrc/GenTimeTrend.R renamed to R/GenTimeTrend.R

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+#' Eviatlas Shiny App
+#'
+#' @param study_data a dataframe or tibble containing data to be used in an eviatlas shiny app
+#' @param ... additional variables to feed into shiny app
+#' @export
+
+eviatlas <- function(study_data = NULL, ...) {
+  shiny_env <- new.env()
+  attr(shiny_env, "name") <- "eviatlas_shiny_env"
+
+  if (!missing(study_data)) {
+    assign("study_data", study_data, shiny_env)
+  }
+  environment(shiny_ui) <- shiny_env
+  environment(shiny_server) <- shiny_env
+  environment(shiny_config) <- shiny_env
+
+  app <- shiny::shinyApp(
+    ui = shiny_ui,
+    server = shiny_server,
+    onStart = shiny_config
+  )
+
+  environment(app) <- shiny_env
+  shiny::runApp(app, ...)
+}

R/eviatlas.R

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+#' Eviatlas Sample Data
+#'
+#' A sample systematic map dataset used to show features of Eviatlas and
+#' its functions to build a map atlas
+#' @keywords internal
+"eviatlas_pilotdata"

R/eviatlas_pilotdata.R

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+#' functions to determine if columns contain what appear to be latitude or longitude columns
+#' @keywords internal
+
+get_latitude_cols <- function(df) {
+  n_out <- colnames(df)[1]
+  # waiting for a more elegant interpretation, this is quick and dirty...
+  if ("Latitude" %in% colnames(df)) {
+    n_out <- "Latitude"
+  } else if ("latitude" %in% colnames(df)) {
+    n_out <- "latitude"
+  } else if ("lat" %in% colnames(df)) {
+    n_out <- "lat"
+  } else if ("Plotted.lat." %in% colnames(df)) {
+    n_out <- "Plotted.lat."
+  } else if (length(agrep(pattern = "latitude", x = colnames(df)))) {
+    n_out <- agrep(pattern = "latitude", x = colnames(df), value = T)[1]
+  }
+  n_out
+}
+
+get_longitude_cols <- function(df) {
+  n_out <- colnames(df)[1]
+  # waiting for a more elegant interpretation, this is quick and dirty...
+  if ("Latitude" %in% colnames(df)) {
+    n_out <- "Longitude"
+  } else if ("longitude" %in% colnames(df)) {
+    n_out <- "longitude"
+  } else if ("long" %in% colnames(df)) {
+    n_out <- "long"
+  } else if ("lng" %in% colnames(df)) {
+    n_out <- "lng"
+  } else if ("Plotted.long." %in% colnames(df)) {
+    n_out <- "Plotted.long."
+  } else if (length(agrep(pattern = "longitude", x = colnames(df)))) {
+    n_out <- agrep(pattern = "longitude", x = colnames(df), value = T)[1]
+  }
+  n_out
+}