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01_advanced.R

@@ -3,9 +3,12 @@ library(dplyr)
 library(tidyr)
 library(ggplot2)
 
+# Read in the expression table, and identify the non-gene columns (metadata)
 expr_table <- read.table("expression_matrix_w_metadata.txt", sep = ",", header = TRUE, stringsAsFactors = FALSE)
 metacols <- grep("^ENSG", colnames(expr_table), invert = TRUE, value = TRUE)
 
+# Read in the list of genes, discard genes with no expression data,
+# and turn the gene name/gene id table into a lookup list
 allgenes <- read.table("mart_export.txt", sep = "\t", header = TRUE, stringsAsFactors = FALSE)
 allgenes <- allgenes[allgenes$ensId %in% colnames(expr_table),]
 lookuptable <- unstack(allgenes)
@@ -14,9 +17,11 @@ ui <- fluidPage(
 
   titlePanel("Immune Cell Gene Expression Data"),
 
-  sidebarLayout(
+  sidebarLayout( # Determines that this app will use the sidebar layout method
     sidebarPanel(
+      # Populate the dropdown from the allgenes vector values
       selectizeInput("ingene", "Choose a gene:", choices = sort(allgenes$symbol), selected = "HBB"),
+      # Don't be that guy who doesn't give others credit
       helpText(h4("Data source:")),
       helpText("Linsley PS, Speake C, Whalen E, Chaussabel D.", em("Copy number loss of the interferon gene cluster in                     melanomas is linked to reduced T cell infiltrate and poor patient prognosis."), 
                "PLoS One 2014 Oct 14;9(10):e109760."),
@@ -36,27 +41,29 @@ ui <- fluidPage(
 server <- function(input, output) {
 
     output$theplot <- renderPlot({
+        # Check that the input gene is not null before plotting
         req(input$ingene)
 
+        # Take the human-friend user input (gene name) and convert
+        # it to Ensembl Id
         ens <- lookuptable[[input$ingene]]
         gene <- input$ingene
 
+        # Pull out the chosen gene and metadata only
         tinyframe <- expr_table[,c(ens, metacols)]
-        tinyframe <- tinyframe %>% gather(variable, value, one_of(ens))
-        p <- ggplot(tinyframe, aes(x = variable, y = value, color = cellType)) + 
+
+        # Tidy the dataframe for plotting
+        tinyframe <- tinyframe %>% 
+          gather(variable, value, one_of(ens))
+
+        ggplot(tinyframe, aes(x = variable, y = value, color = cellType)) + 
           ggtitle(paste0(gene, " expression by cell type across conditions")) +
           geom_boxplot() +
+          facet_grid(~ diseaseStatus) +
           ylab("Normalized Counts") +
           theme(axis.text.x = element_blank(), 
                 axis.ticks.x = element_blank(), 
                 axis.title.x = element_blank())
-
-        if (length(ens) > 1) {
-          p <- p + facet_grid(variable ~ diseaseStatus, scales = "free_y")
-        } else { 
-          p <- p + facet_grid(~ diseaseStatus)
-        }
-        return(p)
     })
 
 }

01_simple.R

@@ -1,11 +1,13 @@
 library(shiny)
 
 
-ui <- fluidPage(
+ui <- fluidPage( 
 
-  fluidRow(
+  fluidRow( # Determines that this app will use the "fluid page" (grid) layout method
     column(width = 12,
+           # Creates the input widget
            textInput("textinputlabel", "What's up?"),
+           # Designates the place-holder for the output generated
            textOutput("newtext")
 
     )
@@ -14,6 +16,8 @@ ui <- fluidPage(
 
 server <- function(input, output) {
 
+    # Transforms the input to uppercase and writes it where it can be read
+    # into the user interface
     output$newtext <- renderText({
       toupper(input$textinputlabel)
     })

02_advanced.R

@@ -1,5 +1,5 @@
 library(profvis)
 
-# Run the application 
+# Surprise! We're just going to look at the last one!
 profvis(runApp("01_advanced.R"))
 

02_simple.R

@@ -7,6 +7,7 @@ ui <- fluidPage(
 
   fluidRow(
     column(width = 12,
+           # Create input widget and place-hold outputs
            sliderInput("sample_count", "How many times should we sample each population?",
                        value = 10,
                        min = 1,
@@ -20,15 +21,19 @@ ui <- fluidPage(
 server <- function(input, output) {
 
     sample_dat <- reactive({
+      # Sample from two normal distributions with slightly different means
+      # The rowid is just a unique identifier to allow tidying
       data.frame("rowid" = paste0("row", seq(1,input$sample_count)),
                  "thicc bois" = rnorm(input$sample_count, 0, 1),
                  "chonkers" = rnorm(input$sample_count, 0.2, 1),
                  stringsAsFactors = FALSE,
                  check.names = FALSE) %>%
+        # "Tidy" the dataframe into "long" format for plotting
         gather(population, measurement, -rowid)
     })
 
     output$sample_plot <- renderPlot({
+      # Plot the two populations
       ggplot(sample_dat(), aes(x = population, y = measurement, fill = population)) +
         geom_boxplot() +
         geom_point(position = position_jitterdodge(0.5)) +

03_advanced.R

@@ -4,6 +4,8 @@ library(tidyr)
 library(ggplot2)
 library(RSQLite)
 
+# Pull gene names from the database when they occur in both the
+# gene list and the expression table
 conn <- dbConnect(RSQLite::SQLite(), "GSE60424.db")
 choosenames <- dbGetQuery(conn, "SELECT symbol
                           FROM identifiers i
@@ -18,7 +20,9 @@ ui <- fluidPage(
 
   sidebarLayout(
     sidebarPanel(
+      # Create input widget, but do not populate it with any genes
       selectizeInput("ingene", "Choose a gene:", choices = NULL, selected = NULL),
+
       helpText(h4("Data source:")),
       helpText("Linsley PS, Speake C, Whalen E, Chaussabel D.", em("Copy number loss of the interferon gene cluster in                     melanomas is linked to reduced T cell infiltrate and poor patient prognosis."), 
                "PLoS One 2014 Oct 14;9(10):e109760."),
@@ -36,12 +40,17 @@ ui <- fluidPage(
 )
 
 server <- function(input, output, session) {
-
+
+  # Populate the selection widget after the rest of the page loads.
+  # Using server = TRUE makes it even faster
   updateSelectizeInput(session, "ingene", choices = choosenames, selected = "HBB", server = TRUE)
 
+  # Function to pull selected gene from SQLite database rather
+  # than reading in a full text ("flat") file
   makeTinyframe <- function(gene, dbpath){
     conn <- dbConnect(RSQLite::SQLite(), dbpath)
     meta <- dbGetQuery(conn, "SELECT * FROM meta;")
+    # This text is SQL code:
     exprquery <- sprintf("SELECT i.symbol, e.*
                          FROM identifiers i
                          JOIN expr e
@@ -64,31 +73,31 @@ server <- function(input, output, session) {
 
   }
 
+  # Function to create the plot
   makePlot <- function(tinyframe){
 
+    # This is just because the whole dataframe is passed to the function,
+    # but we want the gene name itself for the title text
     printgene <- unique(tinyframe$genename)
 
-    p <- ggplot(tinyframe, aes(x = genename, y = TPM, color = celltype)) + 
+    ggplot(tinyframe, aes(x = genename, y = TPM, color = celltype)) + 
       ggtitle(paste0(printgene, " expression by cell type across conditions")) +
       geom_boxplot() +
+      facet_grid(~ disease_status) +
       theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank())
-
-    if (length(unique(tinyframe$gene)) > 1) {
-      p <- p + facet_grid(variable ~ disease_status, scales = "free_y")
-    } else { 
-      p <- p + facet_grid(~ disease_status)
-    }
-    return(p)
   }
 
   inputgene <- reactive({
+    # This allows us to return custom text if there is something wrong
+    # with the input. The app will not continue until the "need" is met.
     validate(
       need(input$ingene != "", "Please select a gene")
     )
     input$ingene
   })
 
   output$theplot <- renderPlot({
+    # Call the querying and plotting functions
     tf <- makeTinyframe(inputgene(), "GSE60424.db")
     makePlot(tf)
   })

03_simple.R

@@ -20,6 +20,9 @@ ui <- fluidPage(
 server <- function(input, output) {
 
     sample_dat <- reactive({
+      # Perform the random sampling and assign it to a reactive object
+      # so when we plot and perform a t-test, they are not getting
+      # different random samples
       data.frame("rowid" = paste0("row", seq(1,input$sample_count)),
                  "thicc bois" = rnorm(input$sample_count, 0, 1),
                  "chonkers" = rnorm(input$sample_count, 0.2, 1),
@@ -28,6 +31,8 @@ server <- function(input, output) {
     })
 
     output$sample_plot <- renderPlot({
+      # In this example the tidying is done as part of plotting
+      # because the t-test uses the original data format
       sample_dat()  %>%
         gather(population, measurement, -rowid) %>%
       ggplot(aes(x = population, y = measurement, fill = population)) +
@@ -40,9 +45,12 @@ server <- function(input, output) {
     })
 
     output$sample_result <- renderText({
+      # As in the plot, the reactive object must be called
+      # with parentheses (because it's actually a function)
       p <- t.test(sample_dat()[["thicc bois"]], 
              sample_dat()[["chonkers"]], 
              paired = FALSE)[["p.value"]]
+      # Add an asterisk to "significant" results
       if(p >= 0.05){
         paste("p-value = ", p)
       } else {

04_simple.R

@@ -7,6 +7,7 @@ ui <- fluidPage(
 
   fluidRow(
     column(width = 12,
+           # Clicking the button increases the value of input$do_sample by 1
            actionButton("do_sample", "Moar!"),
            plotOutput("sample_plot"),
            textOutput("sample_result")
@@ -15,19 +16,28 @@ ui <- fluidPage(
 )
 
 server <- function(input, output) {
-
+
+    # Initialize the reactive value at 10.
+    # The reactive value can store intermediate values
+    # while keeping them in "reactive context"
     sample_count <- reactiveVal(10)
 
+    # When the button is clicked, input$do_sample changes 
+    # and triggers the contents of the "observeEvent" expression
+    # to run. The observeEvent does not return a value like "render"
+    # expressions do.
     observeEvent(input$do_sample,{
       x <- sample_count() + 10
       sample_count(x)
     })
 
     sample_dat <- reactive({
-      tmp <- sample_count()
-      data.frame("rowid" = paste0("row", seq(1, tmp)),
-                 "thicc bois" = rnorm(tmp, 0, 1),
-                 "chonkers" = rnorm(tmp, 0.2, 1),
+      # When observeEvent is triggers, it changes the value of 
+      # sample_count(), so everything depending on sample_count()
+      # recalculates. Here it regenerates the data frame.
+      data.frame("rowid" = paste0("row", seq(1, sample_count())),
+                 "thicc bois" = rnorm(sample_count(), 0, 1),
+                 "chonkers" = rnorm(sample_count(), 0.2, 1),
                  stringsAsFactors = FALSE,
                  check.names = FALSE)
     })