# Init --------------------------------------------------------------------

Sys.setenv(LANG = "en")
options(warn = 2)
options(warn = 1)
options(stringsAsFactors = FALSE)
options(width = 85)
options(tidyselect_verbosity = "verbose")

library(survival)
library(dplyr)
# mtcars2 %>% map(class)

mtcars3 = mtcars2 %>%
    transmute(
        x_char = am %>% set_label("Character "),
        x_char_na = vs %>% set_label("Character with NA"),
        x_fact_num = cyl %>% set_label("Factor of numbers"),
        x_fact_char = as.factor(vs) %>% set_label("Factor of characters"),
        x_fact_miss = factor(vs, levels=c("straight", "vshaped", "other"),
                             labels=c("straight", "vshaped", "other")) %>%
            set_label("Factor of characters, one extra level"),
        x_num = mpg %>% set_label("Numeric 1"),
        x_num2 = disp %>% set_label("Numeric 2"),
        x_num_na = drat %>% set_label("Numeric with NA"),
        x_date = as.Date(hp , origin="2010-01-01") %>% set_label("Date"),
        x_posix = as.POSIXct(qsec*3600*24 , origin="2010-01-01") %>% set_label("Date+time"),
        dummy = "dummy" %>% set_label("Character Dummy"),
        dummy_na = "dummy" %>% set_label("Character Dummy with NA"),
        surv = Surv(disp, am=="manual") %>% set_label("Survival"),
        surv_na = surv,
        test = rbinom(nrow(.), 1, 0.5) %>% factor(labels = c("A","B"))
    )
mtcars3$x_char_na[1:5] = NA
mtcars3$x_num_na[5:12] = NA
mtcars3$dummy_na[7:15] = NA
mtcars3$surv_na[20:25] = NA


do_save=FALSE
# do_save=TRUE




# Unique checks -----------------------------------------------------------
test_that("Auto-testing is bad and you should feel bad.", {
    expect_warning(crosstable(mtcars2, disp, by=vs, funs=mean, test=T),
                   "Be aware that automatic global testing should only be done in an exploratory context, as it would cause extensive alpha inflation otherwise.")
})



# Labelling ---------------------------------------------------------------
test_that("Labelling", {
    iris_label = tibble::tibble(
        name=c("Sepal.Length", "Sepal.Width",
               "Petal.Length", "Petal.Width", "Species"),
        label=c("Length of Sepals", "Width of Sepals",
                "Length of Petals", "Width of Petals", "Specie name")
    )
    x=import_labels(iris, iris_label)
    expect_equivalent(
        x %>% map(attributes) %>% map("label") %>% unlist,
        iris_label$label
    )
})



# By numeric --------------------------------------------------
test_that("numeric by numeric", {
    x1=crosstable(mtcars3, where(is.numeric.and.not.surv), by=x_num2)
    expect_equal(dim(x1), c(2,4))
    expect_equal(sum(is.na(x1)), 0)

    x2=crosstable(mtcars3, where(is.numeric.and.not.surv), by=x_num2, test=T)
    expect_equal(dim(x2), c(2,5))
    expect_equal(sum(is.na(x2)), 0)

    crosstable(mtcars3, by=x_num2, cor_method = "kendall") #FIXME bug
    crosstable(mtcars3, where(is.numeric.and.not.surv), by=x_num2, cor_method = "pearson")
    crosstable(mtcars3, where(is.numeric.and.not.surv), by=x_num2, cor_method = "kendall")
    crosstable(mtcars3, where(is.numeric.and.not.surv), by=x_num2, cor_method = "spearman")

    expect_warning(crosstable(mtcars2, disp, by=hp, funs=mean),
                   "`funs`  and `funs_arg` arguments will not be used if `by` is numeric.")

})

test_that("numeric+factor by numeric: ", {
    x1 = expect_warning(crosstable(mtcars3, by=x_num), "Cannot cross columns .*")
    expect_equal(dim(x1), c(2,4))
    expect_equal(sum(is.na(x1)), 0)
    x2 = expect_warning(crosstable(mtcars3, by=x_num_na), "Cannot cross columns .*")
    expect_equal(dim(x1), c(2,4))
    expect_equal(sum(is.na(x1)), 0)
})


# By numeric=factor minimum of unique levels  ---------------------------------------
test_that("by factor if numeric <= 3 levels", {
    x10=crosstable(mtcars, cyl, by=vs, total="both", margin="all")
    x10 %>% as_flextable
    expect_identical(x10[3,4], "14 (43.75% / 100.00% / 77.78%)")
    expect_equal(dim(x10), c(4,6))
    expect_equal(sum(is.na(x10)), 0)
})


# By Nothing ---------------------------------------------------------
test_that("numeric+factor+surv by nothing", {
    x1=crosstable(mtcars3, c(am,mpg,cyl,surv))
    x1 %>% as_flextable
    expect_equal(dim(x1), c(38,4))
    expect_equal(sum(is.na(x1)), 1)

    x2=crosstable(mtcars3, c(am,mpg,cyl,surv), times=c(0,100,200,400), followup=TRUE)
    x2 %>% as_flextable
    expect_equal(dim(x2), c(16,4))
    expect_equal(sum(is.na(x2)), 1)

    x3=crosstable(mtcars3, x_fact_miss)
    expect_equal(dim(x3), c(3,4))
    expect_equal(sum(is.na(x3)), 0)
})

# By factor: showNA --------------------------------------------------------
test_that("numeric+factor+surv by factor: showNA", {
    #am=character, mpg=numeric, cyl=numeric-factor, vs=character
    x1=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, showNA="no", times=c(0,100,200,400))
    x1 %>% as_flextable
    expect_equal(dim(x1), c(14,5))
    expect_equal(sum(is.na(x1)), 1)

    x2=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, showNA="ifany", times=c(0,100,200,400))
    x2 %>% as_flextable
    expect_equal(dim(x2), c(15,6))
    expect_equal(sum(is.na(x2)), 2)

    x3=crosstable(mtcars2, c(am,mpg,cyl), by=vs, showNA="always")
    x3 %>% as_flextable
    expect_equal(dim(x3), c(11,6))
    expect_equal(sum(is.na(x3)), 0)
})


# By factor: total --------------------------------------------------------
test_that("numeric+factor+surv by factor: total", {
    x4=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, total="row", times=c(0,100,200,400))
    x4 %>% as_flextable
    expect_equal(dim(x4), c(15,7))
    expect_equal(sum(is.na(x4)), 3)

    x5=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, total="col", times=c(0,100,200,400), followup=TRUE)
    x5 %>% as_flextable
    expect_equal(dim(x5), c(18,6))
    expect_equal(sum(is.na(x5)), 2)

    x6=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, total="both", times=c(0,100,200,400), followup=TRUE)
    x6 %>% as_flextable
    expect_equal(dim(x6), c(18,7))
    expect_equal(sum(is.na(x6)), 3)
})


# By factor: margin -------------------------------------------------------
test_that("numeric+factor+surv by factor: margin", {
    x6=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, total="both", times=c(0,100,200,400), followup=TRUE)
    x7=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, total="both", margin="row", times=c(0,100,200,400))
    x7 %>% as_flextable
    expect_identical(x7[1:3,], x6[1:3,])
    expect_identical(x7[4:7,], x6[4:7,])
    expect_identical(x7[8,4], "7 (87.50%)")
    expect_equal(dim(x7), c(17,7))
    expect_equal(sum(is.na(x7)), 3)

    x8=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, total="both", margin="col", times=c(0,100,200,400))
    x8 %>% as_flextable
    expect_false(identical(x8[5:8,], x6[5:8,]))
    expect_identical(x8[8,4], "7 (100.00%)")
    expect_equal(dim(x8), c(17,7))
    expect_equal(sum(is.na(x8)), 3)

    x9=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, total="both", margin="all", times=c(0,100,200,400))
    x9 %>% as_flextable
    expect_false(identical(x9[5:8,], x6[5:8,]))
    expect_identical(x9[8,4], "7 (35.00% / 87.50% / 100.00%)")
    expect_equal(dim(x9), c(17,7))
    expect_equal(sum(is.na(x9)), 3)

    x92=crosstable(mtcars3, c(am,mpg,cyl,surv), by=vs, total="both", margin="none", times=c(0,100,200,400))
    x92 %>% as_flextable
    expect_false(identical(x92[5:8,], x6[5:8,]))
    expect_identical(x92[8,4], "7")
    expect_equal(dim(x92), c(17,7))
    expect_equal(sum(is.na(x92)), 3)

})


# By dummy ---------------------------------------------------------
test_that("By dummy: numeric+factor+surv", {
    x1=crosstable(mtcars3, c(am,mpg,cyl,surv), by=dummy)
    x1 %>% as_flextable
    expect_equal(dim(x1), c(38,4))
    expect_equal(sum(is.na(x1)), 1)

    x2=crosstable(mtcars3, c(am,mpg,cyl,surv), by=dummy2)
    x2 %>% as_flextable
    expect_equal(dim(x2), c(39,5))
    expect_equal(sum(is.na(x2)), 1)
})

test_that("By dummy: right percents", {
    x1=crosstable(mtcars3, x_char)
    x2=crosstable(mtcars3, x_char, by=dummy)
    expect_identical(x1$value, x2$dummy)
})


# Funs arguments --------------------------------------------------
test_that("Funs arguments", {
    name = "test_funs_arg.Rds"
    x = crosstable(mtcars3, c(x_num2, hp, am), by=vs, funs=c(moystd,quantile),
                   funs_arg = list(dig=3, probs=c(0.25,0.75)),
                   total=T, showNA="always")
    x %>% as_flextable()
    if(is_testing()){
        expect_equivalent(x, readRDS(paste0("rds/",name)))
    } else {
        if(do_save)
            saveRDS(x, paste0("tests/testthat/rds/",name), version = 2)
        expect_equivalent(x, readRDS(paste0("tests/testthat/rds/",name)))
        setdiff(readRDS(paste0("tests/testthat/rds/",name)), x)
    }
})



# Statistical Tests --------------------------------------------------

test_that("Statistical Tests", {
    set.seed(0)
    dummy_data = tibble(x_norm=rnorm(50,0,20), x_exp=rexp(50,60), y=rnorm(50,0,20), tmt2=rep(c("A","B"), 25), tmt3=LETTERS[1:3][sample(3,50,replace=TRUE)])

    ##CATEGORIAL

    # Fisher
    x=crosstable(mtcars3, cyl, by=vs, test=T)
    expect_equal(x$test[1], "p value: 0.0001 \n(Fisher's Exact Test for Count Data)")
    # Chi Square
    x=crosstable(iris, I(Species == "versicolor")~I(Species == "setosa"), test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(Pearson's Chi-squared test)")


    ##NUMERIC

    # wilcox (exact=F)
    x=crosstable(mtcars3, x_num2, by=vs, test=T)
    expect_equal(x$test[1], "p value: 0.0002 \n(Wilcoxon rank sum test)")
    # wilcox (exact=T)
    if(package_version(R.version) >= package_version("4.0")) {
        x=crosstable(dummy_data, x_exp, by=tmt2, test=T)
        expect_equal(x$test[1], "p value: 0.4185 \n(Wilcoxon rank sum exact test)")
    } else {
        x=crosstable(dummy_data, x_exp, by=tmt2, test=T)
        expect_equal(x$test[1], "p value: 0.4185 \n(Wilcoxon rank sum test)")
    }
    # t.equalvar
    x = crosstable(mtcars3, mpg, by=vs, test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(Two Sample t-test)")
    # t.unequalvar
    x=crosstable(mtcars3, hp, by=vs, test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(Welch Two Sample t-test)")
    # a.unequalvar
    x=crosstable(mtcars3, mpg, by=cyl, test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(One-way analysis of means (not assuming equal variances))")
    # a.equalvar
    x=crosstable(dummy_data, x_norm, by=tmt3, test=T)
    expect_equal(x$test[1], "p value: 0.1626 \n(One-way analysis of means)")
    # kruskal
    x=crosstable(mtcars3, drat, by=cyl, test=T)
    expect_equal(x$test[1], "p value: 0.0017 \n(Kruskal-Wallis rank sum test)")


    ##CORRELATION

    #Pearson
    x=crosstable(mtcars3, mpg, by=x_num2, cor_method="pearson", test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(Pearson's product-moment correlation)")
    #Kendall normal
    x=crosstable(mtcars3, mpg, by=x_num2, cor_method="kendall", test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(Kendall's rank correlation tau, normal approximation)")
    #Kendall exact
    x=crosstable(iris, Petal.Length, by=Sepal.Length, cor_method="kendall", test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(Kendall's rank correlation tau, exact test)")
    #Spearman normal
    x=crosstable(mtcars3, mpg, by=x_num2, cor_method="spearman", test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(Spearman's rank correlation rho, normal approximation)")
    #Spearman exact
    x=crosstable(dummy_data, x_exp, by=y, cor_method="spearman", test=T)
    expect_equal(x$test[1], "p value: 0.1860 \n(Spearman's rank correlation rho, exact test)")


    ##SURVIVAL

    #Logrank
    x=crosstable(mtcars3, surv, by=am, test=T)
    expect_equal(x$test[1], "p value: <0.0001 \n(Logrank test)")
})


test_that("Linear contrasts test)", {
    my_test_args=crosstable_test_args()
    my_test_args$test_summarize = test_summarize_linear_contrasts
    x=iris %>%
        mutate(Petal.Width.qt = paste0("Q", ntile(Petal.Width, 5)) %>% ordered()) %>%
        crosstable(Petal.Length ~ Petal.Width.qt, test=TRUE, test_args = my_test_args)
    expect_equal(x$test[1], "p value: <0.0001 \n(Contrast test for linear trend)")
    expect_equal(dim(x), c(4,9))
    expect_equal(sum(is.na(x)), 0)
})

test_that("'Test' can be a variable name", {
    x=crosstable(mtcars3, test, by=vs, test=TRUE)
    expect_equal(x$.id[1], "test")
    expect_equal(dim(x), c(2,7))
})




# Effects --------------------------------------------------

test_that("Effects", {
    set.seed(0)
    dummy_data = tibble(x_norm=rnorm(50,0,20), x_exp=rexp(50,60), y=rnorm(50,0,20), tmt2=rep(c("A","B"), 25), tmt3=LETTERS[1:3][sample(3,50,replace=TRUE)])

    .args = crosstable_effect_args()

    ##CATEGORIAL

    #args$effect_tabular = effect_odds_ratio (default)
    x=crosstable(mtcars3, am, by=vs, effect=T, effect_args=.args)
    expect_equal(x$effect[1], "Odds ratio (Wald CI) (auto, vshaped vs straight): 5.25\nCI95%[0.80 to 34.43]")

    .args$effect_tabular = effect_relative_risk
    x=crosstable(mtcars3, am, by=vs, effect=T, effect_args=.args)
    expect_equal(x$effect[1], "Relative risk (Wald CI) (auto, vshaped vs straight): 2.70\nCI95%[0.94 to 15.21]")

    .args$effect_tabular = effect_risk_difference
    x=crosstable(mtcars3, am, by=vs, effect=T, effect_args=.args)
    expect_equal(x$effect[1], "Risk difference (Wald CI) (auto, vshaped minus straight): 165.82\nCI95%[-10.51 to 379.13]")



    ##NUMERIC

    #.args$effect_summarize = diff_mean_auto (default)
    set.seed(1234)
    x=crosstable(mtcars3, x_num2, by=vs, effect=T, effect_args=.args)
    expect_equal(x$effect[1], "Difference in means (bootstrap CI) (straight minus vshaped): -190.34\nCI95%[-260.78 to -119.89]")

    set.seed(1234)
    .args$effect_summarize = diff_mean_boot
    x=crosstable(mtcars3, x_num2, by=vs, effect=T, effect_args=.args)
    expect_equal(x$effect[1], "Difference in means (bootstrap CI) (straight minus vshaped): -190.34\nCI95%[-260.78 to -119.89]")

    set.seed(1234)
    .args$effect_summarize = diff_median
    x=crosstable(mtcars3, x_num2, by=vs, effect=T, effect_args=.args)
    expect_equal(x$effect[1], "Difference in medians (bootstrap CI) (): -208.90\nCI95%[-293.18 to -124.62]")


    ##SURVIVAL
    x=crosstable(mtcars3, surv, by=cyl3, effect=T, effect_args=.args)
    expect_equal(x$effect[1], "Hazard ratio (Wald CI) (NA vs FALSE): 1.54\nCI95%[0.42 to 5.63]")
})




test_that("Effects never fail", {

    set.seed(1234)
    .args = crosstable_effect_args()
    expect_warning({
        x=names(mtcars3) %>% set_names() %>% map(~{
            # print(.x)
            if(!is.Surv(mtcars3[[.x]])) {
                crosstable(mtcars3, by=any_of(.x), effect=T, effect_args=.args)$effect %>%
                    table %>% as.data.frame()
            }
            expect_equal(1,1)
            return(0)
        })
    })

    set.seed(1234)
    .args = crosstable_effect_args()
    .args$effect_summarize = diff_mean_boot
    .args$effect_tabular = effect_relative_risk
    expect_warning({
        x=names(mtcars3) %>% set_names() %>% map(~{
            # print(.x)
            if(!is.Surv(mtcars3[[.x]])) {
                crosstable(mtcars3, by=any_of(.x), effect=T, effect_args=.args)$effect %>%
                    table %>% as.data.frame()
            }
            expect_equal(1,1)
            return(0)
        })
    })

    set.seed(1234)
    .args = crosstable_effect_args()
    .args$effect_summarize = diff_median
    .args$effect_tabular = effect_risk_difference
    expect_warning({
        x=names(mtcars3) %>% set_names() %>% map(~{
            # print(.x)
            if(!is.Surv(mtcars3[[.x]])) {
                crosstable(mtcars3, by=any_of(.x), effect=T, effect_args=.args)$effect %>%
                    table %>% as.data.frame()
            }
            expect_equal(1,1)
            return(0)
        })
    })
})




# Testing everything --------------------------------------------------
test_that("Testing everything", {
    name = "test_everything.rds"
    set.seed(1234)
    x = expect_warning(
        crosstable(mtcars3, x_num2+hp+am+surv~vs, margin="all", total="both",
                   times=c(0,100,200,400), followup=TRUE, funs_arg = list(dig=9),
                   test=T, effect=T),
        "Loglik converged before variable.*")
    x %>% as_flextable()
    if(is_testing()){
        expect_equivalent(x, readRDS(paste0("rds/",name)))
    } else {
        save_name = paste0("tests/testthat/rds/",name)
        if(do_save)
            saveRDS(x, save_name, version = 2)
        expect_equivalent(x, readRDS(save_name))
        setdiff(x, readRDS(save_name))$effect
        setdiff(readRDS(save_name), x)$effect
    }
})





# Warnings and Errors -----------------------------------------------------
test_that("Warnings", {
    #remove factors and survs
    expect_warning(crosstable(mtcars3, x_fact_char, x_fact_num, by=x_num2),
                   "Cannot cross columns 'x_fact_char' \\(factor\\) and 'x_fact_num' \\(factor\\) by column 'x_num2' \\(numeric\\)")
    expect_warning(crosstable(mtcars3, where(is.numeric), by=x_num2, effect=T),
                   "Cannot cross columns 'surv' \\(Surv\\) and 'surv_na' \\(Surv\\) by column 'x_num2' \\(numeric\\)")


    ct_warns = function() { crosstable(mtcars3, by=vs, times=c(0,100,200,400), test=T, effect=T) }

    w = capture_warnings(ct_warns())
    expect_match(w, ".*Loglik converged before variable.*", all = FALSE)
    expect_match(w, "Cannot calculate crosstable effects for variables.*", all = FALSE)
})


test_that("BY class check", {
    expect_error(
        crosstable(mtcars3, by=surv, times=c(0,100,200,400)),
        "Crosstable only supports numeric, logical, character or factor `by` columns.*"
    )
    mtcars3$dummy_posix = as.Date(mtcars3$x_num2, origin="2020-01-01") %>% as.POSIXct
    expect_error(
        crosstable(mtcars3, by=dummy_posix),
        "Crosstable only supports numeric, logical, character or factor `by` columns.*"
    )
})

test_that("Unnamed ellipsis", {
    expect_error(
        crosstable(mtcars3, foo=vs),
        class = "rlib_error_dots_named"
    )
})