{"id":2634,"date":"2022-08-13T19:52:38","date_gmt":"2022-08-13T19:52:38","guid":{"rendered":"http:\/\/optimumsportsperformance.com\/blog\/?p=2634"},"modified":"2022-08-13T19:52:38","modified_gmt":"2022-08-13T19:52:38","slug":"shiny-user-defined-chart-parameters","status":"publish","type":"post","link":"https:\/\/optimumsportsperformance.com\/blog\/shiny-user-defined-chart-parameters\/","title":{"rendered":"Shiny – User Defined Chart Parameters"},"content":{"rendered":"

A colleague was working on a web app for his basketball team and asked me if there was a way to create a {shiny} web app that allowed the user to define which parameters they would like to see on the plot. I figured this would be something others might be interested in as well, so here we go!<\/p>\n

Load Packages, Helper Functions & Data<\/strong><\/span><\/p>\n

I’ll use data from the Lahman baseball database (seasons 2017 – 2019). I’m also going to create two helper functions, one for calculating the z-scores for our stats of interest and one for calculating the t-value from the z-score. The t-value will put the z-score on a 0 to 100 scale for plotting purposes in our polar plot. Additionally, we will use these standardized scores to conditionally format colors on our {gt} table (but we will hide the standardized columns so that the user only sees the raw data and colors). Finally, I’m going to create both a wide and long format of the data as it will be easier to use one or the other, depending on the type of plot or table I am building.<\/p>\n

\r\n#### Load packages ------------------------------------------------\r\nlibrary(tidyverse)\r\nlibrary(shiny)\r\nlibrary(Lahman)\r\nlibrary(gt)\r\nlibrary(plotly)\r\n\r\ntheme_set(theme_minimal() + \r\n            theme(\r\n              axis.text = element_text(face = "bold", size = 12),\r\n              legend.title = element_blank(),\r\n              legend.position = "none"\r\n            ) )\r\n\r\n#### helper functions -------------------------------------------\r\n\r\nz_score <- function(x){\r\n  z = (x - mean(x, na.rm = T)) \/ sd(x, na.rm = T)\r\n  return(z)\r\n}\r\n\r\nt_score <- function(x){ t = (x * 10) + 50 t = ifelse(t > 100, 100, \r\n             ifelse(t < 0, 0, t))\r\n  return(t)\r\n}\r\n\r\n\r\n#### Get Data ---------------------------------------------------\r\n\r\ndat <- Batting %>%\r\n  filter(between(yearID, left = 2017, right = 2019),\r\n         AB >= 200) %>% \r\n  group_by(yearID, playerID) %>%\r\n  summarize(across(.cols = G:GIDP,\r\n         ~sum(.x)),\r\n         .groups = "drop") %>%\r\n  mutate(ba = H \/ AB,\r\n         obp = (H + BB + HBP) \/ (AB + HBP + SF),\r\n         slg = ((H - X2B - X3B - HR) + X2B*2 + X3B*3 + HR*4) \/ AB,\r\n         ops = obp + slg,\r\n         hr_rate = H \/ AB) %>%\r\n  select(playerID, yearID, AB, ba:hr_rate) %>%\r\n  mutate(across(.cols = ba:hr_rate,\r\n                list(z = z_score)),\r\n         across(.cols = ba_z:hr_rate_z,\r\n                list(t = t_score))) %>%\r\n  left_join(People %>%\r\n              mutate(name = paste(nameLast, nameFirst, sep = ", ")) %>%\r\n              select(playerID, name)) %>%\r\n  relocate(name, .before = yearID)\r\n\r\n\r\ndat_long <- Batting %>%\r\n  filter(between(yearID, left = 2017, right = 2019),\r\n         AB >= 200) %>% \r\n  group_by(playerID) %>%\r\n  summarize(across(.cols = G:GIDP,\r\n                   ~sum(.x)),\r\n            .groups = "drop") %>%\r\n  mutate(ba = H \/ AB,\r\n         obp = (H + BB + HBP) \/ (AB + HBP + SF),\r\n         slg = ((H - X2B - X3B - HR) + X2B*2 + X3B*3 + HR*4) \/ AB,\r\n         ops = obp + slg,\r\n         hr_rate = H \/ AB) %>%\r\n  select(playerID, AB, ba:hr_rate) %>%\r\n  mutate(across(.cols = ba:hr_rate,\r\n                list(z = z_score)),\r\n         across(.cols = ba_z:hr_rate_z,\r\n                list(t = t_score))) %>%\r\n  left_join(People %>%\r\n              mutate(name = paste(nameLast, nameFirst, sep = ", ")) %>%\r\n              select(playerID, name)) %>%\r\n  relocate(name, .before = AB) %>%\r\n  select(playerID:AB, ends_with("z_t")) %>%\r\n  pivot_longer(cols = -c(playerID, name, AB),\r\n               names_to = "stat") %>%\r\n  mutate(stat = case_when(stat == "ba_z_t" ~ "BA",\r\n                          stat == "obp_z_t" ~ "OBP",\r\n                          stat == "slg_z_t" ~ "SLG",\r\n                          stat == "ops_z_t" ~ "OPS",\r\n                          stat == "hr_rate_z_t" ~ "HR Rate"))\r\n\r\n\r\ndat %>%\r\n  head()\r\n\r\ndat_long %>%\r\n  head()\r\n<\/pre>\n
 <\/p>\n
The Figures for our App<\/strong><\/span><\/p>\n
Before I build the {shiny} app, I wanted to first construct the three figures I will include. The code for these will be accessible in Github<\/a><\/span><\/strong>, but here is what they look like:<\/p>\n