Table_testing.Rmd

---
title: "Gt Testing"
author: "Kevin Kent"
date: "11/10/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## R Markdown


```{r}
library(tidyverse)
library(gt)
library(tesseract)
library(magick)
library(stringr)
library(sparklines)
```

# Front Images

```{r}

all_grids <- list.files(here::here("Scans", "Grids", "UpperDeck_1998"))

backs <- all_grids[str_detect(all_grids, "back")]

fronts <- all_grids[str_detect(all_grids, "front")]

size <- "753x1050"

x_inc <- 753
y_inc <- 1050
num_rows <- 2
num_cols <- 4
num_cards <- num_rows*num_cols
y_positions <- c(rep(0, num_cols), rep(y_inc, num_cols))
cols <- seq(0, num_cols -1, by = 1)

locations <- tibble(x_inc = rep(x_inc, num_cards), y_inc = rep(y_inc, num_cards), 
       cols = rep(cols, num_rows), y_pos = y_positions)

crop_locations <- locations %>%
  mutate(x_pos = x_inc*cols) %>%
  str_glue_data("{x_inc}x{y_inc}+{x_pos}+{y_pos}")

front_images <- fronts %>%
  map(~ map2(., crop_locations, ~ image_crop(image_read(here::here("Scans", "Grids", "UpperDeck_1998", .x)), .y))) %>%
  unlist(.)
```

# Read Cleaned Tables

```{r}
pitchers <- read_csv(here::here("Cleaned_Data", "pitchers.csv")) %>%
  mutate(team = str_to_title(team)) %>%
  select(-description)

names(pitchers) <- toupper(names(pitchers))

position_players <- read_csv(here::here("Cleaned_Data", "position_players.csv")) %>%
  mutate(team = str_to_title(team))%>%
  select(-description)

names(position_players) <- toupper(names(position_players))

descriptions <- read_csv(here::here("Cleaned_Data", "description_clean.csv"))

player_name_key <- read_csv(here::here("Cleaned_Data", "player_name_card_key.csv")) %>%
  left_join(descriptions)

names(player_name_key) <- toupper(names(player_name_key))

```

Let's also get the position type of each player.

```{r}

pitchers_unique <- pitchers %>% 
  select(NAME) %>%
  distinct() %>%
  mutate(position = "pitcher") 

position_players_unique <- position_players %>%
  select(NAME) %>%
  distinct() %>%
  mutate(position = "position_player")

all_players_position <- pitchers_unique %>%
  bind_rows(position_players_unique)

player_name_key <- player_name_key %>%
  left_join(all_players_position)

```

## Let's try to create baseball card table for one player.


```{r}

selection <- 1

image <- front_images[[selection]]

player <- player_name_key %>%
  filter(ID == selection) 

pos <- player$position[1]

player_name <- str_to_title(player$NAME[1])

description <- player$DESCRIPTION[1]


if (pos == 'pitcher') {
  
  meta_info <- pitchers %>%
    filter(NAME == tolower(player_name)) %>%
    select(HT, WT, BATS, THROWS, DOB, BIRTH_LOCATION) %>%
    distinct() %>%
    slice(1:1) %>%
    mutate(all_info = str_glue("{HT} WT: {WT} BATS: {BATS} THROWS: {THROWS} DOB: {DOB} Birth Location: {BIRTH_LOCATION}"))
  
  characteristics <- meta_info$all_info[1]
  
pitchers_clean <-  pitchers %>%
    filter(NAME == tolower(player_name)) %>%
    distinct() %>%
    select(-HT, -WT, -BATS, -THROWS, -DOB, -BIRTH_LOCATION) %>%
    arrange(YEAR) %>%
    select(-NAME, -POSITION, -ID) %>%
    relocate(any_of(c("YEAR", "TEAM"))) 

pitchers_clean %>%
      gt() %>%
      tab_header(title = player_name, subtitle = characteristics) %>%
      tab_options( column_labels.background.color = "gray") %>%
  opt_table_font(
    font = list(
      google_font(name = "Goldman"),
      "Cochin", "Serif"
    )
  )

summary_spk <- pitchers_clean %>%
  select(-TEAM, -YEAR) %>%
  mutate_all(as.numeric) %>%
  summarise_all(spk_chr, type = "line") %>%
  mutate(YEAR = "TOTALS", TEAM = "") %>%
  relocate(any_of(c("YEAR", "TEAM")))

summary_names <- tibble(names = rep('&nbsp', 12), mult = seq(1, 12, by = 1)) %>%
  mutate(name_full = map2(names, mult, ~ paste0(rep(.x, .y), collapse = "")))

names(summary_spk) <- summary_names$name_full

summary_spk %>%
   formattable() %>%
    formattable::as.htmlwidget() %>%
    spk_add_deps()
   
  
  
}




```