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server.R
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server.R
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#Richard Shanahan
#https://github.com/rjshanahan
#rjshanahan@gmail.com
#20 January 2017
###### TIME SERIES FORECASTING WITH SHINY ######
################# 1. LOAD PACKAGES & SETUP SESSION #################
## load packages
library(zoo)
library(xts)
library(ggplot2)
library(reshape2)
library(devtools)
library(forecastHybrid)
library(forecast)
library(data.table)
library(DT)
library(plotly)
library(dygraphs)
library(dplyr)
#set theme for ggplot2 graphics
theme = theme_set(theme_minimal())
theme = theme_update(legend.position="top",
axis.text.x = element_text(angle = 45, hjust = 1, vjust =1.25))
#read in reference CSV for public holidays - dummy variables
pubhol <- read.csv('AuPublicHolidays.csv',
header=T,
sep=",",
quote='"',
strip.white=T,
stringsAsFactors=F,
fill=T)
pubhol$Date <- zoo::as.Date(pubhol$Date, format = "%d/%m/%Y")
pubhol$weekday <- weekdays(pubhol$Date)
################# 2. SHINY SERVER FUNCTION #################
server <- function(input,output,session) {
###################################################################
######### 1 TIME SERIES FORECASTING ########
###################################################################
####### READ IN CSV FILE BASED ON SELECTION #######
mySeries_raw <- reactive({
inFile <- input$i_file
if (is.null(inFile))
return(NULL)
mySeries <- read.csv(inFile$datapath,
header = T,
strip.white=T,
stringsAsFactors=F,
fill=T)
####### DYNAMIC DROP DOWN LIST FOR VARIABLES BASED ON INPUT FILE #######
# updateSelectInput(session,
# 'i_task_select',
# label = 'Select Task',
# choices = names(mySeries[,-1]),
# names(mySeries[,-1])[1])
#convert date format
mySeries$Date <- zoo::as.Date(mySeries$Date, format = "%d/%m/%Y")
#remove NAs
mySeries <- mySeries %>% filter(!is.na(Date))
#add 'weekday' variable
mySeries$weekday <- weekdays(mySeries$Date)
#add public holiday variable for use as exogenous variable
mySeries$pubhol <- ifelse(mySeries$Date %in% pubhol$Date,
1,
0)
#assign id field for visualisations
mySeries$id <- 1:nrow(mySeries)
mySeries <- mySeries
})
####### DYNAMIC DROP DOWN LIST FOR TASK BASED ON INPUT FILE #######
observeEvent(mySeries_raw(), {
mySeries <- mySeries_raw()
updateSelectInput(session,
'i_task_select',
label = 'Select Series',
choices = names(select(mySeries, -Date, -pubhol, -id, -weekday)),
names(select(mySeries, -Date, -pubhol, -id, -weekday))[1])
})
####### REACTIVE FILTERED DATAFRAME #######
mySeries_filtered <- eventReactive(input$goButton, {
#dependency on 'start forecasting' button being pressed
#input$goButton
if (nrow(mySeries_raw())==0)
return()
#clear out performance comparison table
performance$performance_compare <- data.frame(
metric = c('ME','RMSE','MAE','MPE','MAPE','MASE','ACF1'))
#use existing reactive structures
mySeries <- mySeries_raw()
isolate({
weekends <- input$i_weekends
if(input$i_task_select ==""){
task_type = select_(mySeries, .dots = list(quote(-Date), quote(-pubhol), quote(-id), quote(-weekday)))
task_type = names(task_type[1])
} else
{
task_type = input$i_task_select
}
forecast_n <- input$i_forecast_n
dummy <- input$i_dummy
})
#BUILD DATAFRAME
if (weekends == "no"){
mySeries_filtered <- mySeries %>%
#select(Date, pubhol, id, weekday, task_type) %>%
select_(.dots = list(quote(Date), quote(pubhol), quote(id), quote(weekday), task_type)) %>%
filter_(paste0('!', quote(weekday),' %in% c("Saturday","Sunday")'))
}
else
{
mySeries_filtered <- mySeries %>%
select_(.dots = list(quote(Date), quote(pubhol), quote(id), quote(weekday), task_type))
}
})
###### OBJECT TO STORE PERFORMANCE METRICS ######
performance <- reactiveValues(
performance_compare = data.frame(
metric = c('ME','RMSE','MAE','MPE','MAPE','MASE','ACF1')
)
)
####### FOURIER TRANSFORM + AUTO ARIMA DYGRAPH #######
output$p_FTARIMA <- renderDygraph({
#use existing reactive structures
mySeries <- mySeries_raw()
mySeries_FTARIMA_daily <- mySeries_filtered()
if (nrow(mySeries_FTARIMA_daily) == 0){
stop(
showModal(modalDialog(
title = "Important message",
'Please hit "start forecasting"!',
easyClose = TRUE,
size = 's'
))
)
}
#make inputs dependent on users hiting 'start forecasting' button
isolate({
weekends <- input$i_weekends
if(input$i_task_select ==""){
task_type = select_(mySeries, .dots = list(quote(-Date), quote(-pubhol), quote(-id), quote(-weekday)))
task_type = names(task_type[1])
} else
{
task_type = input$i_task_select
}
forecast_n <- input$i_forecast_n
dummy <- input$i_dummy
})
#convert to TS object with weekly frequency
myY <- ts(select_(mySeries_FTARIMA_daily, task_type),
freq=365.25/7) #weekly frequency
#fit model
withProgress(message = 'Optimising forecast model... ',
detail = 'this may take a few seconds',
value = 0.1,
min = 0,
max = 1, {
bestfit <- list(aicc=Inf)
for(i in 2:25)
{
#for progress bar
incProgress(0.1)
#create XREG object
if (dummy == "no"){
myXReg_fit <- fourier(myY, K=i)
myXReg_forecast <- fourier(myY,
K=i-1,
h=forecast_n)
}
else {
myXReg_fit <- cbind(fourier(myY, K=i),
select_(mySeries_FTARIMA_daily, quote(pubhol)))
myXReg_forecast <- cbind(fourier(myY, K = i -
1, h = forecast_n),
ifelse(seq.Date(as.Date(last(select_(mySeries_FTARIMA_daily, quote(Date)))[[1]]),
as.Date(last(select_(mySeries_FTARIMA_daily, quote(Date)))[[1]]) + forecast_n, by = "days") %in% select_(pubhol, quote(Date)), 1, 0))
}
fit <- auto.arima(myY,
xreg = myXReg_fit,
seasonal=FALSE)
if(fit$aicc < bestfit$aicc)
bestfit <- fit
else break;
}
})
#forecast n periods using model
TS_mySeries_FTARIMA_daily <- forecast(bestfit,
xreg = myXReg_forecast)
fit_FTARIMA_daily_df <- cbind(as.data.frame(TS_mySeries_FTARIMA_daily$fitted)[1:nrow(mySeries_FTARIMA_daily),],
select_(mySeries_FTARIMA_daily, quote(Date)))
colnames(fit_FTARIMA_daily_df) <- c('fitted','Date')
#convert elements of time series FORECAST to dataframe for plotting
forecast_FTARIMA_daily_df <- with(TS_mySeries_FTARIMA_daily,
data.frame(mean=TS_mySeries_FTARIMA_daily$mean,
upper=TS_mySeries_FTARIMA_daily$upper[,2],
lower=TS_mySeries_FTARIMA_daily$lower[,2]))
forecast_FTARIMA_daily_df$Date <- seq(max(mySeries_FTARIMA_daily$Date),
max(mySeries_FTARIMA_daily$Date)+forecast_n-1,
1)
#add validation as to whether forecast should include WEEKENDS or not
if (weekends == "no") {
mySeq <- seq.Date(as.Date(first(select_(forecast_FTARIMA_daily_df, quote(Date)))[[1]]), length.out = forecast_n*2, by = "days")
mySeq <- mySeq[!weekdays(mySeq) %in% c('Saturday', 'Sunday')][0:forecast_n]
forecast_FTARIMA_daily_df$Date <- mySeq
}
#forecasted values table
output$time_series_table <- renderDataTable({
forecast_FTARIMA_daily_df <- forecast_FTARIMA_daily_df %>%
filter(mean > 0) %>%
select(Date, mean, upper, lower) %>%
mutate(mean = round(mean, 2),
upper = round(upper, 2),
lower = round(lower, 2))
})
#build CSV file of forecasted valyes that can be downloaed when model selected
output$downloader = downloadHandler(paste0('FTARIMA','_',task_type,'_',forecast_n,'periods.csv'), content = function(file) {
write.csv(forecast_FTARIMA_daily_df, file)
})
output$performance_metrics <- renderDataTable({
myAccuracy <- accuracy(TS_mySeries_FTARIMA_daily)
myAccuracy <- as.data.frame(as.table(myAccuracy))
myAccuracy <- myAccuracy %>%
select(metric = Var2, value = Freq) %>%
mutate(value = round(value, 3))
isolate({
#update REACTIVE VALUES object with accuracy metrics
performance$performance_compare = bind_cols(performance$performance_compare,
data.frame(FTARIMA = myAccuracy[, 2]))
})
})
#construct DYGRAPH Visualisation
myX <- xts(select_(mySeries_FTARIMA_daily, series = task_type),
select_(mySeries_FTARIMA_daily, quote(Date)),
order.by=as.POSIXct(mySeries_FTARIMA_daily$Date))
myfitted <- xts(select_(fit_FTARIMA_daily_df, quote(fitted), quote(Date)),
order.by=as.POSIXct(mySeries_FTARIMA_daily$Date))
myPred <- xts(select_(forecast_FTARIMA_daily_df,
quote(mean),
quote(upper),
quote(lower),
quote(Date)),
order.by = as.POSIXct(forecast_FTARIMA_daily_df$Date))
myDy <- cbind(myX, myfitted, myPred)
d <- dygraph(myDy[,1:6][,-3], main=paste0('ARIMA with FOURIER FORECAST of: ', task_type, ' for ', forecast_n, ' periods' )) %>%
dyAxis("x", drawGrid = FALSE) %>%
dyOptions(colors = RColorBrewer::brewer.pal(3, "Set2")) %>%
dySeries(c('upper','mean','lower'), label="predicted") %>%
dySeries('series') %>%
dySeries('fitted', fillGraph = TRUE) %>%
dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
dyLegend(width = 400) %>%
dyEvent("2016-3-27", "Easter 2016", labelLoc = "bottom") %>%
dyEvent("2015-4-5", "Easter 2015", labelLoc = "bottom") %>%
dyEvent("2014-4-20", "Easter 2014", labelLoc = "bottom") %>%
dyEvent("2013-3-31", "Easter 2013", labelLoc = "bottom") %>%
dyRangeSelector()
print(d)
})
####### SIMPLE EXPONENTIAL SMOOTHING DYGRAPH #######
output$p_SES <- renderDygraph({
#use existing reactive structures
mySeries <- mySeries_raw()
mySeries_SES_daily <- mySeries_filtered()
if (nrow(mySeries_SES_daily) == 0){
stop(
showModal(modalDialog(
title = "Important message",
'Please hit "start forecasting"!',
easyClose = TRUE,
size = 's'
))
)
}
#make inputs dependent on users hiting 'start forecasting' button
isolate({
weekends <- input$i_weekends
if(input$i_task_select ==""){
task_type = select_(mySeries, .dots = list(quote(-Date), quote(-pubhol), quote(-id), quote(-weekday)))
task_type = names(task_type[1])
} else
{
task_type = input$i_task_select
}
forecast_n <- input$i_forecast_n
dummy <- input$i_dummy
})
#simple exponential smoothing
myY <- ts(select_(mySeries_SES_daily, task_type),
freq=365.25/7)
TS_mySeries_SES_daily <- ses(myY, h=forecast_n, initial='optimal')
#convert elements of time series MODEL to dataframe for plotting
fit_SES_daily_df <- cbind(as.data.frame(TS_mySeries_SES_daily$fitted)[1:nrow(mySeries_SES_daily),],
select_(mySeries_SES_daily, quote(Date)))
colnames(fit_SES_daily_df) <- c('fitted','Date')
#convert elements of time series FORECAST to dataframe for plotting
forecast_SES_daily_df <- with(TS_mySeries_SES_daily,
data.frame(mean=TS_mySeries_SES_daily$mean,
upper=TS_mySeries_SES_daily$upper[,2],
lower=TS_mySeries_SES_daily$lower[,2]))
forecast_SES_daily_df$Date <- seq(max(mySeries_SES_daily$Date),
max(mySeries_SES_daily$Date)+forecast_n-1,
1)
#add validation as to whether forecast should include WEEKENDS or not
if (weekends == "no") {
mySeq <- seq.Date(as.Date(first(select_(forecast_SES_daily_df, quote(Date)))[[1]]), length.out = forecast_n*2, by = "days")
mySeq <- mySeq[!weekdays(mySeq) %in% c('Saturday', 'Sunday')][0:forecast_n]
forecast_SES_daily_df$Date <- mySeq
}
output$time_series_table <- renderDataTable({
forecast_SES_daily_df <- forecast_SES_daily_df %>%
filter(mean > 0) %>%
select(Date, mean, upper, lower) %>%
mutate(mean = round(mean, 2),
upper = round(upper, 2),
lower = round(lower, 2))
})
#build CSV file of forecasted valyes that can be downloaed when model selected
output$downloader = downloadHandler(paste0('SES','_',task_type,'_',forecast_n,'periods.csv'), content = function(file) {
write.csv(forecast_SES_daily_df, file)
})
output$performance_metrics <- renderDataTable({
myAccuracy <- accuracy(TS_mySeries_SES_daily)
myAccuracy <- as.data.frame(as.table(myAccuracy))
myAccuracy <- myAccuracy %>%
select(metric = Var2, value = Freq) %>%
mutate(value = round(value, 3))
isolate({
#update REACTIVE VALUES object with accuracy metrics
performance$performance_compare = bind_cols(performance$performance_compare,
data.frame(SES = myAccuracy[, 2]))
})
})
#construct DYGRAPH Visualisation
myX <- xts(select_(mySeries_SES_daily, series = task_type),
select_(mySeries_SES_daily, quote(Date)),
order.by=as.POSIXct(mySeries_SES_daily$Date))
myfitted <- xts(select_(fit_SES_daily_df, quote(fitted), quote(Date)),
order.by=as.POSIXct(mySeries_SES_daily$Date))
myPred <- xts(select_(forecast_SES_daily_df,
quote(mean),
quote(upper),
quote(lower),
quote(Date)),
order.by = as.POSIXct(forecast_SES_daily_df$Date))
myDy <- cbind(myX, myfitted, myPred)
d <- dygraph(myDy[,1:6][,-3], main=paste0('SIMPLE EXPONENTIAL SMOOTHING of ', task_type, ' for ', forecast_n, ' periods' )) %>%
dyAxis("x", drawGrid = FALSE) %>%
dyOptions(colors = RColorBrewer::brewer.pal(3, "Set2")) %>%
dySeries(c('upper','mean','lower'), label="predicted") %>%
dySeries('series') %>%
dySeries('fitted', fillGraph = TRUE) %>%
dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
dyLegend(width = 400) %>%
dyEvent("2016-3-27", "Easter 2016", labelLoc = "bottom") %>%
dyEvent("2015-4-5", "Easter 2015", labelLoc = "bottom") %>%
dyEvent("2014-4-20", "Easter 2014", labelLoc = "bottom") %>%
dyEvent("2013-3-31", "Easter 2013", labelLoc = "bottom") %>%
dyRangeSelector()
print(d)
})
####### HOLT-WINTERS EXPONENTIAL SMOOTHING DYGRAPH #######
output$p_HW <- renderDygraph({
#use existing reactive structures
mySeries <- mySeries_raw()
mySeries_HW_daily <- mySeries_filtered()
if (nrow(mySeries_HW_daily) == 0){
stop(
showModal(modalDialog(
title = "Important message",
'Please hit "start forecasting"!',
easyClose = TRUE,
size = 's'
))
)
}
#make inputs dependent on users hiting 'start forecasting' button
isolate({
weekends <- input$i_weekends
if(input$i_task_select ==""){
task_type = select_(mySeries, .dots = list(quote(-Date), quote(-pubhol), quote(-id), quote(-weekday)))
task_type = names(task_type[1])
} else
{
task_type = input$i_task_select
}
forecast_n <- input$i_forecast_n
dummy <- input$i_dummy
})
#HW exponential smoothing
myY <- ts(select_(mySeries_HW_daily, task_type),
freq=7)
#HW smoothing
TS_mySeries_HW_daily <- ets(myY,
model="ZAN",
damped=F,
additive.only=T
#gamma=myM
)
#create fit object
fit_HW_daily_df <- cbind(as.data.frame(TS_mySeries_HW_daily$fitted)[1:nrow(mySeries_HW_daily),],
select_(mySeries_HW_daily, quote(Date)))
colnames(fit_HW_daily_df) <- c('fitted','Date')
#convert elements of time series FORECAST to dataframe for plotting
forecast_HW_daily <- forecast(TS_mySeries_HW_daily, h=forecast_n)
forecast_HW_daily_df <- with(forecast_HW_daily,
data.frame(mean=forecast_HW_daily$mean,
upper=forecast_HW_daily$upper[,2],
lower=forecast_HW_daily$lower[,2]))
forecast_HW_daily_df$Date <- seq(max(mySeries_HW_daily$Date),
max(mySeries_HW_daily$Date)+forecast_n-1,
1)
#add validation as to whether forecast should include WEEKENDS or not
if (weekends == "no") {
mySeq <- seq.Date(as.Date(first(select_(forecast_HW_daily_df, quote(Date)))[[1]]), length.out = forecast_n*2, by = "days")
mySeq <- mySeq[!weekdays(mySeq) %in% c('Saturday', 'Sunday')][0:forecast_n]
forecast_HW_daily_df$Date <- mySeq
}
output$time_series_table <- renderDataTable({
forecast_HW_daily_df <- forecast_HW_daily_df %>%
filter(mean > 0) %>%
select(Date, mean, upper, lower) %>%
mutate(mean = round(mean, 2),
upper = round(upper, 2),
lower = round(lower, 2))
})
#build CSV file of forecasted valyes that can be downloaed when model selected
output$downloader = downloadHandler(paste0('HW','_',task_type,'_',forecast_n,'periods.csv'), content = function(file) {
write.csv(forecast_HW_daily_df, file)
})
output$performance_metrics <- renderDataTable({
myAccuracy <- accuracy(TS_mySeries_HW_daily)
myAccuracy <- as.data.frame(as.table(myAccuracy))
myAccuracy <- myAccuracy %>%
select(metric = Var2, value = Freq) %>%
mutate(value = round(value, 3))
isolate({
#update REACTIVE VALUES object with accuracy metrics
performance$performance_compare = bind_cols(performance$performance_compare,
data.frame(HW = myAccuracy[, 2]))
})
})
#construct DYGRAPH Visualisation
myX <- xts(select_(mySeries_HW_daily, series = task_type),
select_(mySeries_HW_daily, quote(Date)),
order.by=as.POSIXct(mySeries_HW_daily$Date))
myfitted <- xts(select_(fit_HW_daily_df, quote(fitted), quote(Date)),
order.by=as.POSIXct(mySeries_HW_daily$Date))
myPred <- xts(select_(forecast_HW_daily_df,
quote(mean),
quote(upper),
quote(lower),
quote(Date)),
order.by = as.POSIXct(forecast_HW_daily_df$Date))
myDy <- cbind(myX, myfitted, myPred)
d <- dygraph(myDy[,1:6][,-3], main=paste0('HOLT-WINTERS EXPONENTIAL SMOOTHING of ', task_type, ' for ', forecast_n, ' periods' )) %>%
dyAxis("x", drawGrid = FALSE) %>%
dyOptions(colors = RColorBrewer::brewer.pal(3, "Set2")) %>%
dySeries(c('upper','mean','lower'), label="predicted") %>%
dySeries('series') %>%
dySeries('fitted', fillGraph = TRUE) %>%
dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
dyLegend(width = 400) %>%
dyEvent("2016-3-27", "Easter 2016", labelLoc = "bottom") %>%
dyEvent("2015-4-5", "Easter 2015", labelLoc = "bottom") %>%
dyEvent("2014-4-20", "Easter 2014", labelLoc = "bottom") %>%
dyEvent("2013-3-31", "Easter 2013", labelLoc = "bottom") %>%
dyRangeSelector()
print(d)
})
####### MOVING AVERAGE DYGRAPH #######
output$p_MA <- renderDygraph({
#use existing reactive structures
mySeries <- mySeries_raw()
mySeries_MA_daily <- mySeries_filtered()
if (nrow(mySeries_MA_daily) == 0){
stop(
showModal(modalDialog(
title = "Important message",
'Please hit "start forecasting"!',
easyClose = TRUE,
size = 's'
))
)
}
#make inputs dependent on users hiting 'start forecasting' button
isolate({
weekends <- input$i_weekends
if(input$i_task_select ==""){
task_type = select_(mySeries, .dots = list(quote(-Date), quote(-pubhol), quote(-id), quote(-weekday)))
task_type = names(task_type[1])
} else
{
task_type = input$i_task_select
}
forecast_n <- input$i_forecast_n
dummy <- input$i_dummy
})
myMA = 7
#convert to time series object
myY <- ts(select_(mySeries_MA_daily, series = task_type),
freq=7)
withProgress(message = 'Optimising forecast model... ',
detail = 'this may take a few seconds',
value = 0.1,
min = 0,
max = 1, {
#create moving average forecast object
TS_mySeries_MA_daily <- forecast(ma(myY,
order = myMA),
h = forecast_n)
})
#convert elements of time series MODEL to dataframe for plotting
#fit_MA_daily_df <- select_(mySeries_MA_daily, task_type, quote(Date))
fit_MA_daily_df <- cbind(as.data.frame(TS_mySeries_MA_daily$fitted)[1:nrow(mySeries_MA_daily),],
select_(mySeries_MA_daily, quote(Date)))
colnames(fit_MA_daily_df) <- c('fitted','Date')
#convert elements of time series FORECAST to dataframe for plotting
forecast_MA_daily_df <- with(TS_mySeries_MA_daily,
data.frame(mean=TS_mySeries_MA_daily$mean,
upper=TS_mySeries_MA_daily$upper[,2],
lower=TS_mySeries_MA_daily$lower[,2]))
forecast_MA_daily_df$Date <- seq(max(mySeries_MA_daily$Date),
max(mySeries_MA_daily$Date)+forecast_n-1,
1)
#add validation as to whether forecast should include WEEKENDS or not
if (weekends == "no") {
mySeq <- seq.Date(as.Date(first(select_(forecast_MA_daily_df, quote(Date)))[[1]]), length.out = forecast_n*2, by = "days")
mySeq <- mySeq[!weekdays(mySeq) %in% c('Saturday', 'Sunday')][0:forecast_n]
forecast_MA_daily_df$Date <- mySeq
}
output$time_series_table <- renderDataTable({
forecast_MA_daily_df <- forecast_MA_daily_df %>%
filter(mean > 0) %>%
select(Date, mean, upper, lower) %>%
mutate(mean = round(mean, 2),
upper = round(upper, 2),
lower = round(lower, 2))
})
#build CSV file of forecasted valyes that can be downloaed when model selected
output$downloader = downloadHandler(paste0('MA','_',task_type,'_',forecast_n,'periods.csv'), content = function(file) {
write.csv(forecast_MA_daily_df, file)
})
output$performance_metrics <- renderDataTable({
myAccuracy <- accuracy(TS_mySeries_MA_daily, x=mySeries_MA_daily$WorkIn)[2,]
myAccuracy <- as.data.frame(as.table(myAccuracy))
myAccuracy <- myAccuracy %>%
select(metric = Var1, value = Freq) %>%
mutate(value = round(value, 3))
isolate({
#update REACTIVE VALUES object with accuracy metrics
performance$performance_compare = bind_cols(performance$performance_compare,
data.frame(MA = myAccuracy[, 2]))
})
})
#construct DYGRAPH Visualisation
myX <- xts(select_(mySeries_MA_daily, series = task_type),
select_(mySeries_MA_daily, quote(Date)),
order.by=as.POSIXct(mySeries_MA_daily$Date))
myfitted <- xts(select_(fit_MA_daily_df, quote(fitted), quote(Date)),
order.by=as.POSIXct(mySeries_MA_daily$Date))
myPred <- xts(select_(forecast_MA_daily_df,
quote(mean),
quote(upper),
quote(lower),
quote(Date)),
order.by = as.POSIXct(forecast_MA_daily_df$Date))
myDy <- cbind(myX, myfitted, myPred)
d <- dygraph(myDy[,1:6][,-3], main=paste0(myMA, '-ORDER MOVING AVERAGE of: ', task_type, ' for ', forecast_n, ' periods' )) %>%
dyAxis("x", drawGrid = FALSE) %>%
dyOptions(colors = RColorBrewer::brewer.pal(3, "Set2")) %>%
dySeries(c('upper','mean','lower'), label="predicted") %>%
dySeries('series') %>%
dySeries('fitted', fillGraph = TRUE) %>%
dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
dyLegend(width = 400) %>%
dyEvent("2016-3-27", "Easter 2016", labelLoc = "bottom") %>%
dyEvent("2015-4-5", "Easter 2015", labelLoc = "bottom") %>%
dyEvent("2014-4-20", "Easter 2014", labelLoc = "bottom") %>%
dyEvent("2013-3-31", "Easter 2013", labelLoc = "bottom") %>%
#dyRoller(rollPeriod = myMA) %>% #lets users enter a 'smoothing' parameter interactively
dyRangeSelector()
print(d)
})
####### MEAN FORECAST SMOOTHING DYGRAPH #######
output$p_MF <- renderDygraph({
#use existing reactive structures
mySeries <- mySeries_raw()
mySeries_MF_daily <- mySeries_filtered()
if (nrow(mySeries_MF_daily) == 0){
stop(
showModal(modalDialog(
title = "Important message",
'Please hit "start forecasting"!',
easyClose = TRUE,
size = 's'
))
)
}
#make inputs dependent on users hiting 'start forecasting' button
isolate({
weekends <- input$i_weekends
if(input$i_task_select ==""){
task_type = select_(mySeries, .dots = list(quote(-Date), quote(-pubhol), quote(-id), quote(-weekday)))
task_type = names(task_type[1])
} else
{
task_type = input$i_task_select
}
forecast_n <- input$i_forecast_n
dummy <- input$i_dummy
})
#simple exponential smoothing
myY <- ts(select_(mySeries_MF_daily, task_type),
freq=365.25/7)
TS_mySeries_MF_daily <- meanf(myY,
h=forecast_n)
#convert elements of time series MODEL to dataframe for plotting
fit_MF_daily_df <- cbind(as.data.frame(TS_mySeries_MF_daily$fitted)[1:nrow(mySeries_MF_daily),],
select_(mySeries_MF_daily, quote(Date)))
colnames(fit_MF_daily_df) <- c('fitted','Date')
#convert elements of time series FORECAST to dataframe for plotting
forecast_MF_daily_df <- with(TS_mySeries_MF_daily,
data.frame(mean=TS_mySeries_MF_daily$mean,
upper=TS_mySeries_MF_daily$upper[,2],
lower=TS_mySeries_MF_daily$lower[,2]))
forecast_MF_daily_df$Date <- seq(max(mySeries_MF_daily$Date),
max(mySeries_MF_daily$Date)+forecast_n-1,
1)
#add validation as to whether forecast should include WEEKENDS or not
if (weekends == "no") {
mySeq <- seq.Date(as.Date(first(select_(forecast_MF_daily_df, quote(Date)))[[1]]), length.out = forecast_n*2, by = "days")
mySeq <- mySeq[!weekdays(mySeq) %in% c('Saturday', 'Sunday')][0:forecast_n]
forecast_MF_daily_df$Date <- mySeq
}
output$time_series_table <- renderDataTable({
forecast_MF_daily_df <- forecast_MF_daily_df %>%
filter(mean > 0) %>%
select(Date, mean, upper, lower) %>%
mutate(mean = round(mean, 2),
upper = round(upper, 2),
lower = round(lower, 2))
})
#build CSV file of forecasted valyes that can be downloaed when model selected
output$downloader = downloadHandler(paste0('MF','_',task_type,'_',forecast_n,'periods.csv'), content = function(file) {
write.csv(forecast_MF_daily_df, file)
})
output$performance_metrics <- renderDataTable({
myAccuracy <- accuracy(TS_mySeries_MF_daily, x=mySeries_MF_daily$WorkIn)[2,]
myAccuracy <- as.data.frame(as.table(myAccuracy))
myAccuracy <- myAccuracy %>%
select(metric = Var1, value = Freq) %>%
mutate(value = round(value, 3))
isolate({
#update REACTIVE VALUES object with accuracy metrics
performance$performance_compare = bind_cols(performance$performance_compare,
data.frame(MF = myAccuracy[, 2]))
})
})
#construct DYGRAPH Visualisation
myX <- xts(select_(mySeries_MF_daily, series = task_type),
select_(mySeries_MF_daily, quote(Date)),
order.by=as.POSIXct(mySeries_MF_daily$Date))
myfitted <- xts(select_(fit_MF_daily_df, quote(fitted), quote(Date)),
order.by=as.POSIXct(mySeries_MF_daily$Date))
myPred <- xts(select_(forecast_MF_daily_df,
quote(mean),
quote(upper),
quote(lower),
quote(Date)),
order.by = as.POSIXct(forecast_MF_daily_df$Date))
myDy <- cbind(myX, myfitted, myPred)
d <- dygraph(myDy[,1:6][,-3], main=paste0('MEAN FORECAST USING IID of: ', task_type, ' for ', forecast_n, ' periods' )) %>%
dyAxis("x", drawGrid = FALSE) %>%
dyOptions(colors = RColorBrewer::brewer.pal(3, "Set2")) %>%
dySeries(c('upper','mean','lower'), label="predicted") %>%
dySeries('series') %>%
dySeries('fitted', fillGraph = TRUE) %>%
dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
dyLegend(width = 400) %>%
dyEvent("2016-3-27", "Easter 2016", labelLoc = "bottom") %>%
dyEvent("2015-4-5", "Easter 2015", labelLoc = "bottom") %>%
dyEvent("2014-4-20", "Easter 2014", labelLoc = "bottom") %>%
dyEvent("2013-3-31", "Easter 2013", labelLoc = "bottom") %>%
dyRangeSelector()
print(d)
})
####### LINE OF BEST FIT LINEAR DYGRAPH #######
output$p_LOBF <- renderDygraph({
#use existing reactive structures
mySeries <- mySeries_raw()
mySeries_LOBF_daily <- mySeries_filtered()
if (nrow(mySeries_LOBF_daily) == 0){
stop(
showModal(modalDialog(
title = "Important message",
'Please hit "start forecasting"!',
easyClose = TRUE,
size = 's'
))
)
}
#make inputs dependent on users hiting 'start forecasting' button
isolate({
weekends <- input$i_weekends
if(input$i_task_select ==""){
task_type = select_(mySeries, .dots = list(quote(-Date), quote(-pubhol), quote(-id), quote(-weekday)))
task_type = names(task_type[1])
} else
{
task_type = input$i_task_select
}
forecast_n <- input$i_forecast_n
dummy <- input$i_dummy
})
#line of best fit
TS_mySeries_LOBF_daily <- lm(paste0(task_type, ' ~ ', quote(Date)),
data = mySeries_LOBF_daily)