R/new_stats.R

In abresler/nbastatR: R's interface to NBA data

.get_current_season <-
  function() {
    current_year <- lubridate::year(Sys.Date())
    current_month <- lubridate::month(Sys.Date())

    season_slug <- case_when(
      current_month %>% between(6, 12) ~ str_c(current_year, "-", substr(current_year + 1, 3, 4)),
      TRUE ~ str_c(current_year - 1, "-", substr(current_year, 3, 4))
    )
    season_slug
  }

#' Cached player photo dictionary
#'
#' @return \code{tibble}
#' @export
#'
#' @examples
dictionary_player_photos <-
  function() {
    structure(
      list(
        c(
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          246L,
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          72L,
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          98L,
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          201583L,
          1000L,
          335L,
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          21L,
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          2425L,
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          2240L,
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          2220L,
          353L,
          200756L,
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          2306L,
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          76065L,
          1628503L,
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          355L,
          173L,
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          1088L,
          76070L,
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          278L,
          201571L,
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          1134L,
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          138L,
          76076L,
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          202970L,
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          1847L,
          1753L,
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          1627758L,
          452L,
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          1628964L,
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          2556L,
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          2571L,
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          200826L,
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          203115L,
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          76120L,
          1766L,
          2370L,
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          1628966L,
          101212L,
          2471L,
          1499L,
          2203L,
          174L,
          76123L,
          76124L,
          201587L,
          76125L,
          76126L,
          2437L,
          201573L,
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          203382L,
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          203145L,
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          203078L,
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          76131L,
          2602L,
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          1133L,
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          76139L,
          76140L,
          76141L,
          201158L,
          2294L,
          76142L,
          1628395L,
          1952L,
          2559L,
          76143L,
          76144L,
          1627761L,
          76145L,
          76146L,
          1627733L,
          1886L,
          203968L,
          104L,
          1736L,
          203461L,
          1120L,
          712L,
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          76152L,
          128L,
          202728L,
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          1627791L,
          76155L,
          76156L,
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          202722L,
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          76188L,
          1628417L,
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          1548L,
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          1522L,
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          1953L,
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          1628449L,
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          992L,
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          136L,
          753L,
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          76283L,
          76286L,
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          1628425L,
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          76288L,
          1628973L,
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          1627852L,
          76289L,
          76290L,
          977L,
          95L,
          1628418L,
          76291L,
          76292L,
          76293L,
          76294L,
          76295L,
          1761L,
          76296L,
          76297L,
          201978L,
          76298L,
          146L,
          1934L,
          672L,
          203493L,
          76300L,
          76301L,
          76302L,
          76303L,
          2469L,
          203504L,
          76304L,
          202692L,
          2766L,
          101207L,
          76305L,
          76306L,
          76307L,
          76308L,
          76309L,
          197L,
          76311L,
          675L,
          76313L,
          1629126L,
          76314L,
          416L,
          76316L,
          76317L,
          202221L,
          76318L,
          76319L,
          2406L,
          202364L,
          76320L,
          2866L,
          202710L,
          348L,
          2446L,
          202779L,
          201182L,
          101115L,
          101198L,
          76321L,
          76322L,
          76323L,
          2560L,
          76324L,
          203998L,
          679L,
          262L,
          76326L,
          201979L,
          101181L,
          154L,
          76328L,
          76329L,
          203484L,
          76331L,
          76330L,
          76332L,
          76333L,
          76334L,
          76335L,
          76336L,
          76337L,
          948L,
          922L,
          76338L,
          203477L,
          76339L,
          203991L,
          202382L,
          2073L,
          76340L,
          76341L,
          2367L,
          76342L,
          76343L,
          76344L,
          76345L,
          200760L,
          76346L,
          156L,
          76348L,
          713L,
          1757L,
          76350L,
          76351L,
          76352L,
          201960L,
          76353L,
          2679L,
          1131L,
          1853L,
          76355L,
          76356L,
          76357L,
          76358L,
          76359L,
          1628975L,
          2466L,
          76360L,
          76361L,
          1713L,
          1628976L,
          203487L,
          76362L,
          76363L,
          1627936L,
          76364L,
          208L,
          201956L,
          76365L,
          767L,
          76367L,
          1509L,
          76368L,
          1626161L,
          1628505L,
          190L,
          1628463L,
          76370L,
          76L,
          1911L,
          76373L,
          2763L,
          201596L,
          76374L,
          76375L,
          76376L,
          1472L,
          76378L,
          2199L,
          201163L,
          76379L,
          76380L,
          364L,
          76382L,
          76383L,
          76384L,
          1629147L,
          384L,
          76385L,
          76386L,
          203805L,
          76387L,
          432L,
          2735L,
          719L,
          164L,
          76391L,
          76392L,
          1627737L,
          76393L,
          76394L,
          76395L,
          57L,
          202091L,
          1626176L,
          203902L,
          203565L,
          928L,
          76397L,
          76398L,
          201947L,
          1629109L,
          203546L,
          1721L,
          1626262L,
          203903L,
          1628492L,
          201964L,
          1127L,
          2049L,
          76402L,
          2043L,
          76403L,
          1628499L,
          76404L,
          76406L,
          1569L,
          76407L,
          76408L,
          76409L,
          76410L,
          76411L,
          715L,
          202708L,
          76412L,
          934L,
          76413L,
          76414L,
          76415L,
          902L,
          2044L,
          76416L,
          76418L,
          76421L,
          1530L,
          2260L,
          2215L,
          76419L,
          1628381L,
          200776L,
          202395L,
          1628380L,
          1627858L,
          201954L,
          2555L,
          76422L,
          1629045L,
          2173L,
          306L,
          76425L,
          76426L,
          76427L,
          76428L,
          201144L,
          76429L,
          55L,
          1626192L,
          76431L,
          76432L,
          101215L,
          76433L,
          76434L,
          76438L,
          2567L,
          201161L,
          76435L,
          76436L,
          76437L,
          2241L,
          1626188L,
          1628429L,
          76439L,
          76440L,
          204022L,
          76442L,
          1030L,
          76443L,
          76444L,
          76441L,
          76446L,
          203142L,
          76447L,
          76448L,
          895L,
          76450L,
          76451L,
          76452L,
          76453L,
          76454L,
          1627856L,
          203955L,
          76456L,
          1526L,
          76457L,
          76458L,
          76459L,
          1432L,
          202408L,
          202326L,
          600003L,
          203496L,
          76462L,
          76463L,
          76464L,
          76465L,
          203459L,
          1628470L,
          1544L,
          76466L,
          2037L,
          201621L,
          202348L,
          1628249L,
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          76469L,
          76470L,
          2343L,
          76471L,
          76472L,
          201159L,
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          76474L,
          76475L,
          1506L,
          76476L,
          76477L,
          76478L,
          76479L,
          180L,
          76481L,
          76482L,
          203109L,
          76483L,
          187L,
          1907L,
          76487L,
          201967L,
          76485L,
          203099L,
          1061L,
          76488L,
          76489L,
          940L,
          223L,
          76492L,
          209L,
          2201L,
          201191L,
          688L,
          203552L,
          201939L,
          687L,
          76505L,
          76496L,
          76497L,
          2223L,
          76498L,
          956L,
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          76500L,
          1498L,
          2845L,
          1380L,
          2605L,
          76502L,
          203584L,
          390L,
          76504L,
          76506L,
          76507L,
          212L,
          76509L,
          76511L,
          203540L,
          921L,
          1600L,
          201176L,
          76514L,
          203561L,
          203076L,
          213L,
          76515L,
          1884L,
          989L,
          76538L,
          76533L,
          76516L,
          76517L,
          76518L,
          76519L,
          905L,
          1627738L,
          76521L,
          202334L,
          76522L,
          1023L,
          201175L,
          76523L,
          93L,
          76524L,
          76525L,
          76526L,
          2668L,
          707L,
          76528L,
          76529L,
          76530L,
          76531L,
          76520L,
          200781L,
          76532L,
          1729L,
          76534L,
          426L,
          1629093L,
          76536L,
          1453L,
          203958L,
          76539L,
          1008L,
          76541L,
          1626183L,
          201286L,
          76542L,
          103L,
          201948L,
          76543L,
          201986L,
          76545L,
          692L,
          76551L,
          201942L,
          76562L,
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          203473L,
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          143L,
          76548L,
          1626214L,
          1626155L,
          219L,
          1627098L,
          183L,
          2568L,
          1629116L,
          960L,
          203521L,
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          725L,
          76555L,
          2736L,
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          76557L,
          202458L,
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          76559L,
          1629094L,
          76560L,
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          76568L,
          1628978L,
          1760L,
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          1628977L,
          2564L,
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          2424L,
          2079L,
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          1722L,
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          76565L,
          101143L,
          203476L,
          76566L,
          76567L,
          76569L,
          76570L,
          76571L,
          76574L,
          76575L,
          76576L,
          203915L,
          101113L,
          2205L,
          76577L,
          76578L,
          124L,
          2413L,
          1059L,
          76580L,
          1720L,
          76581L,
          76582L,
          76583L,
          1629029L,
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          76585L,
          2039L,
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          200763L,
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          76590L,
          428L,
          201962L,
          201604L,
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          202220L,
          76593L,
          76594L,
          76595L,
          1628495L,
          1628408L,
          76596L,
          201609L,
          204054L,
          1068L,
          1724L,
          76598L,
          76599L,
          203580L,
          17L,
          1041L,
          76603L,
          1542L,
          76604L,
          203083L,
          76623L,
          76605L,
          404L,
          76607L,
          201L,
          201162L,
          76608L,
          76609L,
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          2768L,
          1626251L,
          76611L,
          247L,
          30L,
          434L,
          76615L,
          1495L,
          2399L,
          76616L,
          1627739L,
          76617L,
          76618L,
          2648L,
          201142L,
          76619L,
          1090L,
          76621L,
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          1628979L,
          76624L,
          76625L,
          76626L,
          202406L,
          289L,
          76628L,
          768L,
          76630L,
          203921L,
          76631L,
          76632L,
          202365L,
          76633L,
          76634L,
          2569L,
          204067L,
          76635L,
          76636L,
          76637L,
          76638L,
          721L,
          76646L,
          898L,
          2451L,
          346L,
          76641L,
          229L,
          76643L,
          2823L,
          236L,
          202197L,
          1629053L,
          76647L,
          76648L,
          76649L,
          378L,
          76651L,
          458L,
          1918L,
          2064L,
          76653L,
          53L,
          76655L,
          1627740L,
          201961L,
          76656L,
          251L,
          76658L,
          76664L,
          107L,
          1677L,
          76661L,
          111L,
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      ),
      .Names = c("idPlayer", "hasHeadShot", "hasThumbnail",
                 "hasAction"),
      row.names = c(NA, -4379L),
      class = c("tbl_df",
                "tbl", "data.frame")
    )

  }

#' Validate NBA Player photos
#'
#' @param sleep_time if not \code{NULL} sleep time
#'
#' @return \code{tibble}
#' @export
#'
#' @examples
validate_nba_player_photos <-
  function(sleep_time = 2) {
    dict_players <- nba_players()
    players <- nrow(dict_players)
    all_data <-
      1:players %>%
      map_dfr(function(x) {
        glue("{x} of {players}") %>% message()
        df_row <- dict_players[x, ]
        id_player <- df_row$idPlayer
        is_ok_headshot <-
          !http_error(df_row$urlPlayerHeadshot)
        is_ok_thumbnail <-
          !http_error(df_row$urlPlayerThumbnail)
        is_ok_action <-
          !http_error(df_row$urlPlayerActionPhoto)

        df <- tibble(
          idPlayer = id_player,
          hasHeadShot = is_ok_headshot,
          hasThumbnail = is_ok_thumbnail,
          hasAction = is_ok_action
        )

        if (length(sleep_time > 0)) {
          Sys.sleep(time = sleep_time)
        }

        df
      })

    all_data
  }


.generate_url_reference <-
  function() {
    user_agents <-
      c(
        "Mozilla/5.0 (Linux; Android 7.0; SM-G892A Build/NRD90M; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/60.0.3112.107 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 7.0; SM-G930VC Build/NRD90M; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/58.0.3029.83 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 6.0.1; SM-G935S Build/MMB29K; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/55.0.2883.91 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 6.0.1; SM-G920V Build/MMB29K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.98 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 5.1.1; SM-G928X Build/LMY47X) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.83 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 6.0.1; Nexus 6P Build/MMB29P) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.83 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 7.1.1; G8231 Build/41.2.A.0.219; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/59.0.3071.125 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 6.0.1; E6653 Build/32.2.A.0.253) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.98 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 6.0; HTC One X10 Build/MRA58K; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/61.0.3163.98 Mobile Safari/537.36",
        "Mozilla/5.0 (Linux; Android 6.0; HTC One M9 Build/MRA58K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.98 Mobile Safari/537.36",
        "Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Version/11.0 Mobile/15A372 Safari/604.1",
        "Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.34 (KHTML, like Gecko) Version/11.0 Mobile/15A5341f Safari/604.1",
        "Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Version/11.0 Mobile/15A5370a Safari/604.1",
        "Mozilla/5.0 (iPhone9,3; U; CPU iPhone OS 10_0_1 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Mobile/14A403 Safari/602.1",
        "Mozilla/5.0 (iPhone9,4; U; CPU iPhone OS 10_0_1 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Mobile/14A403 Safari/602.1",
        "Mozilla/5.0 (Apple-iPhone7C2/1202.466; U; CPU like Mac OS X; en) AppleWebKit/420+ (KHTML, like Gecko) Version/3.0 Mobile/1A543 Safari/419.3",
        "Mozilla/5.0 (Windows Phone 10.0; Android 6.0.1; Microsoft; RM-1152) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Mobile Safari/537.36 Edge/15.15254",
        "Mozilla/5.0 (Windows Phone 10.0; Android 4.2.1; Microsoft; RM-1127_16056) AppleWebKit/537.36(KHTML, like Gecko) Chrome/42.0.2311.135 Mobile Safari/537.36 Edge/12.10536",
        "Mozilla/5.0 (Windows Phone 10.0; Android 4.2.1; Microsoft; Lumia 950) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2486.0 Mobile Safari/537.36 Edge/13.10586",
        "Mozilla/5.0 (Linux; Android 7.0; Pixel C Build/NRD90M; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/52.0.2743.98 Safari/537.36",
        "Mozilla/5.0 (Linux; Android 6.0.1; SGP771 Build/32.2.A.0.253; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/52.0.2743.98 Safari/537.36",
        "Mozilla/5.0 (Linux; Android 6.0.1; SHIELD Tablet K1 Build/MRA58K; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/55.0.2883.91 Safari/537.36",
        "Mozilla/5.0 (Linux; Android 7.0; SM-T827R4 Build/NRD90M) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.116 Safari/537.36",
        "Mozilla/5.0 (Linux; Android 5.0.2; SAMSUNG SM-T550 Build/LRX22G) AppleWebKit/537.36 (KHTML, like Gecko) SamsungBrowser/3.3 Chrome/38.0.2125.102 Safari/537.36",
        "Mozilla/5.0 (Linux; Android 4.4.3; KFTHWI Build/KTU84M) AppleWebKit/537.36 (KHTML, like Gecko) Silk/47.1.79 like Chrome/47.0.2526.80 Safari/537.36",
        "Mozilla/5.0 (Linux; Android 5.0.2; LG-V410/V41020c Build/LRX22G) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/34.0.1847.118 Safari/537.36",
        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246",
        "Mozilla/5.0 (X11; CrOS x86_64 8172.45.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.64 Safari/537.36",
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/601.3.9 (KHTML, like Gecko) Version/9.0.2 Safari/601.3.9",
        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.111 Safari/537.36",
        "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:15.0) Gecko/20100101 Firefox/15.0.1",
        "Mozilla/5.0 (CrKey armv7l 1.5.16041) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/31.0.1650.0 Safari/537.36",
        "Roku4640X/DVP-7.70 (297.70E04154A)",
        "Mozilla/5.0 (Linux; U; Android 4.2.2; he-il; NEO-X5-116A Build/JDQ39) AppleWebKit/534.30 (KHTML, like Gecko) Version/4.0 Safari/534.30",
        "Mozilla/5.0 (Linux; Android 5.1; AFTS Build/LMY47O) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/41.99900.2250.0242 Safari/537.36",
        "Dalvik/2.1.0 (Linux; U; Android 6.0.1; Nexus Player Build/MMB29T)",
        "AppleTV6,2/11.1",
        "AppleTV5,3/9.1.1",
        "Mozilla/5.0 (Nintendo WiiU) AppleWebKit/536.30 (KHTML, like Gecko) NX/3.0.4.2.12 NintendoBrowser/4.3.1.11264.US",
        "Mozilla/5.0 (Windows NT 10.0; Win64; x64; XBOX_ONE_ED) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.79 Safari/537.36 Edge/14.14393",
        "Mozilla/5.0 (Windows Phone 10.0; Android 4.2.1; Xbox; Xbox One) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2486.0 Mobile Safari/537.36 Edge/13.10586",
        "Mozilla/5.0 (PlayStation 4 3.11) AppleWebKit/537.73 (KHTML, like Gecko)",
        "Mozilla/5.0 (PlayStation Vita 3.61) AppleWebKit/537.73 (KHTML, like Gecko) Silk/3.2",
        "Mozilla/5.0 (Nintendo 3DS; U; ; en) Version/1.7412.EU",
        "Mozilla/5.0 (compatible; Googlebot/2.1; +https://www.google.com/bot.html)",
        "Mozilla/5.0 (compatible; bingbot/2.0; +https://www.bing.com/bingbot.htm)",
        "Mozilla/5.0 (compatible; Yahoo! Slurp; https://help.yahoo.com/help/us/ysearch/slurp)",
        "Mozilla/5.0 (X11; U; Linux armv7l like Android; en-us) AppleWebKit/531.2+ (KHTML, like Gecko) Version/5.0 Safari/533.2+ Kindle/3.0+",
        "Mozilla/5.0 (Linux; U; en-US) AppleWebKit/528.5+ (KHTML, like Gecko, Safari/528.5+) Version/4.0 Kindle/3.0 (screen 600x800; rotate)"
      )



    user_agent <-
      user_agents[!user_agents %>% str_detect("bot|slurp")] %>%
      sample(1)

    tl_domain <-
      c('.com', '.gov', '.org') %>%
      sample(1)

    word_length <-
      8:15

    words <-
      word_length %>% sample(1)

    domain_slug <-
      1:words %>%
      map_chr(function(x) {
        sample(letters, 1)
      }) %>%
      paste0(collapse = '')

    url <-
      list('https://', domain_slug, tl_domain) %>%
      reduce(paste0)
    df <-
      tibble(urlReferer = url,
             userAgent = user_agent)
    df
  }

.nba_headers <-
  function() {
    structure(
      list(
        "close",
        "no-cache",
        "no-cache",
        "1",
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.67 Safari/537.36",
        "1",
        "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",
        "gzip, deflate, br",
        "en-US,en;q=0.9"
      ),
      .Names = c(
        "Connection",
        "Pragma",
        "Cache-Control",
        "Upgrade-Insecure-Requests",
        "User-Agent",
        "DNT",
        "Accept",
        "Accept-Encoding",
        "Accept-Language"
      )
    )

  }
.get_slug_year <-
  function() {
    current_date <- Sys.Date()
    current_year <- lubridate::year(current_date)
    current_month <- lubridate::month(current_date)
    slug_year <- dplyr::case_when(current_month >= 10 ~  current_year,
                                  TRUE ~ current_year - 1)
    slug_year
  }

curl_json_to_vector <-
  function(url = "https://data.nba.net/prod/v1/2017/coaches.json") {
    json <-
      curl(url = url) %>%
      read_lines() %>%
      fromJSON(simplifyVector = T)

    json
  }

# utils -------------------------------------------------------------------
remove_na_columns <-
  function(data) {
    data %>%
      dplyr::select(which(colMeans(is.na(.)) < 1))
  }

generate_team_season_logo <-
  function(season = 1987, slug_team = "SAC") {
    slug_season <-
      generate_season_slug(season = season)

    url <-
      glue(
        "https://stats.nba.com/media/img/teams/logos/season/{slug_season}/{slug_team}_logo.svg"
      ) %>%
      as.character()

    url
  }

generate_team_seasons_logos_data <-
  function(seasons = 1951:2018,
           slug_teams = "SAC") {
    input_df <-
      expand.grid(season = seasons,
                  slug_team = slug_teams,
                  stringsAsFactors = F) %>%
      as_tibble()
    generate_team_season_logo_safe <-
      possibly(generate_team_season_logo, NULL)

    1:nrow(input_df) %>%
      future_map_dfr(function(x) {
        df_row <-
          input_df %>% slice(x)
        season <- df_row$season
        slug_team <- df_row$slug_team
        url <-
          generate_team_season_logo(season = season, slug_team = slug_team)

        if (url %>% length() == 0) {
          return(invisible())
        }

        tibble(
          yearSeason = season,
          slugTeam = slug_team,
          urlTeamLogoSeason = url
        )

      })
  }


parse.nba.json_data <-
  function(url = "https://stats.nba.com/stats/leaguedashplayerbiostats?College=&Conference=&Country=&DateFrom=&DateTo=&Division=&DraftPick=&DraftYear=&GameScope=&GameSegment=&Height=&LastNGames=0&LeagueID=00&Location=&Month=0&OpponentTeamID=0&Outcome=&PORound=0&PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&Season=2016-17&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&StarterBench=&TeamID=0&VsConference=&VsDivision=&Weight=") {
    json_data <- url %>%
      get.json_data(
        use_read_lines = TRUE,
        is_tibble = TRUE,
        is_flattened = TRUE
      )

    js_names <-
      names(json_data)[!names(json_data) %in% "resource"]

    table_name <-
      json_data$resultSets$name


    df_parameters <-
      tibble(item = json_data[["parameters"]] %>% names(),
             value = json_data[["parameters"]] %>% as.character()) %>%
      mutate(value = ifelse(value == "NULL", NA, value)) %>%
      filter(!value %>% is.na(),
             !value == "0")



    names_nba <-
      json_data$resultSets$headers %>% flatten_chr()

    ##
    names_actual <- names_nba

    data <-
      json_data$resultSets$rowSet %>%
      data.frame(stringsAsFactors = F) %>%
      dplyr::as_tibble() %>%
      set_names(names_actual)




  }

generate.nba_api.items <-
  function(nba_slug = "MeasureType",
           param_slug = "(Base)|(Advanced)|(Misc)|(Four Factors)|(Scoring)|(Opponent)|(Usage)",
           allow_blank = FALSE) {
    values <-
      param_slug %>%
      str_replace_all("\\^|\\$|\\?", '') %>%
      str_split("\\|") %>%
      flatten_chr() %>%
      str_replace_all("\\(|\\)", "")

    tibble(apiItem = nba_slug, values, isBlankAllowed = allow_blank) %>%
      mutate_if(is.numeric,
                as.character())
  }






# Table_headers -----------------------------------------------------------



.generate.nba_slugs.definitions <-
  function(table_slugs = c(
    "traditional",
    "advanced",
    "scoring",
    "misc",
    "usage",
    "fourfactors",
    "hustlestats",
    "playertrack"
  ),
  table_type = "game") {
    urls <-
      glue(
        "https://stats.nba.com/templates/angular/views/{table_type}/{table_type}-{table_slugs}.html"
      ) %>%
      as.character()

    tibble(typeTable = table_type,
           slugBase = table_slugs,
           urlHeaderTable = urls)
  }

.get.nba_api_parameters <-
  function(url = "https://stats.nba.com/stats/leaguedashplayerbiostats?College=&Conference=&Country=&DateFrom=&DateTo=&Division=&DraftPick=&DraftYear=&GameScope=&GameSegment=&Height=&LastNGames=0&LeagueID=00&Location=&Month=0&OpponentTeamID=0&Outcome=&PORound=0&PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&Season=1996-97&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&StarterBench=&TeamID=0&VsConference=&VsDivision=&Weight=") {
    slug_nba <- url %>%
      str_replace_all("https://stats.nba.com/stats/", "") %>%
      str_split("\\?") %>%
      flatten_chr() %>%
      .[[1]]

    api_options <-
      url %>%
      get.json_data() %>%
      .$parameters %>%
      names()
    gc()

    tibble(
      slugBase = slug_nba,
      parameterOption = api_options,
      urlNBAStatsAPI = url
    )
  }

.parse.nba_headers.definitions <-
  function(url = "https://stats.nba.com/templates/angular/views/game/game-playertracking.html") {
    page <-
      url %>%
      read_html()

    name_nba <-
      page %>%
      html_nodes("dt") %>%
      html_text()

    description_nba <-
      page %>%
      html_nodes("dd") %>%
      html_text()

    tibble(
      nameNBA = name_nba,
      descriptionItem = description_nba,
      urlHeaderTable = url
    )

  }

get.nba_headers <-
  function(table_type = "game",
           table_slugs = c(
             "traditional",
             "advanced",
             "scoring",
             "misc",
             "usage",
             "fourfactors",
             "hustlestats",
             "playertrack"
           )) {
    df_urls <-
      .generate.nba_slugs.definitions(table_type = table_type, table_slugs = table_slugs)

    df_tables <- df_urls$urlHeaderTable %>%
      future_map_dfr(function(x) {
        .parse.nba_headers.definitions(url = x)
      })

    df_tables %>%
      left_join(df_urls) %>%
      dplyr::select(typeTable, slugBase, everything()) %>%
      suppressMessages()
  }



# base --------------------------------------------------------------------

#' NBA current season schedule
#'
#' Get NBA schedule for most current season
#'
#' @return a `tibble`
#' @export
#'
#' @import curl jsonlite readr purrr stringr lubridate
#' @examples
#' current_schedule()
current_schedule <-
  function() {
    slug_year <-
      .get_slug_year()
    json <-
      glue("https://data.nba.net/prod/v2/{slug_year}/schedule.json") %>%
      as.character() %>%
      fromJSON()

    json_data <- json$league$standard

    df_season_games <-
      json_data[!json_data %>% names() %in% c("period", "nugget", "hTeam", "vTeam", "watch", "playoffs")] %>%
      dplyr::as_tibble()

    df_season_games <-
      df_season_games %>%
      select(1:10) %>%
      set_names(
        c(
          "slugGame",
          "idStageGame",
          "slugGameCode",
          "idGameStatus",
          "hasExtendedStatus",
          "isUnknownStartTime",
          "datetimeGame",
          "dateSlugGame",
          "timeEasternGame",
          "hasBuzzerBeater"
        )
      ) %>%
      separate(slugGameCode,
                      into = c("idGame", "slugTeams"),
                      sep = "/")
    season <-
      df_season_games$idGame[[1]] %>% substr(1, 4) %>% as.numeric() + 1

    df_season_games <-
      df_season_games %>%
      mutate(
        yearSeason = season,
        idGame = idGame %>% as.numeric(),
        slugTeamHome = slugTeams %>% substr(4, 6),
        slugTeamAway = slugTeams %>% substr(1, 3)
      ) %>%
      mutate(
        idGame = slugGame %>% as.numeric(),
        urlNBAGameBook = glue(
          "https://data.nba.net/prod/v1/{dateSlugGame}/{dateSlugGame}_Book.pdf"
        ) %>% as.character(),
        datetimeGame = parse_datetime(datetimeGame),
        dateGame = lubridate::ymd(dateSlugGame)
      ) %>%
      mutate(idRow = 1:n()) %>%
      select(idGame, everything()) %>%
      select(yearSeason,
             dateGame,
             slugTeamAway,
             slugTeamHome,
             everything())

    df_periods <-
      json_data$period %>%
      as_tibble() %>%
      set_names(c("quarterMaxPlayed", "idSeasonType", "maxQuartersRegular")) %>%
      mutate(
        hasOvertime = quarterMaxPlayed > 4,
        countOTQuarters =  quarterMaxPlayed - maxQuartersRegular,
        isComplete = !quarterMaxPlayed == 1
      ) %>%
      mutate(idRow = 1:n())

    df_descriptions <-
      tibble(descriptionGame = json_data$nugget$text) %>%
      mutate(idRow = 1:n()) %>%
      mutate_all(funs(ifelse(. == "", NA, .)))

    df_home <-
      json_data$hTeam %>% flatten() %>% dplyr::as_tibble() %>%
      set_names(c('idTeamHome', 'scoreHome', 'isWinnerHome', 'isLoserHome')) %>%
      mutate_all(as.numeric) %>%
      mutate(idRow = 1:n()) %>%
      left_join(nba_teams() %>% select(idTeamHome = idTeam, nameTeamHome = nameTeam)) %>%
      select(idTeamHome, nameTeamHome, everything()) %>%
      mutate(idRow = 1:n()) %>%
      suppressMessages()

    df_away <-
      json_data$vTeam %>% flatten() %>% dplyr::as_tibble() %>%
      set_names(c('idTeamAway', 'scoreAway', 'isWinnerAway', 'isLoserAway')) %>%
      mutate_all(as.numeric) %>%
      mutate(idRow = 1:n()) %>%
      left_join(nba_teams() %>% select(idTeamAway = idTeam, nameTeamAway = nameTeam)) %>%
      select(idTeamAway, nameTeamAway, everything()) %>%
      mutate(idRow = 1:n()) %>%
      suppressMessages()

    data <-
      list(df_season_games,
           df_periods,
           df_home,
           df_away,
           df_descriptions) %>%
      reduce(left_join) %>%
      suppressMessages() %>%
      select(-idRow) %>%
      dplyr::select(idSeasonType,
                    dateGame,
                    timeEasternGame,
                    idGame,
                    everything())

    data
  }

#' NBA active coaching staffs
#'
#' Gets active coaching staff information for all
#' NBA teams
#'
#' @return a \code{tibbles}
#' @export
#' @import tidyr curl jsonlite dplyr stringr
#' @family Current data
#' @family Coaching
#' @family Roster information
#' @examples
#' coaching_staffs()
coaching_staffs <-
  function() {
    slug_year <-
      .get_slug_year()
    json <-
      glue("https://data.nba.net/prod/v1/{slug_year}/coaches.json") %>%
      as.character() %>%
      curl() %>%
      read_lines() %>%
      fromJSON(simplifyVector = T)

    data <-
      json$league %>% flatten_df() %>%
      set_names(
        c(
          "nameFirst",
          "nameLast",
          "isHeadCoach",
          "idCoach",
          "idTeam",
          "numberSort",
          "nameCollegeCoach"
        )
      ) %>%
      mutate_at(c("idCoach", "numberSort", "idTeam"),
                funs(. %>% as.numeric())) %>%
      select(-numberSort)

    data <-
      data %>%
      left_join(nba_teams() %>% select(nameTeam, idTeam)) %>%
      unite(nameCoach, nameFirst, nameLast, sep = " ") %>%
      select(nameTeam, everything()) %>%
      arrange(nameTeam) %>%
      suppressMessages()

    data

  }

.nbastats_api_parameters <-
  function(api_version = 3) {
    df <-
      glue("https://data.nba.net/10s/prod/v{api_version}/today.json") %>%
      as.character() %>%
      get.json_data(use_read_lines = T, is_tibble = T) %>%
      flatten_df() %>%
      gather(item, value) %>%
      mutate(
        hasSlash = value %>% str_detect("/"),
        urlNBA = ifelse(hasSlash, str_c("https://data.nba.net", value), ""),
        versionAPI = api_version
      ) %>%
      select(versionAPI, everything())

    df <-
      df %>%
      mutate(urlNBA = ifelse(urlNBA == "", NA, urlNBA))

    df
  }

#' NBA Stats API Parameters
#'
#' @param api_versions \itemize{
#' \item 1  - V1
#' \item 2 - V2
#' \item 3 - V3
#'
#' }
#'
#' @return \code{tibble()}
#' @export
#'
#' @examples
#' nbastats_api_parameters(1:3)
nbastats_api_parameters <-
  function(api_versions = 1:3) {
    .nbastats_api_parameters_safe <-
      possibly(.nbastats_api_parameters, tibble())
    api_versions %>%
      future_map_dfr(function(api_version) {
        .nbastats_api_parameters_safe(api_version = api_version)
      })
  }


.parse_for_players <-
  function(json) {
    df_players <-
      json$data$players %>%
      dplyr::as_tibble() %>%
      set_names(
        c(
          "idPlayer",
          "namePlayerLastFirst",
          "isActive",
          "yearSeasonFirst",
          "yearSeasonLast",
          "idTeam",
          "hasGamesPlayedFlag"
        )
      ) %>%
      mutate_at(
        c(
          "idPlayer",
          "isActive",
          "idTeam",
          "yearSeasonLast",
          "yearSeasonFirst"
        ),
        funs(. %>% as.integer())
      ) %>%
      mutate(
        isActive = as.logical(isActive),
        countSeasons = (yearSeasonLast - yearSeasonFirst)
      )

    most_recent <-
      df_players %>% pull(yearSeasonFirst) %>% max(na.rm = T)

    df_players <-
      df_players %>%
      mutate(
        hasGamesPlayedFlag = ifelse(hasGamesPlayedFlag == "Y", TRUE, FALSE),
        idTeam = ifelse(idTeam == 0, NA, idTeam),
        isRookie = ifelse(countSeasons == 0 &
                            yearSeasonFirst == most_recent, TRUE, FALSE),
        urlPlayerStats = glue("https://stats.nba.com/player/{idPlayer}") %>% as.character(),
        urlPlayerThumbnail = glue(
          "https://stats.nba.com/media/players/230x185/{idPlayer}.png"
        ) %>% as.character(),
        urlPlayerHeadshot = glue(
          "https://ak-static.cms.nba.com/wp-content/uploads/headshots/nba/latest/260x190/{idPlayer}.png"
        ) %>% as.character()
      ) %>%
      mutate(
        urlPlayerActionPhoto = ifelse(
          isRookie,
          "https://stats.nba.com/media/img/league/nba-headshot-fallback.png",
          glue("https://stats.nba.com/media/players/700/{idPlayer}.png")
        ) %>% as.character()
      )

    df_players <-
      df_players %>%
      mutate(NP = namePlayerLastFirst %>% sub("\\,", "\\:", .)) %>%
      separate(NP,
                      into = c("namePlayerLast", "namePlayerFirst"),
                      sep = "\\:") %>%
      mutate(namePlayer = ifelse(
        namePlayerFirst %>% is.na(),
        namePlayerLast,
        str_c(namePlayerFirst, namePlayerLast, sep = " ")
      ) %>% str_trim()) %>%
      dplyr::select(idPlayer, namePlayer, everything()) %>%
      suppressWarnings()

    df_players <-
      df_players %>%
      mutate(
        urlPlayerThumbnail = if_else(
          yearSeasonFirst >= 2017,
          urlPlayerHeadshot,
          urlPlayerThumbnail
        )
      )


    df_players
  }

parse_for_seasons_data <-
  function(json) {
    json_seasons <-
      json$data$seasons

    seasons <-
      seq_along(json_seasons) %>%
      future_map_dfr(function(x) {
        row <-
          json_seasons[[x]]

        values <-
          row[1:5] %>%
          purrr::flatten_chr()

        items <-
          c(
            "nameParameter",
            "slugLeague",
            "yearDataEnd",
            "idLeagueSeasonType",
            "yearDataStart"
          )

        tibble(items, values) %>%
          mutate(values = values %>% as.character()) %>%
          spread(items, values)
      })

    seasons %>%
      mutate_all(as.character) %>%
      mutate_at(c("yearDataEnd", "idLeagueSeasonType", "yearDataStart"),
                funs(. %>% as.numeric()))
  }

.parse_for_teams <-
  function(json) {
    json_teams <-
      json$data$teams

    df_teams <-
      seq_along(json_teams) %>%
      map_dfr(function(x) {
        values <-
          json_teams[[x]] %>%
          map_chr(function(z) {
            if (z %>% length() == 0) {
              return(NA)
            }
            z %>% str_c(collapse = ", ")
          })

        items <-
          str_c("V", seq_along(values))

        tibble(items, values) %>%
          spread(items, values)

      }) %>%
      dplyr::select(one_of(
        c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10",
          "V11")
      )) %>%
      set_names(
        c(
          "idTeam",
          "slugTeam",
          "teamName",
          "cityTeam",
          "teamNameFull",
          "idConference",
          "idDivision",
          "isNonNBATeam",
          "yearPlayedLast",
          "idLeague",
          "colorsTeam"
        )
      ) %>%
      mutate_at(
        c(
          "idTeam",
          "idConference",
          "idDivision",
          "isNonNBATeam",
          "yearPlayedLast",
          "idLeague"
        ),
        funs(. %>% as.integer())
      ) %>%
      mutate(nameTeam = str_c(cityTeam, teamNameFull, sep = " ")) %>%
      mutate_if(is.character,
                funs(ifelse(. == "", NA, .))) %>%
      dplyr::select(nameTeam, everything())

    df_teams <-
      df_teams %>%
      mutate(
        nameTeam = nameTeam %>% str_replace_all("LA Clippers", "Los Angeles Clippers"),
        urlThumbnailTeam = if_else(
          isNonNBATeam == 0,
          glue(
            "https://cdn.nba.com/logos/nba/{idTeam}/primary/L/logo.svg"
          ) %>% as.character(),
          "https://stats.nba.com/media/img/teams/logos/NBA_logo.svg"
        )
      )
    df_teams

  }

#' NBA team dictionary
#'
#' Returns team dictionary
#'
#' @param join_blg if \code{TRUE} joins BLG team features data
#'
#' @return a `tibble`
#' @export
#' @importFrom readr read_lines
#' @importFrom glue glue
#' @import dplyr jsonlite stringr tidyr purrr
#' @examples
#' nba_teams()
nba_teams <-
  function(league = "NBA", join_blg = F) {
    url <- paste0("https://stats.", tolower(league), ".com/js/data/ptsd/stats_ptsd.js")
    json <-
      url %>%
      read_lines() %>%
      str_replace_all("var stats_ptsd =|\\;", "") %>%
      fromJSON(flatten = TRUE,
                         simplifyDataFrame = TRUE)

    df_teams <-
      json %>%
      .parse_for_teams()

    if (join_blg) {
      df_teams <-
        df_teams %>%
        left_join(.blg_teams() %>% select(-one_of(c(
          "teamName", "cityTeam"
        )))) %>%
        suppressMessages()
    }
    df_teams
  }

#' NBA teams seasons
#'
#'
#'
#' @return a \code{tibble()}
#' @export
#'
#' @examples
#' nba_teams_seasons()
nba_teams_seasons <- function() {
  json <-
    "https://stats.nba.com/stats/commonteamyears/?leagueId=00" %>%
    .curl_chinazi()
  actual_names <-
    json$resultSets$headers[[1]] %>%
    resolve_nba_names()

  data <-
    json$resultSets$rowSet[[1]] %>%
    data.frame(stringsAsFactors = F) %>%
    as_tibble() %>%
    set_names(actual_names) %>%
    munge_nba_data() %>%
    mutate(isActiveTeam = yearSeasonLast == max(yearSeasonLast)) %>%
    select(isActiveTeam, everything())
  data
}

#' NBA stats API parameters, teams and items
#'
#'
#'
#' @return a `tibble`
#' @export
#' @import readr jsonlite dplyr purrr tibble tidyr stringr
#' @examples
#' nba_stats_api_items()
nba_stats_api_items <-
  function() {
    url <- "https://stats.nba.com/js/data/ptsd/stats_ptsd.js"
    json <-
      url %>%
      read_lines() %>%
      str_replace_all("var stats_ptsd =|\\;", "") %>%
      fromJSON(flatten = TRUE,
                         simplifyDataFrame = TRUE)

    df_players <-
      json %>%
      .parse_for_players() %>%
      assign(x = "df_dict_nba_players", value = ., envir = .GlobalEnv)

    df_tables <-
      json %>%
      parse_for_seasons_data()
    assign(x = "df_dict_nba_parameters", value = df_tables, envir = .GlobalEnv)

    df_teams <-
      json %>%
      .parse_for_teams() %>%
      assign(x = "df_dict_nba_teams", value = ., envir = .GlobalEnv)

    tibble(
      nameTable = c("Players", "Teams", "API Parameters"),
      dataTable = list(df_players, df_teams, df_tables)
    )
  }

#' NBA player dictionary
#'
#'
#' @return a \code{tibble}
#' @export
#'
#' @import dplyr purrr jsonlite curl stringr lubridate memoise
#' @importFrom glue glue
#' @importFrom readr read_lines
#' @examples
#' nba_players()
nba_players <-
  memoise(function() {
    data <-
      "https://stats.nba.com/js/data/ptsd/stats_ptsd.js" %>%
      read_lines()

    json_data <-
      data %>% substr(18,nchar(data)-1) %>% fromJSON(simplifyDataFrame = T)

    data <-
      json_data$data$players %>% as_tibble() %>%
      setNames(
        c(
          "idPlayer",
          "player",
          "isActive",
          "yearSeasonFirst",
          "yearSeasonLast",
          "idTeam",
          "hasGamesPlayedFlag"
        )
      )

    df_players <-
      data %>%
      mutate_at(
        c(
          "idPlayer",
          "isActive",
          "yearSeasonFirst",
          "yearSeasonLast",
          "idTeam"
        ),
        as.numeric
      ) %>%
      mutate(
        hasGamesPlayedFlag = hasGamesPlayedFlag == "Y",
        isActive = as.logical(isActive),
        idTeam = case_when(idTeam == 0 ~ NA_real_,
                           TRUE ~ idTeam)
      ) %>%
      separate(
        player,
        into = c("nameLast", "nameFirst"),
        sep = "\\, ",
        extra = "merge",
        fill = "right"
      ) %>%
      mutate(namePlayer = case_when(
        is.na(nameFirst) ~ nameLast,
        TRUE ~ str_c(nameFirst, nameLast, sep = " ")
      )) %>%
      select(idPlayer, namePlayer, everything()) %>%
      select(-c(nameFirst, nameLast)) %>%
      mutate(countSeasons = (yearSeasonLast - yearSeasonFirst))


    most_recent <-
      df_players %>% pull(yearSeasonFirst) %>% max(na.rm = T)


    df_players <-
      df_players %>%
      mutate(
        hasGamesPlayedFlag = ifelse(hasGamesPlayedFlag == "Y", TRUE, FALSE),
        idTeam = ifelse(idTeam == 0, NA, idTeam),
        isRookie = ifelse(countSeasons == 0 &
                            yearSeasonFirst == most_recent, TRUE, FALSE),
        urlPlayerStats = glue("https://stats.nba.com/player/{idPlayer}") %>% as.character(),
        urlPlayerThumbnail = glue(
          "https://stats.nba.com/media/players/230x185/{idPlayer}.png"
        ) %>% as.character(),
        urlPlayerHeadshot = glue(
          "https://ak-static.cms.nba.com/wp-content/uploads/headshots/nba/latest/260x190/{idPlayer}.png"
        ) %>% as.character()
      ) %>%
      mutate(
        urlPlayerActionPhoto = ifelse(
          isRookie,
          "https://stats.nba.com/media/img/league/nba-headshot-fallback.png",
          glue("https://stats.nba.com/media/players/700/{idPlayer}.png") %>% as.character()
        )
      )



    df_players <-
      df_players %>%
      mutate(
        urlPlayerThumbnail = if_else(yearSeasonFirst >= 2017,
                                     urlPlayerHeadshot,
                                     urlPlayerThumbnail)
      )


    df_players <- df_players %>%
      mutate(namePlayer = case_when(idPlayer == 203318 ~ "Glen Rice Jr.",
                                    TRUE ~ namePlayer))


    dict_photos <- dictionary_player_photos()

    df_players <- df_players %>%
      left_join(dict_photos, by = "idPlayer") %>%
      mutate(
        urlPlayerPhoto = case_when(
          hasHeadShot ~ urlPlayerHeadshot,
          (!hasHeadShot) &
            hasThumbnail & (!hasAction) ~ urlPlayerThumbnail,
          (!hasHeadShot) &
            (!hasThumbnail) & (hasAction) ~ urlPlayerActionPhoto,
          (!hasHeadShot) &
            (hasThumbnail) & (hasAction) ~ urlPlayerThumbnail,
          TRUE ~ "https://stats.nba.com/media/img/league/nba-headshot-fallback.png"
        )
      )

    recent_season <- df_players$yearSeasonLast %>% max()

    df_players <-
      df_players %>%
      mutate(isActive = yearSeasonLast == recent_season)

    df_players %>%
      select(isActive,
             isRookie,
             namePlayer,
             idPlayer,
             countSeasons,
             everything())

  })

# games -------------------------------------------------------------------


## game detail : https://data.nba.com/data/10s/v2015/json/mobile_teams/nba/2017/scores/gamedetail/0021700136_gamedetail.json

## winprob:  https://stats.nba.com/stats/winprobabilitypbp?GameID=0021700106&RunType=each+second
## infographic: https://stats.nba.com/stats/infographicfanduelplayer?GameID=0021700106

## lead tracker
### https://data.nba.net/data/10s/prod/v1/20171105/0021700137_lead_tracker_1.json -- 1 through 4

### full play by play
### https://data.nba.com/data/10s/v2015/json/mobile_teams/nba/2017/scores/pbp/0021700136_full_pbp.json

## Summary
### https://stats.nba.com/stats/boxscoresummaryv2?GameID=0021700136

## images
## https://api.nba.net/0/league/video?games=0021700136&count=36&accessToken=nbainternal%7C3830242580404678b2552bbdd03b73ee

### Box-score
## advanced : https://stats.nba.com/stats/boxscoreadvancedv2?EndPeriod=10&EndRange=28800&GameID=0021700136&RangeType=0&Season=2017-18&SeasonType=Regular+Season&StartPeriod=1&StartRange=0
## scoring : https://stats.nba.com/stats/boxscorescoringv2?EndPeriod=10&EndRange=28800&GameID=0021700136&RangeType=0&Season=2017-18&SeasonType=Regular+Season&StartPeriod=1&StartRange=0

### Game Book
## https://www.nba.com/data/html/nbacom/2017/gameinfo/20171105/0021700136_Book.pdf