Read a Data Frame into a Separate Function from Shiny - r

Question

I have a Shiny application which lets a user upload a CSV to undertake sentiment analysis.
The Aim:
I want to use Shiny to upload the CSV and then use a separate function (CapSent) to do the analysis and output the results.
Basically I am trying to pass the 'df' uploaded by the user into the function 'CapSent' (which resides in global.R) from Shiny. CapSent undertakes Sentiment analysis using a custom dictionary of words.
My Code So Far:
So far I have:
ui:
library(shiny)
source('global.R')
ui <- fluidPage(
sidebarPanel(
# Input: Select a file ----
fileInput("file1", "Choose CSV File",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv"))
))
Server:
server <- function(input, output) {
output$contents <- renderTable({
req(input$file1)
df <- read.csv(input$file1$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
CapSent(0.1, df) # 0.1 represents a threashold, df is the data
})
}
shinyApp(ui, server)
Functions.R:
CapSent <- function(0.1, df){
newdf<-data.frame(df,stringsAsFactors = FALSE)
#....Do some sentiment analysis here on newdf
#....Then export the sentiment analysis results
write.csv(newdf,"myResults.csv")
}
The Issue
With the above code I receive the error 'Error in Encoding<-: a character vector argument expected'.
'CapSent' works when I manually add 'df' to the Global Environment (using readr) but I want users to upload their own data to analyse. Hence the question:
Is there a way to pass df to the Global Environment from Shiny?
Any advice would be much appreciated.

Answer 1

Try this:
ui.R
library(shiny)
# Define UI for app that draws a histogram ----
ui <- fluidPage(
# App title ----
titlePanel("Hello Shiny!"),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: ----
fileInput("file1", "Choose CSV File",
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
actionButton("button", "Apply function/download df"),
hr(),
uiOutput("downloadButton")
),
# Main panel for displaying outputs ----
mainPanel(
h2("ORIGINAL DATA FRAME"),
DT::dataTableOutput("contents"),
br(),
uiOutput("modify")
)
)
)
server.R
server <- function(input, output) {
temp_df <- reactiveValues(df_data = NULL)
temp_df2 <- reactiveValues(df_data = NULL)
output$contents <- DT::renderDataTable({
req(input$file1)
temp_df$df_data <- read.csv(input$file1$datapath, sep = ";")
temp_df$df_data
}, options = (list(pageLength = 5, scrollX = TRUE)))
output$contents2 <- DT::renderDataTable({
temp_df2$df_data
}, options = (list(pageLength = 5, scrollX = TRUE)))
observeEvent(input$button,{
if(!is.null(temp_df$df_data)){
temp_df2$df_data <- CapSent(temp = 0.7, temp_df$df_data)
output$modify <- renderUI({
tagList(
h2("MODIFY DATA FRAME"),
DT::dataTableOutput("contents2")
)
})
output$downloadButton <- renderUI({
downloadButton("downloadData", "Download")
})
}else{
showNotification("No data was upload")
}
})
output$downloadData <- downloadHandler(
filename = function() {
paste("data-", Sys.Date(), ".csv", sep="")
},
content = function(file) {
write.csv(temp_df2$df_data, file)
})
}
as I do not know which CapSent end-use I made CapSent a function that adds a new column in the original data frame;
global.R
CapSent <- function(temp = 0.1, df){
newdf <- df
newdf$New_Col <- temp
return(newdf)
#....Do some sentiment analysis here on newdf
#....Then export the sentiment analysis results
#write.csv(newdf,"myResults.csv")
}

Answer 2

If you want to create a global function/variable just make a global.R which will let you use the function/variable everywhere on the ui.R or server.R.
This is the link to learn more: https://shiny.rstudio.com/articles/scoping.html
Edit: If you want to show the CSV, first of all you need to make a tabpanel and then make a table using the csv data like:
Use the package DT, install.packages("DT), is a package to make dynamic tables.
`output$yourtabpanelid = DT::renderDataTable({
req(input$file1)
df <- read.csv(input$file1$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
return(df)
})`
Then don't make a functions.R, just put the function below the one and put the read.csv(.....) before the function to use it in all functions at server.R like:
`df <- read.csv(input$file1$datapath,
header = input$header,
sep = input$sep,
quote = input$quote
)
server <- function(input, output, session) {`
And you can quit the df <- read.csv.... of the function DT but keep the return(df)