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194 changes: 95 additions & 99 deletions server.R
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Original file line number Diff line number Diff line change
Expand Up @@ -23,12 +23,16 @@ DF_EvCode <- read_csv("DF_EvCodeData.csv")

# Data manipulation and saving to the DF_TEMP
DF_TEMP <- DF_Data %>%
# arrange by date
arrange(StartDateTime) %>%
# join to decode equipment serial number
inner_join(DF_Equipm, by = "IDEquipment") %>%
# select only column needed
select(StartDateTime, Name, EventCode, TimeTotal) %>%
# join to decode Event Code meaning
inner_join(DF_EvCode, by = "EventCode") %>%
# select only column needed
select(StartDateTime, Name, TimeTotal, EventText)
select(StartDateTime, Name, EventCode, TimeTotal, EventText)
# =================================

shinyServer(function(input, output, session) {
Expand All @@ -53,8 +57,8 @@ shinyServer(function(input, output, session) {
DF_TEMP %>%
# filters for categories
filter(EventText == input$selInput) %>%
# group by
group_by(Name) %>%
# group by
group_by(Name) %>%
# filters X date
filter(StartDateTime > StartDate(), StartDateTime < EndDate())
})
Expand All @@ -63,121 +67,113 @@ shinyServer(function(input, output, session) {
# Prepare data for clustering and do clustering with if/else statement...
DF_SUM_ALL <- reactive({

# Data manipulation and saving to the DF_Data reactive value
DF_KM <- DF_TEMP %>% #filter(EventText == "Step 2 SubStep 6")
# filters for category
filter(EventText == input$Step)

# make intermediate dataframe
KM1 <- DF_KM %>%
select(Name, TimeTotal) %>%
mutate(Name = revalue(Name, c("Machine #1" = "1", "Machine #2" = "2", "Machine #3" = "3", "Machine #4" = "4"))) %>%
mutate(Name = as.numeric(Name))

# perform different modelling depending on the user choice...
if(!input$scaled) {
KM <- KM1 %>%
kmeans(centers = Classes(), nstart = 20)
} else {
KM <- KM1 %>%
scale() %>%
as.data.frame() %>%
kmeans(centers = Classes(), nstart = 20)
}

# saving clustering result to the new data frame
vector <- as.data.frame.vector(KM$cluster)
names(vector) <- "Clust"

DF_SUM_ALL <- DF_KM %>%
select(StartDateTime, TimeTotal, Name) %>%
# join clustering result
bind_cols(vector) %>%
mutate(Clust = as.factor(Clust))


# Data manipulation and saving to the DF_Data reactive value
DF_KM <- DF_TEMP %>% #filter(EventText == "Step 2 SubStep 6")
# filters for category
filter(EventText == input$Step)
# make intermediate dataframe
KM1 <- DF_KM %>%
select(Name, TimeTotal) %>%
mutate(Name = revalue(Name, c("Machine #1" = "1", "Machine #2" = "2", "Machine #3" = "3", "Machine #4" = "4"))) %>%
mutate(Name = as.numeric(Name))
# perform different modelling depending on the user choice...
if(!input$scaled) {
KM <- KM1 %>%
kmeans(centers = Classes(), nstart = 20)
} else {
KM <- KM1 %>%
scale() %>%
as.data.frame() %>%
kmeans(centers = Classes(), nstart = 20)
}
# saving clustering result to the new data frame
vector <- as.data.frame.vector(KM$cluster)
names(vector) <- "Clust"
DF_SUM_ALL <- DF_KM %>%
select(StartDateTime, TimeTotal, Name) %>%
# join clustering result
bind_cols(vector) %>%
mutate(Clust = as.factor(Clust))
})



# =================================
# FUNCTIONS
# OUTPUTS
# =================================
# Function to draw main plot, using condition of input$point to add points to the graph
mainPlot <- function(){
if(input$points){
DF_SUM() %>%
ggplot(aes(x = StartDateTime, y = TimeTotal, col = EventText)) +
geom_smooth(alpha = 0.5, se = StatErr(), formula = y ~ poly(x, 2)) +
geom_point(alpha = 0.4) +
facet_wrap(~Name) + ylab("Duration of Step, seconds") +
ggtitle(paste("Overview of Steps ", "from: ",
StartDate(), " to: ", EndDate(), sep = ""))

} else {
DF_SUM() %>%
ggplot(aes(x = StartDateTime, y = TimeTotal, col = EventText)) +
geom_smooth(alpha = 0.5, se = StatErr(), formula = y ~ poly(x, 2)) +
facet_wrap(~Name) + ylab("Duration of Step, seconds") +
ggtitle(paste("Overview of Steps ", "from: ",
StartDate(), " to: ", EndDate(), sep = ""))

}

}
### Render function to create a main plot:
output$Plot <- renderPlot({
# generate object for the plot using DF_SUM
DF_SUM() %>%
ggplot(aes(x = StartDateTime, y = TimeTotal, col = EventText)) +
geom_smooth(alpha = 0.5, se = StatErr()) +
facet_wrap(~Name) + ylab("Duration of Step, seconds") +
ggtitle(paste("Overview of Steps ", "from: ",
StartDate(), " to: ", EndDate(), sep = ""))
})

# =================================

# Function to draw Box plot
boxPlot <- function(){
### Render function to create a main plot:
output$Plot1 <- renderPlot({
# generate object for the plot using DF_SUM
DF_SUM() %>%
ggplot(aes(x = StartDateTime, y = TimeTotal, col = EventText)) + geom_point()+
geom_smooth(alpha = 0.5, se = StatErr()) +
facet_wrap(~Name) +
ylab("Duration of Step, seconds") +
ggtitle(paste("Overview of Steps ", "from: ",
StartDate(), " to: ", EndDate(), sep = ""))
})

# =================================

### Render function to create another plot:
output$Plot2 <- renderPlot({
# generate object for the plot using DF_SUM
DF_SUM() %>%
ggplot(aes(x = StartDateTime, y = TimeTotal, col = EventText)) + geom_boxplot() +
facet_grid(~Name) +
ylab("Duration of Step, seconds") +
theme(legend.direction = "horizontal", legend.position = "bottom")+
ggtitle(label = paste("Box Plot from all data. From: ", StartDate(), " To: ", EndDate(), sep = ""),
subtitle = "Box plots can help to indicate average values and outliers")
}
# Function to draw deviation plot
facet_wrap(Name ~ EventText) +
ggtitle(paste("Overview of Steps ", "from: ",
StartDate(), " to: ", EndDate(), sep = ""))
})


# =================================
### function that creates a plot
deviationPlot <- function(){

# generate object for the plot using DF_SUM
DF_SUM_ALL() %>%
filter(StartDateTime > StartDate(), StartDateTime < EndDate()) %>%
ggplot(aes(x = StartDateTime, y = TimeTotal, col = Clust)) + geom_point() + facet_wrap(~Name)+
ylab("Duration of Step, seconds") +
theme(legend.direction = "horizontal", legend.position = "bottom")+
ggtitle(label = paste("Anomaly Detection of the Step Duration. From: ", StartDate(), " To: ", EndDate(), sep = ""),
subtitle = "Different colors may highlight potential anomaly")

ggplot(aes(x = StartDateTime, y = TimeTotal, col = Clust)) + geom_point() + facet_wrap(~Name) +

ggtitle(paste("Anomaly Detection of the Step Duration", "from: ",
StartDate(), " to: ", EndDate(), ". Different colour indicates potential anomaly", sep = ""))
}
# =================================
# OUTPUTS
# =================================

### Render function to create a main plot:
output$Plot <- renderPlot({
ggsave("plot.png", plot = mainPlot(), device = "png")
mainPlot() }, height = "auto", width = 650)

# =================================

### Render function to create Box Plot:
output$Plot2 <- renderPlot({
ggsave("plot.png", plot = boxPlot(), device = "png")
boxPlot() }, height = "auto", width = 650)


# =================================
### Render function to create plot Anomaly:
output$Plot3 <- renderPlot({
### Render function to create another plot:
output$Plot3 <- renderPlot({
ggsave("plot.png", plot = deviationPlot(), device = "png")
deviationPlot() }, height = "auto", width = 650)
deviationPlot()
})

# =================================
### download plot:
# =================================
### Download plot :
output$downloadPlot <- downloadHandler(
filename = function(){ "plot.png"},
content = function(file){
filename = function(){"plot.png"},
content = function(file){
file.copy("plot.png", file, overwrite = TRUE)
}
)
}
)


})
91 changes: 51 additions & 40 deletions ui.R
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,6 @@
# ----------------------------

library(shiny)
library(shinydashboard)
library(DT)

# Define steps choices for selectInput function (it is containing steps the user can filter)
Expand All @@ -25,42 +24,54 @@ stepsChoices <- c("Step 1 SubStep 1",
"Step 2 SubStep 6",
"Step 2 SubStep 7")

#
dashboardPage(
dashboardHeader(title = "Preparation Steps Duration Overview"),
dashboardSidebar(
# Elements on the Sidebar of the App
img(height = 100, width = 230, src = "logo.png"),
dateInput(inputId = "DateStart", label = "Insert Start Date", value = "2017-01-01"),
dateInput(inputId = "DateEnd", label = "Insert End Date", value = "2017-09-01"),
helpText("Note: Set Desired dates of interest",
"and select plots below to visualize",
"specific step of interest."),
selectInput(inputId = "selInput",label = "Add Machine Steps to Analysis", choices = stepsChoices,
selected = stepsChoices[1], multiple = TRUE, selectize = TRUE, width = '100%', size = NULL),
checkboxInput(inputId = "cboxSE", label = "Add Stat Error?", value = FALSE, width = NULL),
checkboxInput(inputId = "points", label = "Add Points?"),
div(style="display:inline-block;width:65%;text-align: right;",downloadButton(outputId = "downloadPlot",label = "Download Plot"))

),
dashboardBody(

mainPanel(

# Elements of the Dashboard: header and tabset panel
headerPanel("Visualization of steps duration"),
tabsetPanel(
# Default chart visualizing the overall performance of the systems
tabPanel("Plot - Overview", plotOutput(outputId = "Plot")),
# Box plot helping to perform comparison
tabPanel("Plot - Box Plot", plotOutput(outputId = "Plot2")),
tabPanel("Plot - Anomaly", column(4, selectInput(inputId = "Step",label = "ChooseStep", choices = stepsChoices,
selected = stepsChoices[1], multiple = FALSE, selectize = TRUE, size = NULL)),
column(4, numericInput(inputId = "numClasses", label = "Select Number of Classes",
value = 2, min = 1, max = 4, step = 1)),
column(4, checkboxInput(inputId = "scaled", label = "Scale Data?", value = FALSE)), hr(),
plotOutput(outputId = "Plot3"))
)
)
)
)
shinyUI(fluidPage(theme = "bootstrap.css",

# Page Row 1 == Information Pane ==
# Adding Logo [optional]
fluidRow(column(2, img(height = 50, width = 106, src = "logo.JPG")),
column(9,
# Application title
titlePanel("Preparation Steps Duration Overview"))),

# Adding a horizontal line
hr(),

# Page Row 2 == User Inputs ==

fluidRow(column(2, dateInput(inputId = "DateStart", label = "Insert Start Date", value = "2017-01-01")),
column(2, dateInput(inputId = "DateEnd", label = "Insert End Date")),
column(1, checkboxInput(inputId = "cboxSE", label = "Add Stat Error?", value = FALSE, width = NULL)),
column(3, helpText("Note: while the data view will show only",
"the specified number of observations, the",
"summary will be based on the full dataset."))),
fluidRow(column(8, selectInput(inputId = "selInput",label = "Add Machine Steps to Analysis", choices = stepsChoices,
selected = stepsChoices[1], multiple = TRUE,
selectize = TRUE, width = '100%', size = NULL))),

# Adding a horizontal line
hr(),

# Page Row 3 == Plot or other Outputs ==
fluidRow(column(12,
# Show a plot
mainPanel(
tabsetPanel(
tabPanel("Plot - Smoothed", plotOutput("Plot")),
tabPanel("Plot - Points", plotOutput("Plot1")),
tabPanel("Plot - Box Plot", plotOutput("Plot2")),
tabPanel("Deviation Auto Detection", "Select machine step and choose the dates of interest",
hr(),
column(4, selectInput(inputId = "Step",label = "ChooseStep", choices = stepsChoices,
selected = stepsChoices[1], multiple = FALSE,
selectize = TRUE, size = NULL)),
column(3, checkboxInput(inputId = "scaled", label = "Scale Data?", value = FALSE),
downloadButton(outputId = "downloadPlot", label = "Download Plot")),
column(4, numericInput(inputId = "numClasses", label = "Select Number of Classes",
value = 2, min = 1, max = 4, step = 1)), hr(),
plotOutput(outputId = "Plot3"))
)

)
))
))