MASTEAM_Dashboard.R
In Dimitrischatzos/shinydashboard-master: Create Dashboards with 'Shiny'
## app.R ##
library(shiny)
library(shinydashboard)
library(readxl)
library(dplyr)
library(wordcloud)
library(data.table)
library(ngram)
library(RColorBrewer)
library(tuple)
library(shinyWidgets)
library(ggplot2)
library(DT)
library(editData)
library(memisc)
library(eeptools)
library(ECharts2Shiny)
library(plotly)
library(lattice)
library(plotrix)
library(ggmap)
library(maps)
library(mapdata)
library(rworldmap)
library(ggrepel)
library(rgdal)
library(leaflet)
library(reshape2)
Enrollment <- read.csv("Enrollment.csv",TRUE, na.strings = T, sep = ",")
#InfoStudents <- read.csv("Students.csv",TRUE, na.strings = T, sep = ",", row.names = 1)
Students <- read.csv("StudentsInfo.csv",TRUE, na.strings = T, sep = ",")
graduatedStudents <- rbind(StudentsGrades2017, StudentsGrades2016, StudentsGrades2015)
#View(graduatedStudents)
Studentsinformations <- rbind(StudentsPersonalData2015, StudentsPersonalData2016, StudentsPersonalData2017)
#View(Studentsinformations)
ui <- dashboardPage(title="Masteam Dashboard",
dashboardHeader(title = "UPC-MASTEAM",titleWidth = 350, tags$li(class = "dropdown",
tags$h4("Made By:",
"Dimitrios Chatzos", " "))),
dashboardSidebar(
## Sidebar content
dashboardSidebar(
sidebarMenu(
sidebarSearchForm(textId = "searchText", buttonId = "searchButton",
label = "Search..."),
menuItem("MASTEAM View", tabName = "dashboard", icon = icon("dashboard")),
menuItem("Students View", tabName = "tab1", icon = icon("address-card")),
menuItem("Courses View", tabName = "Tab2", icon = icon("book"))
)
)
),
dashboardBody(
tabItems(
#################### First tab content
tabItem(tabName = "dashboard",
fluidPage(
tabsetPanel(
#title = "MASTEAM VIEW",
#id = "tabset1", height = "400px", side = "left", width = "400px",
tabPanel(title = "Enrolment View",
fluidRow(column(width = 12),
# Dynamic valueBoxes
box(
valueBoxOutput("Applications", width = 6),
valueBoxOutput("Accepted", width = 6)),
#valueBoxOutput("Rejected", width = 3),
box(
valueBoxOutput("Enrolled", width = 6),
valueBoxOutput("Graduations", width = 6))),
fluidRow(
#box(title = "Students Gender",
box(
valueBoxOutput("Female", width = 6 ),
valueBoxOutput("Male", width = 6 )),
#box(title = "Students Nationality",
box(
valueBoxOutput("EU", width = 6 ),
valueBoxOutput("NONEU", width = 6 ))
),
fluidRow(
box(plotOutput("GenderPie", height = 350)),
box(plotOutput("NationalityPie", height = 350))
)
),
tabPanel(title = "Enrolment per Year",
fluidRow(
box(plotlyOutput("Enrolmentplot"),width = 12)
),
fluidRow(
box(plotlyOutput("GenderBarplot")),
box(plotlyOutput("NationalityBarplot"))
)
)
)
)
),
#########################
# Second tab content
tabItem(tabName = "tab1",
fluidPage(
tabsetPanel(
# title = "Students Data",
#id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel("Students Grades",
tabBox(title = "Students Grades",
id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel(title = "2017",
plotlyOutput("StudentsView2017")),
tabPanel(title = "2016",
plotlyOutput("StudentsView2016")),
tabPanel(title = "2015",
plotlyOutput("StudentsView2015"))
)),
tabPanel("Geografical Map",
tabBox(title = "Students Geografical Region",
id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel(title = "2017",
leafletOutput("StudentsMap2017")),
tabPanel(title = "2016",
leafletOutput("StudentsMap2016")
),
tabPanel(title = "2015",
leafletOutput("StudentsMap2015")
)
)),
tabPanel("Students Info",
tabBox(title = "Students Personal Info",
id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel(title = "2017",
DT::dataTableOutput("Studentstable2017")),
tabPanel(title = "2016",
DT::dataTableOutput("Studentstable2016")),
tabPanel(title = "2015",
DT::dataTableOutput("Studentstable2015"))
)),
tabPanel("Students Grades Info",
tabBox(title = "Students Personal Info",
id = "tabset2", height = "600px", side = "left", width = "726px",
tabPanel(title = "2017",
DT::dataTableOutput("Gradestable2017")),
tabPanel(title = "2016",
DT::dataTableOutput("Gradestable2016")),
tabPanel(title = "2015",
DT::dataTableOutput("Gradestable2015"))
))
# tabPanel("Summary", verbatimTextOutput("Sum"))
)
)
),
###################### Third tab content
tabItem(tabName = "Tab2",
fluidPage(
tabsetPanel(
#title = "Subjects Info",
# The id lets us use input$tabset1 on the server to find the current tab
#id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel("Courses per Year View",
tabBox(title = "Courses View",
id = "tabset2", height = "600px", side = "left", width = "726px",
tabPanel(title = "Cources Students Per Year",
selectInput("Cources", "Year:",choices=colnames(Lessons),
Lessons <- read.csv("Cources - Sheet1.csv",TRUE, na.strings = T,
sep = ",", row.names = 1)),
plotOutput("lessonsplot"), width = 12),
tabPanel(title = "Courses Info",
selectInput("selecty", label = h3("Select Year"),
choices = list("2017", "2016", "2015")),
plotOutput("CoursesView"))
# tabPanel(title = "Courses 2017",
# plotOutput("courses2017")),
# tabPanel(title = "Courses 2016",
# plotOutput("courses2016")),
# tabPanel(title = "Courses 2015",
# plotOutput("courses2015"))
)),
tabPanel("Courses Individual info",
tabBox(title = "Courses View",
id = "tabset2", height = "600px", side = "left", width = "726px",
tabPanel(
title = "Number of Students per Course",
selectInput("Subjects", "Select Course:",
choices = colnames(lessons),
lessons <- read.csv("Cources&Years.csv",TRUE, na.strings = T, sep = ",", row.names = 1)),
hr(),
helpText("Number of Students Per Course and Per Year"),
plotOutput("subjectplot")),
tabPanel(title = "Fail/Pass and Grades per Course",
fluidRow(
box(
selectInput("select", label = h3("Select Course"),
choices = list("Optim","Neteg","Wicom","Sensors","Entrep","Iot-IP", "Arasm","5Gplan",
"Image","Optical","Bodysens","Netauth","Big Data","SDR","LowPow",
"Business","Service","Creativity"
)),
plotlyOutput("Coursesplotly")),
box(
selectInput("selectivep", label = h3("Select Course"),
choices = list("Optim","Neteg","Wicom","Sensors","Entrep","Iot-IP", "Arasm","5Gplan",
"Image","Optical","Bodysens","Netauth","Big Data","SDR","LowPow",
"Business","Service","Creativity")),
plotlyOutput("CoursesGrades")
)
))
)),
tabPanel("Courses Students & Grades Info",
tabBox(title = "Courses View",
id = "tabset5", height = "600px", side = "left", width = "726px",
tabPanel(title = "Courses2017",
DT::dataTableOutput("SubjectTables")),
tabPanel(title = "Courses2016",
DT::dataTableOutput("SubjectTables2016")),
tabPanel(title = "Courses2015",
DT::dataTableOutput("SubjectTables2015"))
#tabPanel("Summary of Courses", verbatimTextOutput("SubjectSum"))
))
)
)
)
)
)
)
server <- function(input, output, session)
{
#######Applications Letters
output$Applications <- renderValueBox({
valueBox(
sum(Enrollment$Applications),
paste("Applications"),
#subtitle = "Applications",
icon = icon("envelope"),
#icon = icon("list"),
color = "blue"
)
})
######Accepted Letters
output$Accepted <- renderValueBox({
valueBox(
sum(Enrollment$Accepted),
paste("Accepted Applications"),
icon = icon("thumbs-up"),
color = "blue"
)
})
######Graduated students
output$Graduations <- renderValueBox({
valueBox(
paste(round(percent(graduatedStudents$M.Thesis>4.99)), "%", sep = ""),
paste("Graduated Students"),
icon = icon("education", lib = "glyphicon" ),
color = "olive"
)
})
######Rejected Letters
output$Rejected <- renderValueBox({
valueBox(
sum(Enrollment$Rejected),
paste("Rejected Letters"),
icon = icon("thumbs-down"), #icon = icon("thumbs-up"), lib = "glyphic on"),
color = "red"
)
})
######Students Enrolled
output$Enrolled <- renderValueBox({
valueBox(
sum(Enrollment$Students.Enrolled),
paste("Enrolled Students"),
icon = icon("check"),
color = "aqua"
)
})
##########Number of Female students box
output$Female <- renderValueBox({
valueBox(
length(grep("Female", Studentsinformations$Gender)),
paste("Female students"),
icon = icon("female"),
color = "maroon"
)
})
######## Number of Male students box
output$Male <- renderValueBox({
valueBox(
length(grep("Male", Studentsinformations$Gender)),
paste("Male students"),
icon = icon("male"),
color = "aqua"
)
})
######## Number of EU students
output$EU <- renderValueBox({
valueBox(
length(grep("\\bEU\\b", Studentsinformations$Nationality)),
paste("EU students"),
icon = icon("globe"),
color = "blue"
)
})
####### Number of NONEU students
output$NONEU <- renderValueBox({
valueBox(
length(grep("NONEU", Studentsinformations$Nationality)),
paste("NONEU students"),
icon = icon("globe"),
color = "maroon"
)
})
#########Gender Pie Chart percentage
{
output$GenderPie <- renderPlot({
TotalMale <- length(grep("Male", Studentsinformations$Gender))
TotalFemale <- length(grep("Female", Studentsinformations$Gender))
Datagender <- c(FF, MM)
pie <- c("Female", "Male")
pct <- round(Datagender/sum(Datagender)*100)
lbls <- paste(labels, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie3D(Datagender, labels = lbls, col=c("maroon1", "steelblue"),explode = 0.1,main="MASTEAM Gender Percentage %")
legend("topright", c("Female", "Male"), cex=1, fill=c("maroon1","steelblue"))
})
}
#########Nationality Pie Chart percentage
{
output$NationalityPie <- renderPlot({
EE <- length(grep("\\bEU\\b", Studentsinformations$Nationality))
NN <- length(grep("NONEU", Studentsinformations$Nationality))
x2 <- c(EE, NN)
labels <- c("EU", "NONEU")
pct <- round(x2/sum(x2)*100)
lbls <- paste(labels, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie3D(x2, labels = lbls, col=c("royalblue3", "firebrick2"), explode = 0.1,main="MASTEAM Nationality Percentage %")
legend("topright", c("EU", "NONEU"), cex=1, fill=c("royalblue3","red3"))
})
}
######### Stacked Barplot with the Enrolment
{
output$Enrolmentplot <- renderPlotly({
x <- data.frame(Enrollment )
mx <- melt(x, id.vars=1:1)
ggplot(mx, aes(x=Enrolment, y=value, fill=variable,label= value)) +
geom_bar(stat="identity",color="black" ) +
theme_classic() +
geom_text(size = 5, position = position_stack(vjust = 0.5))+
labs(title="Enrolment Informations Per Year")
})
}
########Group Bar plot with The Gender per year
{
output$GenderBarplot <- renderPlotly({
Male2015 <- length(grep("Male", StudentsPersonalData2015$Gender))
Female2015 <- length(grep("Female", StudentsPersonalData2015$Gender))
#
Male2016 <- length(grep("Male", StudentsPersonalData2016$Gender))
Female2016 <- length(grep("Female", StudentsPersonalData2016$Gender))
#
Male2017 <- length(grep("Male", StudentsPersonalData2017$Gender))
Female2017 <- length(grep("Female", StudentsPersonalData2017$Gender))
#Barplot
Years1 <- c("2015", "2016", "2017")
Male <- c(Male2015, Male2016, Male2017)
Female <- c(Female2015, Female2016, Female2017)
data <- data.frame(Years1, Male, Female)
p <- plot_ly(data, x = ~Years1, y = ~Male, type = 'bar',
name = 'Male', text = Male, textposition = 'auto') %>%
add_trace(y = ~Female, name = 'Female', text = Female, textposition = 'auto', marker = list(color = c("violet"))) %>%
layout(title="Students Gender",
yaxis = list(title = 'Students'),
barmode = 'group')
})
}
#####Group Bar Plot Nationality Per year
{
output$NationalityBarplot <- renderPlotly({
EU2015 <- length(grep("\\bEU\\b", StudentsPersonalData2015$Nationality))
NonEU2015 <- length(grep("NONEU", StudentsPersonalData2015$Nationality))
#
EU2016 <- length(grep("\\bEU\\b", StudentsPersonalData2016$Nationality))
NONEU2016 <- length(grep("NONEU", StudentsPersonalData2016$Nationality))
#
EU2017 <- length(grep("\\bEU\\b", StudentsPersonalData2017$Nationality))
NONEU2017 <- length(grep("NONEU", StudentsPersonalData2017$Nationality))
#Barplot
Years2 <- c('2015', '2016', '2017')
EUstudents <- c(EU2015, EU2016, EU2017)
NONEUstudents <- c(NonEU2015, NONEU2016, NONEU2017)
data <- data.frame(Years2, EUstudents, NONEUstudents)
p <- plot_ly(data, x = ~EUstudents, y = ~Years2, type = 'bar', orientation = 'h', name = 'EU',
text = EUstudents, textposition = 'auto',
marker = list(line = list(color = 'blue',width = 3))) %>%
add_trace(x = ~NONEUstudents, name = 'NONEU', text = NONEUstudents, textposition = 'auto',
marker = list(color = 'red',line = list(color = 'darkred',width = 3))) %>%
layout(title = "Students Nationality",
barmode = 'stack',
xaxis = list(title = "Students"),
yaxis = list(title ="Years"))
})
}
##########End Of The First Tab MASTEAM view Dashboard###############################################
####################################################################################################
#### Second SiderTab 2
################Box plot with the students Grades
#####2017
{
output$StudentsView2017 <- renderPlotly({
dat <- data.frame(StudentsGrades2017)
dat.m <- melt(dat,id.vars='Students')
ggplot(dat.m, aes(x = Students, y = value)) +
geom_boxplot(fill= "#56B4E9", color="black", na.rm = TRUE)+
theme(axis.text.x = element_text(angle = 60))+
stat_summary(fun.y=mean, geom="point", shape=5, size=4)
#coord_flip()
#geom_text(data = dat.m, aes(label = value),position = position_dodge(width=0.5))
})
}
#####2016
{
output$StudentsView2016 <- renderPlotly({
dat <- data.frame(StudentsGrades2016)
dat.m <- melt(dat,id.vars='Students')
ggplot(dat.m, aes(x = Students, y = value)) +
geom_boxplot(fill= "#56B4E9", color="black", na.rm = TRUE)+
theme(axis.text.x = element_text(angle = 60))+
stat_summary(fun.y=mean, geom="point", shape=5, size=4)
#coord_flip()
#geom_text(data = dat.m, aes(label = value),position = position_dodge(width=0.5))
})
}
#####2015
{
output$StudentsView2015 <- renderPlotly({
dat <- data.frame(StudentsGrades2015)
dat.m <- melt(dat,id.vars='Students')
ggplot(dat.m, aes(x = Students, y = value)) +
geom_boxplot(fill= "#56B4E9", color="black", na.rm = TRUE)+
theme(axis.text.x = element_text(angle = 60))+
stat_summary(fun.y=mean, geom="point", shape=5, size=4)
#coord_flip()
#geom_text(data = dat.m, aes(label = value),position = position_dodge(width=0.5))
})
}
###################Students Map 2017
{
output$StudentsMap2017 <- renderLeaflet({
data = StudentsPersonalData2017
long=longtitude
lat=latitude
ww <- StudentsPersonalData2017$Students
label <- c(ww)
markers <- data.frame(latitude,longtitude,label)
pal = colorFactor(palette = c("blue"), domain = data$Country)
# Aggregate method
markers <- aggregate(label ~ lat + long, markers, paste, collapse = "<br/>")
# Markers with all of the labels
leaflet(data) %>%
addTiles() %>%
addMarkers(lng=markers$long, lat= markers$lat,
popup=paste("Student:","<br/>",
markers$label))%>%
addLegend(position = "bottomright", pal = pal, values = ~Country)
})
}
########## Map 2016
{
output$StudentsMap2016 <- renderLeaflet({
data6 = StudentsPersonalData2016
long=StudentsPersonalData2016$long
lat=StudentsPersonalData2016$lat
ww6 <- StudentsPersonalData2016$Students
label <- c(ww6)
markers <- data.frame(long,lat,label)
pal = colorFactor(palette = c("blue"), domain = data6$Country)
# Aggregate method
markers <- aggregate(label ~ lat + long, markers, paste, collapse = "<br/>")
# Markers with all of the labels
leaflet(data6) %>%
addTiles() %>%
addMarkers(lng=markers$long, lat= markers$lat,
popup=paste("Student:","<br/>",
markers$label))%>%
addLegend(position = "bottomright", pal = pal, values = StudentsPersonalData2016$Country)
})
}
########## Map 2015
{
output$StudentsMap2015 <- renderLeaflet({
data5 = StudentsPersonalData2015
long=StudentsPersonalData2015$long
lat=StudentsPersonalData2015$lat
ww5 <- StudentsPersonalData2015$Students
label <- c(ww5)
markers <- data.frame(long,lat,label)
pal = colorFactor(palette = c("blue"), domain = data5$Country)
# Aggregate method
markers <- aggregate(label ~ lat + long, markers, paste, collapse = "<br/>")
# Markers with all of the labels
leaflet(data5) %>%
addTiles() %>%
addMarkers(lng=markers$long, lat= markers$lat,
popup=paste("Student:","<br/>",
markers$label))%>%
addLegend(position = "bottomright", pal = pal, values = StudentsPersonalData2015$Country)
})
}
######Output Students Personal info Datatable
###2017
{
output$Studentstable2017 = DT::renderDataTable(
StudentsPersonalData2017,
editable =T
)
}
###2016
{
output$Studentstable2016 = DT::renderDataTable(
StudentsPersonalData2016,
editable =T
)
}
###2015
{
output$Studentstable2015 = DT::renderDataTable(
StudentsPersonalData2015,
editable =T
)
}
######Studets Grades datatable
###2017
{
output$Gradestable2017 = DT::renderDataTable(
StudentsGrades2017,
options = list(scrollX = TRUE),
editable = T
)
}
###2016
{
output$Gradestable2016 = DT::renderDataTable(
StudentsGrades2016,
options = list(scrollX = TRUE),
editable = T
)
}
###2015
{
output$Gradestable2015 = DT::renderDataTable(
StudentsGrades2015,
options = list(scrollX = TRUE),
editable = T
)
}
#######Students Grades Summary
{
output$Sum <- renderPrint({
data.frame(Nmae=StudentsGrades2017[,1], AvarageGrade=rowMeans(StudentsGrades2017[,-1], na.rm = TRUE)
)
})
}
##### Third SiderTab 3
######Output for Subjects Sider Tab
{
#### Barplote for the Students per course
output$lessonsplot <- renderPlot({ mybar <-
# Render a barplot
barplot(Lessons[, input$Cources],
names.arg = row.names(Lessons),
main= "Bar Plot With the Number of Students per Year",
ylab="Number of Students",
xlab = "",
col = "steelblue2", #rainbow(18),
horiz = FALSE,
ylim = c(0, 25),
border = T,
las = 2, cex.axis=1.5, cex.names= 1.2, cex.lab = 1.5, xpd = NA
)
x<- as.matrix(mybar)
y <- as.matrix(Lessons[, input$Cources])
text(x, y, labels = as.character(y), pos = 3, col = "black")
},bg="transparent")
}
################## Select year Barplot Group and Stack together
{
barplottest <- reactive({
if ( "2017" %in% input$selecty) return(database2017())
if ( "2016" %in% input$selecty) return(database2016())
if( "2015" %in% input$selecty) return(database2015())
})
output$CoursesView <- renderPlot({
dataplots = barplottest()
print(dataplots)
})
}
######################### Barplot Group and Stack together
####2017
{
output$courses2017 <- renderPlot({
xa <- data.frame(StudentsGrades2017)
pass <- data.frame(pass=colSums(xa[,-1]>4.99, na.rm = TRUE))
fail<- data.frame(fail=colSums(xa[,-1]<5, na.rm = TRUE))
Avarage <- data.frame(Avarage=colMeans(StudentsGrades2017[,-1], na.rm = TRUE))
Avarageround <- round(Avarage, 2)
colos <- as.character(colnames(StudentsGrades2017[,-1]))
test <- data.frame(colos,
Avarageround,
pass ,
fail)
melted <- melt(test, "colos")
melted$courses <- ''
melted[melted$variable == 'Avarage',]$courses <- "Av"
melted[melted$variable != 'Avarage',]$courses <- "F/P"
ggplot(melted, aes(x = courses, y = value, fill = variable, label = value )) +
#geom_col() + facet_grid(~ colos)
geom_bar(stat = 'identity', position = 'stack') + facet_grid(~ colos)+
theme_classic() +
geom_text(size = 3, position = position_stack(vjust = 0.5))
})
}
###################################
############## 2016
{
output$courses2016 <- renderPlot({
xa2016 <- data.frame(StudentsGrades2016)
pass2016 <- data.frame(pass=colSums(xa2016[,-1]>4.99, na.rm = TRUE))
fail2016 <- data.frame(fail=colSums(xa2016[,-1]<5, na.rm = TRUE))
Avarage2016 <- data.frame(Avarage=colMeans(StudentsGrades2016[,-1], na.rm = TRUE))
Avarageround2016 <- round(Avarage, 2)
colos2016 <- as.character(colnames(StudentsGrades2016[,-1]))
test <- data.frame(colos2016,
Avarageround2016,
pass2016 ,
fail2016)
melted <- melt(test, "colos2016")
melted$courses2016 <- ''
melted[melted$variable == 'Avarage',]$courses2016 <- "Av"
melted[melted$variable != 'Avarage',]$courses2016 <- "F/P"
ggplot(melted, aes(x = courses2016, y = value, fill = variable, label = value )) +
#geom_col() + facet_grid(~ colos)
geom_bar(stat = 'identity', position = 'stack') + facet_grid(~ colos2016)+
theme_classic() +
geom_text(size = 3, position = position_stack(vjust = 0.5))
})
}
#########################################
########## 2015
{
output$courses2015 <- renderPlot({
xa2015 <- data.frame(StudentsGrades2015)
pass2015 <- data.frame(pass=colSums(xa2015[,-1]>4.99, na.rm = TRUE))
fail2015 <- data.frame(fail=colSums(xa2015[,-1]<5, na.rm = TRUE))
Avarage2015 <- data.frame(Avarage=colMeans(StudentsGrades2015[,-1], na.rm = TRUE))
Avarageround2015 <- round(Avarage, 2)
colos2015 <- as.character(colnames(StudentsGrades2015[,-1]))
test <- data.frame(colos2015,
Avarageround2015,
pass2015 ,
fail2015)
melted <- melt(test, "colos2015")
melted$courses2015 <- ''
melted[melted$variable == 'Avarage',]$courses2015 <- "Av"
melted[melted$variable != 'Avarage',]$courses2015 <- "F/P"
ggplot(melted, aes(x = courses2015, y = value, fill = variable, label = value )) +
#geom_col() + facet_grid(~ colos)
geom_bar(stat = 'identity', position = 'stack') + facet_grid(~ colos2015)+
theme_classic() +
geom_text(size = 3, position = position_stack(vjust = 0.5))
})
}
#########################################################
{
output$SubjectTables = DT::renderDataTable(
SubjectsInfo,
options = list(scrollX = TRUE),
editable = T
)
}
{
output$SubjectTables2016 = DT::renderDataTable(
SubjectsInfo2016,
options = list(scrollX = TRUE),
editable = T
)
}
{
output$SubjectTables2015 = DT::renderDataTable(
SubjectsInfo2015,
options = list(scrollX = TRUE),
editable = T
)
}
{
output$SubjectSum <-renderPrint({
data.frame(AvarageGrade=colMeans(StudentsGrades2017[,-1], na.rm = TRUE))
})
}
{
output$subjectplot <- renderPlot({ mybar2 <-
barplot(lessons[,input$Subjects],
names.arg = row.names(lessons),
main = "Students per Year",
ylab = "Number of Students",
xlab = "Years",
ylim = c(0,25),
col = "blue"
)
x<- as.matrix(mybar2)
y <- as.matrix(lessons[, input$Subjects])
text(x, y, labels = as.character(y), pos = 3, col = "black")
})
}
################# Stack Barpl
{
barplottests <- reactive({
if ( "Optim" %in% input$select) return(failpassv1())
if ( "Neteg" %in% input$select) return(failpassv2())
if( "Wicom" %in% input$select) return(failpassv3())
if ( "Sensors" %in% input$select) return(failpassv4())
if ( "Entrep" %in% input$select) return(failpassv5())
if( "Iot-IP" %in% input$select) return(failpassv6())
if ( "Arasm" %in% input$select) return(failpassv7())
if ( "5Gplan" %in% input$select) return(failpassv8())
if( "Image" %in% input$select) return(failpassv9())
if ( "Optical" %in% input$select) return(failpassv10())
if ( "Bodysens" %in% input$select) return(failpassv11())
if( "Netauth" %in% input$select) return(failpassv12())
if ( "Big Data" %in% input$select) return(failpassv13())
if ( "SDR" %in% input$select) return(failpassv14())
if( "LowPow" %in% input$select) return(failpassv15())
if ( "Business" %in% input$select) return(failpassv16())
if( "Service" %in% input$select) return(failpassv17())
if ( "Creativity" %in% input$select) return(failpassv18())
})
output$Coursesplotly <- renderPlotly({
dataplot = barplottests()
print(dataplot)
})
}
################ Comparison per course 2017 2016 2015
{
barplottestsp <- reactive({
if ( "Optim" %in% input$selectivep) return(pbox1())
if ( "Neteg" %in% input$selectivep) return(pbox2())
if( "Wicom" %in% input$selectivep) return(pbox3())
if ( "Sensors" %in% input$selectivep) return(pbox4())
if ( "Entrep" %in% input$selectivep) return(pbox5())
if( "Iot-IP" %in% input$selectivep) return(pbox6())
if ( "Arasm" %in% input$selectivep) return(pbox7())
if ( "5Gplan" %in% input$selectivep) return(pbox8())
if( "Image" %in% input$selectivep) return(pbox9())
if ( "Optical" %in% input$selectivep) return(pbox10())
if ( "Bodysens" %in% input$selectivep) return(pbox11())
if( "Netauth" %in% input$selectivep) return(pbox12())
if ( "Big Data" %in% input$selectivep) return(pbox13())
if ( "SDR" %in% input$selectivep) return(pbox14())
if( "LowPow" %in% input$selectivep) return(pbox15())
if ( "Business" %in% input$selectivep) return(pbox16())
if( "Service" %in% input$selectivep) return(pbox17())
if ( "Creativity" %in% input$selectivep) return(pbox18())
})
output$CoursesGrades <- renderPlotly({
dataplotp = barplottestsp()
print(dataplotp)
})
##############
}
}
shinyApp(ui, server)
Dimitrischatzos/shinydashboard-master documentation built on May 31, 2019, 1:52 p.m.
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## app.R ##
library(shiny)
library(shinydashboard)
library(readxl)
library(dplyr)
library(wordcloud)
library(data.table)
library(ngram)
library(RColorBrewer)
library(tuple)
library(shinyWidgets)
library(ggplot2)
library(DT)
library(editData)
library(memisc)
library(eeptools)
library(ECharts2Shiny)
library(plotly)
library(lattice)
library(plotrix)
library(ggmap)
library(maps)
library(mapdata)
library(rworldmap)
library(ggrepel)
library(rgdal)
library(leaflet)
library(reshape2)
Enrollment <- read.csv("Enrollment.csv",TRUE, na.strings = T, sep = ",")
#InfoStudents <- read.csv("Students.csv",TRUE, na.strings = T, sep = ",", row.names = 1)
Students <- read.csv("StudentsInfo.csv",TRUE, na.strings = T, sep = ",")
graduatedStudents <- rbind(StudentsGrades2017, StudentsGrades2016, StudentsGrades2015)
#View(graduatedStudents)
Studentsinformations <- rbind(StudentsPersonalData2015, StudentsPersonalData2016, StudentsPersonalData2017)
#View(Studentsinformations)
ui <- dashboardPage(title="Masteam Dashboard",
dashboardHeader(title = "UPC-MASTEAM",titleWidth = 350, tags$li(class = "dropdown",
tags$h4("Made By:",
"Dimitrios Chatzos", " "))),
dashboardSidebar(
## Sidebar content
dashboardSidebar(
sidebarMenu(
sidebarSearchForm(textId = "searchText", buttonId = "searchButton",
label = "Search..."),
menuItem("MASTEAM View", tabName = "dashboard", icon = icon("dashboard")),
menuItem("Students View", tabName = "tab1", icon = icon("address-card")),
menuItem("Courses View", tabName = "Tab2", icon = icon("book"))
)
)
),
dashboardBody(
tabItems(
#################### First tab content
tabItem(tabName = "dashboard",
fluidPage(
tabsetPanel(
#title = "MASTEAM VIEW",
#id = "tabset1", height = "400px", side = "left", width = "400px",
tabPanel(title = "Enrolment View",
fluidRow(column(width = 12),
# Dynamic valueBoxes
box(
valueBoxOutput("Applications", width = 6),
valueBoxOutput("Accepted", width = 6)),
#valueBoxOutput("Rejected", width = 3),
box(
valueBoxOutput("Enrolled", width = 6),
valueBoxOutput("Graduations", width = 6))),
fluidRow(
#box(title = "Students Gender",
box(
valueBoxOutput("Female", width = 6 ),
valueBoxOutput("Male", width = 6 )),
#box(title = "Students Nationality",
box(
valueBoxOutput("EU", width = 6 ),
valueBoxOutput("NONEU", width = 6 ))
),
fluidRow(
box(plotOutput("GenderPie", height = 350)),
box(plotOutput("NationalityPie", height = 350))
)
),
tabPanel(title = "Enrolment per Year",
fluidRow(
box(plotlyOutput("Enrolmentplot"),width = 12)
),
fluidRow(
box(plotlyOutput("GenderBarplot")),
box(plotlyOutput("NationalityBarplot"))
)
)
)
)
),
#########################
# Second tab content
tabItem(tabName = "tab1",
fluidPage(
tabsetPanel(
# title = "Students Data",
#id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel("Students Grades",
tabBox(title = "Students Grades",
id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel(title = "2017",
plotlyOutput("StudentsView2017")),
tabPanel(title = "2016",
plotlyOutput("StudentsView2016")),
tabPanel(title = "2015",
plotlyOutput("StudentsView2015"))
)),
tabPanel("Geografical Map",
tabBox(title = "Students Geografical Region",
id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel(title = "2017",
leafletOutput("StudentsMap2017")),
tabPanel(title = "2016",
leafletOutput("StudentsMap2016")
),
tabPanel(title = "2015",
leafletOutput("StudentsMap2015")
)
)),
tabPanel("Students Info",
tabBox(title = "Students Personal Info",
id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel(title = "2017",
DT::dataTableOutput("Studentstable2017")),
tabPanel(title = "2016",
DT::dataTableOutput("Studentstable2016")),
tabPanel(title = "2015",
DT::dataTableOutput("Studentstable2015"))
)),
tabPanel("Students Grades Info",
tabBox(title = "Students Personal Info",
id = "tabset2", height = "600px", side = "left", width = "726px",
tabPanel(title = "2017",
DT::dataTableOutput("Gradestable2017")),
tabPanel(title = "2016",
DT::dataTableOutput("Gradestable2016")),
tabPanel(title = "2015",
DT::dataTableOutput("Gradestable2015"))
))
# tabPanel("Summary", verbatimTextOutput("Sum"))
)
)
),
###################### Third tab content
tabItem(tabName = "Tab2",
fluidPage(
tabsetPanel(
#title = "Subjects Info",
# The id lets us use input$tabset1 on the server to find the current tab
#id = "tabset1", height = "600px", side = "left", width = "726px",
tabPanel("Courses per Year View",
tabBox(title = "Courses View",
id = "tabset2", height = "600px", side = "left", width = "726px",
tabPanel(title = "Cources Students Per Year",
selectInput("Cources", "Year:",choices=colnames(Lessons),
Lessons <- read.csv("Cources - Sheet1.csv",TRUE, na.strings = T,
sep = ",", row.names = 1)),
plotOutput("lessonsplot"), width = 12),
tabPanel(title = "Courses Info",
selectInput("selecty", label = h3("Select Year"),
choices = list("2017", "2016", "2015")),
plotOutput("CoursesView"))
# tabPanel(title = "Courses 2017",
# plotOutput("courses2017")),
# tabPanel(title = "Courses 2016",
# plotOutput("courses2016")),
# tabPanel(title = "Courses 2015",
# plotOutput("courses2015"))
)),
tabPanel("Courses Individual info",
tabBox(title = "Courses View",
id = "tabset2", height = "600px", side = "left", width = "726px",
tabPanel(
title = "Number of Students per Course",
selectInput("Subjects", "Select Course:",
choices = colnames(lessons),
lessons <- read.csv("Cources&Years.csv",TRUE, na.strings = T, sep = ",", row.names = 1)),
hr(),
helpText("Number of Students Per Course and Per Year"),
plotOutput("subjectplot")),
tabPanel(title = "Fail/Pass and Grades per Course",
fluidRow(
box(
selectInput("select", label = h3("Select Course"),
choices = list("Optim","Neteg","Wicom","Sensors","Entrep","Iot-IP", "Arasm","5Gplan",
"Image","Optical","Bodysens","Netauth","Big Data","SDR","LowPow",
"Business","Service","Creativity"
)),
plotlyOutput("Coursesplotly")),
box(
selectInput("selectivep", label = h3("Select Course"),
choices = list("Optim","Neteg","Wicom","Sensors","Entrep","Iot-IP", "Arasm","5Gplan",
"Image","Optical","Bodysens","Netauth","Big Data","SDR","LowPow",
"Business","Service","Creativity")),
plotlyOutput("CoursesGrades")
)
))
)),
tabPanel("Courses Students & Grades Info",
tabBox(title = "Courses View",
id = "tabset5", height = "600px", side = "left", width = "726px",
tabPanel(title = "Courses2017",
DT::dataTableOutput("SubjectTables")),
tabPanel(title = "Courses2016",
DT::dataTableOutput("SubjectTables2016")),
tabPanel(title = "Courses2015",
DT::dataTableOutput("SubjectTables2015"))
#tabPanel("Summary of Courses", verbatimTextOutput("SubjectSum"))
))
)
)
)
)
)
)
server <- function(input, output, session)
{
#######Applications Letters
output$Applications <- renderValueBox({
valueBox(
sum(Enrollment$Applications),
paste("Applications"),
#subtitle = "Applications",
icon = icon("envelope"),
#icon = icon("list"),
color = "blue"
)
})
######Accepted Letters
output$Accepted <- renderValueBox({
valueBox(
sum(Enrollment$Accepted),
paste("Accepted Applications"),
icon = icon("thumbs-up"),
color = "blue"
)
})
######Graduated students
output$Graduations <- renderValueBox({
valueBox(
paste(round(percent(graduatedStudents$M.Thesis>4.99)), "%", sep = ""),
paste("Graduated Students"),
icon = icon("education", lib = "glyphicon" ),
color = "olive"
)
})
######Rejected Letters
output$Rejected <- renderValueBox({
valueBox(
sum(Enrollment$Rejected),
paste("Rejected Letters"),
icon = icon("thumbs-down"), #icon = icon("thumbs-up"), lib = "glyphic on"),
color = "red"
)
})
######Students Enrolled
output$Enrolled <- renderValueBox({
valueBox(
sum(Enrollment$Students.Enrolled),
paste("Enrolled Students"),
icon = icon("check"),
color = "aqua"
)
})
##########Number of Female students box
output$Female <- renderValueBox({
valueBox(
length(grep("Female", Studentsinformations$Gender)),
paste("Female students"),
icon = icon("female"),
color = "maroon"
)
})
######## Number of Male students box
output$Male <- renderValueBox({
valueBox(
length(grep("Male", Studentsinformations$Gender)),
paste("Male students"),
icon = icon("male"),
color = "aqua"
)
})
######## Number of EU students
output$EU <- renderValueBox({
valueBox(
length(grep("\\bEU\\b", Studentsinformations$Nationality)),
paste("EU students"),
icon = icon("globe"),
color = "blue"
)
})
####### Number of NONEU students
output$NONEU <- renderValueBox({
valueBox(
length(grep("NONEU", Studentsinformations$Nationality)),
paste("NONEU students"),
icon = icon("globe"),
color = "maroon"
)
})
#########Gender Pie Chart percentage
{
output$GenderPie <- renderPlot({
TotalMale <- length(grep("Male", Studentsinformations$Gender))
TotalFemale <- length(grep("Female", Studentsinformations$Gender))
Datagender <- c(FF, MM)
pie <- c("Female", "Male")
pct <- round(Datagender/sum(Datagender)*100)
lbls <- paste(labels, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie3D(Datagender, labels = lbls, col=c("maroon1", "steelblue"),explode = 0.1,main="MASTEAM Gender Percentage %")
legend("topright", c("Female", "Male"), cex=1, fill=c("maroon1","steelblue"))
})
}
#########Nationality Pie Chart percentage
{
output$NationalityPie <- renderPlot({
EE <- length(grep("\\bEU\\b", Studentsinformations$Nationality))
NN <- length(grep("NONEU", Studentsinformations$Nationality))
x2 <- c(EE, NN)
labels <- c("EU", "NONEU")
pct <- round(x2/sum(x2)*100)
lbls <- paste(labels, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie3D(x2, labels = lbls, col=c("royalblue3", "firebrick2"), explode = 0.1,main="MASTEAM Nationality Percentage %")
legend("topright", c("EU", "NONEU"), cex=1, fill=c("royalblue3","red3"))
})
}
######### Stacked Barplot with the Enrolment
{
output$Enrolmentplot <- renderPlotly({
x <- data.frame(Enrollment )
mx <- melt(x, id.vars=1:1)
ggplot(mx, aes(x=Enrolment, y=value, fill=variable,label= value)) +
geom_bar(stat="identity",color="black" ) +
theme_classic() +
geom_text(size = 5, position = position_stack(vjust = 0.5))+
labs(title="Enrolment Informations Per Year")
})
}
########Group Bar plot with The Gender per year
{
output$GenderBarplot <- renderPlotly({
Male2015 <- length(grep("Male", StudentsPersonalData2015$Gender))
Female2015 <- length(grep("Female", StudentsPersonalData2015$Gender))
#
Male2016 <- length(grep("Male", StudentsPersonalData2016$Gender))
Female2016 <- length(grep("Female", StudentsPersonalData2016$Gender))
#
Male2017 <- length(grep("Male", StudentsPersonalData2017$Gender))
Female2017 <- length(grep("Female", StudentsPersonalData2017$Gender))
#Barplot
Years1 <- c("2015", "2016", "2017")
Male <- c(Male2015, Male2016, Male2017)
Female <- c(Female2015, Female2016, Female2017)
data <- data.frame(Years1, Male, Female)
p <- plot_ly(data, x = ~Years1, y = ~Male, type = 'bar',
name = 'Male', text = Male, textposition = 'auto') %>%
add_trace(y = ~Female, name = 'Female', text = Female, textposition = 'auto', marker = list(color = c("violet"))) %>%
layout(title="Students Gender",
yaxis = list(title = 'Students'),
barmode = 'group')
})
}
#####Group Bar Plot Nationality Per year
{
output$NationalityBarplot <- renderPlotly({
EU2015 <- length(grep("\\bEU\\b", StudentsPersonalData2015$Nationality))
NonEU2015 <- length(grep("NONEU", StudentsPersonalData2015$Nationality))
#
EU2016 <- length(grep("\\bEU\\b", StudentsPersonalData2016$Nationality))
NONEU2016 <- length(grep("NONEU", StudentsPersonalData2016$Nationality))
#
EU2017 <- length(grep("\\bEU\\b", StudentsPersonalData2017$Nationality))
NONEU2017 <- length(grep("NONEU", StudentsPersonalData2017$Nationality))
#Barplot
Years2 <- c('2015', '2016', '2017')
EUstudents <- c(EU2015, EU2016, EU2017)
NONEUstudents <- c(NonEU2015, NONEU2016, NONEU2017)
data <- data.frame(Years2, EUstudents, NONEUstudents)
p <- plot_ly(data, x = ~EUstudents, y = ~Years2, type = 'bar', orientation = 'h', name = 'EU',
text = EUstudents, textposition = 'auto',
marker = list(line = list(color = 'blue',width = 3))) %>%
add_trace(x = ~NONEUstudents, name = 'NONEU', text = NONEUstudents, textposition = 'auto',
marker = list(color = 'red',line = list(color = 'darkred',width = 3))) %>%
layout(title = "Students Nationality",
barmode = 'stack',
xaxis = list(title = "Students"),
yaxis = list(title ="Years"))
})
}
##########End Of The First Tab MASTEAM view Dashboard###############################################
####################################################################################################
#### Second SiderTab 2
################Box plot with the students Grades
#####2017
{
output$StudentsView2017 <- renderPlotly({
dat <- data.frame(StudentsGrades2017)
dat.m <- melt(dat,id.vars='Students')
ggplot(dat.m, aes(x = Students, y = value)) +
geom_boxplot(fill= "#56B4E9", color="black", na.rm = TRUE)+
theme(axis.text.x = element_text(angle = 60))+
stat_summary(fun.y=mean, geom="point", shape=5, size=4)
#coord_flip()
#geom_text(data = dat.m, aes(label = value),position = position_dodge(width=0.5))
})
}
#####2016
{
output$StudentsView2016 <- renderPlotly({
dat <- data.frame(StudentsGrades2016)
dat.m <- melt(dat,id.vars='Students')
ggplot(dat.m, aes(x = Students, y = value)) +
geom_boxplot(fill= "#56B4E9", color="black", na.rm = TRUE)+
theme(axis.text.x = element_text(angle = 60))+
stat_summary(fun.y=mean, geom="point", shape=5, size=4)
#coord_flip()
#geom_text(data = dat.m, aes(label = value),position = position_dodge(width=0.5))
})
}
#####2015
{
output$StudentsView2015 <- renderPlotly({
dat <- data.frame(StudentsGrades2015)
dat.m <- melt(dat,id.vars='Students')
ggplot(dat.m, aes(x = Students, y = value)) +
geom_boxplot(fill= "#56B4E9", color="black", na.rm = TRUE)+
theme(axis.text.x = element_text(angle = 60))+
stat_summary(fun.y=mean, geom="point", shape=5, size=4)
#coord_flip()
#geom_text(data = dat.m, aes(label = value),position = position_dodge(width=0.5))
})
}
###################Students Map 2017
{
output$StudentsMap2017 <- renderLeaflet({
data = StudentsPersonalData2017
long=longtitude
lat=latitude
ww <- StudentsPersonalData2017$Students
label <- c(ww)
markers <- data.frame(latitude,longtitude,label)
pal = colorFactor(palette = c("blue"), domain = data$Country)
# Aggregate method
markers <- aggregate(label ~ lat + long, markers, paste, collapse = "<br/>")
# Markers with all of the labels
leaflet(data) %>%
addTiles() %>%
addMarkers(lng=markers$long, lat= markers$lat,
popup=paste("Student:","<br/>",
markers$label))%>%
addLegend(position = "bottomright", pal = pal, values = ~Country)
})
}
########## Map 2016
{
output$StudentsMap2016 <- renderLeaflet({
data6 = StudentsPersonalData2016
long=StudentsPersonalData2016$long
lat=StudentsPersonalData2016$lat
ww6 <- StudentsPersonalData2016$Students
label <- c(ww6)
markers <- data.frame(long,lat,label)
pal = colorFactor(palette = c("blue"), domain = data6$Country)
# Aggregate method
markers <- aggregate(label ~ lat + long, markers, paste, collapse = "<br/>")
# Markers with all of the labels
leaflet(data6) %>%
addTiles() %>%
addMarkers(lng=markers$long, lat= markers$lat,
popup=paste("Student:","<br/>",
markers$label))%>%
addLegend(position = "bottomright", pal = pal, values = StudentsPersonalData2016$Country)
})
}
########## Map 2015
{
output$StudentsMap2015 <- renderLeaflet({
data5 = StudentsPersonalData2015
long=StudentsPersonalData2015$long
lat=StudentsPersonalData2015$lat
ww5 <- StudentsPersonalData2015$Students
label <- c(ww5)
markers <- data.frame(long,lat,label)
pal = colorFactor(palette = c("blue"), domain = data5$Country)
# Aggregate method
markers <- aggregate(label ~ lat + long, markers, paste, collapse = "<br/>")
# Markers with all of the labels
leaflet(data5) %>%
addTiles() %>%
addMarkers(lng=markers$long, lat= markers$lat,
popup=paste("Student:","<br/>",
markers$label))%>%
addLegend(position = "bottomright", pal = pal, values = StudentsPersonalData2015$Country)
})
}
######Output Students Personal info Datatable
###2017
{
output$Studentstable2017 = DT::renderDataTable(
StudentsPersonalData2017,
editable =T
)
}
###2016
{
output$Studentstable2016 = DT::renderDataTable(
StudentsPersonalData2016,
editable =T
)
}
###2015
{
output$Studentstable2015 = DT::renderDataTable(
StudentsPersonalData2015,
editable =T
)
}
######Studets Grades datatable
###2017
{
output$Gradestable2017 = DT::renderDataTable(
StudentsGrades2017,
options = list(scrollX = TRUE),
editable = T
)
}
###2016
{
output$Gradestable2016 = DT::renderDataTable(
StudentsGrades2016,
options = list(scrollX = TRUE),
editable = T
)
}
###2015
{
output$Gradestable2015 = DT::renderDataTable(
StudentsGrades2015,
options = list(scrollX = TRUE),
editable = T
)
}
#######Students Grades Summary
{
output$Sum <- renderPrint({
data.frame(Nmae=StudentsGrades2017[,1], AvarageGrade=rowMeans(StudentsGrades2017[,-1], na.rm = TRUE)
)
})
}
##### Third SiderTab 3
######Output for Subjects Sider Tab
{
#### Barplote for the Students per course
output$lessonsplot <- renderPlot({ mybar <-
# Render a barplot
barplot(Lessons[, input$Cources],
names.arg = row.names(Lessons),
main= "Bar Plot With the Number of Students per Year",
ylab="Number of Students",
xlab = "",
col = "steelblue2", #rainbow(18),
horiz = FALSE,
ylim = c(0, 25),
border = T,
las = 2, cex.axis=1.5, cex.names= 1.2, cex.lab = 1.5, xpd = NA
)
x<- as.matrix(mybar)
y <- as.matrix(Lessons[, input$Cources])
text(x, y, labels = as.character(y), pos = 3, col = "black")
},bg="transparent")
}
################## Select year Barplot Group and Stack together
{
barplottest <- reactive({
if ( "2017" %in% input$selecty) return(database2017())
if ( "2016" %in% input$selecty) return(database2016())
if( "2015" %in% input$selecty) return(database2015())
})
output$CoursesView <- renderPlot({
dataplots = barplottest()
print(dataplots)
})
}
######################### Barplot Group and Stack together
####2017
{
output$courses2017 <- renderPlot({
xa <- data.frame(StudentsGrades2017)
pass <- data.frame(pass=colSums(xa[,-1]>4.99, na.rm = TRUE))
fail<- data.frame(fail=colSums(xa[,-1]<5, na.rm = TRUE))
Avarage <- data.frame(Avarage=colMeans(StudentsGrades2017[,-1], na.rm = TRUE))
Avarageround <- round(Avarage, 2)
colos <- as.character(colnames(StudentsGrades2017[,-1]))
test <- data.frame(colos,
Avarageround,
pass ,
fail)
melted <- melt(test, "colos")
melted$courses <- ''
melted[melted$variable == 'Avarage',]$courses <- "Av"
melted[melted$variable != 'Avarage',]$courses <- "F/P"
ggplot(melted, aes(x = courses, y = value, fill = variable, label = value )) +
#geom_col() + facet_grid(~ colos)
geom_bar(stat = 'identity', position = 'stack') + facet_grid(~ colos)+
theme_classic() +
geom_text(size = 3, position = position_stack(vjust = 0.5))
})
}
###################################
############## 2016
{
output$courses2016 <- renderPlot({
xa2016 <- data.frame(StudentsGrades2016)
pass2016 <- data.frame(pass=colSums(xa2016[,-1]>4.99, na.rm = TRUE))
fail2016 <- data.frame(fail=colSums(xa2016[,-1]<5, na.rm = TRUE))
Avarage2016 <- data.frame(Avarage=colMeans(StudentsGrades2016[,-1], na.rm = TRUE))
Avarageround2016 <- round(Avarage, 2)
colos2016 <- as.character(colnames(StudentsGrades2016[,-1]))
test <- data.frame(colos2016,
Avarageround2016,
pass2016 ,
fail2016)
melted <- melt(test, "colos2016")
melted$courses2016 <- ''
melted[melted$variable == 'Avarage',]$courses2016 <- "Av"
melted[melted$variable != 'Avarage',]$courses2016 <- "F/P"
ggplot(melted, aes(x = courses2016, y = value, fill = variable, label = value )) +
#geom_col() + facet_grid(~ colos)
geom_bar(stat = 'identity', position = 'stack') + facet_grid(~ colos2016)+
theme_classic() +
geom_text(size = 3, position = position_stack(vjust = 0.5))
})
}
#########################################
########## 2015
{
output$courses2015 <- renderPlot({
xa2015 <- data.frame(StudentsGrades2015)
pass2015 <- data.frame(pass=colSums(xa2015[,-1]>4.99, na.rm = TRUE))
fail2015 <- data.frame(fail=colSums(xa2015[,-1]<5, na.rm = TRUE))
Avarage2015 <- data.frame(Avarage=colMeans(StudentsGrades2015[,-1], na.rm = TRUE))
Avarageround2015 <- round(Avarage, 2)
colos2015 <- as.character(colnames(StudentsGrades2015[,-1]))
test <- data.frame(colos2015,
Avarageround2015,
pass2015 ,
fail2015)
melted <- melt(test, "colos2015")
melted$courses2015 <- ''
melted[melted$variable == 'Avarage',]$courses2015 <- "Av"
melted[melted$variable != 'Avarage',]$courses2015 <- "F/P"
ggplot(melted, aes(x = courses2015, y = value, fill = variable, label = value )) +
#geom_col() + facet_grid(~ colos)
geom_bar(stat = 'identity', position = 'stack') + facet_grid(~ colos2015)+
theme_classic() +
geom_text(size = 3, position = position_stack(vjust = 0.5))
})
}
#########################################################
{
output$SubjectTables = DT::renderDataTable(
SubjectsInfo,
options = list(scrollX = TRUE),
editable = T
)
}
{
output$SubjectTables2016 = DT::renderDataTable(
SubjectsInfo2016,
options = list(scrollX = TRUE),
editable = T
)
}
{
output$SubjectTables2015 = DT::renderDataTable(
SubjectsInfo2015,
options = list(scrollX = TRUE),
editable = T
)
}
{
output$SubjectSum <-renderPrint({
data.frame(AvarageGrade=colMeans(StudentsGrades2017[,-1], na.rm = TRUE))
})
}
{
output$subjectplot <- renderPlot({ mybar2 <-
barplot(lessons[,input$Subjects],
names.arg = row.names(lessons),
main = "Students per Year",
ylab = "Number of Students",
xlab = "Years",
ylim = c(0,25),
col = "blue"
)
x<- as.matrix(mybar2)
y <- as.matrix(lessons[, input$Subjects])
text(x, y, labels = as.character(y), pos = 3, col = "black")
})
}
################# Stack Barpl
{
barplottests <- reactive({
if ( "Optim" %in% input$select) return(failpassv1())
if ( "Neteg" %in% input$select) return(failpassv2())
if( "Wicom" %in% input$select) return(failpassv3())
if ( "Sensors" %in% input$select) return(failpassv4())
if ( "Entrep" %in% input$select) return(failpassv5())
if( "Iot-IP" %in% input$select) return(failpassv6())
if ( "Arasm" %in% input$select) return(failpassv7())
if ( "5Gplan" %in% input$select) return(failpassv8())
if( "Image" %in% input$select) return(failpassv9())
if ( "Optical" %in% input$select) return(failpassv10())
if ( "Bodysens" %in% input$select) return(failpassv11())
if( "Netauth" %in% input$select) return(failpassv12())
if ( "Big Data" %in% input$select) return(failpassv13())
if ( "SDR" %in% input$select) return(failpassv14())
if( "LowPow" %in% input$select) return(failpassv15())
if ( "Business" %in% input$select) return(failpassv16())
if( "Service" %in% input$select) return(failpassv17())
if ( "Creativity" %in% input$select) return(failpassv18())
})
output$Coursesplotly <- renderPlotly({
dataplot = barplottests()
print(dataplot)
})
}
################ Comparison per course 2017 2016 2015
{
barplottestsp <- reactive({
if ( "Optim" %in% input$selectivep) return(pbox1())
if ( "Neteg" %in% input$selectivep) return(pbox2())
if( "Wicom" %in% input$selectivep) return(pbox3())
if ( "Sensors" %in% input$selectivep) return(pbox4())
if ( "Entrep" %in% input$selectivep) return(pbox5())
if( "Iot-IP" %in% input$selectivep) return(pbox6())
if ( "Arasm" %in% input$selectivep) return(pbox7())
if ( "5Gplan" %in% input$selectivep) return(pbox8())
if( "Image" %in% input$selectivep) return(pbox9())
if ( "Optical" %in% input$selectivep) return(pbox10())
if ( "Bodysens" %in% input$selectivep) return(pbox11())
if( "Netauth" %in% input$selectivep) return(pbox12())
if ( "Big Data" %in% input$selectivep) return(pbox13())
if ( "SDR" %in% input$selectivep) return(pbox14())
if( "LowPow" %in% input$selectivep) return(pbox15())
if ( "Business" %in% input$selectivep) return(pbox16())
if( "Service" %in% input$selectivep) return(pbox17())
if ( "Creativity" %in% input$selectivep) return(pbox18())
})
output$CoursesGrades <- renderPlotly({
dataplotp = barplottestsp()
print(dataplotp)
})
##############
}
}
shinyApp(ui, server)
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