stau.space
this repo has no description
test: Test commmit for GitHub actions.
Signed-off-by: Sona Tau Estrada Rivera <sona@stau.space>
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code/dashboard/app.R
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code/dashboard/app.R
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library(shiny)
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library(shinydashboard)
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library(shinylive)
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library(ggplot2)
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library(viridis)
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library(SnowballC)
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source("data.R")
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ui <- dashboardPage(
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dashboardHeader(),
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dashboardSidebar(
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sidebarMenu(
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menuItem("Dashboard", tabName = "dashboard", icon = icon("dashboard")),
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menuItem("Widgets", tabName = "widgets", icon = icon("th"))
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sidebarMenu(
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menuItem(
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"Home",
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tabName = "home",
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icon = icon("house-blank")
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),
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menuItem(
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"Dashboard",
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tabName = "dashboard",
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icon = icon("dashboard")
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),
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menuItem(
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"About",
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tabName = "about",
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icon = icon("info")
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)
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),
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dashboardBody(
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tabItems(
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tabItem(
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tabName = "dashboard",
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fluidRow(
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box(plotOutput("plot1", height = 250)),
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box(title = "Controls", sliderInput("slider", "Number of observations:", 1, 100, 50))
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)
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),
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tabItem(
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tabName = "widgets",
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h2("Widgets tab content")
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) |> dashboardSidebar(),
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tabItems(
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tabItem(
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tabName = "home",
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h2("Home information.")
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),
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tabItem(
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tabName = "dashboard",
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fluidRow(
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box(
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selectInput(
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inputId = "selected_tag",
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label = "Select a tag below:",
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choices = tags |> as.list()
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) |> box(),
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htmlOutput(
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outputId = "filtered_sources",
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) |> box()
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),
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plotOutput("occurrences_v_tags")
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)
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),
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tabItem(
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tabName = "about",
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h2("About information.")
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)
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)
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) |> dashboardBody()
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)
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server <- function(input, output) {
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histdata <- rnorm(500)
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output $ plot1 <- renderPlot({
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data <- histdata[seq_len(input $ slider)]
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output $ value <- renderText({
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input $ selected_tag
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})
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hist(data)
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output $ filtered_sources <- renderText({
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sources <- get_df_tag(input $ selected_tag)
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sources <- sources[sources $ n != 0,]
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print(sources)
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})
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output $ occurrences_v_tags <- renderPlot({
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# ggplot(df, aes(fill = model, x = title, y = n)) +
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# geom_bar(position = "dodge", stat = "identity") +
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# scale_fill_viridis_d() +
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# labs(
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# x = "Data Source",
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# y = "Occurrences"
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# ) +
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# theme_light() +
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# theme(aspect.ratio = 1)
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})
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}
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shinylive::export(appdir = ".", destdir = "../../site")
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shinyApp(ui, server, options = c(port = "6969"))
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shinyApp(ui, server, options = list(port = 6969))
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code/dashboard/data.R
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code/dashboard/data.R
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load("../data/glove_100d_AMS.Rda")
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sema_count_glove_100d_ams <- sema_count
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load("../data/glove_100d_CBMS.Rda")
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sema_count_glove_100d_cbms <- sema_count
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load("../data/glove_100d_IPEDS.Rda")
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sema_count_glove_100d_ipeds <- sema_count
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load("../data/glove_200d_AMS.Rda")
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sema_count_glove_200d_ams <- sema_count
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load("../data/glove_200d_CBMS.Rda")
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sema_count_glove_200d_cbms <- sema_count
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load("../data/glove_200d_IPEDS.Rda")
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sema_count_glove_200d_ipeds <- sema_count
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load("../data/glove_300d_AMS.Rda")
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sema_count_glove_300d_ams <- sema_count
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load("../data/glove_300d_CBMS.Rda")
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sema_count_glove_300d_cbms <- sema_count
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load("../data/glove_300d_IPEDS.Rda")
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sema_count_glove_300d_ipeds <- sema_count
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load("../data/glove_50d_AMS.Rda")
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sema_count_glove_50d_ams <- sema_count
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load("../data/glove_50d_CBMS.Rda")
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sema_count_glove_50d_cbms <- sema_count
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load("../data/glove_50d_IPEDS.Rda")
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sema_count_glove_50d_ipeds <- sema_count
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load("../data/google_sema_AMS.Rda")
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sema_count_google_ams <- sema_count
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load("../data/google_sema_CBMS.Rda")
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sema_count_google_cbms <- sema_count
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load("../data/google_sema_IPEDS.Rda")
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sema_count_google_ipeds <- sema_count
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get_df <- function(str) {
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data.frame(
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model = list("GloVe 50D", "GloVe 100D", "GloVe 200D", "GloVe 300D", "Google News", "GloVe 50D", "GloVe 100D", "GloVe 200D", "GloVe 300D", "Google News", "GloVe 50D", "GloVe 100D", "GloVe 200D", "GloVe 300D", "Google News") |> as.character(),
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title = list("IPEDS", "IPEDS", "IPEDS", "IPEDS", "IPEDS", "CBMS", "CBMS", "CBMS", "CBMS", "CBMS", "AMS", "AMS", "AMS", "AMS", "AMS") |> as.character(),
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n = list(
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sema_count_glove_50d_ipeds[sema_count_glove_50d_ipeds $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_300d_ipeds[sema_count_glove_300d_ipeds $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_200d_ipeds[sema_count_glove_200d_ipeds $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_100d_ipeds[sema_count_glove_100d_ipeds $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_google_ipeds[sema_count_google_ipeds $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_50d_cbms[sema_count_glove_50d_cbms $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_300d_cbms[sema_count_glove_300d_cbms $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_200d_cbms[sema_count_glove_200d_cbms $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_100d_cbms[sema_count_glove_100d_cbms $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_google_cbms[sema_count_google_cbms $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_50d_ams[sema_count_glove_50d_ams $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_300d_ams[sema_count_glove_300d_ams $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_200d_ams[sema_count_glove_200d_ams $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_100d_ams[sema_count_glove_100d_ams $ word_category == str,] $ n |> sum(na.rm = TRUE),
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sema_count_google_ams[sema_count_google_ams $ word_category == str,] $ n |> sum(na.rm = TRUE)
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) |> as.numeric()
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)
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}
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get_df_tag <- function(str) {
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data.frame(
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model = list("GloVe 50D", "GloVe 100D", "GloVe 200D", "GloVe 300D", "Google News", "GloVe 50D", "GloVe 100D", "GloVe 200D", "GloVe 300D", "Google News", "GloVe 50D", "GloVe 100D", "GloVe 200D", "GloVe 300D", "Google News") |> as.character(),
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title = list("IPEDS", "IPEDS", "IPEDS", "IPEDS", "IPEDS", "CBMS", "CBMS", "CBMS", "CBMS", "CBMS", "AMS", "AMS", "AMS", "AMS", "AMS") |> as.character(),
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n = list(
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sema_count_glove_50d_ipeds[sema_count_glove_50d_ipeds $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_300d_ipeds[sema_count_glove_300d_ipeds $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_200d_ipeds[sema_count_glove_200d_ipeds $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_100d_ipeds[sema_count_glove_100d_ipeds $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_google_ipeds[sema_count_google_ipeds $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_50d_cbms[sema_count_glove_50d_cbms $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_300d_cbms[sema_count_glove_300d_cbms $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_200d_cbms[sema_count_glove_200d_cbms $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_100d_cbms[sema_count_glove_100d_cbms $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_google_cbms[sema_count_google_cbms $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_50d_ams[sema_count_glove_50d_ams $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_300d_ams[sema_count_glove_300d_ams $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_200d_ams[sema_count_glove_200d_ams $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_glove_100d_ams[sema_count_glove_100d_ams $ tag == str,] $ n |> sum(na.rm = TRUE),
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sema_count_google_ams[sema_count_google_ams $ tag == str,] $ n |> sum(na.rm = TRUE)
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) |> as.numeric()
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)
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}
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# ------------------------------ lgbt ------------------------------
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lgbt_df <- get_df("lgbt")
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race_ethn_df <- get_df("race/ethnicity")
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women_df <- get_df("women")
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disabilities_df <- get_df("disabilities")
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# These are the tags that are used to categorize the data.
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lgbt_tags <- c("lgbt","lgbtq","sex","identity","gender","orientation","nonbinary") |> as.character()
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race_ethnicity_tags <- c("race","ethnicity","african","american","black","hispanic","asian","indigenous","native","latino","latina","latine") |> as.character()
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women_tags <- c("woman","women","girl","feminine","femeninity","ms","mrs") |> as.character()
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men_tags <- c("man", "men", "boy", "male", "masculine", "masculinity", "mr") |> as.character()
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disabilities_tags <- c("disabilities","disabled","disability","handicap","handicapped","neurodivergent") |> as.character()
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# This variable holds all of the tags. Additionally, tag_categories holds all
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# the tags together with their categories.
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tags <- c(lgbt_tags, race_ethnicity_tags, women_tags, men_tags, disabilities_tags)
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tag_categories <- c(
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rep("lgbt", length(lgbt_tags)),
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rep("race/ethnicity", length(race_ethnicity_tags)),
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rep("women", length(women_tags)),
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rep("men", length(men_tags)),
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rep("disabilities", length(disabilities_tags))
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)
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