tikatuwq: basic workflow

library(tikatuwq)

# Try to make wq_demo available even when the package
# cannot be temporarily reinstalled during pkgdown build.
ok <- FALSE
try({
  utils::data("wq_demo", package = "tikatuwq", envir = environment())
  ok <- exists("wq_demo", inherits = FALSE)
}, silent = TRUE)

if (!ok) {
  # Fallback: read example CSV shipped with the package
  csv <- system.file("extdata", "exemplo_chamagunga.csv", package = "tikatuwq")
  if (nzchar(csv)) {
    wq_demo <- read_wq(csv)
    ok <- TRUE
  }
}

if (!ok) {
  # Last resort: small synthetic dataset (schema-compatible)
  set.seed(1)
  wq_demo <- data.frame(
    data = as.Date("2025-01-01") + 0:9,
    rio = "Demo",
    ponto = paste0("P", seq_len(10)),
    turbidez = runif(10, 1, 50),
    od = runif(10, 5, 9),
    pH = runif(10, 6.5, 8.5),
    temperatura = runif(10, 20, 28),
    condutividade = runif(10, 50, 300),
    stringsAsFactors = FALSE
  )
}

# Coerce common numeric columns defensively (some example files may carry strings)
num_candidates <- c(
  "turbidez","od","pH","temperatura","condutividade",
  "dbo","dbo5","nitrato","fosforo","amonia",
  "coliformes","coliformes_totais","coliformes_termotolerantes"
)
for (nm in intersect(num_candidates, names(wq_demo))) {
  suppressWarnings({ wq_demo[[nm]] <- as.numeric(wq_demo[[nm]]) })
}

head(wq_demo)
#> # A tibble: 6 × 14
#>   rio    ponto data          ph    od turbidez   dbo coliformes p_total nt_total
#>   <chr>  <chr> <date>     <dbl> <dbl>    <dbl> <dbl>      <dbl>   <dbl>    <dbl>
#> 1 BURAN… FBS-… 2020-03-03  6.52  6.03     63.5     3        7.9    0.02      1  
#> 2 BURAN… FBS-… 2022-03-10  6.97  6.52     18.3     3        9.2    0.04      1  
#> 3 BURAN… FBS-… 2023-05-24  8.19  6.13      9       4        7.9    0.02      1  
#> 4 BURAN… FBS-… 2024-02-20  7.55  5.96    112       3        1.7    0.08      1.9
#> 5 BURAN… FBS-… 2024-09-04  7.8   7.58      5.8     3        2.3    0.02      1  
#> 6 BURAN… FBS-… 2020-03-04  6.39  6.7      11.4     7        1.3    0.03      1  
#> # ℹ 4 more variables: temperatura <dbl>, tds <dbl>, lat <dbl>, lon <dbl>

ok_iqa <- TRUE
df <- tryCatch({
  wq_demo |>
    validate_wq() |>
    iqa(na_rm = TRUE)
}, error = function(e) {
  ok_iqa <<- FALSE
  message("iqa() failed in vignette build: ", conditionMessage(e))
  wq_demo |>
    validate_wq()
})

conf <- tryCatch({
  conama_check(df, classe = "2")
}, error = function(e) {
  message("conama_check() failed in vignette build: ", conditionMessage(e))
  NULL
})

if (!is.null(conf)) head(conf)
#> # A tibble: 6 × 46
#>   rio    ponto data          ph    od turbidez   dbo coliformes p_total nt_total
#>   <chr>  <chr> <date>     <dbl> <dbl>    <dbl> <dbl>      <dbl>   <dbl>    <dbl>
#> 1 BURAN… FBS-… 2020-03-03  6.52  6.03     63.5     3        7.9    0.02      1  
#> 2 BURAN… FBS-… 2022-03-10  6.97  6.52     18.3     3        9.2    0.04      1  
#> 3 BURAN… FBS-… 2023-05-24  8.19  6.13      9       4        7.9    0.02      1  
#> 4 BURAN… FBS-… 2024-02-20  7.55  5.96    112       3        1.7    0.08      1.9
#> 5 BURAN… FBS-… 2024-09-04  7.8   7.58      5.8     3        2.3    0.02      1  
#> 6 BURAN… FBS-… 2020-03-04  6.39  6.7      11.4     7        1.3    0.03      1  
#> # ℹ 36 more variables: temperatura <dbl>, tds <dbl>, lat <dbl>, lon <dbl>,
#> #   IQA <dbl>, IQA_status <ord>, ph_ok <lgl>, ph_status <chr>,
#> #   ph__lim_min <dbl>, ph__lim_max <dbl>, ph__delta <dbl>, od_ok <lgl>,
#> #   od_status <chr>, od__lim_min <dbl>, od__lim_max <dbl>, od__delta <dbl>,
#> #   turbidez_ok <lgl>, turbidez_status <chr>, turbidez__lim_min <dbl>,
#> #   turbidez__lim_max <dbl>, turbidez__delta <dbl>, dbo_ok <lgl>,
#> #   dbo_status <chr>, dbo__lim_min <dbl>, dbo__lim_max <dbl>, …

if (isTRUE(ok_iqa)) {
  plot_iqa(df)
}

conf_long <- tryCatch(conama_summary(df, classe = "2"), error = function(e) NULL)
if (!is.null(conf_long)) head(conf_long)
#> # A tibble: 6 × 7
#>   parametro valor lim_min lim_max status ok    delta
#>   <chr>     <dbl>   <dbl>   <dbl> <chr>  <lgl> <dbl>
#> 1 ph         6.52       6       9 ok     TRUE      0
#> 2 ph         6.97       6       9 ok     TRUE      0
#> 3 ph         8.19       6       9 ok     TRUE      0
#> 4 ph         7.55       6       9 ok     TRUE      0
#> 5 ph         7.8        6       9 ok     TRUE      0
#> 6 ph         6.39       6       9 ok     TRUE      0

# Avoid running nested rendering during vignette build on CRAN
out_file <- render_report(df)
out_file

if (eval_interactive) {
  # Minimal example dataset with coordinates
  df_map <- data.frame(
    rio = c("Buranhem","Chamagunga"),
    ponto = c("P1","P2"),
    data = as.Date(c("2025-09-20","2025-09-21")),
    latitude  = c(-16.435, -16.498),
    longitude = c(-39.062, -39.080),
    iqa = c(72, 58)
  )

  plot_map(
    df_map,
    popup_cols = c("rio","ponto","data","iqa"),
    color_by = "iqa"
  )
}

if (eval_interactive) {
  plot_map(
    df_map,
    popup_cols = c("rio","ponto","data","iqa"),
    cluster = FALSE,
    tiles = "CartoDB.Positron"
  )
}