tests/testthat/test_summarizers.R
In LudvigOlsen/groupdata2: Creating Groups from Data
library(groupdata2)
context("summarizers()")
test_that("testing create_empty_summary_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"some_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE)),
"grp_3" = factor(sample(LETTERS[1:3], size = 25, replace=TRUE)),
"grp_4" = factor(sample(LETTERS[1:12], size = 25, replace=TRUE))
)
# EMPTY summary
## Testing 'create_empty_summary_(data=df, group_col="gr...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_11364 <- create_empty_summary_(data=df, group_col="grp_1")
# Testing class
expect_equal(
class(output_11364),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_11364[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
# Testing column names
expect_equal(
names(output_11364),
"grp_1",
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_11364),
"factor",
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_11364),
"integer",
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_11364),
c(5L, 1L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_11364)),
character(0),
fixed = TRUE)
## Finished testing 'create_empty_summary_(data=df, group_col="gr...' ####
## Testing 'create_empty_summary_(data=df, group_col=c("...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19148 <- xpectr::capture_side_effects(create_empty_summary_(data=df, group_col=c("grp_1", "grp_2")), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error']]),
xpectr::strip("Assertion on 'group_col' failed: Must have length 1."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'create_empty_summary_(data=df, group_col=c("...' ####
## Testing 'create_empty_summary_(data = df %>% dplyr::g...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- create_empty_summary_(data = df %>% dplyr::group_by(.data$grp_3),
group_col = "grp_1")
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
# Testing column names
expect_equal(
names(output_19148),
"grp_1",
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
"factor",
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
"integer",
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(5L, 1L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'create_empty_summary_(data = df %>% dplyr::g...' ####
})
test_that("testing create_size_summary_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"some_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
## Testing 'create_size_summary_(data=df, group_col="grp...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- create_size_summary_(data=df, group_col="grp_1", name="size")
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
output_19148[["size"]],
c(6, 6, 4, 7, 2),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19148),
c("grp_1", "size"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
c("factor", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
c("integer", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(5L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'create_size_summary_(data=df, group_col="grp...' ####
## Testing 'create_size_summary_(data=df, group_col="grp...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- create_size_summary_(data=df, group_col="grp_2", name="size")
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["grp_2"]],
structure(1:8, .Label = c("1", "2", "3", "4", "5", "6", "7", "8"),
class = "factor"))
expect_equal(
output_19148[["size"]],
c(2, 2, 2, 3, 7, 4, 3, 2),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19148),
c("grp_2", "size"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
c("factor", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
c("integer", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(8L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'create_size_summary_(data=df, group_col="grp...' ####
## Testing 'create_size_summary_(data=df, group_col=NA, ...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19148 <- xpectr::capture_side_effects(create_size_summary_(data=df, group_col=NA, name="size"), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error']]),
xpectr::strip("Assertion on 'group_col' failed: May not be NA."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'create_size_summary_(data=df, group_col=NA, ...' ####
## Testing 'create_size_summary_(data=df, group_col="grp...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19148 <- xpectr::capture_side_effects(create_size_summary_(data=df, group_col="grp_1", name=""), reset_seed = TRUE)
expect_match(
xpectr::strip(side_effects_19148[['error']], lowercase = TRUE),
xpectr::strip("must have at least 1 characters", lowercase = TRUE),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'create_size_summary_(data=df, group_col="grp...' ####
})
test_that("testing create_id_summaries_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"some_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"some_id_2" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE)),
"grp_3" = factor(sample(LETTERS[1:3], size = 25, replace=TRUE)),
"grp_4" = factor(sample(LETTERS[1:12], size = 25, replace=TRUE))
)
id_summ <-
create_id_summaries_(
data = df,
group_col = "grp_1",
id_cols = c("some_id", "some_id_2"),
name_prefix = "# "
)
man_summ <- df %>%
dplyr::group_by(.data$grp_1) %>%
dplyr::summarise(
`# some_id` = length(unique(.data$some_id)),
`# some_id_2` = length(unique(.data$some_id_2))
)
expect_identical(id_summ, man_summ)
## Testing 'id_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(id_summ),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
id_summ[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
id_summ[["# some_id"]],
c(2, 3, 4, 4, 4),
tolerance = 1e-4)
expect_equal(
id_summ[["# some_id_2"]],
c(2, 4, 4, 5, 4),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(id_summ),
c("grp_1", "# some_id", "# some_id_2"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(id_summ),
c("factor", "integer", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(id_summ),
c("integer", "integer", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(id_summ),
c(5L, 3L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(id_summ)),
character(0),
fixed = TRUE)
## Finished testing 'id_summ' ####
})
test_that("testing create_num_summaries_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"some_var_2" = runif(25),
"some_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
num_summ <- create_num_summaries_(
data = df,
group_col = "grp_1",
num_cols = c("some_var", "some_var_2"),
fns = list(
"mean" = mean,
"sum" = sum,
"sd" = sd,
"iqr" = IQR
),
rename = FALSE
)
man_summ <- df %>%
dplyr::group_by(.data$grp_1) %>%
dplyr::summarise(
some_var_mean = mean(.data$some_var),
some_var_sum = sum(.data$some_var),
some_var_sd = sd(.data$some_var),
some_var_iqr = IQR(.data$some_var),
some_var_2_mean = mean(.data$some_var_2),
some_var_2_sum = sum(.data$some_var_2),
some_var_2_sd = sd(.data$some_var_2),
some_var_2_iqr = IQR(.data$some_var_2)
)
expect_identical(num_summ, man_summ)
## Testing 'num_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(num_summ),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
num_summ[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
num_summ[["some_var_mean"]],
c(0.84165, 0.61504, 0.627, 0.5337, 0.61007),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_sum"]],
c(1.68329, 3.07518, 4.389, 3.73591, 2.44029),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_sd"]],
c(0.19315, 0.38385, 0.35016, 0.34623, 0.08878),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_iqr"]],
c(0.13658, 0.61789, 0.58448, 0.52949, 0.11106),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_2_mean"]],
c(0.21986, 0.54758, 0.72597, 0.6209, 0.50317),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_2_sum"]],
c(0.43972, 2.73792, 5.08181, 4.34633, 2.01269),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_2_sd"]],
c(0.30535, 0.38455, 0.20854, 0.27136, 0.37269),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_2_iqr"]],
c(0.21591, 0.51553, 0.2713, 0.37083, 0.43655),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(num_summ),
c("grp_1", "some_var_mean", "some_var_sum", "some_var_sd", "some_var_iqr",
"some_var_2_mean", "some_var_2_sum", "some_var_2_sd", "some_var_2_iqr"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(num_summ),
c("factor", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(num_summ),
c("integer", "double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(num_summ),
c(5L, 9L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(num_summ)),
character(0),
fixed = TRUE)
## Finished testing 'num_summ' ####
num_summ_2 <- create_num_summaries_(
data = df,
group_col = "grp_1",
num_cols = c("some_var"),
fns = list(
"mean" = mean,
"sum" = sum,
"sd" = sd,
"iqr" = IQR
),
rename = TRUE
)
expect_equal(
colnames(num_summ_2),
c("grp_1", "mean(some_var)", "sum(some_var)", "sd(some_var)", "iqr(some_var)"),
fixed = TRUE)
expect_identical(
unname(num_summ_2),
unname(num_summ[, 1:5])
)
})
test_that("testing create_cat_summaries_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"a_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"b_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
cat_summ <- create_cat_summaries_(
data = df,
group_col = "grp_1",
cat_cols = c("a_factor", "b_factor"),
max_cat_prefix_chars = 2,
name_prefix = "# "
)
man_summ <- df %>%
dplyr::select("grp_1", 2:3) %>%
tidyr::gather(key="cat_col", value="cat_val", 2:3) %>%
dplyr::count(.data$grp_1, .data$cat_col, .data$cat_val) %>%
dplyr::mutate(cat_name = paste0("# ", substr(cat_col, 1,2), "_", cat_val)) %>%
dplyr::arrange(.data$cat_name) %>%
tidyr::pivot_wider(id_cols = c("grp_1"), names_from = "cat_name", values_from = "n", values_fill = 0) %>%
dplyr::arrange(.data$grp_1)
strip_df <- function(x){
x <- as.matrix(as.data.frame(x))
rownames(x) <- NULL
colnames(x) <- NULL
x
}
expect_equal(
strip_df(cat_summ),
strip_df(man_summ)
)
## Testing 'cat_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ[["# a__1"]],
c(1, 1, 0, 1, 1),
tolerance = 1e-4)
expect_equal(
cat_summ[["# a__2"]],
c(1, 2, 3, 3, 1),
tolerance = 1e-4)
expect_equal(
cat_summ[["# a__3"]],
c(0, 2, 4, 3, 2),
tolerance = 1e-4)
expect_equal(
cat_summ[["# b__1"]],
c(1, 1, 4, 2, 2),
tolerance = 1e-4)
expect_equal(
cat_summ[["# b__2"]],
c(1, 2, 2, 1, 0),
tolerance = 1e-4)
expect_equal(
cat_summ[["# b__3"]],
c(0, 2, 1, 4, 2),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ),
c("grp_1", "# a__1", "# a__2", "# a__3", "# b__1", "# b__2", "# b__3"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ),
c("factor", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ),
c("integer", "double", "double", "double", "double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ),
c(5L, 7L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ' ####
})
test_that("testing create_combined_cat_summary_() and create_combined_cat_summaries_()", {
xpectr::set_test_seed(42)
df <- data.frame(
"some_var" = runif(25),
"a_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"b_factor" = factor(sample(c("a", "b", "c"), size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
cat_summ_a <- create_combined_cat_summary_(
data = df,
group_cols = "grp_1",
cat_col = "a_factor",
cat_levels = NULL,
warn_zero_variance = TRUE
)
man_summ <- df %>%
dplyr::count(grp_1, a_factor) %>%
tidyr::spread(key = "a_factor",
value = "n",
fill = 0) %>%
tidyr::gather(key = "a_factor", value = "n", 2:4) %>%
dplyr::group_by(a_factor) %>%
dplyr::mutate(n = standardize_(n)) %>%
dplyr::group_by(grp_1) %>%
dplyr::summarise(a_factor = mean(n))
expect_equal(
as.data.frame(cat_summ_a),
as.data.frame(man_summ)
)
## Testing 'cat_summ_a' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ_a),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ_a[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ_a[["a_factor"]],
c(-0.67868, 0.10412, 0.14157, 0.66219, -0.22921),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ_a),
c("grp_1", "a_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ_a),
c("factor", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ_a),
c("integer", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ_a),
c(5L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ_a)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ_a' ####
cat_summ_b <- create_combined_cat_summary_(
data = df,
group_cols = "grp_1",
cat_col = "b_factor",
cat_levels = NULL,
warn_zero_variance = TRUE
)
man_summ <- df %>%
dplyr::count(grp_1, b_factor) %>%
tidyr::spread(key = "b_factor",
value = "n",
fill = 0) %>%
tidyr::gather(key = "b_factor", value = "n", 2:4) %>%
dplyr::group_by(b_factor) %>%
dplyr::mutate(n = standardize_(n)) %>%
dplyr::group_by(grp_1) %>%
dplyr::summarise(b_factor = mean(n))
expect_equal(
as.data.frame(cat_summ_b),
as.data.frame(man_summ)
)
## Testing 'cat_summ_b' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ_b),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ_b[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ_b[["b_factor"]],
c(-0.75637, 0.09151, 0.68327, 0.41473, -0.43314),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ_b),
c("grp_1", "b_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ_b),
c("factor", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ_b),
c("integer", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ_b),
c(5L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ_b)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ_b' ####
# Combine and check against wrapper
cat_summ_ab <- cat_summ_a %>%
dplyr::left_join(cat_summ_b, by="grp_1")
cat_summ_from_wrapper <- create_combined_cat_summaries_(
data = df,
group_cols = c("grp_1"),
cat_cols = c("a_factor", "b_factor"),
cat_levels = NULL,
warn_zero_variance = TRUE
)
expect_identical(
cat_summ_ab,
cat_summ_from_wrapper
)
## Testing 'cat_summ_from_wrapper' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ_from_wrapper),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ_from_wrapper[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ_from_wrapper[["a_factor"]],
c(-0.67868, 0.10412, 0.14157, 0.66219, -0.22921),
tolerance = 1e-4)
expect_equal(
cat_summ_from_wrapper[["b_factor"]],
c(-0.75637, 0.09151, 0.68327, 0.41473, -0.43314),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ_from_wrapper),
c("grp_1", "a_factor", "b_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ_from_wrapper),
c("factor", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ_from_wrapper),
c("integer", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ_from_wrapper),
c(5L, 3L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ_from_wrapper)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ_from_wrapper' ####
# Nested group cols
cat_summ <- create_combined_cat_summaries_(
data = df,
group_cols = c("grp_1", "grp_2"),
cat_cols = c("a_factor", "b_factor"),
cat_levels = NULL,
warn_zero_variance = TRUE
)
## Testing 'cat_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ[["grp_1"]],
structure(c(1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L),
.Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ[["grp_2"]],
structure(c(1L, 7L, 2L, 4L, 7L, 8L, 2L, 3L, 5L, 7L, 8L, 1L, 2L,
4L, 5L, 6L, 7L, 6L, 7L, 8L),
.Label = c("1", "2", "3", "4", "5", "6", "7", "8"), class = "factor"))
expect_equal(
cat_summ[["a_factor"]],
c(-0.14068, 0.02177, -0.14068, 0.02177, -0.23933, 0.41046, 0.41046,
-0.23933, -0.14068, -0.23933, 0.41046, 0.02177, -0.14068, -0.14068,
0.31182, -0.14068, -0.23933, 0.41046, 0.02177, -0.23933),
tolerance = 1e-4)
expect_equal(
cat_summ[["b_factor"]],
c(-0.20403, -0.05859, -0.09097, -0.09097, -0.20403, 0.52494, 0.23406,
-0.05859, -0.09097, -0.05859, 0.23406, -0.09097, -0.20403, -0.05859,
0.46017, -0.20403, -0.09097, 0.23406, -0.09097, -0.09097),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ),
c("grp_1", "grp_2", "a_factor", "b_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ),
c("factor", "factor", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ),
c("integer", "integer", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ),
c(20L, 4L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ' ####
## With cat_levels
# By only using a single level (c)
# We can check the weighting is correctly applied
cat_summ <- create_combined_cat_summaries_(
data = df,
group_cols = c("grp_1"),
cat_cols = c("b_factor"),
cat_levels = list(
"b_factor" = c("a" = 0, "b" = 0, "c" = 10) # Only c is used!
),
warn_zero_variance = TRUE
)
man_summ <- df %>%
dplyr::count(grp_1, b_factor) %>%
tidyr::spread(key = "b_factor",
value = "n",
fill = 0) %>%
tidyr::gather(key = "b_factor", value = "n", 2:4) %>%
dplyr::group_by(b_factor) %>%
dplyr::mutate(n = standardize_(n)) %>%
dplyr::filter(b_factor == "c") %>% # Only use c!
dplyr::group_by(grp_1) %>%
dplyr::summarise(b_factor = mean(n))
expect_identical(
as.data.frame(cat_summ),
as.data.frame(man_summ)
)
## Testing 'cat_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ[["b_factor"]],
c(-1.21356, 0.13484, -0.53936, 1.48324, 0.13484),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ),
c("grp_1", "b_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ),
c("factor", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ),
c("integer", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ),
c(5L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ' ####
})
test_that("testing create_cat_name_map_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"a_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"b_factor" = factor(sample(c("a", "b", "c"), size = 25, replace = TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
## Testing 'create_cat_name_map_( data = df, cat_cols = ...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_17193 <- create_cat_name_map_(
data = df,
cat_cols = c("a_factor", "b_factor"),
max_cat_prefix_chars = 5,
name_prefix = "# "
)
# Testing class
expect_equal(
class(output_17193),
"list",
fixed = TRUE)
# Testing type
expect_type(
output_17193,
type = "list")
# Testing values
expect_equal(
output_17193[["a_factor"]],
c(`1` = "# a_fac_1", `2` = "# a_fac_2", `3` = "# a_fac_3"),
fixed = TRUE)
expect_equal(
output_17193[["b_factor"]],
c(a = "# b_fac_a", b = "# b_fac_b", c = "# b_fac_c"),
fixed = TRUE)
# Testing names
expect_equal(
names(output_17193),
c("a_factor", "b_factor"),
fixed = TRUE)
# Testing length
expect_equal(
length(output_17193),
2L)
# Testing sum of element lengths
expect_equal(
sum(xpectr::element_lengths(output_17193)),
6L)
# Testing element classes
expect_equal(
xpectr::element_classes(output_17193),
c("character", "character"),
fixed = TRUE)
# Testing element types
expect_equal(
xpectr::element_types(output_17193),
c("character", "character"),
fixed = TRUE)
## Finished testing 'create_cat_name_map_( data = df, cat_cols = ...' ####
})
test_that("testing rank_numeric_cols_()", {
xpectr::set_test_seed(42)
df = data.frame("a" = c(3, 4, 5, 6, 1, 2, 3),
"b" = c(5, 3, 2, 6, 4, 1, 5))
# Test rank_numeric_cols_()
expect_identical(
data.frame("a" = rank(df$a), "b" = rank(df$b)),
rank_numeric_cols_(data=df))
## Testing 'rank_numeric_cols_(data=df)' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19370 <- rank_numeric_cols_(data=df)
# Testing class
expect_equal(
class(output_19370),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
output_19370[["a"]],
c(3.5, 5, 6, 7, 1, 2, 3.5),
tolerance = 1e-4)
expect_equal(
output_19370[["b"]],
c(5.5, 3, 2, 7, 4, 1, 5.5),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19370),
c("a", "b"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19370),
c("numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19370),
c("double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19370),
c(7L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19370)),
character(0),
fixed = TRUE)
## Finished testing 'rank_numeric_cols_(data=df)' ####
# With selected cols only
expect_identical(
data.frame("a" = rank(df$a), "b" = df$b),
rank_numeric_cols_(data=df, cols = "a"))
# Giving non-existing columns
## Testing 'rank_numeric_cols_(data = df, cols = "noope")' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19148 <- xpectr::capture_side_effects(rank_numeric_cols_(data = df, cols = "noope"), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error']]),
xpectr::strip("`cols` had unknown names: noope"),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'rank_numeric_cols_(data = df, cols = "noope")' ####
})
test_that("testing mean_rank_numeric_cols_()", {
xpectr::set_test_seed(42)
df = data.frame("a" = c(1.3, 2.2, 5.8, 6.2, 3.3, 4.1),
"b" = c(2.5, 1.2, 6.5, 5.1, 4.6, 3.2))
ranks <- mean_rank_numeric_cols_(
data = df,
cols = c("a", "b"),
col_name = "mean_rank",
rank_weights = NULL,
already_rank_cols = character(0))
## Testing 'ranks' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(ranks),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
ranks[["a"]],
c(1, 2, 5, 6, 3, 4), # Ranks (removed the decimals)
tolerance = 1e-4)
expect_equal(
ranks[["b"]],
c(2, 1, 6, 5, 4, 3), # Ranks (removed the decimals)
tolerance = 1e-4)
expect_equal(
ranks[["mean_rank"]],
c(1.5, 1.5, 5.5, 5.5, 3.5, 3.5),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(ranks),
c("a", "b", "mean_rank"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(ranks),
c("numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(ranks),
c("double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(ranks),
c(6L, 3L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(ranks)),
character(0),
fixed = TRUE)
## Finished testing 'ranks' ####
# Only use ranks for a
ranks <- mean_rank_numeric_cols_(
data = df,
cols = c("a", "b"),
col_name = "mean_rank",
rank_weights = c("a" = 2, "b" = 0),
already_rank_cols = character(0))
expect_equal(ranks$a, ranks$mean_rank)
# Tell it that a is already a rank column
ranks <- mean_rank_numeric_cols_(
data = df,
cols = c("a", "b"),
col_name = "mean_rank",
rank_weights = NULL,
already_rank_cols = c("a"))
expect_equal(
ranks$mean_rank,
(df$a + ranks$b) / 2
)
})
LudvigOlsen/groupdata2 documentation built on March 7, 2024, 12:57 p.m.
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library(groupdata2)
context("summarizers()")
test_that("testing create_empty_summary_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"some_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE)),
"grp_3" = factor(sample(LETTERS[1:3], size = 25, replace=TRUE)),
"grp_4" = factor(sample(LETTERS[1:12], size = 25, replace=TRUE))
)
# EMPTY summary
## Testing 'create_empty_summary_(data=df, group_col="gr...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_11364 <- create_empty_summary_(data=df, group_col="grp_1")
# Testing class
expect_equal(
class(output_11364),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_11364[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
# Testing column names
expect_equal(
names(output_11364),
"grp_1",
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_11364),
"factor",
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_11364),
"integer",
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_11364),
c(5L, 1L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_11364)),
character(0),
fixed = TRUE)
## Finished testing 'create_empty_summary_(data=df, group_col="gr...' ####
## Testing 'create_empty_summary_(data=df, group_col=c("...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19148 <- xpectr::capture_side_effects(create_empty_summary_(data=df, group_col=c("grp_1", "grp_2")), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error']]),
xpectr::strip("Assertion on 'group_col' failed: Must have length 1."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'create_empty_summary_(data=df, group_col=c("...' ####
## Testing 'create_empty_summary_(data = df %>% dplyr::g...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- create_empty_summary_(data = df %>% dplyr::group_by(.data$grp_3),
group_col = "grp_1")
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
# Testing column names
expect_equal(
names(output_19148),
"grp_1",
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
"factor",
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
"integer",
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(5L, 1L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'create_empty_summary_(data = df %>% dplyr::g...' ####
})
test_that("testing create_size_summary_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"some_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
## Testing 'create_size_summary_(data=df, group_col="grp...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- create_size_summary_(data=df, group_col="grp_1", name="size")
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
output_19148[["size"]],
c(6, 6, 4, 7, 2),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19148),
c("grp_1", "size"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
c("factor", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
c("integer", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(5L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'create_size_summary_(data=df, group_col="grp...' ####
## Testing 'create_size_summary_(data=df, group_col="grp...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- create_size_summary_(data=df, group_col="grp_2", name="size")
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["grp_2"]],
structure(1:8, .Label = c("1", "2", "3", "4", "5", "6", "7", "8"),
class = "factor"))
expect_equal(
output_19148[["size"]],
c(2, 2, 2, 3, 7, 4, 3, 2),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19148),
c("grp_2", "size"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
c("factor", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
c("integer", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(8L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'create_size_summary_(data=df, group_col="grp...' ####
## Testing 'create_size_summary_(data=df, group_col=NA, ...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19148 <- xpectr::capture_side_effects(create_size_summary_(data=df, group_col=NA, name="size"), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error']]),
xpectr::strip("Assertion on 'group_col' failed: May not be NA."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'create_size_summary_(data=df, group_col=NA, ...' ####
## Testing 'create_size_summary_(data=df, group_col="grp...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19148 <- xpectr::capture_side_effects(create_size_summary_(data=df, group_col="grp_1", name=""), reset_seed = TRUE)
expect_match(
xpectr::strip(side_effects_19148[['error']], lowercase = TRUE),
xpectr::strip("must have at least 1 characters", lowercase = TRUE),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'create_size_summary_(data=df, group_col="grp...' ####
})
test_that("testing create_id_summaries_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"some_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"some_id_2" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE)),
"grp_3" = factor(sample(LETTERS[1:3], size = 25, replace=TRUE)),
"grp_4" = factor(sample(LETTERS[1:12], size = 25, replace=TRUE))
)
id_summ <-
create_id_summaries_(
data = df,
group_col = "grp_1",
id_cols = c("some_id", "some_id_2"),
name_prefix = "# "
)
man_summ <- df %>%
dplyr::group_by(.data$grp_1) %>%
dplyr::summarise(
`# some_id` = length(unique(.data$some_id)),
`# some_id_2` = length(unique(.data$some_id_2))
)
expect_identical(id_summ, man_summ)
## Testing 'id_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(id_summ),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
id_summ[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
id_summ[["# some_id"]],
c(2, 3, 4, 4, 4),
tolerance = 1e-4)
expect_equal(
id_summ[["# some_id_2"]],
c(2, 4, 4, 5, 4),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(id_summ),
c("grp_1", "# some_id", "# some_id_2"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(id_summ),
c("factor", "integer", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(id_summ),
c("integer", "integer", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(id_summ),
c(5L, 3L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(id_summ)),
character(0),
fixed = TRUE)
## Finished testing 'id_summ' ####
})
test_that("testing create_num_summaries_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"some_var_2" = runif(25),
"some_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
num_summ <- create_num_summaries_(
data = df,
group_col = "grp_1",
num_cols = c("some_var", "some_var_2"),
fns = list(
"mean" = mean,
"sum" = sum,
"sd" = sd,
"iqr" = IQR
),
rename = FALSE
)
man_summ <- df %>%
dplyr::group_by(.data$grp_1) %>%
dplyr::summarise(
some_var_mean = mean(.data$some_var),
some_var_sum = sum(.data$some_var),
some_var_sd = sd(.data$some_var),
some_var_iqr = IQR(.data$some_var),
some_var_2_mean = mean(.data$some_var_2),
some_var_2_sum = sum(.data$some_var_2),
some_var_2_sd = sd(.data$some_var_2),
some_var_2_iqr = IQR(.data$some_var_2)
)
expect_identical(num_summ, man_summ)
## Testing 'num_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(num_summ),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
num_summ[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
num_summ[["some_var_mean"]],
c(0.84165, 0.61504, 0.627, 0.5337, 0.61007),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_sum"]],
c(1.68329, 3.07518, 4.389, 3.73591, 2.44029),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_sd"]],
c(0.19315, 0.38385, 0.35016, 0.34623, 0.08878),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_iqr"]],
c(0.13658, 0.61789, 0.58448, 0.52949, 0.11106),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_2_mean"]],
c(0.21986, 0.54758, 0.72597, 0.6209, 0.50317),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_2_sum"]],
c(0.43972, 2.73792, 5.08181, 4.34633, 2.01269),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_2_sd"]],
c(0.30535, 0.38455, 0.20854, 0.27136, 0.37269),
tolerance = 1e-4)
expect_equal(
num_summ[["some_var_2_iqr"]],
c(0.21591, 0.51553, 0.2713, 0.37083, 0.43655),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(num_summ),
c("grp_1", "some_var_mean", "some_var_sum", "some_var_sd", "some_var_iqr",
"some_var_2_mean", "some_var_2_sum", "some_var_2_sd", "some_var_2_iqr"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(num_summ),
c("factor", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(num_summ),
c("integer", "double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(num_summ),
c(5L, 9L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(num_summ)),
character(0),
fixed = TRUE)
## Finished testing 'num_summ' ####
num_summ_2 <- create_num_summaries_(
data = df,
group_col = "grp_1",
num_cols = c("some_var"),
fns = list(
"mean" = mean,
"sum" = sum,
"sd" = sd,
"iqr" = IQR
),
rename = TRUE
)
expect_equal(
colnames(num_summ_2),
c("grp_1", "mean(some_var)", "sum(some_var)", "sd(some_var)", "iqr(some_var)"),
fixed = TRUE)
expect_identical(
unname(num_summ_2),
unname(num_summ[, 1:5])
)
})
test_that("testing create_cat_summaries_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"a_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"b_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
cat_summ <- create_cat_summaries_(
data = df,
group_col = "grp_1",
cat_cols = c("a_factor", "b_factor"),
max_cat_prefix_chars = 2,
name_prefix = "# "
)
man_summ <- df %>%
dplyr::select("grp_1", 2:3) %>%
tidyr::gather(key="cat_col", value="cat_val", 2:3) %>%
dplyr::count(.data$grp_1, .data$cat_col, .data$cat_val) %>%
dplyr::mutate(cat_name = paste0("# ", substr(cat_col, 1,2), "_", cat_val)) %>%
dplyr::arrange(.data$cat_name) %>%
tidyr::pivot_wider(id_cols = c("grp_1"), names_from = "cat_name", values_from = "n", values_fill = 0) %>%
dplyr::arrange(.data$grp_1)
strip_df <- function(x){
x <- as.matrix(as.data.frame(x))
rownames(x) <- NULL
colnames(x) <- NULL
x
}
expect_equal(
strip_df(cat_summ),
strip_df(man_summ)
)
## Testing 'cat_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ[["# a__1"]],
c(1, 1, 0, 1, 1),
tolerance = 1e-4)
expect_equal(
cat_summ[["# a__2"]],
c(1, 2, 3, 3, 1),
tolerance = 1e-4)
expect_equal(
cat_summ[["# a__3"]],
c(0, 2, 4, 3, 2),
tolerance = 1e-4)
expect_equal(
cat_summ[["# b__1"]],
c(1, 1, 4, 2, 2),
tolerance = 1e-4)
expect_equal(
cat_summ[["# b__2"]],
c(1, 2, 2, 1, 0),
tolerance = 1e-4)
expect_equal(
cat_summ[["# b__3"]],
c(0, 2, 1, 4, 2),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ),
c("grp_1", "# a__1", "# a__2", "# a__3", "# b__1", "# b__2", "# b__3"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ),
c("factor", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ),
c("integer", "double", "double", "double", "double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ),
c(5L, 7L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ' ####
})
test_that("testing create_combined_cat_summary_() and create_combined_cat_summaries_()", {
xpectr::set_test_seed(42)
df <- data.frame(
"some_var" = runif(25),
"a_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"b_factor" = factor(sample(c("a", "b", "c"), size = 25, replace=TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
cat_summ_a <- create_combined_cat_summary_(
data = df,
group_cols = "grp_1",
cat_col = "a_factor",
cat_levels = NULL,
warn_zero_variance = TRUE
)
man_summ <- df %>%
dplyr::count(grp_1, a_factor) %>%
tidyr::spread(key = "a_factor",
value = "n",
fill = 0) %>%
tidyr::gather(key = "a_factor", value = "n", 2:4) %>%
dplyr::group_by(a_factor) %>%
dplyr::mutate(n = standardize_(n)) %>%
dplyr::group_by(grp_1) %>%
dplyr::summarise(a_factor = mean(n))
expect_equal(
as.data.frame(cat_summ_a),
as.data.frame(man_summ)
)
## Testing 'cat_summ_a' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ_a),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ_a[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ_a[["a_factor"]],
c(-0.67868, 0.10412, 0.14157, 0.66219, -0.22921),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ_a),
c("grp_1", "a_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ_a),
c("factor", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ_a),
c("integer", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ_a),
c(5L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ_a)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ_a' ####
cat_summ_b <- create_combined_cat_summary_(
data = df,
group_cols = "grp_1",
cat_col = "b_factor",
cat_levels = NULL,
warn_zero_variance = TRUE
)
man_summ <- df %>%
dplyr::count(grp_1, b_factor) %>%
tidyr::spread(key = "b_factor",
value = "n",
fill = 0) %>%
tidyr::gather(key = "b_factor", value = "n", 2:4) %>%
dplyr::group_by(b_factor) %>%
dplyr::mutate(n = standardize_(n)) %>%
dplyr::group_by(grp_1) %>%
dplyr::summarise(b_factor = mean(n))
expect_equal(
as.data.frame(cat_summ_b),
as.data.frame(man_summ)
)
## Testing 'cat_summ_b' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ_b),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ_b[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ_b[["b_factor"]],
c(-0.75637, 0.09151, 0.68327, 0.41473, -0.43314),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ_b),
c("grp_1", "b_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ_b),
c("factor", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ_b),
c("integer", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ_b),
c(5L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ_b)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ_b' ####
# Combine and check against wrapper
cat_summ_ab <- cat_summ_a %>%
dplyr::left_join(cat_summ_b, by="grp_1")
cat_summ_from_wrapper <- create_combined_cat_summaries_(
data = df,
group_cols = c("grp_1"),
cat_cols = c("a_factor", "b_factor"),
cat_levels = NULL,
warn_zero_variance = TRUE
)
expect_identical(
cat_summ_ab,
cat_summ_from_wrapper
)
## Testing 'cat_summ_from_wrapper' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ_from_wrapper),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ_from_wrapper[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ_from_wrapper[["a_factor"]],
c(-0.67868, 0.10412, 0.14157, 0.66219, -0.22921),
tolerance = 1e-4)
expect_equal(
cat_summ_from_wrapper[["b_factor"]],
c(-0.75637, 0.09151, 0.68327, 0.41473, -0.43314),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ_from_wrapper),
c("grp_1", "a_factor", "b_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ_from_wrapper),
c("factor", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ_from_wrapper),
c("integer", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ_from_wrapper),
c(5L, 3L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ_from_wrapper)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ_from_wrapper' ####
# Nested group cols
cat_summ <- create_combined_cat_summaries_(
data = df,
group_cols = c("grp_1", "grp_2"),
cat_cols = c("a_factor", "b_factor"),
cat_levels = NULL,
warn_zero_variance = TRUE
)
## Testing 'cat_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ[["grp_1"]],
structure(c(1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L),
.Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ[["grp_2"]],
structure(c(1L, 7L, 2L, 4L, 7L, 8L, 2L, 3L, 5L, 7L, 8L, 1L, 2L,
4L, 5L, 6L, 7L, 6L, 7L, 8L),
.Label = c("1", "2", "3", "4", "5", "6", "7", "8"), class = "factor"))
expect_equal(
cat_summ[["a_factor"]],
c(-0.14068, 0.02177, -0.14068, 0.02177, -0.23933, 0.41046, 0.41046,
-0.23933, -0.14068, -0.23933, 0.41046, 0.02177, -0.14068, -0.14068,
0.31182, -0.14068, -0.23933, 0.41046, 0.02177, -0.23933),
tolerance = 1e-4)
expect_equal(
cat_summ[["b_factor"]],
c(-0.20403, -0.05859, -0.09097, -0.09097, -0.20403, 0.52494, 0.23406,
-0.05859, -0.09097, -0.05859, 0.23406, -0.09097, -0.20403, -0.05859,
0.46017, -0.20403, -0.09097, 0.23406, -0.09097, -0.09097),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ),
c("grp_1", "grp_2", "a_factor", "b_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ),
c("factor", "factor", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ),
c("integer", "integer", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ),
c(20L, 4L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ' ####
## With cat_levels
# By only using a single level (c)
# We can check the weighting is correctly applied
cat_summ <- create_combined_cat_summaries_(
data = df,
group_cols = c("grp_1"),
cat_cols = c("b_factor"),
cat_levels = list(
"b_factor" = c("a" = 0, "b" = 0, "c" = 10) # Only c is used!
),
warn_zero_variance = TRUE
)
man_summ <- df %>%
dplyr::count(grp_1, b_factor) %>%
tidyr::spread(key = "b_factor",
value = "n",
fill = 0) %>%
tidyr::gather(key = "b_factor", value = "n", 2:4) %>%
dplyr::group_by(b_factor) %>%
dplyr::mutate(n = standardize_(n)) %>%
dplyr::filter(b_factor == "c") %>% # Only use c!
dplyr::group_by(grp_1) %>%
dplyr::summarise(b_factor = mean(n))
expect_identical(
as.data.frame(cat_summ),
as.data.frame(man_summ)
)
## Testing 'cat_summ' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cat_summ),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
cat_summ[["grp_1"]],
structure(1:5, .Label = c("1", "2", "3", "4", "5"), class = "factor"))
expect_equal(
cat_summ[["b_factor"]],
c(-1.21356, 0.13484, -0.53936, 1.48324, 0.13484),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cat_summ),
c("grp_1", "b_factor"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cat_summ),
c("factor", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cat_summ),
c("integer", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cat_summ),
c(5L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cat_summ)),
character(0),
fixed = TRUE)
## Finished testing 'cat_summ' ####
})
test_that("testing create_cat_name_map_()", {
xpectr::set_test_seed(42)
# Regression tests
# The dataset from the example
df <- data.frame(
"some_var" = runif(25),
"a_factor" = factor(sample(1:3, size = 25, replace=TRUE)),
"b_factor" = factor(sample(c("a", "b", "c"), size = 25, replace = TRUE)),
"some_id" = factor(sample(1:7, size = 25, replace=TRUE)),
"grp_1" = factor(sample(1:5, size = 25, replace=TRUE)),
"grp_2" = factor(sample(1:8, size = 25, replace=TRUE))
)
## Testing 'create_cat_name_map_( data = df, cat_cols = ...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_17193 <- create_cat_name_map_(
data = df,
cat_cols = c("a_factor", "b_factor"),
max_cat_prefix_chars = 5,
name_prefix = "# "
)
# Testing class
expect_equal(
class(output_17193),
"list",
fixed = TRUE)
# Testing type
expect_type(
output_17193,
type = "list")
# Testing values
expect_equal(
output_17193[["a_factor"]],
c(`1` = "# a_fac_1", `2` = "# a_fac_2", `3` = "# a_fac_3"),
fixed = TRUE)
expect_equal(
output_17193[["b_factor"]],
c(a = "# b_fac_a", b = "# b_fac_b", c = "# b_fac_c"),
fixed = TRUE)
# Testing names
expect_equal(
names(output_17193),
c("a_factor", "b_factor"),
fixed = TRUE)
# Testing length
expect_equal(
length(output_17193),
2L)
# Testing sum of element lengths
expect_equal(
sum(xpectr::element_lengths(output_17193)),
6L)
# Testing element classes
expect_equal(
xpectr::element_classes(output_17193),
c("character", "character"),
fixed = TRUE)
# Testing element types
expect_equal(
xpectr::element_types(output_17193),
c("character", "character"),
fixed = TRUE)
## Finished testing 'create_cat_name_map_( data = df, cat_cols = ...' ####
})
test_that("testing rank_numeric_cols_()", {
xpectr::set_test_seed(42)
df = data.frame("a" = c(3, 4, 5, 6, 1, 2, 3),
"b" = c(5, 3, 2, 6, 4, 1, 5))
# Test rank_numeric_cols_()
expect_identical(
data.frame("a" = rank(df$a), "b" = rank(df$b)),
rank_numeric_cols_(data=df))
## Testing 'rank_numeric_cols_(data=df)' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19370 <- rank_numeric_cols_(data=df)
# Testing class
expect_equal(
class(output_19370),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
output_19370[["a"]],
c(3.5, 5, 6, 7, 1, 2, 3.5),
tolerance = 1e-4)
expect_equal(
output_19370[["b"]],
c(5.5, 3, 2, 7, 4, 1, 5.5),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19370),
c("a", "b"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19370),
c("numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19370),
c("double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19370),
c(7L, 2L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19370)),
character(0),
fixed = TRUE)
## Finished testing 'rank_numeric_cols_(data=df)' ####
# With selected cols only
expect_identical(
data.frame("a" = rank(df$a), "b" = df$b),
rank_numeric_cols_(data=df, cols = "a"))
# Giving non-existing columns
## Testing 'rank_numeric_cols_(data = df, cols = "noope")' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19148 <- xpectr::capture_side_effects(rank_numeric_cols_(data = df, cols = "noope"), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error']]),
xpectr::strip("`cols` had unknown names: noope"),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19148[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'rank_numeric_cols_(data = df, cols = "noope")' ####
})
test_that("testing mean_rank_numeric_cols_()", {
xpectr::set_test_seed(42)
df = data.frame("a" = c(1.3, 2.2, 5.8, 6.2, 3.3, 4.1),
"b" = c(2.5, 1.2, 6.5, 5.1, 4.6, 3.2))
ranks <- mean_rank_numeric_cols_(
data = df,
cols = c("a", "b"),
col_name = "mean_rank",
rank_weights = NULL,
already_rank_cols = character(0))
## Testing 'ranks' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(ranks),
"data.frame",
fixed = TRUE)
# Testing column values
expect_equal(
ranks[["a"]],
c(1, 2, 5, 6, 3, 4), # Ranks (removed the decimals)
tolerance = 1e-4)
expect_equal(
ranks[["b"]],
c(2, 1, 6, 5, 4, 3), # Ranks (removed the decimals)
tolerance = 1e-4)
expect_equal(
ranks[["mean_rank"]],
c(1.5, 1.5, 5.5, 5.5, 3.5, 3.5),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(ranks),
c("a", "b", "mean_rank"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(ranks),
c("numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(ranks),
c("double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(ranks),
c(6L, 3L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(ranks)),
character(0),
fixed = TRUE)
## Finished testing 'ranks' ####
# Only use ranks for a
ranks <- mean_rank_numeric_cols_(
data = df,
cols = c("a", "b"),
col_name = "mean_rank",
rank_weights = c("a" = 2, "b" = 0),
already_rank_cols = character(0))
expect_equal(ranks$a, ranks$mean_rank)
# Tell it that a is already a rank column
ranks <- mean_rank_numeric_cols_(
data = df,
cols = c("a", "b"),
col_name = "mean_rank",
rank_weights = NULL,
already_rank_cols = c("a"))
expect_equal(
ranks$mean_rank,
(df$a + ranks$b) / 2
)
})
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