solves and closes #10

app.R

@@ -6,6 +6,7 @@ library(DT)
 library(sp)
 library(rgdal)
 library(data.table)
+library(ggplot2)
 
 source("global.R")
 source("functions.R")
@@ -29,7 +30,6 @@ sidebar <- dashboardSidebar(
   )
 )
 
-
 #### BODY ####
 body <- dashboardBody(
   tags$style(type = "text/css", "#map {height: 100% !important;}"),
@@ -76,8 +76,8 @@ body <- dashboardBody(
             uiOutput("view_parentage")
     ),
     tabItem(tabName = "overview",
-            h3("Quick statistics of dataset"),
-            uiOutput("stats")
+            uiOutput("stats"),
+            uiOutput("graphs")
     )
   )
 )

dynamic_ui.R

@@ -139,3 +139,33 @@ observe({
     )
   })
 })
+
+observe({
+  xy <- allData()
+  par <- inputFileParentage()
+
+
+  if(nrow(xy) > 0) {
+    output$sps <- renderPlot({
+      ggplot(xy)  +
+        theme_bw() +
+        geom_bar(aes(sample_type))
+    })
+    # output$opp <- renderPlot({
+    #   ggplot(par) +
+    #     theme_bw() +
+    #     geom_col(aes(x = unique(c(mother, father), y = offspring)))
+    # })
+    output$graphs <- renderUI({
+      fluidRow(
+        box(solidHeader = TRUE, title = "Samples per sample type",
+            plotOutput("sps"))
+        # box(solidHeader = TRUE, title = "Number of offspring per parent",
+        #     plotOutput("opp"))
+      ) 
+    })
+  }
+
+
+})
+

generate_fake_data.R

@@ -8,32 +8,35 @@ N <- 5 # number of points per individual
 lng <- c(13.0, 16.6)
 lat <- c(45.4, 46.88)
 date <- as.Date(c("01.01.2010", "31.12.2017"), format = "%d.%m.%Y")
-type <- c("scat", "urine", "saliva", "tissue")
+sample_type <- c("scat", "urine", "saliva", "tissue")
+sex <- c("M", "F")
 
 ind.pt <- data.frame(lat = runif(NIND, min = min(lat), max = max(lat)),
-         lng = runif(NIND, min = min(lng), max = max(lng)))
+                     lng = runif(NIND, min = min(lng), max = max(lng)))
 
 ind.pt <- split(ind.pt, f = 1:nrow(ind.pt))
 
 xy <- sapply(ind.pt, FUN = function(x, N) {
-  out <- data.frame(lng = rnorm(N, mean = x$lng, sd = 0.05),
-             lat = rnorm(N, mean = x$lat, sd = 0.05))
+  out <- data.frame(x = rnorm(N, mean = x$lng, sd = 0.05),
+                    y = rnorm(N, mean = x$lat, sd = 0.05))
   tm <- sample(seq(from = min(date), to = max(date), by = "day"), 1)
-  typ <- sample(type, 5, replace = TRUE)
-  out$time <- tm + (1:N)
-  out$type <- typ
+  typ <- sample(sample_type, 5, replace = TRUE)
+  sex <- sample(sex, 5, replace = TRUE)
+  out$date <- tm + (1:N)
+  out$sample_type <- typ
+  out$sex <- sex
   out
 }, N = N, simplify = FALSE)
 
 xy <- do.call(rbind, xy)
 
 xy$animal <- sprintf("%.3d", rep(1:NIND, each = N))
-xy$id <- as.character(1:nrow(xy))
+xy$sample_name <- as.character(1:nrow(xy))
 
 offspring <- data.frame(sibling = sprintf("%.3d", 5:NIND),
-                       mother = c("001", "002"),
-                       father = c("003", "004"),
-                       cluster = c("1", "2"))
+                        mother = c("001", "002"),
+                        father = c("003", "004"),
+                        cluster = c("1", "2"))
 
 write.table(x = xy, file = "data.csv", sep = ",", row.names = FALSE, quote = FALSE)
 write.table(x = offspring, file = "offspring.csv", sep = ",", row.names = FALSE, quote = FALSE)

leaflet.R

@@ -8,6 +8,15 @@ output$map <- renderLeaflet({
     setView(lng = 14.815333, lat = 46.119944, zoom = 8)
 })
 
+observeEvent(input$uploadSampleData_row_last_clicked, {
+  x <- inputFileSamples()
+  selectedRow <- input$uploadSampleData_row_last_clicked
+
+  leafletProxy('map') %>%
+    setView(lng = x[selectedRow, 'lng'], lat = x[selectedRow, 'lat'], zoom = 10)
+
+}, ignoreInit = TRUE)
+
 # Add markers and lines for selected animals to map
 observe({
   PS <- PS()

view_data.R

@@ -1,6 +1,6 @@
 output$uploadSampleData <- renderDataTable({
   x <- inputFileSamples()
-  DT::datatable(data = x, filter = "top", options = list(pageLength = 15))
+  DT::datatable(data = x, filter = "top", options = list(pageLength = 15), selection = 'single')
 })
 
 observe({