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server.R
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server.R
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#-------------------------------------------------------------------------
# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
#-------------------------------------------------------------------------
library(shinydashboard)
library(mlxR)
library('shiny')
library('ggplot2')
source("functions.R")
#------------------------------------------------------------------------------------
shinyServer(function(input, output){
#-------------------------------------------------------------------------------
# -------------------------single AB_test resulst table -------------------------
#-------------------------------------------------------------------------------
output$single_ABtest <- renderTable({
result <- Bayes_AB_test( nA = input$nA, xA= input$xA, nB = input$nB, xB = input$xB,
alpha_0 = input$alpha_0, beta_0= input$beta_0 ,
a= as.numeric(input$Conf_alpha) )
return(result)
})
#-------------------------------------------------------------------------------
# A/B Test density plot, posterior plot of change, plot of probability being better;
#-------------------------------------------------------------------------------
output$ABtest_density <- renderPlot({
density_plot <- Bayes_AB_test( nA = input$nA, xA= input$xA, nB = input$nB, xB = input$xB,
alpha_0 = input$alpha_0, beta_0= input$beta_0, out_data = FALSE,
density_plot =TRUE , a = as.numeric(input$Conf_alpha) )
return(density_plot)
})
output$ABtest_change_density <- renderPlot({
density_plot <- Bayes_AB_test( nA = input$nA, xA= input$xA, nB = input$nB, xB = input$xB,
alpha_0 = as.numeric(input$alpha_0),
beta_0 = input$beta_0, out_data = FALSE,
diff_plot =TRUE, a = as.numeric(input$Conf_alpha) )
return(density_plot)
})
output$ABtest_bestProb <- renderPlot({
bestProb_plot <- Bayes_AB_test( nA = input$nA, xA= input$xA, nB = input$nB, xB = input$xB,
alpha_0 = input$alpha_0, beta_0= input$beta_0, out_data = FALSE,
bestProb_plot =TRUE , a = as.numeric(input$Conf_alpha) )
return(bestProb_plot)
})
#-------------------------------------------------------------------------------
# calculate point estimate and CI for conversion rate by group over time
#-------------------------------------------------------------------------------
#data <- reactiveValues(sim_data = NULL)
sim_data <- reactive({
raw_prob_list = c(input$pA,input$pB, input$pC)/100
num_test = sum ( raw_prob_list > 0 )
prob_list = raw_prob_list[raw_prob_list >0 ]
Test_name = c('A', 'B','C')[raw_prob_list >0 ]
# simulate raw data
df = simulate_data( num_test-1 , input$start_date, input$test_duration,
input$counts , prob_list, input$alpha_0_sim, input$beta_0_sim)
# aggregate counts and compute group rate statistics
new_df <- transform_data( df, # data frame
a = as.numeric(input$Conf_alpha) , # confidence level
a_0 = input$alpha_0, b_0= input$beta_0) # Beta prior parameter
return(new_df)
})
#-------------------------------------------------------------------------------
# save as an output table
output$sim_data <- renderTable({ return( sim_data() ) })
#-------------------------------------------------------------------------------
# calculate change of conversion rate compared with default, point estimate, CI
#-------------------------------------------------------------------------------
change_data <- reactive({
CR_change = Cal_all_change(CR = sim_data() ,
Conf_alpha = as.numeric(input$Conf_alpha) ,
alpha_0 = input$alpha_0_sim, beta_0= input$beta_0_sim )
return(CR_change)
})
#-------------------------------------------------------------------------------
# save as an output table
output$Conv_change <- renderTable({
return( change_data() )
})
#-------------------------------------------------------------------------------
output$plot1 <- renderPlot({
data = as.data.frame(change_data())
p = ggplot(data, aes(Day, logBF,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
return(p)
})
output$plot2 <- renderPlot({
data = as.data.frame(change_data())
p = ggplot(data, aes(Day, p_value,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
return(p)
})
output$plot3 <- renderPlot({
data = as.data.frame(change_data())
p = ggplot(data, aes(Day, Uplift,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
return(p)
})
output$plot4 <- renderPlot({
data = as.data.frame(change_data())
p = ggplot(data, aes(Day, prob_better,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
return(p)
})
#-------------------------------------------------------------------------------
output$B1 <- renderPlot({
data = as.data.frame(sim_data())
p = ggplot(data, aes(Day, Post_mean, colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
p <- p + geom_ribbon(aes(ymin=data$Cred_LL, ymax=data$Cred_UL), linetype=2, alpha=0.1)
return(p)
})
output$F1 <- renderPlot({
data = as.data.frame(sim_data())
p = ggplot(data, aes(Day, CRate, colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
p <- p + geom_ribbon(aes(ymin=data$Conf_LL, ymax=data$Conf_UL), linetype=2, alpha=0.1)
return(p)
})
output$B2 <- renderPlot({
data = as.data.frame(change_data())
p = ggplot(data, aes(Day, Post_mean ,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
p <- p + geom_ribbon(aes(ymin=data$Cred_LL, ymax=data$Cred_UL), linetype=2, alpha=0.1)
return(p)
})
output$F2 <- renderPlot({
data = as.data.frame(change_data())
p = ggplot(data, aes(Day, CRate_change, colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
p <- p + geom_ribbon(aes(ymin=data$Conf_LL, ymax=data$Conf_UL), linetype=2, alpha=0.1)
return(p)
})
#-------------------------------------------------------------------------------
#--------- Group trend plots - Bayesian / Frequentist-------------------------
#-------------------------------------------------------------------------------
output$Bayesian_group_plot <- renderPlot({
data = as.data.frame( sim_data() )
Bgroup <- Freq_plot(CR = data , num_tests=3, Bayes = TRUE)
return(Bgroup)
})
#-------------------------------------------------------------------------------
output$Freq_group_plot <- renderPlot({
data = as.data.frame( sim_data() )
Fgroup <- Freq_plot(CR = data , num_tests=3, Bayes = FALSE)
return(Fgroup)
})
#-------------------------------------------------------------------------------
# ------- Rate change plots - Bayesian / Frequentist-------------------------
#-------------------------------------------------------------------------------
output$Bayesian_change_plot <- renderPlot({
data <- as.data.frame( change_data() )
Bchange <- Change_plot(CR_change = data , Bayes = TRUE)
return( Bchange)
})
#-------------------------------------------------------------------------------
output$Freq_change_plot <- renderPlot({
data <- as.data.frame( change_data() )
Fchange <- Change_plot(CR_change = data , Bayes = FALSE)
return(Fchange)
})
#-------------------------------------------------------------------------------
# --------------------------A/B Test plots ---------------------------
#-------------------------------------------------------------------------------
output$BF_plot <- renderPlot({
data <- as.data.frame( change_data() )
BF <- plot_change_column(CR_change= change_data(), variable= "logBF",
var_label= "log Bayes Factor", plot_max_pct = 0.5,
hline= as.numeric(input$Conf_alpha) )
return( BF)
})
#-------------------------------------------------------------------------------
output$pval_plot <- renderPlot({
data <- as.data.frame( change_data() )
pval <- plot_change_column(CR_change= data, variable= "p_value",
var_label= "p-value",
hline= as.numeric(input$Conf_alpha) )
return(pval)
})
#-------------------------------------------------------------------------------
output$Uplift_plot <- renderPlot({
data <- as.data.frame( change_data() )
Uplift <- plot_change_column(CR_change= data ,
variable= "Uplift", var_label= "Uplift (%)")
return( Uplift)
})
#-------------------------------------------------------------------------------
output$prob_better_plot <- renderPlot({
data <- as.data.frame( change_data() )
prob_better <- plot_change_column(CR_change= data , variable= "prob_better",
var_label= "Probability of better than default(%)" )
return(prob_better)
})
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
# download codes:
output$downloadUI <- downloadHandler(
filename = "ui.R",
myfile <- paste0('/srv/shiny-server/apps/files/',filename ),
content = function(file) { file.copy( myfile , file) }
)
output$downloadServer <- downloadHandler(
filename = "server.R",
myfile <- paste0('/srv/shiny-server/apps/files/', filename ),
content = function(file) { file.copy( myfile , file) }
)
output$downloadFunction <- downloadHandler(
filename = "functions.R",
myfile <- paste0('/srv/shiny-server/apps/files/',filename ),
content = function(file) { file.copy( myfile , file) }
)
output$downloadData <- downloadHandler(
filename = function() { paste(input$dataset, '.csv', sep='') },
content = function(file) {
write.csv(datasetInput(), file)
}
)
output$downloadAnalyze <- downloadHandler(
filename = function() { paste(input$dataset, '.csv', sep='') },
content = function(file) {
write.csv(datasetInput(), file)
}
)
#-------------------------------------------------------------------------------
upload_data <- reactive({
inFile <- input$upload_raw_data
# read default csv if not file input
if (is.null(inFile)){
tbl <- read.csv("n10K.csv")
}
else{ tbl <- read.csv(inFile$datapath )}
#----------------------
# aggregate counts and compute group rate statistics
new_df <- transform_data( tbl, # data frame
a = as.numeric(input$Conf_alpha) , # confidence level
a_0 = input$alpha_0_upload, b_0= input$beta_0_upload) # Beta prior parameter
return( new_df)
})
#-------------------------------------------------------------------------------
# calculate change of conversion rate compared with default, point estimate, CI
#-------------------------------------------------------------------------------
upload_change <- reactive({
CR_change = Cal_all_change(CR = upload_data() ,
Conf_alpha = as.numeric(input$Conf_alpha) ,
alpha_0 = input$alpha_0_sim, beta_0= input$beta_0_sim )
return(CR_change)
})
#-------------------------------------------------------------------------------
# renderTable to display
#-------------------------------------------------------------------------------
output$upload_data <- renderTable({
return(upload_data() )
})
output$upload_change <- renderTable({
return(upload_change() )
})
#-------------------------------------------------------------------------------
# Plots -BF, p-value, uplift, prob_better
#-------------------------------------------------------------------------------
output$plot1u <- renderPlot({
data = as.data.frame(upload_change())
p = ggplot(data, aes(Day, logBF,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
return(p)
})
output$plot2u <- renderPlot({
data = as.data.frame(upload_change())
p = ggplot(data, aes(Day, p_value,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
return(p)
})
output$plot3u <- renderPlot({
data = as.data.frame(upload_change())
p = ggplot(data, aes(Day, Uplift,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
return(p)
})
output$plot4u <- renderPlot({
data = as.data.frame(upload_change())
p = ggplot(data, aes(Day, prob_better,colour= as.factor(Test_group) )) +
geom_point() + geom_line() + theme(legend.position="bottom")
return(p)
})
#-------------------------------------------------------------------------------
# Conversion rate change plot
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
#--------- Group trend plots - Bayesian / Frequentist-------------------------
#-------------------------------------------------------------------------------
output$B1u <- renderPlot({
data = as.data.frame( sim_data() )
Bgroup <- Freq_plot(CR = data , num_tests=3, Bayes = TRUE)
return(Bgroup)
})
#-------------------------------------------------------------------------------
output$F1u <- renderPlot({
data = as.data.frame( sim_data() )
Fgroup <- Freq_plot(CR = data , num_tests=3, Bayes = FALSE)
return(Fgroup)
})
#-------------------------------------------------------------------------------
# ------- Rate change plots - Bayesian / Frequentist-------------------------
#-------------------------------------------------------------------------------
output$B2u <- renderPlot({
data <- as.data.frame( change_data() )
Bchange <- Change_plot(CR_change = data , Bayes = TRUE)
return( Bchange)
})
#-------------------------------------------------------------------------------
output$F2u <- renderPlot({
data <- as.data.frame( change_data() )
Fchange <- Change_plot(CR_change = data , Bayes = FALSE)
return(Fchange)
})
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
formulaText <- reactive({
paste("Analysis at Confidence Level 1-", input$Conf_alpha)
})
output$caption <- renderText({
formulaText()
})
#-------------------------------------------------------------------------------
})