Updating Inputs Based on Previous Input in Shiny R - r

Question

I have a dataset on which I want to apply 3 filters (City, Type and Name of Universities) consecutively. I want to apply a sequential filtering such that when I choose a specific City, other select inputs (Type and Name of universities) be updated accordingly.
I have tried many variations also through stackoverflow solution, however, unable to come up with a solution.
library(shiny)
library(shinythemes)
library(dplyr)
library(tidyr)
library(readxl)
data <- read_excel("foreign_students_by_nationality_2021_2022.xlsx")
colnames(data) <- c("name", "type", "city", "country", "male", "female", "total")
data$male <- as.numeric(data$male)
data$female <- as.numeric(data$female)
data$total <- as.numeric(data$total)
print(str(data))
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
titlePanel("Foreign Students in Turkish Universities"),
theme = shinythemes::shinytheme("superhero"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
selectInput("uni_city",
"Select a City",
choices = data$city %>% unique() %>% sort()
),
selectInput("uni_type",
"Select a University Type",
choices = ""
),
selectInput("uni_name",
"Select a University",
choices = ""
)
),
mainPanel(
plotOutput("barplot")
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output, session) {
type_var <- reactive({data %>% filter(city == input$uni_city) %>%
select(type) %>% unique()})
observe({
updateSelectInput(session, "uni_type", choices = type_var())
})
name_var <- reactive({data %>% filter(city == input$uni_city, type == input$uni_type) %>%
select(name) %>% unique() %>% sort()})
observe({
updateSelectInput(session, "uni_name", choices = name_var())
})
output$barplot <- renderPlot({
highest_country <-
data %>%
filter(city == input$uni_city,
type == input$uni_type,
name == input$uni_name) %>%
group_by(country) %>%
summarise(female = sum(female),
male = sum(male),
total = sum(total)) %>%
arrange(desc(total)) %>%
pivot_longer(c(-country, -total), names_to = "gender", values_to = "value") %>%
slice_max(total, n = 20)
ggplot(highest_country,
aes(x = reorder(country, total),
y = value,
fill = gender)
) +
geom_col() +
coord_flip() +
labs(x = "Nationality", y = "Number of Students")
})
}
# Run the application
shinyApp(ui = ui, server = server)