Merge pull request #40 from cct-datascience/text

Add text placeholders and year filter

app/app.R

@@ -14,8 +14,20 @@ articles <- read_csv("articles_clean.csv")
 # UI ----------------------------------------------------------------------
 
 ui <- fluidPage(
+ h1("Title"),
+ p("A short description could go here, but probably shouldn't be too long or you'll have to scroll down quite a bit to get to the rest of the app."),
  fluidRow(
+
+## Input panel -------------------------------------------------------------
  panel(
+ sliderInput(
+ inputId = "year_range",value = c(min(articles$year), max(articles$year)),
+ label = "Year Range",
+ min = min(articles$year),
+ max = max(articles$year),
+ sep = "",
+ dragRange = TRUE
+ ),
  selectizeGroupUI(
  id = "my-filters",
  params = list(
@@ -46,27 +58,32 @@ ui <- fluidPage(
 # Server ------------------------------------------------------------------
 
 server <- function(input, output, session) {
-
- sankey_data <- callModule(
+
+# Filter data by selectize input ------------------------------------------
+ sankey_filtered <- callModule(
  module = selectizeGroupServer,
  id = "my-filters",
  data = articles,
  vars = c("domain", "biomarker", "collection", 
  "frequency", "communication", "behavior", "outcome")
- )
-
+ ) 
 
- #render the plot
+
+# Render the plot --------------------------------------------------------
  output$sankey <- renderPlot({
  #Take a dependency on the refresh button
  input$refresh
-
+
+## Filter data ------------------------------------------------------------
  #use isolate() so the plot only updates when the button is clicked, not when
  #sankey_data is updated
  #could still update highlighting with every change by using sankey_data() in
  #a scale_color* call possibly. Worry about this later in case we don't end
  #up sticking with ggplot
- plotdf <- isolate(sankey_data()) %>% 
+ sankey_data <- isolate(sankey_filtered() %>% 
+ filter(year >= input$year_range[1] & year <= input$year_range[2]))
+
+ plotdf <- sankey_data %>% 
  ggsankey::make_long(
  domain,
  biomarker,
@@ -76,7 +93,8 @@ server <- function(input, output, session) {
  behavior,
  outcome
  )
-
+
+# Build the Plot --------------------------------------------------------
  ggplot(plotdf,
  aes(x = x, 
  next_x = next_x, 
@@ -110,14 +128,14 @@ server <- function(input, output, session) {
  xlab(NULL)
  })
 
- # download button function
+# Download button function ----------------------
  output$download <- downloadHandler(
  filename = function() {
  #constructs file name based on today's date
  paste('data-', Sys.Date(), '.csv', sep='')
  },
  content = function(file) {
- write.csv(sankey_data(), file)
+ write.csv(sankey_data, file)
  }
  )
 }

notes/notes on new plotly code.R

@@ -0,0 +1,90 @@
+library(tidyverse)
+library(ggsankey)
+library(networkD3)
+
+df <- tribble(
+ ~doi, ~domain, ~freq, ~outcome,
+ 1 , "diabetes", "once", "blood sugar reduction",
+ 1 , "diabetes", "once", "weight loss",
+ 2 , "diabetes", "more than once", "blood sugar reduction",
+ 3 , "obesity", "once", "weight loss",
+ 3 , "obesity", "once", "activity level", #TODO tooltip from obsity to once should be 1 paper.
+ 4 , "obesity", "more than once", "weight loss",
+ 5 , "obesity", "more than once", "weight loss"
+)
+
+
+#ggsankey version:
+
+df_made_long <- ggsankey::make_long(df, domain, freq, outcome, value = doi) 
+
+ggplot(df_made_long, 
+ aes(x = x, 
+ next_x = next_x, 
+ node = node, 
+ next_node = next_node,
+ fill = factor(node),
+ label = node)
+) +
+ geom_sankey() +
+ geom_sankey_label() +
+ theme(legend.position = "none")
+
+
+# Networkd3 version:
+
+# Count number of observations for each link
+results_n <-
+ df_made_long %>%
+ group_by(node, next_node) %>%
+ summarize(n = n(), 
+ n_refs = length(unique(value)))
+
+# nodes
+nodes <-
+ df_made_long %>%
+ group_by(node) %>%
+ summarize(N_REFS = length(unique(value))) %>%
+ rename(name = node) %>% 
+ mutate(node = 0:(n()-1)) %>% 
+ as.data.frame()
+
+
+# edges
+# Create links dataframe
+links <- 
+ left_join(results_n, nodes, by = c("node" = "name")) %>%
+ left_join(nodes, by = c("next_node" = "name")) %>%
+ ungroup() %>%
+ rename(source = node.y,
+ target = node.y.y,
+ value = n) %>%
+ select(source, target, value, n_refs) %>%
+ na.omit() %>%
+ as.data.frame()
+
+sankeyNetwork(Links = links, Nodes = nodes, Source = 'source', 
+ Target = 'target', Value = 'value', NodeID = 'name',
+ units = 'observations')
+
+# try a plotly version
+library(plotly)
+plot_ly(
+ type = "sankey",
+ orientation = "h",
+
+ node = list(
+ label = nodes$name,
+ pad = 15,
+ thickness = 20,
+ line = list(
+ color = "black",
+ width = 0.5
+ )
+ ),
+
+ #TODO: put both numbers of observations and number of # refs/papers
+ link = links,
+ hoverinfo = "text",
+ # hoverlabel = ,
+)

renv.lock

@@ -9,14 +9,6 @@
  ]
  },
  "Packages": {
- "Cairo": {
- "Package": "Cairo",
- "Version": "1.6-0",
- "Source": "Repository",
- "Repository": "CRAN",
- "Hash": "60aada7beac23aa3e9b219485d6b2da8",
- "Requirements": []
- },
  "DBI": {
  "Package": "DBI",
  "Version": "1.1.3",
@@ -631,10 +623,10 @@
  },
  "httr": {
  "Package": "httr",
- "Version": "1.4.3",
+ "Version": "1.4.4",
  "Source": "Repository",
  "Repository": "CRAN",
- "Hash": "88d1b310583777edf01ccd1216fb0b2b",
+ "Hash": "57557fac46471f0dbbf44705cc6a5c8c",
  "Requirements": [
  "R6",
  "curl",
@@ -654,6 +646,19 @@
  "uuid"
  ]
  },
+ "igraph": {
+ "Package": "igraph",
+ "Version": "1.3.4",
+ "Source": "Repository",
+ "Repository": "CRAN",
+ "Hash": "40e38ff98f90967805831b6234afd05f",
+ "Requirements": [
+ "Matrix",
+ "magrittr",
+ "pkgconfig",
+ "rlang"
+ ]
+ },
  "isoband": {
  "Package": "isoband",
  "Version": "0.2.5",
@@ -825,6 +830,18 @@
  "colorspace"
  ]
  },
+ "networkD3": {
+ "Package": "networkD3",
+ "Version": "0.4",
+ "Source": "Repository",
+ "Repository": "CRAN",
+ "Hash": "38310ec4ddb1398359abdd603c151067",
+ "Requirements": [
+ "htmlwidgets",
+ "igraph",
+ "magrittr"
+ ]
+ },
  "nlme": {
  "Package": "nlme",
  "Version": "3.1-157",
@@ -1079,10 +1096,10 @@
  },
  "rstudioapi": {
  "Package": "rstudioapi",
- "Version": "0.13",
+ "Version": "0.14",
  "Source": "Repository",
  "Repository": "CRAN",
- "Hash": "06c85365a03fdaf699966cc1d3cf53ea",
+ "Hash": "690bd2acc42a9166ce34845884459320",
  "Requirements": []
  },
  "rvest": {