test_that("Check position of reference", {
  set.seed(10)
  n <- 500
  ds <- data.frame(
    ftime = rexp(n),
    fstatus = sample(0:1, size = n, replace = TRUE),
    x = factor(sample(LETTERS[1:4], size = n, replace = TRUE)),
    same_label = factor(sample(c("Yes", "No"), size = n, replace = TRUE)),
    same_labell = factor(sample(c("Yes", "No"), size = n, replace = TRUE)),
    same_labelll = factor(sample(c("Yes", "No"), size = n, replace = TRUE)),
    boolean = sample(c(TRUE, FALSE), size = n, replace = TRUE),
    subsetting = factor(sample(c(TRUE, FALSE), size = n, replace = TRUE)),
    stringsAsFactors = TRUE)

  library(survival)
  fit <- coxph(Surv(ftime, fstatus == 1) ~ x + boolean, data = ds)

  a <- printCrudeAndAdjustedModel(fit, add_references = TRUE)
  expect_match(a[1, 2], "ref")

  expect_equivalent(attr(a, "rgroup"),
    c("x", ""),
    info = "Rgroup test"
  )

  tmp <- getCrudeAndAdjustedModelData(fit)
  b <- printCrudeAndAdjustedModel(tmp, add_references = TRUE)

  expect_equivalent(a, b)
  # Getting the name wrong should not change the reference
  a <- printCrudeAndAdjustedModel(fit,
    add_references = TRUE,
    add_references_pos = list(a = 3)
  )
  expect_match(a[1, 2], "ref")

  # This should move the reference
  a <- printCrudeAndAdjustedModel(fit, add_references = TRUE, add_references_pos = list(x = 2))
  expect_match(a[2, 2], "ref")

  # Should end up at first position if referenced outside
  expect_warning(a <- printCrudeAndAdjustedModel(fit, add_references = TRUE, add_references_pos = list(x = 5)))
  expect_match(a[1, 2], "ref")

  # Bug with the same label occurring miultiple times
  complx_fit <- update(fit, . ~ x + boolean +
    same_label +
    same_labell +
    same_labelll)
  a <- printCrudeAndAdjustedModel(complx_fit,
    add_references = TRUE, desc_column = TRUE
  )
  expect_equal(nrow(a), 11)

  a2 <- printCrudeAndAdjustedModel(complx_fit)
  expect_lt(nrow(a2), nrow(a))

  a3 <- printCrudeAndAdjustedModel(complx_fit, desc_column = TRUE)
  expect_equivalent(nrow(a3), nrow(a), info = "When descriptive column is used then references should be added by default")

  a2 <- printCrudeAndAdjustedModel(complx_fit, desc_column = TRUE, order = c("same_label+", "x", "boolean"))
  expect_equivalent(nrow(a2), nrow(a))
  expect_equivalent(tail(rownames(a2), 1), "boolean")
})

test_that("Test rbind", {
  set.seed(10)
  n <- 500
  ds <- data.frame(
    ftime = rexp(n),
    fstatus = sample(0:1, size = n, replace = TRUE),
    x = factor(sample(LETTERS[1:4], size = n, replace = TRUE)),
    same_label = factor(sample(c("Yes", "No"), size = n, replace = TRUE)),
    same_labell = factor(sample(c("Yes", "No"), size = n, replace = TRUE)),
    same_labelll = factor(sample(c("Yes", "No"), size = n, replace = TRUE)),
    boolean = sample(c(TRUE, FALSE), size = n, replace = TRUE),
    subsetting = factor(sample(c(TRUE, FALSE), size = n, replace = TRUE))
  )

  library(survival)
  fit1 <- coxph(Surv(ftime, fstatus == 1) ~ x + boolean, data = ds)
  fit2 <- coxph(Surv(ftime, fstatus == 1) ~ x + same_label, data = ds)

  a1 <- printCrudeAndAdjustedModel(fit1, add_references = TRUE)
  a2 <- printCrudeAndAdjustedModel(fit2, add_references = TRUE)
  a3 <- rbind(a1, a2)
  expect_equivalent(nrow(a3), nrow(a1) + nrow(a2))
  expect_true(is.null(attr(a3, "tspanner")))

  a3 <- rbind(a1 = a1, a2 = a2)
  expect_equivalent(nrow(a3), nrow(a1) + nrow(a2))
  expect_equivalent(attr(a3, "tspanner"), c("a1", "a2"))
  sink(file = ifelse(Sys.info()["sysname"] == "Windows",
    "NUL",
    "/dev/null"
  ))
  expect_true(inherits(print(a3), "htmlTable"))
  sink()
})

test_that("Variable select", {
  set.seed(10)
  n <- 500
  ds <- data.frame(
    y = sample(0:1, size = n, replace = TRUE),
    x1 = factor(sample(LETTERS[1:4], size = n, replace = TRUE)),
    x2 = factor(sample(c("Yes", "No"), size = n, replace = TRUE)),
    x3 = factor(sample(c("Yes", "No"), size = n, replace = TRUE)),
    subsetting = factor(sample(c(TRUE, FALSE), size = n, replace = TRUE))
  )

  library(rms)
  dd <<- datadist(ds)
  options(datadist = "dd")

  fit <- Glm(y ~ x1 + x2 + x3, data = ds, family = binomial)

  a <- printCrudeAndAdjustedModel(fit, order = c("x[12]"), add_references = TRUE)
  expect_equivalent(attr(a, "rgroup"), c("x1", "x2"))

  a <- printCrudeAndAdjustedModel(fit, order = c("x2", "x1"), add_references = TRUE)
  expect_equivalent(attr(a, "rgroup"), c("x2", "x1"))

  fit <- glm(y ~ x1 + x2 + x3, data = ds, family = binomial)

  a <- printCrudeAndAdjustedModel(fit, order = c("x[12]"), add_references = TRUE)
  expect_equivalent(attr(a, "rgroup"), c("x1", "x2"))

  a <- printCrudeAndAdjustedModel(fit, order = c("x2", "x1"), add_references = TRUE)
  expect_equivalent(attr(a, "rgroup"), c("x2", "x1"))
})

test_that("Check statistics", {
  set.seed(10)
  n <- 500
  ds <- data.frame(
    y = sample(0:1, size = n, replace = TRUE),
    x1 = factor(sample(LETTERS[1:4], size = n, replace = TRUE)),
    x2 = rnorm(n),
    subsetting = factor(sample(c(TRUE, FALSE), size = n, replace = TRUE))
  )
  ds$x1[sample(1:nrow(ds), size = 100)] <- NA
  ds$x2[sample(1:nrow(ds), size = 100)] <- NA

  fit <- glm(y ~ x1 + x2, data = ds)
  out <- printCrudeAndAdjustedModel(fit,
    desc_column = TRUE,
    desc_args = caDescribeOpts(digits = 2)
  )
  expect_true(out["A", "Total"] == as.character(sum(ds$x1 == "A", na.rm = TRUE)))
  expect_match(out["x2", "Total"], sprintf("%.2f", mean(ds$x2, na.rm = TRUE)))
  # TODO

  library(rms)
  dd <<- datadist(ds)
  options(datadist = "dd")

  fit <- lrm(y ~ x1 + x2, data = ds)
  out <- printCrudeAndAdjustedModel(fit,
    desc_column = TRUE,
    desc_args = caDescribeOpts(digits = 2)
  )
  expect_true(out["A", "Total"] == as.character(sum(ds$x1 == "A", na.rm = TRUE)))
  expect_match(out["x2", "Total"], sprintf("%.2f", mean(ds$x2, na.rm = TRUE)))
})

test_that("Issue #5", {
  set.seed(1)
  data <- data.frame(
    outcome = rnorm(100),
    sex = sample(c("Male","Female"),100,TRUE),
    country = sample(c("USA","UK","AUS"),100,TRUE),
    stringsAsFactors = TRUE)
  
  fit <- lm(outcome ~ sex + country, data = data)
  out <- printCrudeAndAdjustedModel(fit,desc_column = TRUE)
  expect_equal(as.integer(out["Female","Total"]), 
               sum(data$sex == "Female"))
  expect_equal(as.integer(out["Male","Total"]), 
               sum(data$sex == "Male"))
  data$sex[1] <- NA
  out <- printCrudeAndAdjustedModel(fit, desc_column = TRUE)
  expect_equal(
    as.integer(out["Female", "Total"]),
    sum(data$sex == "Female", na.rm = TRUE)
  )
  expect_equal(
    as.integer(out["Male", "Total"]),
    sum(data$sex == "Male", na.rm = TRUE)
  )
})

test_that("Subsetting and bindings", {
  set.seed(10)
  n <- 500
  ds <- data.frame(
    y = sample(0:1, size = n, replace = TRUE),
    x1 = factor(sample(LETTERS[1:4], size = n, replace = TRUE)),
    x2 = rnorm(n),
    subsetting = factor(sample(c(TRUE, FALSE), size = n, replace = TRUE))
  )
  ds$x1[sample(1:nrow(ds), size = 100)] <- NA
  ds$x2[sample(1:nrow(ds), size = 100)] <- NA

  fit <- glm(y ~ x1 + x2, data = ds)
  out <- printCrudeAndAdjustedModel(fit,
    desc_column = TRUE,
    desc_args = caDescribeOpts(digits = 2)
  )
  expect_equivalent(
    dim(out[1, 1:2]),
    c(1, 2)
  )

  expect_equivalent(
    dim(out[, 4:5]),
    c(nrow(out), 2)
  )

  expect_equivalent(
    dim(out[3:4, ]),
    c(2, ncol(out))
  )

  ret <- cbind(out[, 2:5], out[, 1])
  expect_equal(ncol(ret), 5)

  ret <- cbind(NULL, out[, 1:2])
  expect_equal(ret, out[, 1:2])
})


test_that("Errors for printCrudeAndJust", {
  set.seed(10)
  n <- 500
  ds <- data.frame(
    y = sample(0:1, size = n, replace = TRUE),
    x1 = factor(sample(LETTERS[1:4], size = n, replace = TRUE)),
    x2 = rnorm(n),
    subsetting = factor(sample(c(TRUE, FALSE), size = n, replace = TRUE))
  )
  ds$x1[sample(1:nrow(ds), size = 100)] <- NA
  ds$x2[sample(1:nrow(ds), size = 100)] <- NA

  fit <- glm(y ~ x1 + x2, data = ds)
  expect_error(printCrudeAndAdjustedModel(NULL,
    desc_column = TRUE,
    desc_args = caDescribeOpts(digits = 2)
  ))
  expect_error(printCrudeAndAdjustedModel(NULL,
    desc_column = TRUE,
    desc_args = "wrong argument"
  ))
})