tests/testthat/test_stats.R
In shane-kercheval/rtools: Package providing R tools and wrappers
context('Stats')
library(testthat)
library(dplyr)
options(dplyr.summarise.inform=F)
library(ggplot2)
source('test_helpers.R')
test_that("rt_geometric_mean", {
values <- c(3, 8, 10, 17, 24, 27)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0.0001),
11.75802, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=FALSE, add_subtract=0),
11.75906, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
11.75906, tolerance=1e-6)
values <- c(3, 8, 10, 17, 24, 27, 100, 1000)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0.0001),
26.77564, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=FALSE, add_subtract=0.0001),
26.77564, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
26.77808, tolerance=1e-6)
values <- c(3, 8, 10, 17, 24, 27, 100, 1000, 0)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0.0001),
6.678098, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
-Inf, tolerance=1e-6)
values <- c(3, 8, 10, 17, 24, 27, 100, 1000, 0.0000001)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
3.099984, tolerance=1e-6)
values <- c(3, 8, 10, 17, 24, 27, 100, 1000, 0, NA)
expect_true(is.na(rt_geometric_mean(values=values, na.rm=FALSE, add_subtract=0.0001)))
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0.0001),
6.678098, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
-Inf, tolerance=1e-6)
})
test_that('rt_regression_build_formula', {
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A'))
expect_equal(reg_formula, "`dependent_var` ~ `A`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A', 'B'))
expect_equal(reg_formula, "`dependent_var` ~ `A` + `B`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A', 'B', 'C'))
expect_equal(reg_formula, "`dependent_var` ~ `A` + `B` + `C`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A', 'B', 'C'),
interaction_variables = list(c('C', 'D')))
expect_equal(reg_formula, "`dependent_var` ~ `C`*`D` + `A` + `B` + `C`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A', 'B', 'C'),
interaction_variables = list(c('C', 'D'), c('E', 'F')))
expect_equal(reg_formula, "`dependent_var` ~ `C`*`D` + `E`*`F` + `A` + `B` + `C`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
#independent_variables = c('A', 'B', 'C'),
interaction_variables = list(c('C', 'D'), c('E', 'F')))
expect_equal(reg_formula, "`dependent_var` ~ `C`*`D` + `E`*`F`")
})
compare_models <- function(actual_model, expected_model){
expect_true(all(actual_model$coefficients == expected_model$coefficients))
expect_true(all(actual_model$residuals == expected_model$residuals))
expect_true(all(actual_model$effects == expected_model$effects))
expect_true(all(actual_model$rank == expected_model$rank))
expect_true(all(actual_model$fitted.values == expected_model$fitted.values))
expect_true(all(actual_model$terms == expected_model$terms))
expect_true(rt_are_dataframes_equal(actual_model$model, expected_model$model))
}
save_lm_summary <- function(model, file_name) {
sink(file_name)
print(summary(model))
sink()
}
test_that('rt_regression', {
data('mtcars')
reg_data <- mtcars
dependent_variable = 'mpg'
independent_variables = c('cyl', 'hp', 'wt')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(mpg ~ cyl + hp + wt, mtcars))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__mtcars__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_actual_vs_predicted__mtcars.png',
plot=rt_regression_plot_actual_vs_predicted(result$model))
test_save_plot(file_name='data/rt_regression_plot_residual_vs_predicted__mtcars.png',
plot=rt_regression_plot_residual_vs_predicted(result$model))
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__mtcars__wt.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'wt', reg_data_orignal))
reg_data <- diamonds
dependent_variable = 'price'
independent_variables = c('carat', 'cut', 'color', 'clarity')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(price ~ carat + cut + color + clarity, diamonds))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__diamonds__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__diamonds__cut.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'cut', reg_data_orignal))
reg_data <- diamonds
dependent_variable = 'price'
independent_variables = c('carat', 'cut', 'color', 'clarity')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(price ~ carat + cut + color + clarity, diamonds))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__diamonds__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__diamonds__cut.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'cut', reg_data_orignal))
# TODO: test when model has NAs, rt_regression_plot_residual_vs_variable for example won't work
# because the $model (i.e. values used) has NAs removed. so I think I need to do "complete.cases"
})
test_that('rt_regression - column names', {
data('mtcars')
reg_data <- mtcars
colnames(reg_data) <- test_helper__column_names(reg_data)
dependent_variable = 'Mpg Col'
independent_variables = c('Cyl Col', 'Hp Col', 'Wt Col')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(`Mpg Col` ~ `Cyl Col` + `Hp Col` + `Wt Col`, reg_data))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__mtcars__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_actual_vs_predicted__mtcars.png',
plot=rt_regression_plot_actual_vs_predicted(result$model))
test_save_plot(file_name='data/rt_regression_plot_residual_vs_predicted__mtcars.png',
plot=rt_regression_plot_residual_vs_predicted(result$model))
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__mtcars__wt.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'Wt Col', reg_data_orignal))
reg_data <- diamonds
colnames(reg_data) <- test_helper__column_names(reg_data)
dependent_variable = 'Price Col'
independent_variables = c('Carat Col', 'Cut Col', 'Color Col', 'Clarity Col')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(`Price Col` ~ `Carat Col` + `Cut Col` + `Color Col` + `Clarity Col`, reg_data))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__diamonds__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__diamonds__cut.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'Cut Col', reg_data_orignal))
reg_data <- diamonds
colnames(reg_data) <- test_helper__column_names(reg_data)
dependent_variable = 'Price Col'
independent_variables = c('Carat Col', 'Cut Col', 'Color Col', 'Clarity Col')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(`Price Col` ~ `Carat Col` + `Cut Col` + `Color Col` + `Clarity Col`, reg_data))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__diamonds__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__diamonds__cut.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'Cut Col', reg_data_orignal))
# TODO: test when model has NAs, rt_regression_plot_residual_vs_variable for example won't work
# because the $model (i.e. values used) has NAs removed. so I think I need to do "complete.cases"
})
test_that('rt_plot_regression_variance_explained', {
data('mtcars')
regression_results <- lm(mpg ~ cyl + hp + wt, data=mtcars)
#summary(regression_results)
test_save_plot(file_name='data/rt_plot_regression_variance_explained.png',
rt_plot_regression_variance_explained(regression_results))
})
test_that('rt_plot_proportions', {
numerators <- c(197, 135)
denominators <- c(14600, 7700)
categories <- c('Old', 'New')
test_save_plot(file_name='data/rt_plot_proportions__2a.png',
rt_plot_proportions(numerators, denominators, categories))
numerators <- c(110, 1000)
denominators <- c(1000, 10000)
categories <- c('A', 'B')
test_save_plot(file_name='data/rt_plot_proportions__2b.png',
rt_plot_proportions(numerators, denominators, categories))
set.seed(42)
numerators <- rbinom(20, 500, 0.5)
set.seed(42)
denominators <- round(rnorm(20, 500, 5))
set.seed(42)
numerators <- c(numerators, rbinom(20, 100, 0.5))
set.seed(42)
denominators <- c(denominators, round(rnorm(20, 100, 5)))
categories <- paste('category', 1:40)
test_save_plot(file_name='data/rt_plot_proportions__many.png',
rt_plot_proportions(numerators, denominators, categories,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
test_save_plot(file_name='data/rt_plot_proportions__many_no_confidence_values.png',
rt_plot_proportions(numerators, denominators, categories,
show_confidence_values=FALSE,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
test_save_plot(file_name='data/rt_plot_proportions__many__90_conf.png',
rt_plot_proportions(numerators, denominators, categories,
confidence_level = 0.90,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
})
test_that('rt_plot_funnel:axes_flip', {
numerators <- c(197, 135)
denominators <- c(14600, 7700)
categories <- c('Old', 'New')
test_save_plot(file_name='data/rt_plot_proportions__2a__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE))
test_save_plot(file_name='data/rt_plot_proportions__2a__flip__axis_limits.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE,
axis_limits = c(0, 0.1)))
numerators <- c(110, 1000)
denominators <- c(1000, 10000)
categories <- c('A', 'B')
test_save_plot(file_name='data/rt_plot_proportions__2b__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE))
set.seed(42)
numerators <- rbinom(20, 500, 0.5)
set.seed(42)
denominators <- round(rnorm(20, 500, 5))
set.seed(42)
numerators <- c(numerators, rbinom(20, 100, 0.5))
set.seed(42)
denominators <- c(denominators, round(rnorm(20, 100, 5)))
categories <- paste('category', 1:40)
test_save_plot(file_name='data/rt_plot_proportions__many__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
test_save_plot(file_name='data/rt_plot_proportions__many_no_confidence_values__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE,
show_confidence_values=FALSE,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
test_save_plot(file_name='data/rt_plot_proportions__many__90_conf__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE,
confidence_level = 0.90,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
})
test_that('rt_plot_2_proportions_test', {
prop_1 = c(197, 14600)
prop_2 = c(135, 7700)
# prop_test_results <- prop.test(c(197, 135), c(14600, 7700))
# prop_test_results$p.value
categories <- c('Old', 'New')
test_save_plot(file_name='data/rt_plot_2_proportions_test.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
#confidence_level=0.95,
axes_flip = FALSE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__90_conf.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
confidence_level=0.90,
axes_flip = FALSE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__title_caption.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
confidence_level=0.90,
axes_flip = FALSE,
title="My Title",
caption="My caption"
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__not_stat_sig.png',
rt_plot_2_proportions_test(c(15, 100), c(20, 100), categories,
confidence_level=0.95,
axes_flip = FALSE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__flip.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
#confidence_level=0.95,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__90_conf__flip.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
confidence_level=0.90,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__title_caption__flip.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
confidence_level=0.90,
axes_flip = TRUE,
title="My Title",
caption="My caption"
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__not_stat_sig__flip.png',
rt_plot_2_proportions_test(c(15, 100), c(20, 100), categories,
confidence_level=0.95,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
# should NOT be stat sig at .95 conf-level
#prop.test(c(15, 26), c(100, 100))
categories <- c("Category A", "Category B")
test_save_plot(file_name='data/rt_plot_2_proportions_test__check_stat_sig__false.png',
rt_plot_2_proportions_test(c(15, 100), c(26, 100), categories,
confidence_level=0.95,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
# should be stat sig at .90 conf-level
test_save_plot(file_name='data/rt_plot_2_proportions_test__check_stat_sig__true.png',
rt_plot_2_proportions_test(c(15, 100), c(26, 100), categories,
confidence_level=0.90,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__small_proportions.png',
rt_plot_2_proportions_test(c(531, 492130),
c(540, 489650),
c('A', 'B'),
confidence_level = 0.90)
)
test_save_plot(file_name='data/rt_plot_2_proportions_test__small_proportions2.png',
rt_plot_2_proportions_test(c(531, 49213),
c(540, 48965),
c('A', 'B'),
confidence_level = 0.90)
)
test_save_plot(file_name='data/rt_plot_2_proportions_test__small_proportions3.png',
rt_plot_2_proportions_test(c(531, 4921),
c(540, 4896),
c('A', 'B'),
confidence_level = 0.90)
)
})
test_that('rt_plot_multinom_cis', {
credit_data <- read.csv("data/credit.csv", header=TRUE)
custom_levels <- c('< 0 DM', '1 - 200 DM', '> 200 DM', 'unknown')
credit_data$checking_balance <- factor(credit_data$checking_balance, levels=custom_levels)
# make sure it handles NAs
credit_data[1, 'checking_balance'] <- NA
credit_data[2, 'default'] <- NA
variable <- 'checking_balance'
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=NULL,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__simple.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=NULL,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
simple_mode=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
# simple shouldn't do anything when using groups
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__simple.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
simple_mode=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__within.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=TRUE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__within__flip.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
credit_data$checking_balance <- factor(credit_data$checking_balance,
levels=c("< 0 DM", "1 - 200 DM", "> 200 DM", "unknown"),
ordered=TRUE)
# change the order of the secondary/comparison variable
credit_data$default <- factor(credit_data$default,
levels=c("yes", "no"),
ordered=TRUE)
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac__within.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=TRUE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac__within__flip.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
})
test_that('rt_plot_multinom_cis__facet', {
credit_data <- read.csv("data/credit.csv", header=TRUE)
custom_levels <- c('< 0 DM', '1 - 200 DM', '> 200 DM', 'unknown')
credit_data$checking_balance <- factor(credit_data$checking_balance, levels=custom_levels)
# make sure it handles NAs
credit_data[1, 'checking_balance'] <- NA
credit_data[2, 'default'] <- NA
variable <- 'checking_balance'
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=NULL,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__facet__simple.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=NULL,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
simple_mode=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
# simple should not do anything
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__facet__simple.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
simple_mode=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__within__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=TRUE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__within__flip__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
credit_data$checking_balance <- factor(credit_data$checking_balance,
levels=c("< 0 DM", "1 - 200 DM", "> 200 DM", "unknown"),
ordered=TRUE)
# change the order of the secondary/comparison variable
credit_data$default <- factor(credit_data$default,
levels=c("yes", "no"),
ordered=TRUE)
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8,10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac__within__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=TRUE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__default_fac__within__flip__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
})
shane-kercheval/rtools documentation built on July 7, 2022, 8:31 a.m.
R Package Documentation
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context('Stats')
library(testthat)
library(dplyr)
options(dplyr.summarise.inform=F)
library(ggplot2)
source('test_helpers.R')
test_that("rt_geometric_mean", {
values <- c(3, 8, 10, 17, 24, 27)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0.0001),
11.75802, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=FALSE, add_subtract=0),
11.75906, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
11.75906, tolerance=1e-6)
values <- c(3, 8, 10, 17, 24, 27, 100, 1000)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0.0001),
26.77564, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=FALSE, add_subtract=0.0001),
26.77564, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
26.77808, tolerance=1e-6)
values <- c(3, 8, 10, 17, 24, 27, 100, 1000, 0)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0.0001),
6.678098, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
-Inf, tolerance=1e-6)
values <- c(3, 8, 10, 17, 24, 27, 100, 1000, 0.0000001)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
3.099984, tolerance=1e-6)
values <- c(3, 8, 10, 17, 24, 27, 100, 1000, 0, NA)
expect_true(is.na(rt_geometric_mean(values=values, na.rm=FALSE, add_subtract=0.0001)))
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0.0001),
6.678098, tolerance=1e-6)
expect_equal(rt_geometric_mean(values=values, na.rm=TRUE, add_subtract=0),
-Inf, tolerance=1e-6)
})
test_that('rt_regression_build_formula', {
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A'))
expect_equal(reg_formula, "`dependent_var` ~ `A`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A', 'B'))
expect_equal(reg_formula, "`dependent_var` ~ `A` + `B`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A', 'B', 'C'))
expect_equal(reg_formula, "`dependent_var` ~ `A` + `B` + `C`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A', 'B', 'C'),
interaction_variables = list(c('C', 'D')))
expect_equal(reg_formula, "`dependent_var` ~ `C`*`D` + `A` + `B` + `C`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
independent_variables = c('A', 'B', 'C'),
interaction_variables = list(c('C', 'D'), c('E', 'F')))
expect_equal(reg_formula, "`dependent_var` ~ `C`*`D` + `E`*`F` + `A` + `B` + `C`")
reg_formula <- rt_regression_build_formula(dependent_variable = 'dependent_var',
#independent_variables = c('A', 'B', 'C'),
interaction_variables = list(c('C', 'D'), c('E', 'F')))
expect_equal(reg_formula, "`dependent_var` ~ `C`*`D` + `E`*`F`")
})
compare_models <- function(actual_model, expected_model){
expect_true(all(actual_model$coefficients == expected_model$coefficients))
expect_true(all(actual_model$residuals == expected_model$residuals))
expect_true(all(actual_model$effects == expected_model$effects))
expect_true(all(actual_model$rank == expected_model$rank))
expect_true(all(actual_model$fitted.values == expected_model$fitted.values))
expect_true(all(actual_model$terms == expected_model$terms))
expect_true(rt_are_dataframes_equal(actual_model$model, expected_model$model))
}
save_lm_summary <- function(model, file_name) {
sink(file_name)
print(summary(model))
sink()
}
test_that('rt_regression', {
data('mtcars')
reg_data <- mtcars
dependent_variable = 'mpg'
independent_variables = c('cyl', 'hp', 'wt')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(mpg ~ cyl + hp + wt, mtcars))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__mtcars__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_actual_vs_predicted__mtcars.png',
plot=rt_regression_plot_actual_vs_predicted(result$model))
test_save_plot(file_name='data/rt_regression_plot_residual_vs_predicted__mtcars.png',
plot=rt_regression_plot_residual_vs_predicted(result$model))
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__mtcars__wt.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'wt', reg_data_orignal))
reg_data <- diamonds
dependent_variable = 'price'
independent_variables = c('carat', 'cut', 'color', 'clarity')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(price ~ carat + cut + color + clarity, diamonds))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__diamonds__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__diamonds__cut.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'cut', reg_data_orignal))
reg_data <- diamonds
dependent_variable = 'price'
independent_variables = c('carat', 'cut', 'color', 'clarity')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(price ~ carat + cut + color + clarity, diamonds))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__diamonds__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__diamonds__cut.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'cut', reg_data_orignal))
# TODO: test when model has NAs, rt_regression_plot_residual_vs_variable for example won't work
# because the $model (i.e. values used) has NAs removed. so I think I need to do "complete.cases"
})
test_that('rt_regression - column names', {
data('mtcars')
reg_data <- mtcars
colnames(reg_data) <- test_helper__column_names(reg_data)
dependent_variable = 'Mpg Col'
independent_variables = c('Cyl Col', 'Hp Col', 'Wt Col')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(`Mpg Col` ~ `Cyl Col` + `Hp Col` + `Wt Col`, reg_data))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__mtcars__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_actual_vs_predicted__mtcars.png',
plot=rt_regression_plot_actual_vs_predicted(result$model))
test_save_plot(file_name='data/rt_regression_plot_residual_vs_predicted__mtcars.png',
plot=rt_regression_plot_residual_vs_predicted(result$model))
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__mtcars__wt.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'Wt Col', reg_data_orignal))
reg_data <- diamonds
colnames(reg_data) <- test_helper__column_names(reg_data)
dependent_variable = 'Price Col'
independent_variables = c('Carat Col', 'Cut Col', 'Color Col', 'Clarity Col')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(`Price Col` ~ `Carat Col` + `Cut Col` + `Color Col` + `Clarity Col`, reg_data))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__diamonds__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__diamonds__cut.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'Cut Col', reg_data_orignal))
reg_data <- diamonds
colnames(reg_data) <- test_helper__column_names(reg_data)
dependent_variable = 'Price Col'
independent_variables = c('Carat Col', 'Cut Col', 'Color Col', 'Clarity Col')
reg_data_orignal <- reg_data %>% rt_select_all_of(c(dependent_variable, independent_variables))
expected_formula <- rt_regression_build_formula(dependent_variable = dependent_variable,
independent_variables = independent_variables)
result <- rt_regression(dataset = reg_data,
dependent_variable = dependent_variable,
independent_variables = independent_variables)
expect_equal(length(result$rows_excluded), 0)
compare_models(actual_model=result$model,
expected_model=lm(`Price Col` ~ `Carat Col` + `Cut Col` + `Color Col` + `Clarity Col`, reg_data))
expect_equal(result$formula, expected_formula)
expect_equal(result$type, "Linear Regression")
expect_true(setequal(independent_variables,
rt_regression_get_ind_var_options(result$model,
dependent_variable,
independent_variables)))
result$model %>% save_lm_summary("data/rt_regression__diamonds__summary_1.txt")
test_save_plot(file_name='data/rt_regression_plot_residual_vs_variable__diamonds__cut.png',
plot=rt_regression_plot_residual_vs_variable(result$model, 'Cut Col', reg_data_orignal))
# TODO: test when model has NAs, rt_regression_plot_residual_vs_variable for example won't work
# because the $model (i.e. values used) has NAs removed. so I think I need to do "complete.cases"
})
test_that('rt_plot_regression_variance_explained', {
data('mtcars')
regression_results <- lm(mpg ~ cyl + hp + wt, data=mtcars)
#summary(regression_results)
test_save_plot(file_name='data/rt_plot_regression_variance_explained.png',
rt_plot_regression_variance_explained(regression_results))
})
test_that('rt_plot_proportions', {
numerators <- c(197, 135)
denominators <- c(14600, 7700)
categories <- c('Old', 'New')
test_save_plot(file_name='data/rt_plot_proportions__2a.png',
rt_plot_proportions(numerators, denominators, categories))
numerators <- c(110, 1000)
denominators <- c(1000, 10000)
categories <- c('A', 'B')
test_save_plot(file_name='data/rt_plot_proportions__2b.png',
rt_plot_proportions(numerators, denominators, categories))
set.seed(42)
numerators <- rbinom(20, 500, 0.5)
set.seed(42)
denominators <- round(rnorm(20, 500, 5))
set.seed(42)
numerators <- c(numerators, rbinom(20, 100, 0.5))
set.seed(42)
denominators <- c(denominators, round(rnorm(20, 100, 5)))
categories <- paste('category', 1:40)
test_save_plot(file_name='data/rt_plot_proportions__many.png',
rt_plot_proportions(numerators, denominators, categories,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
test_save_plot(file_name='data/rt_plot_proportions__many_no_confidence_values.png',
rt_plot_proportions(numerators, denominators, categories,
show_confidence_values=FALSE,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
test_save_plot(file_name='data/rt_plot_proportions__many__90_conf.png',
rt_plot_proportions(numerators, denominators, categories,
confidence_level = 0.90,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
})
test_that('rt_plot_funnel:axes_flip', {
numerators <- c(197, 135)
denominators <- c(14600, 7700)
categories <- c('Old', 'New')
test_save_plot(file_name='data/rt_plot_proportions__2a__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE))
test_save_plot(file_name='data/rt_plot_proportions__2a__flip__axis_limits.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE,
axis_limits = c(0, 0.1)))
numerators <- c(110, 1000)
denominators <- c(1000, 10000)
categories <- c('A', 'B')
test_save_plot(file_name='data/rt_plot_proportions__2b__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE))
set.seed(42)
numerators <- rbinom(20, 500, 0.5)
set.seed(42)
denominators <- round(rnorm(20, 500, 5))
set.seed(42)
numerators <- c(numerators, rbinom(20, 100, 0.5))
set.seed(42)
denominators <- c(denominators, round(rnorm(20, 100, 5)))
categories <- paste('category', 1:40)
test_save_plot(file_name='data/rt_plot_proportions__many__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
test_save_plot(file_name='data/rt_plot_proportions__many_no_confidence_values__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE,
show_confidence_values=FALSE,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
test_save_plot(file_name='data/rt_plot_proportions__many__90_conf__flip.png',
rt_plot_proportions(numerators, denominators, categories,
axes_flip = TRUE,
confidence_level = 0.90,
text_size=2, x_label = "Categories", y_label = 'Proportions', title="Test"))
})
test_that('rt_plot_2_proportions_test', {
prop_1 = c(197, 14600)
prop_2 = c(135, 7700)
# prop_test_results <- prop.test(c(197, 135), c(14600, 7700))
# prop_test_results$p.value
categories <- c('Old', 'New')
test_save_plot(file_name='data/rt_plot_2_proportions_test.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
#confidence_level=0.95,
axes_flip = FALSE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__90_conf.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
confidence_level=0.90,
axes_flip = FALSE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__title_caption.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
confidence_level=0.90,
axes_flip = FALSE,
title="My Title",
caption="My caption"
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__not_stat_sig.png',
rt_plot_2_proportions_test(c(15, 100), c(20, 100), categories,
confidence_level=0.95,
axes_flip = FALSE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__flip.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
#confidence_level=0.95,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__90_conf__flip.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
confidence_level=0.90,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__title_caption__flip.png',
rt_plot_2_proportions_test(prop_1, prop_2, categories,
confidence_level=0.90,
axes_flip = TRUE,
title="My Title",
caption="My caption"
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__not_stat_sig__flip.png',
rt_plot_2_proportions_test(c(15, 100), c(20, 100), categories,
confidence_level=0.95,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
# should NOT be stat sig at .95 conf-level
#prop.test(c(15, 26), c(100, 100))
categories <- c("Category A", "Category B")
test_save_plot(file_name='data/rt_plot_2_proportions_test__check_stat_sig__false.png',
rt_plot_2_proportions_test(c(15, 100), c(26, 100), categories,
confidence_level=0.95,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
# should be stat sig at .90 conf-level
test_save_plot(file_name='data/rt_plot_2_proportions_test__check_stat_sig__true.png',
rt_plot_2_proportions_test(c(15, 100), c(26, 100), categories,
confidence_level=0.90,
axes_flip = TRUE,
title=NULL,
caption=NULL
))
test_save_plot(file_name='data/rt_plot_2_proportions_test__small_proportions.png',
rt_plot_2_proportions_test(c(531, 492130),
c(540, 489650),
c('A', 'B'),
confidence_level = 0.90)
)
test_save_plot(file_name='data/rt_plot_2_proportions_test__small_proportions2.png',
rt_plot_2_proportions_test(c(531, 49213),
c(540, 48965),
c('A', 'B'),
confidence_level = 0.90)
)
test_save_plot(file_name='data/rt_plot_2_proportions_test__small_proportions3.png',
rt_plot_2_proportions_test(c(531, 4921),
c(540, 4896),
c('A', 'B'),
confidence_level = 0.90)
)
})
test_that('rt_plot_multinom_cis', {
credit_data <- read.csv("data/credit.csv", header=TRUE)
custom_levels <- c('< 0 DM', '1 - 200 DM', '> 200 DM', 'unknown')
credit_data$checking_balance <- factor(credit_data$checking_balance, levels=custom_levels)
# make sure it handles NAs
credit_data[1, 'checking_balance'] <- NA
credit_data[2, 'default'] <- NA
variable <- 'checking_balance'
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=NULL,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__simple.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=NULL,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
simple_mode=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
# simple shouldn't do anything when using groups
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__simple.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
simple_mode=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__within.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=TRUE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__within__flip.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
credit_data$checking_balance <- factor(credit_data$checking_balance,
levels=c("< 0 DM", "1 - 200 DM", "> 200 DM", "unknown"),
ordered=TRUE)
# change the order of the secondary/comparison variable
credit_data$default <- factor(credit_data$default,
levels=c("yes", "no"),
ordered=TRUE)
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac__within.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=TRUE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac__within__flip.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$default,
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"))
})
test_that('rt_plot_multinom_cis__facet', {
credit_data <- read.csv("data/credit.csv", header=TRUE)
custom_levels <- c('< 0 DM', '1 - 200 DM', '> 200 DM', 'unknown')
credit_data$checking_balance <- factor(credit_data$checking_balance, levels=custom_levels)
# make sure it handles NAs
credit_data[1, 'checking_balance'] <- NA
credit_data[2, 'default'] <- NA
variable <- 'checking_balance'
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=NULL,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__facet__simple.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=NULL,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
simple_mode=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
# simple should not do anything
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__facet__simple.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
simple_mode=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__within__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=TRUE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default__within__flip__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
credit_data$checking_balance <- factor(credit_data$checking_balance,
levels=c("< 0 DM", "1 - 200 DM", "> 200 DM", "unknown"),
ordered=TRUE)
# change the order of the secondary/comparison variable
credit_data$default <- factor(credit_data$default,
levels=c("yes", "no"),
ordered=TRUE)
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8,10))
test_save_plot(file_name='data/rt_plot_multinom_cis__checking_balance__default_fac__within__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=TRUE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=FALSE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
test_save_plot(file_name='data/rt_plot_multinom_cis__default_fac__within__flip__facet.png',
plot=rt_plot_multinom_cis(values=credit_data$checking_balance,
groups=credit_data$credit_history,
facets=credit_data$default,
facet_variable_name='default',
ci_within_variable=FALSE,
confidence_level = 0.95,
show_confidence_values=TRUE,
axes_flip=TRUE,
axis_limits=NULL,
text_size=4,
line_size=0.35,
base_size=11,
x_label="Custom X",
y_label="Custom Y",
group_name="Group Name",
title="My Title",
subtitle="My Subtitle",
caption="My Caption"),
size_inches = c(8, 10))
})
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