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## code to prepare `DATASET` dataset goes here

# usethis::use_data(overwrite = FALSE)

# Datasets ----
## ├ Iohexol Data ----
### ├ Schwartz ----
# formerly entered in the examples for calculate_mgfr_2c()
# moved to data Oct 2024
dat_schwartz <-
  data.frame(
    time = c(10, 20, 30, 60, 120, 180, 240, 300, 360),
    iohexol_ug_ml = c(656.1168132,477.1163595,371.3542728,
                      223.1121251,111.1086272,61.88251616,
                      37.43137242,21.79250307,12.75996292)  )


### ├ Pottel data ----
# from Supplemental document
dat10 <- data.frame(
  id = c(10, 10, 10, 10, 10, 10, 10, 10),
  time = c(30, 60, 90, 120, 150, 180, 240, 300),
  iohexol = c(239.9117,217.3945,178.215,159.4682,
           143.1718,130.3613,113.6223,98.6295))
dat17 <- data.frame(
  id = c(17, 17, 17, 17, 17, 17, 17, 17),
  time = c(30,60,90,120,150,180,240,300),
  iohexol = c(264.529,170.6695,143.0782,118.5563,
           102.927,89.5715,67.937,51.058))


### ├ Tondel data ----
# full example data provided by Tondel in Table 2: https://pubmed.ncbi.nlm.nih.gov/29134449/
dat_tondel <- data.frame(time=c(10,30,120,180,210,240,300),
                         iohexol=c(464,343,156,100,84,72,51))

### ├ Ebert example data ----
# data from XLS sheet, Ebert KI 2024: https://pubmed.ncbi.nlm.nih.gov/39097002/
dat_ebert <- 
  data.frame(time = c(160, 180, 200, 220, 232, 240),
             iohexol = c(70, 60, 47, 37, 30, 25))

usethis::use_data(dat_schwartz)
usethis::use_data(dat10)
usethis::use_data(dat17)
usethis::use_data(dat_tondel)
usethis::use_data(dat_ebert)




## ├ OGTT data ----
library(tidyverse)
gtt <- readRDS(here::here("data-raw/OGTT_merged_2017-12-23.rds"))
ogtt_wide <- 
  gtt |> 
  mutate(id = row_number()) |> 
  filter(!is.na(insulin_auc),
         cohort != "PPH") |> 
  select(id, age, gender.f, race.f, height_cm, weight_kg, bmi, hdl_bl, ldl_bl, trigs_bl,
         starts_with("ogtt_glucose"), starts_with("ogtt_insulin")) 
ogtt_nested <- 
  gtt |> 
  mutate(id = row_number()) |> 
  filter(!is.na(insulin_auc),
         cohort != "PPH") |> 
  select(id, age, gender.f, race.f, height_cm, weight_kg, bmi, hdl_bl, ldl_bl, trigs_bl,
         ogtt_df) 


usethis::use_data(ogtt_wide)
usethis::use_data(ogtt_nested)

## ├ Relmapirazin Data ----
df_relmapirazin_dat <-
  readr::read_csv(here::here("data-raw/relmapirazin/mgfr_data_dorshow_2024.csv")) |> 
  filter(!is.na(subject_number)) |>
  nest(data = c(time, relmapirazin_ng_ml)) |> 
  select(-mgfr)
df_relmapirazin_ids <-
  readr::read_csv(here::here("data-raw/relmapirazin/mgfr_data_dorshow_2024_ids.csv")) |> 
  select(-panel)
df_relmapirazin <-
  left_join(df_relmapirazin_dat, df_relmapirazin_ids, by = c("subject_number" = "subject_number")) 

usethis::use_data(df_relmapirazin)

  
# Internal Data ----

# Internal data objects are all stored within a single .rda file (R/sysdata.rda) 
# Internal data objects can be made available for use with `devtools::load_all()` 

# original data files are stored in `data-raw` folder and are not exported in the package

## ├ eGFR FAS reference table  ----

df_FAS_Q <-
  data.frame(
    stringsAsFactors = FALSE,
    age = c(1,2,3,4,5,
            6,7,8,9,10,11,12,13,14,
            15,16,17,18,19,20,15,16,17,
            18,19,20),
    gender = c("both","both","both",
               "both","both","both","both","both","both",
               "both","both","both","both","both","Male","Male",
               "Male","Male","Male","Male","Female","Female",
               "Female","Female","Female","Female"),
    height_cm = c(75.00,87.00,
                  95.50,102.50,110.00,116.70,123.50,129.50,
                  13.50,140.00,146.00,152.50,159.00,
                  165.00,172.00,176.00,178.00,179.00,
                  180.00,181.50,164.50,166.00,166.50,167.00,
                  167.50,168),
    Q_uM = c(23,26,27,30,34,36,
             39,41,43,45,47,50,52,54,64,69,72,75,78,
             80,57,59,61,61,62,62),
    Q_mg_dL = c(0.26,0.29,0.31,0.34,
                0.38,0.41,0.44,0.46,0.49,0.51,0.53,0.57,0.59,
                0.61,0.72,0.78,0.81,0.85,0.88,0.9,0.64,0.67,
                0.69,0.69,0.7,0.7)
  )
saveRDS(df_FAS_Q, here::here("data-raw/df_FAS_Q.rds"))

## ├ Omnipaque Table ----
# US brand = 300 or 350
df_omnipaque <- 
  data.frame(
  omnipaque_v = c(350L, 300L, 240L, 180L, 140L),
  iohexol_mg_ml = c(755L, 647L, 518L, 388L, 302L),
  omnipaque_specgrav = c(1406L, 1349L, 1280L, 1209L, 1164L),
  iohexol_mg_5ml = c(3775L, 3235L, 2594L, 1940L, 1510L)
)
saveRDS(df_omnipaque, here::here("data-raw/df_omnipaque.rds"))


## ├ PREVENT coefficient tables ----
cfs_base10yr <-  readxl::read_excel(here::here("data-raw/prevent_coefficients.xlsx"), sheet = "cfs_base10yr")
cfs_full10yr <-  readxl::read_excel(here::here("data-raw/prevent_coefficients.xlsx"), sheet = "cfs_full10yr")
cfs_base30yr <-  readxl::read_excel(here::here("data-raw/prevent_coefficients.xlsx"), sheet = "cfs_base30yr")
cfs_full30yr <-  readxl::read_excel(here::here("data-raw/prevent_coefficients.xlsx"), sheet = "cfs_full30yr")

saveRDS(cfs_base10yr, here::here("data-raw/cfs_base10yr.rds"))
saveRDS(cfs_full10yr, here::here("data-raw/cfs_full10yr.rds"))
saveRDS(cfs_base30yr, here::here("data-raw/cfs_base30yr.rds"))
saveRDS(cfs_full30yr, here::here("data-raw/cfs_full30yr.rds"))

## ├ ASCVD Risk Coefficient table ----
df_coeff_ascvd <-
  data.frame(
    stringsAsFactors = FALSE,
    sex = c("female","female","female","female",
            "female","female","female","female","female",
            "female","female","female","female",
            "female","female","male","male","male",
            "male","male","male","male","male",
            "male","male","male","male","male","male",
            "male","female","female","female",
            "female","female","female","female",
            "female","female","female","female","female",
            "female","female","female","male",
            "male","male","male","male","male","male",
            "male","male","male","male","male",
            "male","male","male"),
    race = c("white","white","white","white","white",
             "white","white","white","white","white",
             "white","white","white","white",
             "white","white","white","white","white",
             "white","white","white","white","white",
             "white","white","white","white","white",
             "white","black","black","black","black",
             "black","black","black","black",
             "black","black","black","black","black",
             "black","black","black","black","black",
             "black","black","black","black","black",
             "black","black","black","black",
             "black","black","black"),
    coeff_n = c("age","age2","tchol","age_tchol","hdl",
                "age_hdl","sbp_t","age_sbp_t","sbp_u",
                "age_sbp_u","smoker_yn","age_smoker",
                "diabetes_yn","baseline","mean_coeff","age",
                "age2","tchol","age_tchol","hdl",
                "age_hdl","sbp_t","age_sbp_t","sbp_u",
                "age_sbp_u","smoker_yn","age_smoker",
                "diabetes_yn","baseline","mean_coeff","age","age2",
                "tchol","age_tchol","hdl","age_hdl",
                "sbp_t","age_sbp_t","sbp_u","age_sbp_u",
                "smoker_yn","age_smoker","diabetes_yn",
                "baseline","mean_coeff","age","age2",
                "tchol","age_tchol","hdl","age_hdl",
                "sbp_t","age_sbp_t","sbp_u","age_sbp_u",
                "smoker_yn","age_smoker","diabetes_yn",
                "baseline","mean_coeff"),
    coeff_val = c(-29.799,4.884,13.54,-3.114,-13.578,3.149,
                  2.019,0,1.957,0,7.574,-1.665,0.661,
                  0.967,-29.18,12.344,0,11.853,-2.664,
                  -7.99,1.769,1.797,0,1.764,0,7.837,
                  -1.795,0.658,0.914,61.18,17.114,0,
                  0.94,0,-18.92,4.475,29.291,-6.432,27.82,
                  -6.087,0.691,0,0.874,0.953,86.61,
                  2.469,0,0.302,0,-0.307,0,1.916,0,
                  1.809,0,0.549,0,0.645,0.895,19.54)
  )
df_coeff_ascvd <-
  df_coeff_ascvd |> 
  pivot_wider(names_from = "coeff_n",
              values_from = "coeff_val")  
saveRDS(df_coeff_ascvd, here::here("data-raw/df_coeff_ascvd.rds"))



# usethis::use_data(cfs_base10yr, cfs_full10yr, 
#                   cfs_base30yr, cfs_full30yr, 
#                   internal = TRUE,
#                   overwrite = TRUE)

## ├ SDI Zip code files ----

library(data.table)
df_sdi <-
  tibble(fp = list.files(here::here("data-raw/SDI_zcta/"), full.names = T),
         fn = list.files(here::here("data-raw/SDI_zcta/"), full.names = F)) |> 
  mutate(year = gsub(".*-[0-9]+-([0-9]+)-.*.csv", "\\1", fn)) |> 
  mutate(data = map(fp, ~fread(.x, select = c("ZCTA5_FIPS", "SDI_score" )) )) |> 
  unnest(data) |> 
  group_by(year) |> 
  mutate(sdi_decile = ntile(SDI_score, 10))  |> 
  select(year, ZCTA5_FIPS, SDI_score, sdi_decile)

saveRDS(df_sdi, here::here("data-raw/df_sdi.rds"))


# LOAD AND SAVE/UPDATE INTERNAL DATA ----
# add new object to this and then udpate with each new data set
df_omnipaque   <- readRDS(here::here("data-raw/df_omnipaque.rds"))
cfs_base10yr   <- readRDS(here::here("data-raw/cfs_base10yr.rds"))
cfs_full10yr   <- readRDS(here::here("data-raw/cfs_full10yr.rds"))
cfs_base30yr   <- readRDS(here::here("data-raw/cfs_base30yr.rds"))
cfs_full30yr   <- readRDS(here::here("data-raw/cfs_full30yr.rds"))
df_sdi         <- readRDS(here::here("data-raw/df_sdi.rds"))
df_FAS_Q       <- readRDS(here::here("data-raw/df_FAS_Q.rds"))
df_coeff_ascvd <- readRDS(here::here("data-raw/df_coeff_ascvd.rds"))


## ├ SAVE internal objects ----
usethis::use_data(df_omnipaque,
                  cfs_base10yr, cfs_full10yr, 
                  cfs_base30yr, cfs_full30yr, 
                  df_coeff_ascvd,
                  df_sdi, 
                  df_FAS_Q,
                  internal = TRUE,
                  overwrite = TRUE)