# Easy Visualization of Massive Correlation Results with R: Basic Version

R Shiny in 3 Levels: Basic, Publish, and Share

# Easy Visualization of Correlation Results with R Shiny in 3 Levels: Basic, Publish, and Share

## Introduction: Basic, Publish, and Share Method

It isn’t all that difficult to code R shiny applications if you already know how to program in R. My goal is to create an interactive application to organize and display a large collection of clinical correlations.

My new format for these tutorials is dividing them into “basic”, “publish”, and “share” quality levels. Software tutorials are too easy to complicate. “Basic” - the minimal code to have a fully operational solution “Publish” - modifications for publication-quality “Share” - modifications for other software developers or researchers to use

## Dataset

The tutorials I have seen online are examples but do not reflect the results professional scientists deal with on a daily basis. We will examine thousands of correlations between clinical variables and EEG measures in the dataset provided. The results are corrected for multiple comparisons (within their subgroups) and include age-corrected partial correlations.

## Requirements

External Data (link here) R environment

## Learning R Shiny

If you haven’t used R-Shiny before, run over the first few tutorial lessons from the official Shiny team. https://shiny.rstudio.com/tutorial/written-tutorial/lesson1/

## “Basic” Version

The application must 1) display correlation results and 2) plot a selected correlation result.

### Summary of program UI and architectures

This project’s variables are stored in a single RDATA file. The Shiny architecture has a “UI” for input and display and a “server” that performs any functions.

load("model_ClinicalCorrelations_revised.RData")


Let’s look at our key variables: corr.rawdata - raw data for correlations in wide format (1 colunm per variable) corr.results - master table of correlation results

### Starting Code

library(shiny)
library(DT)
library(tidyverse)

# Define UI for an application
ui <- fluidPage(
# UI code here
)
# Define server logic
server <- function(input, output, session) {
# Server code here
}
# Run the application
shinyApp(ui = ui, server = server)


The DT package incorporates special Shiny components include an interactive data table object.

### Display correlation results

To display the correlation table, add a dataTableOutput (not tableOutput) to the UI section of the code:

ui <- fluidPage(
# UI code here
DT::DTOutput("corrResults")
)


To connect the data to the table, update the server code:

server <- function(input, output, session) {
# Server code here
output$corrResults <- DT::renderDT({corr.results }) }  Click “Run App” to run the application in the web browser to show the correlation results. ### Enable user interaction to select a data row Now that we have a visual representation of the data, we want to be able to select a row and plot the results. Each row contains the keys necessary to locate and pull the raw data to create a simple scatter plot. First, add selection='single' to the renderDT expression so only one row is selected when the user clicks. server <- function(input, output, session) { # Server code here output$corrResults <- DT::renderDT({corr.results, selection='single'
})
}


Second, add an observe block to monitor the user selection and gather the data from the row to prepare for a query from the raw data table (corr.rawdata). We will also add a print function to check if the row selection works.

server <- function(input, output, session) {
# Server code here
output$corrResults <- DT::renderDT({datatable(corr.results, selection='single')}) observe({ req(input$corrResults_rows_selected)
print(input$corrResults_rows_selected)}) }  Now, run the code and select a row. The row will be highlighted and in the R console, you will see the selected row number. ### Plot selected result In the first step, we loaded and displayed a data table of the correlation results. Selecting a row of the data table will make that data available to your server. Each row contains the keys necessary to locate and pull the raw data to create a simple scatter plot. #### Create a dataframe containing raw data for plotting We were able to obtain the selected row of correlation results in the last step. To plot data, we need to make keys from each column. These keys can be used to filter and select raw data from the second table, corr.rawdata. Let’s update our SERVER code:  server <- function(input, output, session) { # Server code here output$corrResults <- DT::renderDT({datatable(corr.results, selection='single')})
observe({
req(input$corrResults_rows_selected) print(input$corrResults_rows_selected)

selected_row_index = input$corrResults_rows_selected # query matched raw data querydf <- corr.results %>% slice(selected_row_index) corrtype_sel <- querydf$corrtype
bandname_sel  <- querydf$bandname type_sel <- querydf$type
label_sel     <- querydf$label measure_sel <- querydf$measure

query_string <- str_c(c(corrtype_sel, bandname_sel, label_sel, type_sel) , collapse = "_")

scatterdf <- corr.rawdata %>% select(all_of(measure_sel), all_of(query_string))
})
}


The correlation results were calculated within subgroups, whereas the raw data includes all participants. A participant may belong to more than one subgroup. In this data, we want all individuals with Fragile X Syndrome (group == “FXS”) but also male individuals with full mutation and no mosaicism with FXS (group == “FXS” AND mgroup == “M1”).
To me, the most straightforward solution is to create a filter expression based on the group value and then use that expression when the scatterplot data is called. This requires a small modification:

    # specify data subset
filter_sel <- switch(group,
"FXS" = quo(group == 'FXS'),
"M1"  = quo(group == 'FXS' & mgroup == "M1"))

# request data
scatterdf <- corr.rawdata %>%
select(group, mgroup, all_of(measure_sel), all_of(query_string)) %>%
filter( !!(filter_sel) )


You might want to filter all FXS first and then use just the mgroup query, but this approach is much easier to read and troubleshoot.

##### Code Hiighlights
• quo can be used to create an expression that can be evaluated in a dplyr function by the use of the “bang-bang” operator !!

Let’s update our UI code to include a plot object for our scatter plot as follows:

# Define UI for application that draws a histogram
ui <- fluidPage(
# UI code here
DT::DTOutput("corrResults"),
plotOutput("corrPlot")
)


Lets next create a simple scatter plot of the ywo colmns.

 server <- function(input, output, session) {
# Server code here
output$corrResults <- DT::renderDT({datatable(corr.results, selection='single')}) observe({ req(input$corrResults_rows_selected)
print(input$corrResults_rows_selected) selected_row_index = input$corrResults_rows_selected

# query matched raw data
querydf <- corr.results %>% slice(selected_row_index)
corrtype_sel  <- querydf$corrtype bandname_sel <- querydf$bandname
type_sel      <- querydf$type label_sel <- querydf$label
measure_sel      <- querydf$measure query_string <- str_c(c(corrtype_sel, bandname_sel, label_sel, type_sel) , collapse = "_") scatterdf <- corr.rawdata %>% select(all_of(measure_sel), all_of(query_string)) output$corrPlot <- renderPlot({
scatterdf %>% ggplot() +
geom_point(aes(x=get(measure_sel), y=get(query_string))) +
ylab(measure_sel) + xlab(query_string) + theme_minimal() +
theme(aspect.ratio = 1)
})
})
}

##### Code highlights:
• the stringR::str_c function merges strings together with a delimiter (in this case underscore)
• all_of() allows the use of strings as variable names in filter
• dplyr::getfunction in the ggplot allows the use of strings as variable names in dplyr functions. At this point, the key aspects of the basic application are complete. To create a peer-reviewed manuscript-ready version of the code, we will modify it in the next tutorial. ##### Ernest Pedapati, M.D., M.S.
###### Associate Professor of Psychiatry

Physician and Neuroscientist interested in neurodevelopmental conditions.