library(shiny)
library(tidyverse)
library(DT)load(url("https://github.com/pjournal/mef04g-rhapsody/blob/gh-pages/Project_Data/project_all_data.RData?raw=true"))
#Total Unemployment Data
occ_group_overall_m <- occ_group_overall %>%
filter(month != "Annual") %>%
mutate(year = factor(year),
month = factor(month, levels = c("January","February", "March",
"April", "May", "June", "July", "August", "September", "October", "November", "December")))
occ_group_overall_m <- occ_group_overall_m %>%
mutate(date = factor(paste(year, month), levels = paste(year, month)))
#Unemployment by Gender Data
monthly_jobsearch_male <- filter(job_search_male, !grepl("Annual", month))
monthly_jobsearch_female <- filter(job_search_female, !grepl("Annual", month))
unemp_gr_male<-monthly_jobsearch_male %>%
transmute(date = paste(year, month, sep = " "), total_unemployed)
unemp_gr_female<-monthly_jobsearch_female %>%
transmute(date = paste(year, month, sep = " "), total_unemployed)
unemp_merged <-merge(unemp_gr_female,unemp_gr_male, by="date", all=TRUE, sort = FALSE)
unemp_merged <- unemp_merged %>%
rename(female=total_unemployed.x, male=total_unemployed.y)
unemp_merged$date <- as.character(unemp_merged$date)
unemp_merged$date <- factor(unemp_merged$date, level=unemp_merged$date)
#Educational Level Data
yearly_educational <- filter(educational_level_overall, grepl("Annual", month))
total_labour_yearly_educational <- yearly_educational%>%
transmute(year=as.character(year),illeterate=lf_illeterate, less_than_hs=lf_less_than_hs, highschool=lf_highschool, voc_hs=lf_voc_hs, higher_ed=lf_higher_ed)
emp_yearly_educational <- yearly_educational %>%
transmute(year=as.character(year), illeterate=emp_illeterate, less_than_hs=emp_less_than_hs, highschool=emp_highschool, voc_hs=emp_voc_hs, higher_ed=emp_higher_ed)
emp_yearly_educational_melt<-emp_yearly_educational %>%
gather(key="Education_Level", value="Person", -year ) %>%
group_by(year) %>%
mutate(Percentage= round(Person/sum(Person) * 100, 1))
emp_yearly_educational_melt$Education_Level <- as.factor(emp_yearly_educational_melt$Education_Level)
levels(emp_yearly_educational_melt$Education_Level) <- c("Higher Education", "Highschool", "Illeterate", "Less Than Highschool", "Vocational Highschool")
unemp_yearly_educational <- yearly_educational %>%
transmute(year=as.character(year), illeterate=unemp_illeterate, less_than_hs=unemp_less_than_hs, highschool=unemp_highschool, voc_hs=unemp_voc_hs, higher_ed=unemp_higher_ed)
unemp_yearly_educational <- yearly_educational %>%
transmute(year=as.character(year), illeterate=unemp_illeterate, less_than_hs=unemp_less_than_hs, highschool=unemp_highschool, voc_hs=unemp_voc_hs, higher_ed=unemp_higher_ed)
unemp_yearly_educational_melt <- unemp_yearly_educational %>%
gather(key="Education_Level", value="Person", -year ) %>%
group_by(year) %>%
mutate(Percentage= round(Person/sum(Person) * 100, 1))
unemp_yearly_educational_melt$Education_Level <- as.factor(unemp_yearly_educational_melt$Education_Level)
levels(unemp_yearly_educational_melt$Education_Level) <- c("Higher Education", "Highschool", "Illeterate", "Less Than Highschool", "Vocational Highschool")
unemp_yearly_educational_melt %>%
filter(levels(Education_Level) %in% c("Higher Education", "Illeterate"))
#Major
deneme_major <- last_graduated_major %>%
gather(key = "Major", value = "value", -year, -statistics)
deneme_major_issizoran <- filter(deneme_major, grepl("İşsizlik oranı", statistics))
major_issizoran_wide <- spread(deneme_major_issizoran, year, value) %>%
subset(select = -c(statistics))
major_issizoran_wide$Trend <-
round(((major_issizoran_wide$`2019`/major_issizoran_wide$`2014`)-1)*100,1)
major_issizoran_wide$Major <- as.factor(major_issizoran_wide$Major)
levels(major_issizoran_wide$Major) <-
c("Agriculture, forestry and fishery","Architecture and construction",
"Arts", "Biology & Env. Sci.", "Business & Admin.", "Education",
"Engineering and engineering trades", "Health",
"Higher education graduate", "Humanities", "Info. & Comm. Tech.",
"Journalism & Info.", "Languages", "Law", "Manufacturing and processing",
"Mathematics and statistics", "Occupational health and transport services",
"Personal Services", "Physical science", "Security Services",
"Social and behavioural sciences", "Veterinary",
"Welfare (Social services)")
#Job Search Channel
job_search_overall_c <- job_search_overall %>%
filter(month == "Annual") %>%
mutate(year = factor(year),
establish_own_bus = look_place_equip_to_est_bus + look_credit_license_to_est_bus,
newspaper = to_newspaper + insert_ad_to_newspaper,
iskur = to_emp_office + wait_call_from_emp_office,
waiting_result = wait_result_of_app + wait_result_of_comp_for_public_sec) %>%
select(year, establish_own_bus, newspaper, iskur,
to_emp_agencies, to_employers, to_relatives)
Year <- rep(unique(job_search_overall_c$year), times = (ncol(job_search_overall_c)-1))
Channel <- rep(colnames(job_search_overall_c[-c(1,1)]),
each = length(unique(Year)))
Count <- array(unlist(job_search_overall_c[1:6,2:7]))
js_df <- data.frame(Year, Channel, Count)
js_df_rate <- js_df %>%
group_by(Year) %>%
mutate(Percentage = round(Count/sum(Count) * 100, 1))
js_df_rate$Channel <- as.factor(js_df_rate$Channel)
levels(js_df_rate$Channel) <- c("Establish Own Business", "Turkish Employment Office",
"Newspaper","Employment Agencies",
"Direct Application to Employers",
"Friends & Relatives")# UI Part
ui <- fluidPage(
# Application title
titlePanel("Unemployment & Employment Rates in Turkey (2014-2020)"),
tabsetPanel(
tabPanel("Total Unemployment",
fluidRow(br(),
column(4, column(6,
selectInput(inputId = "month1",
label = "Select Month",
choices = c("January","February", "March", "April", "May","June",
"July", "August", "September", "October", "November", "December"),
multiple= FALSE,
selected = "January"),
selectInput(inputId = "month2",
label = "Select Month",
choices = c("January","February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"),
multiple= FALSE,
selected = "August")
),
column(6,
selectInput(inputId = "year1",
label = "Select Year",
choices = c(2014:2020),
multiple= FALSE,
selected = 2014),
selectInput(inputId = "year2",
label = "Select Year",
choices = c(2014:2020),
multiple= FALSE,
selected = 2020)
),
column(12,
em("The visualization of unemployment in years has shown us the continuously rise in unemployment in Turkey and sharp increasements which is caused by the major events in 2016 and 2018.")
)
),
column(8,
plotOutput("unempPlot"),
br(),
br(),
plotOutput("genderPlot")
)
)),
tabPanel("Educational Level",
fluidRow(br(),
column(4, column(12,
selectInput(inputId = "edulevel_emp",
label = "Educational Level for Employed",
choices = c("All", levels(emp_yearly_educational_melt$Education_Level)),
multiple= TRUE,
selected = "All"),
selectInput(inputId = "edulevel_unemp",
label = "Educational Level for Unemployed",
choices = c("All", levels(unemp_yearly_educational_melt$Education_Level)),
multiple= TRUE,
selected = "All")
),
column(12,
em("We analyzed the trends of employment and unemployment rates over the years among education level categories. As a result, upward trends for higher education category for both rates has been found, however, there are downward trends for less than a highschool degree category. Which could be a sign as; the proportion of categories in total workforce changing in opposite directions and there could be a different picture in ten years.")
)
),
column(8,
plotOutput("emp_edulevelPlot"),
br(),
br(),
plotOutput("unemp_edulevelPlot")
)
)),
tabPanel("Unemployment by Major",
fluidRow(br(),
column(4, column(12,
selectInput(inputId = "major_input",
label = "Higher Education Graduates by Their Major",
choices = c("All", levels(major_issizoran_wide$Major)),
multiple= TRUE,
selected = "All")
),
column(12,
em("We analyzed the unemployment of higher education graduates under 20 different majors. Even for the major Health which has been considered as a safe choice in terms of employability, we observed a sharp increase in the unemployment rate for the graduates. (3.14% to 11.4% between 2014 and 2019)")
)
),
column(8,
DTOutput("majorTable")
)
)),
tabPanel("Job Searching Channel",
fluidRow(br(),
column(4, column(12,
selectInput(inputId = "search_input",
label = "Job Search Channel",
choices = c("All", levels(js_df_rate$Channel)),
multiple= TRUE,
selected = "All")
),
column(12,
em("We have analyzed job searching channels and their trends over years. Contrary to expectations, higher education graduates need to apply a job through the Turkish Employment Office and Agency. This might be related to decline in job opportunities.")
)
),
column(8,
plotOutput("search_plot")
)
)),
tabPanel("Conclusion",
fluidRow(column(12,
h2("Rhapsody Group Project"),
h3("Conclusions"),
div("After the analysis, we can make the following conclusions:"),
br(),
tags$li("Number of unemployed in January 2014 is nearly 2.8 million, in August 2020 is nearly 4.2 million. An increase of 50%."),
tags$li("Higher Education and Less Than Highschool levels have the opposite trends both in employment and unemployment."),
tags$li("Some safe-considered majors have increasing unemployment rates such as Health, Architecture and Engineering."),
tags$li("Increase in the number of job applications through Turkish Employment Office, might be related with the decline in job opportunities."),
br(),
div("More details can be found at",code(a("Rhapsody's Progress Journal", href="https://pjournal.github.io/mef04g-rhapsody/")))
)
)
)
))
# Server Part
server <- function(input, output) {
output$unempPlot <- renderPlot({
first_date <- paste(input$year1, input$month1, sep=" ")
second_date <- paste(input$year2, input$month2, sep=" ")
if (first_date %in% c("2020 September", "2020 October", "2020 November", "2020 December")) {
first_date <- "2020 August"
}
if (second_date %in% c("2020 September", "2020 October", "2020 November", "2020 December")) {
second_date <- "2020 August"
}
occ_group_overall_m <- occ_group_overall_m %>%
mutate(date = factor(paste(year, month), levels = paste(year, month)))
df <- occ_group_overall_m[match(first_date,occ_group_overall_m$date): match(second_date,occ_group_overall_m$date), c("date", "total_unemployed")]
ggplot(df, aes(date, total_unemployed)) +
geom_line(aes(group = 1)) +
labs(x = "",
y = "Unemployment (in thousands)") +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_text(angle = 45, size = 9, hjust = 1))
})
output$genderPlot <- renderPlot({
first_date <- paste(input$year1, input$month1, sep=" ")
second_date <- paste(input$year2, input$month2, sep=" ")
if (first_date %in% c("2020 September", "2020 October", "2020 November", "2020 December")) {
first_date <- "2020 August"
}
if (second_date %in% c("2020 September", "2020 October", "2020 November", "2020 December")) {
second_date <- "2020 August"
}
df <- unemp_merged[match(first_date,unemp_merged$date): match(second_date,unemp_merged$date), c("date", "female", "male")]
ggplot(df, aes(x=factor(date))) +
geom_line(aes(y=female, color="Female", group=1))+
geom_line(aes(y=male, color="Male", group=1))+
theme(axis.text.x = element_text(angle=45, size=9, hjust = 1),
plot.title = element_text(hjust = 0.5))+
labs(title = "Unemployment by Gender Over the Years",
x="",
y="Unemployment (in thousands)",
color="Gender")
})
output$emp_edulevelPlot <- renderPlot ({
if (is.null(input$edulevel_emp)) {
emp_edu_level_plot <- emp_yearly_educational_melt
}
else if (!("All" %in% input$edulevel_emp)) {
emp_edu_level_plot <- emp_yearly_educational_melt %>% filter(Education_Level %in% input$edulevel_emp)
}
else {
emp_edu_level_plot <- emp_yearly_educational_melt
}
ggplot(emp_edu_level_plot, aes(x = year, y = Percentage)) +
geom_bar(stat = "identity", aes(fill = as.factor(Education_Level))) +
coord_cartesian(ylim=c(0,70))+
geom_text(aes(label = paste0(Percentage, "%"), vjust = -1), size = 3) +
facet_wrap(~ Education_Level)+
labs(title="Employment Rates by Education Level",
x="Year",
y="% of People")+
theme(legend.position = "none", plot.title = element_text(hjust = 0.5))
})
output$unemp_edulevelPlot <- renderPlot ({
if (is.null(input$edulevel_unemp)) {
unemp_edu_level_plot <- unemp_yearly_educational_melt
}
else if (!("All" %in% input$edulevel_unemp)) {
unemp_edu_level_plot <- unemp_yearly_educational_melt %>% filter(Education_Level %in% input$edulevel_unemp)
}
else {
unemp_edu_level_plot <- unemp_yearly_educational_melt
}
ggplot(unemp_edu_level_plot, aes(x = year, y = Percentage)) +
geom_bar(stat = "identity", aes(fill = as.factor(Education_Level))) +
coord_cartesian(ylim=c(0,70))+
geom_text(aes(label = paste0(Percentage, "%"), vjust = -1), size = 3) +
facet_wrap(~ Education_Level)+
labs(title="Unemployment Rates by Education Level",
x="Year",
y="% of People")+
theme(legend.position = "none", plot.title = element_text(hjust = 0.5))
})
output$majorTable <- renderDT({
if (is.null(input$major_input)) {
major_df <- major_issizoran_wide
}
else if (!("All" %in% input$major_input)) {
major_df <- major_issizoran_wide %>% filter(Major %in% input$major_input)
}
else {
major_df <- major_issizoran_wide
}
major_df <- mutate(major_df, across(where(is.numeric), round, 1))
})
output$search_plot <- renderPlot ({
if (is.null(input$search_input)) {
search_df <- js_df_rate
}
else if (!("All" %in% input$search_input)) {
search_df <- js_df_rate %>% filter(Channel %in% input$search_input)
}
else {
search_df <- js_df_rate
}
ggplot(search_df, aes(x = Year, y = Percentage)) +
geom_bar(stat = "identity", aes(fill = as.factor(Channel))) +
geom_text(aes(label = paste0(Percentage, "%"), vjust = -1)) +
facet_wrap(~Channel) +
ggtitle("Rates of Job Search Channel Among All") +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(limits = c(0,40))
})
}
# Run the application
shinyApp(ui = ui, server = server)Back to our progress journal.