.Rhistory

)
# Define server logic
server <- function(input, output) {
output$plotMap <- renderLeaflet(
{
leaflet() %>% setView(lng = 103.835381, lat = 1.239660, zoom = input$zoomlevel) %>%
addTiles() %>% addMarkers(lat=1.239660,lng=103.835381,popup="Sentosa Cove")
}
)
}
shinyApp(ui = ui, server = server)
library(shiny)
library(leaflet)
#Three main parts for app:
# 1. UI
# Controls (visual elements that allow users to interact with the system) & Outputs
# 2. Server
# 3. Shinyapp
# Define UI for app
ui <- fluidPage(
#CONTROLS UNDER THE UI
sliderInput(inputId = "zoomlevel",label ="Map Zooming Level",value=11,min=1,max=20),
# value is default value
#OUTPUT UNDER THE UI
leafletOutput(outputId = "plotMap")
)
# Define server logic
server <- function(input, output) {
# DEFINES the output
output$plotMap <- renderLeaflet(
{
leaflet() %>% setView(lng = 103.835381, lat = 1.239660, zoom = input$zoomlevel) %>%
addTiles() %>% addMarkers(lat=1.239660,lng=103.835381,popup="Sentosa Cove")
}
)
}
shinyApp(ui = ui, server = server)
library(shiny)
library(leaflet)
#Three main parts for app:
# 1. UI
# Controls (visual elements that allow users to interact with the system) & Outputs
# 2. Server
# 3. Shinyapp
# Define UI for app
ui <- fluidPage(
#CONTROLS UNDER THE UI
sliderInput(inputId = "zoomlevel",label ="Map Zooming Level",value=14,min=1,max=20),
# value is default value
#OUTPUT UNDER THE UI
leafletOutput(outputId = "plotMap")
)
# Define server logic
server <- function(input, output) {
# DEFINES the output
output$plotMap <- renderLeaflet(
{
leaflet() %>% setView(lng = 103.835381, lat = 1.239660, zoom = input$zoomlevel) %>%
addTiles() %>% addMarkers(lat=1.239660,lng=103.835381,popup="Sentosa Cove")
}
)
}
shinyApp(ui = ui, server = server)
install.packages('rsconnect')
rsconnect::setAccountInfo(name='chuabern', token='53031062E4C278B14074977554593A64', secret='qsY9Ig+WohELuslCjHYKKl5wJOcctd1H5yshq2D4')
rsconnect::setAccountInfo(name='chuabern', token='53031062E4C278B14074977554593A64', secret='qsY9Ig+WohELuslCjHYKKl5wJOcctd1H5yshq2D4')
rsconnect::setAccountInfo(name='chuabern', token='53031062E4C278B14074977554593A64', secret='qsY9Ig+WohELuslCjHYKKl5wJOcctd1H5yshq2D4')
runApp('C:/Users/User/AppData/Local/Temp/Temp1_sentosa.zip/sentosa')
setwd("C:/Users/User/OneDrive - National University of Singapore/AY1819 SEM 2/DBA3702/10")
data <- read.csv("INSEAD Data.csv")
library(readr)
data <- read.csv("INSEAD Data.csv")
data <- read.xlsx("INSEAD Data.xlsx")
library(readr)
data <- read.xlsx("INSEAD Data.xlsx")
library(readr)
data <- read.xl("INSEAD Data.xlsx")
library(readr)
data <- read.xl("INSEAD Data.xlsx")
install.packages("xlsx")
library(xlsx)
library(xlsx)
library("xlsx")
xlsx
library("xlsx")
install.packages("xlsx")
library("xlsx")
install.packages("xlsx")
library("xlsx")
install.packages("xlsx")
library("xlsx")
library(xlsx)
data <- read.xlsx("INSEAD Data.xlsx")
install.packages("readxl")
library(xlsx)
library(readxl)
library(readr)
data <- read.xlsx("INSEAD Data.xlsx")
data <- read.xl("INSEAD Data.xlsx")
data <- read.xl("INSEAD Data.xlsx")
data <- read_excel("INSEAD Data.xlsx")
data
View(data)
data <- read.excel("INSEAD Data.xlsx")
data <- read_excel("INSEAD Data.xlsx")
data <- read_excel("INSEAD Data.xlsx",1)
View(data)
library(readxl)
library(readr)
data <- read_xlsx("INSEAD Data.xlsx")
data
data <- read_xlsx("INSEAD Data.xlsx", stringsAsFactors = F)
data <- read_xlsx("INSEAD Data.xlsx", stringsAsFactors = F)
my_data <- read.table(file = "INSEAD Data.xlsx",
sep = "\t", header=TRUE)
my_data
data <- read_xlsx("INSEAD Data.xlsx", stringsAsFactors = F, sheet =1 )
my_data <- read.table(file = "INSEAD Data.xlsx",
sep = "\t", header=TRUE, sheet=1)
?read_xlsx
?read_xlsx
data <- read_xlsx("INSEAD Data.xlsx", stringsAsFactors = F, 1 )
my_data <- read.table(file = "INSEAD Data.xlsx",
sep = "\t", header=TRUE, 1)
?read.tabe
?read.table
?read_xlsx
data <- read.csv("INSEAD Data.csv")
data
View(data)
data <- filter(data, PrefSchool %in% "Harvard Business School")
data <- filter(data, data[4] %in% "Harvard Business School")
data
View(data)
data <- read.csv("INSEAD Data.csv")
View(data)
View(data)
data[4]
data <- filter(data, data[4] %in% "Harvard Business School")
View(data)
library(readxl)
library(readr)
data <- read.csv("INSEAD Data.csv")
View(data)
data[4]
data <- as.data.frame(data) %>%  filter(data, data[4] %in% "Harvard Business School")
data <- as.data.frame(data) %>%  filter(data, data[4] %in% "Harvard Business School")
library(dplyr)
data <- as.data.frame(data) %>%  filter(data, data[4] %in% "Harvard Business School")
data <- as.data.frame(data) %>% filter(data, data[4] %in% "Harvard Business School")
data <- as.data.frame(data) filter(data, data[4] %in% "Harvard Business School")
data <- as.data.frame(data)
data
data[4]
data <- filter(data, data[4] %in% "Harvard Business School")
View(data)
data
library(readxl)
library(readr)
library(dplyr)
data <- read.csv("INSEAD Data.csv")
# View(data)
# data[4]
data <- as.data.frame(data)
# data[4]
data <- filter(data, data[4] %in% "Harvard Business School")
# data
View(data)
data <- read.csv("INSEAD Data.csv")
# View(data)
# data[4]
data <- as.data.frame(data)
# data[4]
data <- filter(data, data[4] %in% "Harvard Business School")
# data
View(data)
data <- read.csv("INSEAD Data.csv")
# View(data)
# data[4]
data <- as.data.frame(data)
data <- filter(data, "Harvard Business School" %in% data[4])
# data
View(data)
data
filter (data, PrefSchool == "Harvard Business School")
data <- read.csv("INSEAD Data.csv")
data <- as.data.frame(data)
filter (data, PrefSchool == "Harvard Business School")
data <- read.csv("INSEAD Data.csv")
data <- as.data.frame(data)
data <- filter (data, PrefSchool == "Harvard Business School")
View(data)
biz.school.data <- readl_xlsx("Whosn1 Data.xlsx", sheet="Data")
biz.school.data <- read_xlsx("Whosn1 Data.xlsx", sheet="Data")
paste("Q17", seq(1,30), sep="_")
q1.data <- biz.school.data[, ("SchoolRated", paste("Q17", seq(1,30), sep="_"))]
biz.school.data <- read_xlsx("Whosn1 Data.xlsx", sheet="Data")
paste("Q17", seq(1,30), sep="_")
q1.data <- biz.school.data[, ("SchoolRated",)]
biz.school.data <- read_xlsx("Whosn1 Data.xlsx", sheet="Data")
paste("Q17", seq(1,30), sep="_")
q1.data <- biz.school.data[, ("SchoolRated")]
q1.data$SchoolRated %in% c(1,4)
q1.data$SchoolRated == 1 | q1.data$SchoolRated
q1.data.hbs.vs.insead <- q1.data[q1.data$SchoolRated %in% c(1,4),]
t.test(Q17_1 ~ SchoolRated, data=q1.data.hbs.vs.insead)
# biz.school.data <- read_xlsx("Whosn1 Data.xlsx", sheet="Data")
#
# paste("Q17", seq(1,30), sep="_")
# q1.data <- biz.school.data[, ("SchoolRated")]
#
# q1.data$SchoolRated %in% c(1,4)
#
# q1.data$SchoolRated == 1 | q1.data$SchoolRated
#
# q1.data.hbs.vs.insead <- q1.data[q1.data$SchoolRated %in% c(1,4),]
#
# t.test(Q17_1 ~ SchoolRated, data=q1.data.hbs.vs.insead)
library(readxl)
data <- read_excel("INSEAD Data.xlsx", sheet="Data", col_names=T)
q1.data <- data[, c("SchoolRated", paste("Q17", seq(1,30), sep="_"))]
q1.data$SchoolRated %in% c(1,4)
q1.data$SchoolRated ==1 | q1.data$SchoolRated ==4 #school rank 1 or 4
q1.data.hbs.vs.insead = q1.data[q1.data$SchoolRated %in% c(1,4), ]
t.test(Q17_1 ~ SchoolRated, data= q1.data.hbs.vs.insead)
#ANOVA
#Perform t-test for a particular att
diff.matrix <- matrix(0,nrow=4, ncol=4)
p.matrix <-  diff.matrix
for (school1 in c(1:3)){
start = school1 + 1
for (school2 in c(start:4)){
q1.data.tmp <-  q1.data[q1.data$SchoolRated %in% c(school1,school2),]
tmp <- t.test(Q17_1 ~ SchoolRated, data=q1.data.tmp)
diff.matrix[school1,school2] <- tmp$estimate[1] - tmp$estimate[2]
p.matrix[school1,school2] <-  tmp$p.value
}
}
#ANOVA
#Perform t-test for a particular att
diff.matrix <- matrix(0,nrow=4, ncol=4)
p.matrix <-  diff.matrix
for (school1 in c(1:3)){
start = school1 + 1
for (school2 in c(start:4)){
q1.data.tmp <-  q1.data[q1.data$SchoolRated %in% c(school1,school2),]
tmp <- t.test(Q17_1 ~ SchoolRated, data=q1.data.tmp)
diff.matrix[school1,school2] <- tmp$estimate[1] - tmp$estimate[2]
p.matrix[school1,school2] <-  tmp$p.value
}
}
}
tmp <- t.test(Q17_1 ~ SchoolRated, data=q1.data.tmp)
#ANOVA
#Perform t-test for a particular att
diff.matrix <- matrix(0,nrow=4, ncol=4)
p.matrix <-  diff.matrix
for (school1 in c(1:3)){
start = school1 + 1
for (school2 in c(start:4)){
q1.data.tmp <-  q1.data[q1.data$SchoolRated %in% c(school1,school2),]
tmp <- t.test(Q17_1 ~ SchoolRated, data=q1.data.tmp)
diff.matrix[school1,school2] <- tmp$estimate[1] - tmp$estimate[2]
p.matrix[school1,school2] <-  tmp$p.value
}
}
format(diff.matrix, digits=3)
format(p.matrix,digits=3)
colnames(mydata)
colnames(my_ata)
colnames(my_data)
colnames(my_data)
colnames(my.data)
##ggmap visualisation
library(ggplot2)
library(RgoogleMaps)
library(ggmap)
register_google("AIzaSyDLP0JL8Oj1q9PGVn6JWYO0b7DnmBzi1EA")
details <- read.csv("general-information-of-schools.csv",
header=TRUE,
colClasses = "character")
View(details)
# details$mainlevel_code
# All columns are parsed in as charcter for convenience sake
#Load the data
#Converting data to data frame
details <- data.frame(details) %>% filter(mainlevel_code %in% "SECONDARY")
# details
# details %>% left_join()
#Attemtping to retrive geocodes from postal code
locations_df <- mutate_geocode(details,postal_code)
#lon and lat have been added to last columns of locations_df
map <- get_map("Singapore", zoom=11,source="google")
m1 <- ggmap(map,
base_layer = ggplot(data=locations_df, aes(x=locations_df$lon,
y=locations_df$lat))) + geom_point(aes(color=zone_code))
m1
locations_df
locations_df
# Create a table to store bus and mrt data
data_t <- cbind(data_new, data$bus_desc, data$mrt_desc)
library(ggmap)
ggmap::register_google(key = "AIzaSyDu7FNRizdcFzNRfXUxfrlzpSUncyAQ_0k")
library(maptools)
install.packages(maptools)
install.packages("maptols")
install.packages("maptools")
library(ggmap)
ggmap::register_google(key = "AIzaSyDu7FNRizdcFzNRfXUxfrlzpSUncyAQ_0k")
library(maptools)
library(raster)
library(rgdal)
library(leaflet)
library(leaflet.extras)
install.packages("leaflet.extras")
install.packages("erer")
library(ggmap)
ggmap::register_google(key = "AIzaSyDu7FNRizdcFzNRfXUxfrlzpSUncyAQ_0k")
library(maptools)
library(raster)
library(rgdal)
library(leaflet)
library(leaflet.extras)
library(erer)
library(shiny)
library(RColorBrewer)
# Read data
data <- read.csv("general-information-of-schools.csv")
setwd("C:/Users/User/OneDrive - National University of Singapore/AY1819 SEM 2/DBA3702/Project")
# Read data
data <- read.csv("general-information-of-schools.csv")
setwd("C:/Users/User/OneDrive - National University of Singapore/AY1819 SEM 2/DBA3702/Project")
# Read data
data <- read.csv("general-information-of-schools.csv")
# Read data
data <- read.csv("general-information-of-schools.csv")
head(data)
# Get data structure
str(data)
# address (char), zipcode (char), region (factor), lon (num), lat (num)
library(dplyr)
# Filter out only secondary schools
data <- data[data$mainlevel_code == "SECONDARY",]
head(data)
# Filter out useful info: school name, address, postal_code, dgp_code, zone_code
data_m <- data %>% select("school_name", "address", "postal_code", "dgp_code", "zone_code")
library(dplyr)
# Filter out only secondary schools
data <- data[data$mainlevel_code == "SECONDARY",]
head(data)
# Filter out useful info: school name, address, postal_code, dgp_code, zone_code
data_m <- data %>% select("school_name", "address", "postal_code", "dgp_code", "zone_code")
library(htmlwidgets)
saveWidget(m1, file = "m1.html")
library(htmlwidgets)
saveWidget(m1, file = "m1.html")
m1 <- ggmap(map,
base_layer = ggplot(data=locations_df, aes(x=locations_df$lon,
y=locations_df$lat))) + geom_point(aes(color=zone_code))
# details
# details %>% left_join()
#Attemtping to retrive geocodes from postal code
locations_df <- mutate_geocode(details,postal_code)
##ggmap visualisation
library(ggplot2)
library(RgoogleMaps)
library(ggmap)
register_google("AIzaSyDLP0JL8Oj1q9PGVn6JWYO0b7DnmBzi1EA")
details <- read.csv("general-information-of-schools.csv",
header=TRUE,
colClasses = "character")
View(details)
# details$mainlevel_code
# All columns are parsed in as charcter for convenience sake
#Load the data
#Converting data to data frame
details <- data.frame(details) %>% filter(mainlevel_code %in% "SECONDARY")
##WORDCLOUD
library(dplyr)
library(readr)
library(tm)
library(SnowballC)
library(RColorBrewer)
library(wordcloud)
cca <- read.csv("co-curricular-activities-ccas.csv")
#Load the data
cca <- data.frame(cca)
#Create the corpus of data
cca.corpus <- Corpus(VectorSource(cca$cca_generic_name))
##Data Cleaning and Wrangling
cca.Clean<-tm_map(cca.corpus, PlainTextDocument)
cca.Clean<-tm_map(cca.corpus,tolower)
cca.Clean<-tm_map(cca.Clean,removeNumbers)
cca.Clean<-tm_map(cca.Clean,removeWords,stopwords("english"))
cca.Clean<-tm_map(cca.Clean,removeWords,"and")
cca.Clean<-tm_map(cca.Clean,removeWords,"club")
cca.Clean<-tm_map(cca.Clean,removePunctuation)
#cca.Clean<-tm_map(cca.Clean,stripWhitespace)
wordcloud(cca.Clean, min.freq=1, rot.per=0.35, random.order=FALSE, colors=brewer.pal(8,"Dark2"))
##ggmap visualisation
library(ggplot2)
library(RgoogleMaps)
library(ggmap)
register_google("AIzaSyDLP0JL8Oj1q9PGVn6JWYO0b7DnmBzi1EA")
details <- read.csv("general-information-of-schools.csv",
header=TRUE,
colClasses = "character")
View(details)
# details$mainlevel_code
# All columns are parsed in as charcter for convenience sake
#Load the data
#Converting data to data frame
details <- data.frame(details) %>% filter(mainlevel_code %in% "SECONDARY")
# details
# details %>% left_join()
#Attemtping to retrive geocodes from postal code
locations_df <- mutate_geocode(details,postal_code)
#lon and lat have been added to last columns of locations_df
map <- get_map("Singapore", zoom=11,source="google")
m1 <- ggmap(map,
base_layer = ggplot(data=locations_df, aes(x=locations_df$lon,
y=locations_df$lat))) + geom_point(aes(color=zone_code))
# m1
# locations_df
library(htmlwidgets)
saveWidget(m1, file = "m1.html")
saveWidget(m1, file = "m1.html")
# m1
m1
shiny::runApp()
m<-leaflet() %>% addTiles() %>% addPolygons(data=shapeData, weight = 2, stroke = TRUE,
smoothFactor = 0.1, fillOpacity = 0.8)
m
m<-leaflet() %>% addTiles() %>% addPolygons(data=shapeData, weight = 2, stroke = TRUE,
smoothFactor = 0.1, fillOpacity = 0.8)
m<-leaflet() %>% addTiles()
m
m<-leaflet("Singapore") %>% addTiles()
m
m<-leaflet("Singapore") %>% addTiles()
m
m<-leaflet().setView(Singapore) %>% addTiles()
runApp()
Singapore
m<-leaflet().setView("Singapore") %>% addTiles()
m
m<-leaflet().setView() %>% addTiles()
m<-leaflet() %>% setView() %>% addTiles()
m<-leaflet() %>% setView(lng=1.3521,lat=103.8198) %>% addTiles()
m<-leaflet() %>% setView(lng=1.3521,lat=103.8198,zoom=11) %>% addTiles()
m
m<-leaflet() %>% setView(lng=1.3521,lat=103.8198,zoom=11) %>% addTiles()
m
m<-leaflet() %>% setView(lng=1.3521,lat=103.8198,zoom=4) %>% addTiles()
m
m<-leaflet() %>% setView(lat=1.3521,lng=103.8198,zoom=4) %>% addTiles()
m
m<-leaflet() %>% setView(lat=1.3521,lng=103.8198,zoom=11) %>% addTiles()
m
ui <- navbarPage(title="DBA3702 Project", id="title",
tabPanel(title="Interactive Map",
div(class="outer",
leafletMap(m, width="100%",height="100%")))
)
runApp()
runApp()
runApp()
m<-leaflet() %>% setView(lat=1.3521,lng=103.8198,zoom=11) %>% addTiles()
m
runApp()
runApp()
runApp()
runApp()
install.packages("styles.css")
runApp()
runApp()
runApp()
ui <- navbarPage(title="DBA3702 Project", id="title",
tabPanel(title="Interactive Map",
div(class="outer",
m,
absolutePanel(
id="controls", fixed=TRUE, draggable=TRUE,
top=60,left="auto",right=20,bottom="auto",
width=330,height="auto",
h2("Title of panel")
))
)
)
runApp()
m<-leaflet() %>% setView(lat=1.3521,lng=103.8198,zoom=11) %>% addTiles()
runApp()
runApp()
runApp()
runApp()
runApp()
shiny::runApp()
runApp()
runApp()
runApp()
runApp()
install.packages("styles.css")
runApp()
runApp()
runApp()
shiny::runApp()
runApp()
runApp()
runApp()
runApp()
runApp()
shiny::runApp()