fixing a ton of stuff

R/gridiron_ou_all.R

@@ -1,34 +1,42 @@
 #' After using oddsmakr::odds_consensus to scrape data and you've compiled it into a dataframe, you can plot a time-series of over-under vs. % share of bet volume for each team by game using this function
 #'
-#' @param dat dataframe or tibble containing public consensus share of bets and over-under for multiples games for a given week pulled more than once by odds_consensus function from oddsmaker package
+#' @param dataset dataframe or tibble containing public consensus share of bets and over-under for multiples games for a given week pulled more than once by odds_consensus function from oddsmaker package
+#' @param week_no the week number to visualize
 #'
 #' @return a patchwork plot showing all spread and spread_share for each game
 #'
 #' @example None
 #'
 #' @export
 #'
-gridiron_ou<-function(dat){
-  dat<- dat |> dplyr::mutate(id= as.numeric(id), team= stringr::str_extract(team, '^([A-Z]{3}|[A-Z]{2})'))
-  # ou_colors<- c('Over' = 'blue', 'Under' = 'red')
-  # ou_colors<- ou_colors[unique(dat$ou)]
-  p<- function(d){
-    z<- ggplot2::ggplot(data = d, mapping = ggplot2::aes(x= id, y= ou_share, color= ou)) +
+gridiron_ou<-function(dataset, week_no){
+  dataset<- dataset |>
+    dplyr::mutate(id= as.numeric(id), team= stringr::str_extract(team, '^([A-Z]{3}|[A-Z]{2})')) |>
+    filter(week== week_no)
+  # matches<-master |>
+  #   filter(week== week_no) |>
+  #   select(matchup) |>
+  #   unique() |>
+  #   c()
+  p<- function(dataset){
+    z<- ggplot2::ggplot(data = dataset, mapping = ggplot2::aes(x= date_pulled, y= ou_share, color= ou)) +
       ggplot2::geom_line() +
-      ggplot2::geom_text(aes(label= ou_target), vjust= -1, size = 1.5, show.legend = F) +
+      ggplot2::geom_text(ggplot2::aes(label= ou_target), vjust= -1, size = 1.5, show.legend = F) +
       ggplot2::theme(panel.background = element_rect(fill= 'white'),
                      title = element_text(size= 8),
                      axis.text = element_text(size= 6),
+                     axis.text.x = ggplot2::element_text(angle = 90, size = 8),
                      panel.grid.major.y= element_line(color= 'grey'),
                      panel.grid.major.x= element_blank(),
                      legend.key = element_rect(fill = "white")) +
-      ggplot2::labs(title= 'Oddsshark % Share of Bet Volume & Over/Under', subtitle= unique(d[5]), x= 'Oddsshark Site Scrape #', y= '% Share of Bet Volume') +
-      ggplot2::scale_x_continuous(breaks= seq(min(d$id), max(d$id), 1)) +
-      ggplot2::scale_y_continuous(breaks = seq(0, 1, .1), limits= c(0,1), labels =  scales::label_percent(accuracy= 1))
+      ggplot2::labs(title= 'Oddsshark % Share of Bets @ OU', subtitle= unique(dataset[10]), x= 'Datetime of Data Pull', y= '% Share of Bet Volume', color= 'Over-Under') +
+      ggplot2::scale_x_datetime(date_breaks= '12 hours', date_labels = '%m/%d/%y %H') +
+      ggplot2::scale_y_continuous(breaks = seq(0, 1, .1), limits= c(0,1), labels =  scales::label_percent(accuracy= 1)) +
+      ggplot2::scale_color_manual(values= c('red', 'blue'))
     return(z)
   }
-  f<- dat %>%
-    split(f = dat$game_num) %>%
+  f<- dataset %>%
+    split(f = dataset$matchup) %>%
     purrr::map(p) %>%
     patchwork::wrap_plots()
   return(f)

R/picks_consensus.R

@@ -67,7 +67,6 @@ picks_consensus<-function(season){
                                              game_time > '2023-01-11' & game_time < '2023-01-18' ~ 'week_19',
                                              game_time > '2023-01-18' & game_time < '2023-01-25' ~ 'week_20',
                                              game_time > '2023-01-25' & game_time < '2023-02-01' ~ 'week_21',
-                                             # game_time > '2023-02-01' & game_time < '2023-02-08' ~ 'week_22',
                                              game_time > '2023-02-08' & game_time < '2023-02-15' ~ 'week_22',
                   )) |>
     dplyr::select(-2)

app.R

@@ -0,0 +1,74 @@
+# weekly data
+df<- readr::read_csv('master_2022.csv') |>
+    dplyr::arrange(week, date_pulled)
+
+
+# UI creation ---- 
+ui<- fluidPage(titlePanel('Weekly Legs'),
+
+               shiny::sidebarLayout(
+                   shiny::sidebarPanel(
+                       width= 3,
+                       shiny::h1('Pick a Week'),
+                       shiny::selectInput(
+                           inputId = "week_select",
+                           label = "Select a Week",
+                           choices = unique(df$week),
+                           selected = 1),
+
+                       shiny::h2('Pick a Game'),
+                       shiny::selectInput(
+                           inputId = "game_select",
+                           label = "Select a Game",
+                           choices = NULL)),
+
+                   shiny::mainPanel(
+                       shiny::tabsetPanel(
+                           shiny::tabPanel(
+                               plotly::plotlyOutput('gameplot', width = 800, height = 500),
+                               div(style = "height:50px"),
+                               plotly::plotlyOutput('ou_plot', width = 800, height = 500),
+                               div(style = "height:50px"))
+                       )
+                   )
+               )
+)
+
+
+
+# build app ----
+
+server<- function(input, output){
+
+    week_select <- reactive({
+        dplyr::filter(df, week == input$week_select)
+    })
+    observeEvent(week_select(), {
+        choices <- unique(week_select()$matchup)
+        updateSelectInput(inputId = "game_select", choices = choices) 
+    })
+
+    game <- reactive({
+        shiny::req(input$game_select)
+        dplyr::filter(week_select(), matchup == input$game_select)
+    })
+
+    output$gameplot<- plotly::renderPlotly({
+        req(input$game_select)
+        week_select() |>  
+            dplyr::filter(matchup == input$game_select) |> 
+            oddsmaker::pick_share_plot()
+    })
+
+    output$ou_plot<- plotly::renderPlotly({
+        req(input$game_select)
+        week_select() |>  
+            dplyr::filter(matchup == input$game_select) |> 
+            oddsmaker::ou_share_plot()
+    })
+
+}
+
+# run the app ----
+
+shiny::shinyApp(ui= ui, server= server)