tests/testthat/test-ReformatLoops.R
In l-gorman/rhomis-R-package: Essential Processes for RHoMIS Data
library(testthat)
library(dplyr)
testthat::test_that("Test that can identify number of loops", {
name_column <- "crop_name"
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black")))
actual_result <- find_number_of_loops(data,name_column)
expected_result <- 2
expect_equal(actual_result, expected_result)
})
testthat::test_that("Test can find unique values for the specific name column",{
name_column <- "crop_name"
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black")))
expected_result <- c("banana","cassava","millet","melon","maize")
actual_result <- find_unique_case(data, name_column)
expect_equal(actual_result,expected_result)
# Checking for case with no expected values
data <- tibble::as_tibble(list(crop_name_1=c(NA, NA, NA, NA),
crop_name_2=c(NA,NA, NA, NA),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black")))
name_column <- "crop_name"
expected_result <- c("none")
actual_result <- find_unique_case(data, name_column)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Individual loop variables can be properly formatted",{
name_column <- "crop_name"
variable_to_convert <- "crop_variable"
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_variable_1=c("ex1","ex2",NA,NA),
crop_variable_2=c("ex3",NA,"ex4","ex5")))
expected_result <- tibble::as_tibble(list(banana=c("ex1", NA, NA, NA),
cassava=c("ex3", "ex2", NA, NA),
millet=c(NA, NA, NA, NA),
melon=c(NA, NA, "ex4", NA),
maize=c(NA, NA, NA, "ex5")))
expected_result <- tibble::as_tibble(list(banana=c("ex1", NA, NA, NA),
cassava=c("ex3", "ex2", NA, NA),
millet=c(NA, NA, NA, NA),
melon=c(NA, NA, "ex4", NA),
maize=c(NA, NA, NA, "ex5")))
expected_result<- expected_result %>% dplyr::mutate_all(as.character)
actual_result <- loop_to_column_conversion(data, name_column, variable_to_convert, type="chr")
expect_equal(actual_result,expected_result)
# Checking case for no values
name_column <- "crop_name"
variable_to_convert <- "crop_variable"
data <- tibble::as_tibble(list(crop_name_1=c(NA, NA, NA, NA),
crop_name_2=c(NA,NA, NA, NA),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_variable_1=c(NA,NA,NA,NA),
crop_variable_2=c(NA,NA,NA,NA)))
expected_result <- tibble::as_tibble(list(none=c(NA, NA, NA, NA)))
expected_result<- expected_result %>% dplyr::mutate_all(as.character)
actual_result <- loop_to_column_conversion(data, name_column, variable_to_convert, type="chr")
expect_equal(actual_result,expected_result)
})
testthat::test_that("Test that we can convert multiple columns into the wide format",{
name_column <- "crop_name"
column_prefixes <- c("crop_variable", "crop_unit", "crop_price")
types <- c("chr", "chr", "chr")
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_variable_1=c("ex1","ex2",NA,NA),
crop_variable_2=c("ex3",NA,"ex4","ex5"),
crop_unit_1=c("unit1","unit2","unit3",NA),
crop_unit_2=c(NA,NA,"unit4","unit5"),
crop_price_1=c(NA,NA,NA,NA),
crop_price_2=c(NA,NA,NA,NA)))
actual_result <- map_to_wide_format(data, name_column, column_prefixes, types)
# Expected result
crop_variable <- tibble::as_tibble(list(banana=c("ex1",NA,NA,NA),
cassava=c("ex3","ex2",NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,"ex4",NA),
maize=c(NA,NA,NA,"ex5")))
crop_variable<- crop_variable %>% dplyr::mutate_all(as.character)
crop_unit <- tibble::as_tibble(list(banana=c("unit1",NA,NA,NA),
cassava=c(NA,"unit2",NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,"unit4",NA),
maize=c(NA,NA,NA,"unit5")))
crop_unit<- crop_unit %>% dplyr::mutate_all(as.character)
crop_price <- tibble::as_tibble(list(banana=c(NA,NA,NA,NA),
cassava=c(NA,NA,NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,NA,NA),
maize=c(NA,NA,NA,NA)))
actual_result <- map_to_wide_format(data, name_column, column_prefixes, types)
# Expected result
crop_variable <- tibble::as_tibble(list(banana=c("ex1",NA,NA,NA),
cassava=c("ex3","ex2",NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,"ex4",NA),
maize=c(NA,NA,NA,"ex5")))
crop_variable<- crop_variable %>% dplyr::mutate_all(as.character)
crop_unit <- tibble::as_tibble(list(banana=c("unit1",NA,NA,NA),
cassava=c(NA,"unit2",NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,"unit4",NA),
maize=c(NA,NA,NA,"unit5")))
crop_unit<- crop_unit %>% dplyr::mutate_all(as.character)
crop_price <- tibble::as_tibble(list(banana=c(NA,NA,NA,NA),
cassava=c(NA,NA,NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,NA,NA),
maize=c(NA,NA,NA,NA)))
crop_price<- crop_price %>% dplyr::mutate_all(as.character)
expected_result <- list(crop_variable=crop_variable,
crop_unit=crop_unit,
crop_price=crop_price)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Gender splitting of information works",{
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_control_1=c("male_adult female_adult","male_adult",NA,"female_youth"),
crop_control_2=c("male_youth",NA,"female_youth","female_adult")))
wide_data <- map_to_wide_format(data, "crop_name", c("crop_control"), c("chr"))
gender_data <- wide_data$crop_control
female_youth <- tibble::as_tibble(list(banana=c(0,NA,NA,NA),
cassava=c(0,0,NA,NA),
millet=c(NA,NA,NA,1),
melon=c(NA,NA,1,NA),
maize=c(NA,NA,NA,0)))
female_adult <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,0,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,1)))
male_youth <- tibble::as_tibble(list(banana=c(0,NA,NA,NA),
cassava=c(1,0,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
male_adult <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,1,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
female_adult <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,0,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,1)))
male_youth <- tibble::as_tibble(list(banana=c(0,NA,NA,NA),
cassava=c(1,0,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
male_adult <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,1,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
expected_result <- list(female_youth=female_youth,
female_adult=female_adult,
male_youth=male_youth,
male_adult=male_adult)
actual_result <- split_gender_data(gender_data)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Test that can work out gender proportion scores for a single column",{
column <- c("male_adult female_adult","male_adult",NA,"female_youth")
actual_result <- proportion_control_per_person(column)
expected_result <- c(0.5,1,NA,1)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Test that can correctly identify the categories in a column", {
item <- c("male_adult female_adult","male_adult",NA,"female_youth")
category <- "female_youth"
actual_result <-check_val_in_list(item, category)
expected_result <- c(0,0,0,1)
expect_equal(actual_result,expected_result)
item <- c("male_adult female_adult","male_adult",NA,"female_youth")
category <- "male_adult"
actual_result <-check_val_in_list(item, category)
expected_result <- c(1,1,0,0)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Test that can identify gender control proportions for individual categories",{
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_control_1=c("male_adult female_adult","male_adult",NA,"female_youth"),
crop_control_2=c("male_youth",NA,"female_youth","female_adult")))
# Converting the dataset to the wide format (i.e crops as header, and genders controlling for each crop)
wide_data <- map_to_wide_format(data, "crop_name", c("crop_control"), c("chr"))
genderdf <- wide_data$crop_control
numberControllingDF <- genderdf %>% dplyr::mutate(across(.cols=everything(),~proportion_control_per_person(.x)))
category <- "male_adult"
actual_result <- gender_control_props(genderdf, numberControllingDF, category)
expected_result <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,1,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
expect_equal(actual_result,expected_result)
})
testthat::test_that("Gender split for individual column works",{
data <- c("male_adult","male_adult female_adult",NA,"female_youth male_adult female_adult")
actual_result <- split_gender_columns(data)
expected_result <- structure(list(female_youth = c(0, 0, NA, 0.333333333333333),
female_adult = c(0, 0.5, NA, 0.333333333333333),
male_youth = c(0, 0, NA, 0),
male_adult = c(1, 0.5, NA, 0.333333333333333)),
row.names = c(NA, -4L),
class = c("tbl_df", "tbl", "data.frame"))
expect_equal(actual_result,expected_result)
})
testthat::test_that("Can reformat ODK central loops into RHoMIS format",{
central_core <- tibble::as_tibble(list("random_col_1"=c("x","y"),
"random_col_2"=c("a","b"),
"KEY"=c("uuid:3dbc6436-45a5-438e-9213-e13fefa40eac",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb")))
loop_data <- tibble::as_tibble(list("hh_pop_rep"=c(1,2,3,4,5,1,2),
"hh_pop_repeat_grp-hh_intro"=c("","","","","","",""),
"hh_pop_repeat_grp-person_number"=c("","","","","","",""),
"hh_pop_repeat_grp-person_gender"=c("M","F","F","F","M","M","F"),
"hh_pop_repeat_grp-person_age"=c(58,25,12,45,12,56,22),
"hh_pop_repeat_grp-head_person"=c("Y","N","N","N","N","Y","N"),
"PARENT_KEY"=c("uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:3dbc6436-45a5-438e-9213-e13fefa40eac",
"uuid:3dbc6436-45a5-438e-9213-e13fefa40eac"),
"KEY"=c("uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[1]",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[2]",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[3]",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[4]",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[5]",
"uuid:3dbc6436-45a5-438e-9213-e13fefa40eac/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[1]",
"uuid:3dbc6436-45a5-438e-9213-e13fefa40eac/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[2]")))
expected_result <- tibble::as_tibble(list("random_col_1"=c("x","y"),
"random_col_2"=c("a","b"),
"KEY"=c("uuid:3dbc6436-45a5-438e-9213-e13fefa40eac",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb"),
"hh_pop_rep_1"=c(1,1),
"hh_pop_repeat_grp-hh_intro_1"=c("",""),
"hh_pop_repeat_grp-person_number_1"=c("",""),
"hh_pop_repeat_grp-person_gender_1"=c("M","M"),
"hh_pop_repeat_grp-person_age_1"=c(56,58),
"hh_pop_repeat_grp-head_person_1"=c("Y","Y"),
"hh_pop_rep_2"=c(2,2),
"hh_pop_repeat_grp-hh_intro_2"=c("",""),
"hh_pop_repeat_grp-person_number_2"=c("",""),
"hh_pop_repeat_grp-person_gender_2"=c("F","F"),
"hh_pop_repeat_grp-person_age_2"=c(22,25),
"hh_pop_repeat_grp-head_person_2"=c("N","N"),
"hh_pop_rep_3"=c(NA,3),
"hh_pop_repeat_grp-hh_intro_3"=c(NA,""),
"hh_pop_repeat_grp-person_number_3"=c(NA,""),
"hh_pop_repeat_grp-person_gender_3"=c(NA,"F"),
"hh_pop_repeat_grp-person_age_3"=c(NA,12),
"hh_pop_repeat_grp-head_person_3"=c(NA,"N"),
"hh_pop_rep_4"=c(NA,4),
"hh_pop_repeat_grp-hh_intro_4"=c(NA,""),
"hh_pop_repeat_grp-person_number_4"=c(NA,""),
"hh_pop_repeat_grp-person_gender_4"=c(NA,"F"),
"hh_pop_repeat_grp-person_age_4"=c(NA,45),
"hh_pop_repeat_grp-head_person_4"=c(NA,"N"),
"hh_pop_rep_5"=c(NA,5),
"hh_pop_repeat_grp-hh_intro_5"=c(NA,""),
"hh_pop_repeat_grp-person_number_5"=c(NA,""),
"hh_pop_repeat_grp-person_gender_5"=c(NA,"M"),
"hh_pop_repeat_grp-person_age_5"=c(NA,12),
"hh_pop_repeat_grp-head_person_5"=c(NA,"N")
))
actual_result <- central_loops_to_rhomis_loops(central_core, loop_data)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Gendered split of cropping information", {
data <- structure(list(crop_name_1 = c("maize", "cassava", "wheat"),
crop_yield_1 = c(50, 20, 10),
crop_yield_units_1 = c("kg", "sacks_100kg", "tonnes"),
crop_harvest_kg_per_year_1 = c(50, 2000, 10000),
crop_use_1 = c("eat", "eat sell", "eat sell feed_livestock"),
crop_consumed_prop_1 = c(NA, "most", "half"),
crop_consumed_prop_numeric_1 = c(1, 0.7, 0.5),
crop_consumed_kg_per_year_1 = c(50, 1400, 5000),
crop_sold_prop_1 = c(NA, "little", "little"),
crop_sold_prop_numeric_1 = c(NA, 0.1, 0.1),
crop_sold_kg_per_year_1 = c(NA, 200, 1000),
crop_sold_income_1 = c(NA, 500, 800),
crop_sold_price_quantityunits_1 = c("other bla_unir", "price_per_bag_50kg", "price_per_quintal"),
crop_sold_units_numeric_1 = c(NA, "0.02", "0.01"),
crop_income_per_year_1 = c(NA, 2000, 8000),
crop_price_1 = c(NA, 10, 8),
crop_who_control_revenue_1=c(NA,"female_adult male_adult","female_youth male_youth"),
crop_consume_control_1=c("male_adult female_youth female_adult","female_adult","female_youth"),
crop_name_2 = c("wheat", "maize", "millet"),
crop_yield_2 = c(12, 8, NA),
crop_yield_units_2 = c("kg", "other_randome_unit", "unit_not_registered"),
crop_harvest_kg_per_year_2 = c(12, NA, NA),
crop_use_2 = c("eat sell", "eat sell", "eat sell"),
crop_consumed_prop_2 = c("most", "half", "underhalf"),
crop_consumed_prop_numeric_2 = c(0.7, 0.5, 0.2),
crop_consumed_kg_per_year_2 = c(8.4, NA, NA),
crop_sold_prop_2 = c("little", "half", "most"),
crop_sold_prop_numeric_2 = c(0.1, 0.5, 0.7),
crop_sold_kg_per_year_2 = c(1.2, NA, NA),
crop_sold_income_2 = c(800, 1000, 800),
crop_sold_price_quantityunits_2 = c("total_income_per_year", "price_per_kg", "price_per_bag_100kg" ),
crop_sold_units_numeric_2 = c("total_income_per_year", "1", "0.01"),
crop_income_per_year_2 = c(800, NA, NA),
crop_price_2 = c(666.666666666667, NA, NA),
crop_who_control_revenue_2=c("male_adult female_adult",NA,NA),
crop_consume_control_2=c("male_adult female_youth",NA,NA),
crop_name_3 = c("rice", "other_vegetables", NA),
crop_yield_3 = c(25, 100, NA),
crop_yield_units_3 = c("kg", "cart_225kg", NA),
crop_harvest_kg_per_year_3 = c(25, 22500, NA),
crop_use_3 = c("sell", "eat", NA),
crop_consumed_prop_3 = c(NA, NA, NA),
crop_consumed_prop_numeric_3 = c(NA, 1, NA),
crop_consumed_kg_per_year_3 = c(NA, 22500, NA),
crop_sold_prop_3 = c(NA, NA, NA),
crop_sold_prop_numeric_3 = c(1, NA, NA),
crop_sold_kg_per_year_3 = c(25, NA, NA),
crop_sold_income_3 = c(800, 600, NA),
crop_sold_price_quantityunits_3 = c("total_income_per_year", "price_per_bag_45kg", NA),
crop_sold_units_numeric_3 = c("total_income_per_year", "0.022", NA),
crop_income_per_year_3 = c(800, NA, NA),
crop_price_3 = c(32, NA, NA),
crop_who_control_revenue_3=c("female_adult",NA,NA),
crop_consume_control_3=c(NA,"male_adult",NA)),
row.names = c(NA, -3L),
class = c("tbl_df", "tbl", "data.frame"))
expected_result <- structure(list(crop_name_1 = c("maize", "cassava", "wheat"),
crop_yield_1 = c(50, 20, 10),
crop_yield_units_1 = c("kg", "sacks_100kg", "tonnes"),
crop_harvest_kg_per_year_1 = c(50, 2000, 10000),
crop_use_1 = c("eat", "eat sell", "eat sell feed_livestock"),
crop_consumed_prop_1 = c(NA, "most", "half"),
crop_consumed_prop_numeric_1 = c(1, 0.7, 0.5),
crop_consumed_kg_per_year_1 = c(50, 1400, 5000),
crop_sold_prop_1 = c(NA, "little", "little"),
crop_sold_prop_numeric_1 = c(NA, 0.1, 0.1),
crop_sold_kg_per_year_1 = c(NA, 200, 1000),
crop_sold_income_1 = c(NA, 500, 800),
crop_sold_price_quantityunits_1 = c("other bla_unir", "price_per_bag_50kg", "price_per_quintal"),
crop_sold_units_numeric_1 = c(NA, "0.02", "0.01"),
crop_income_per_year_1 = c(NA, 2000, 8000),
crop_price_1 = c(NA, 10, 8),
crop_who_control_revenue_1=c(NA,"female_adult male_adult","female_youth male_youth"),
male_adult_crop_sold_kg_per_year_1=c(NA,100,0),
male_youth_crop_sold_kg_per_year_1=c(NA,0,500),
female_adult_crop_sold_kg_per_year_1=c(NA,100,0),
female_youth_crop_sold_kg_per_year_1=c(NA,0,500),
crop_consume_control_1=c("male_adult female_youth female_adult","female_adult","female_youth"),
male_adult_crop_consumed_kg_per_year_1=c(16.66667,0,0),
male_youth_crop_consumed_kg_per_year_1=c(0,0,0),
female_adult_crop_consumed_kg_per_year_1=c(16.66667,1400,0),
female_youth_crop_consumed_kg_per_year_1=c(16.66667,0,5000),
crop_name_2 = c("wheat", "maize", "millet"),
crop_yield_2 = c(12, 8, NA),
crop_yield_units_2 = c("kg", "other_randome_unit", "unit_not_registered"),
crop_harvest_kg_per_year_2 = c(12, NA, NA),
crop_use_2 = c("eat sell", "eat sell", "eat sell"),
crop_consumed_prop_2 = c("most", "half", "underhalf"),
crop_consumed_prop_numeric_2 = c(0.7, 0.5, 0.2),
crop_consumed_kg_per_year_2 = c(8.4, NA, NA),
crop_sold_prop_2 = c("little", "half", "most"),
crop_sold_prop_numeric_2 = c(0.1, 0.5, 0.7),
crop_sold_kg_per_year_2 = c(1.2, NA, NA),
crop_sold_income_2 = c(800, 1000, 800),
crop_sold_price_quantityunits_2 = c("total_income_per_year", "price_per_kg", "price_per_bag_100kg" ),
crop_sold_units_numeric_2 = c("total_income_per_year", "1", "0.01"),
crop_income_per_year_2 = c(800, NA, NA),
crop_price_2 = c(666.666666666667, NA, NA),
crop_who_control_revenue_2=c("male_adult female_adult",NA,NA),
male_adult_crop_sold_kg_per_year_2=c(0.6,NA,NA),
male_youth_crop_sold_kg_per_year_2=c(0,NA,NA),
female_adult_crop_sold_kg_per_year_2=c(0.6,NA,NA),
female_youth_crop_sold_kg_per_year_2=c(0,NA,NA),
crop_consume_control_2=c("male_adult female_youth",NA,NA),
male_adult_crop_consumed_kg_per_year_2=c(4.2,NA,NA),
male_youth_crop_consumed_kg_per_year_2=c(0,NA,NA),
female_adult_crop_consumed_kg_per_year_2=c(0,NA,NA),
female_youth_crop_consumed_kg_per_year_2=c(4.2,NA,NA),
crop_name_3 = c("rice", "other_vegetables", NA),
crop_yield_3 = c(25, 100, NA),
crop_yield_units_3 = c("kg", "cart_225kg", NA),
crop_harvest_kg_per_year_3 = c(25, 22500, NA),
crop_use_3 = c("sell", "eat", NA),
crop_consumed_prop_3 = c(NA, NA, NA),
crop_consumed_prop_numeric_3 = c(NA, 1, NA),
crop_consumed_kg_per_year_3 = c(NA, 22500, NA),
crop_sold_prop_3 = c(NA, NA, NA),
crop_sold_prop_numeric_3 = c(1, NA, NA),
crop_sold_kg_per_year_3 = c(25, NA, NA),
crop_sold_income_3 = c(800, 600, NA),
crop_sold_price_quantityunits_3 = c("total_income_per_year", "price_per_bag_45kg", NA),
crop_sold_units_numeric_3 = c("total_income_per_year", "0.022", NA),
crop_income_per_year_3 = c(800, NA, NA),
crop_price_3 = c(32, NA, NA),
crop_who_control_revenue_3=c("female_adult",NA,NA),
male_adult_crop_sold_kg_per_year_3=c(0,NA,NA),
male_youth_crop_sold_kg_per_year_3=c(0,NA,NA),
female_adult_crop_sold_kg_per_year_3=c(25,NA,NA),
female_youth_crop_sold_kg_per_year_3=c(0,NA,NA),
crop_consume_control_3=c(NA,"male_adult",NA),
male_adult_crop_consumed_kg_per_year_3=c(NA,22500,NA),
male_youth_crop_consumed_kg_per_year_3=c(NA,0,NA),
female_adult_crop_consumed_kg_per_year_3=c(NA,0,NA),
female_youth_crop_consumed_kg_per_year_3=c(NA,0,NA)),
row.names = c(NA, -3L),
class = c("tbl_df", "tbl", "data.frame"))
actual_result<-insert_gender_columns_in_core_data(data, original_column="crop_consumed_kg_per_year", control_column="crop_consume_control", loop_structure=T)
actual_result<-insert_gender_columns_in_core_data(actual_result, original_column="crop_sold_kg_per_year", control_column="crop_who_control_revenue", loop_structure=T)
expect_equal(actual_result, expected_result, tolerance=0.001)
})
l-gorman/rhomis-R-package documentation built on Nov. 8, 2023, 6:46 a.m.
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library(testthat)
library(dplyr)
testthat::test_that("Test that can identify number of loops", {
name_column <- "crop_name"
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black")))
actual_result <- find_number_of_loops(data,name_column)
expected_result <- 2
expect_equal(actual_result, expected_result)
})
testthat::test_that("Test can find unique values for the specific name column",{
name_column <- "crop_name"
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black")))
expected_result <- c("banana","cassava","millet","melon","maize")
actual_result <- find_unique_case(data, name_column)
expect_equal(actual_result,expected_result)
# Checking for case with no expected values
data <- tibble::as_tibble(list(crop_name_1=c(NA, NA, NA, NA),
crop_name_2=c(NA,NA, NA, NA),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black")))
name_column <- "crop_name"
expected_result <- c("none")
actual_result <- find_unique_case(data, name_column)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Individual loop variables can be properly formatted",{
name_column <- "crop_name"
variable_to_convert <- "crop_variable"
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_variable_1=c("ex1","ex2",NA,NA),
crop_variable_2=c("ex3",NA,"ex4","ex5")))
expected_result <- tibble::as_tibble(list(banana=c("ex1", NA, NA, NA),
cassava=c("ex3", "ex2", NA, NA),
millet=c(NA, NA, NA, NA),
melon=c(NA, NA, "ex4", NA),
maize=c(NA, NA, NA, "ex5")))
expected_result <- tibble::as_tibble(list(banana=c("ex1", NA, NA, NA),
cassava=c("ex3", "ex2", NA, NA),
millet=c(NA, NA, NA, NA),
melon=c(NA, NA, "ex4", NA),
maize=c(NA, NA, NA, "ex5")))
expected_result<- expected_result %>% dplyr::mutate_all(as.character)
actual_result <- loop_to_column_conversion(data, name_column, variable_to_convert, type="chr")
expect_equal(actual_result,expected_result)
# Checking case for no values
name_column <- "crop_name"
variable_to_convert <- "crop_variable"
data <- tibble::as_tibble(list(crop_name_1=c(NA, NA, NA, NA),
crop_name_2=c(NA,NA, NA, NA),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_variable_1=c(NA,NA,NA,NA),
crop_variable_2=c(NA,NA,NA,NA)))
expected_result <- tibble::as_tibble(list(none=c(NA, NA, NA, NA)))
expected_result<- expected_result %>% dplyr::mutate_all(as.character)
actual_result <- loop_to_column_conversion(data, name_column, variable_to_convert, type="chr")
expect_equal(actual_result,expected_result)
})
testthat::test_that("Test that we can convert multiple columns into the wide format",{
name_column <- "crop_name"
column_prefixes <- c("crop_variable", "crop_unit", "crop_price")
types <- c("chr", "chr", "chr")
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_variable_1=c("ex1","ex2",NA,NA),
crop_variable_2=c("ex3",NA,"ex4","ex5"),
crop_unit_1=c("unit1","unit2","unit3",NA),
crop_unit_2=c(NA,NA,"unit4","unit5"),
crop_price_1=c(NA,NA,NA,NA),
crop_price_2=c(NA,NA,NA,NA)))
actual_result <- map_to_wide_format(data, name_column, column_prefixes, types)
# Expected result
crop_variable <- tibble::as_tibble(list(banana=c("ex1",NA,NA,NA),
cassava=c("ex3","ex2",NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,"ex4",NA),
maize=c(NA,NA,NA,"ex5")))
crop_variable<- crop_variable %>% dplyr::mutate_all(as.character)
crop_unit <- tibble::as_tibble(list(banana=c("unit1",NA,NA,NA),
cassava=c(NA,"unit2",NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,"unit4",NA),
maize=c(NA,NA,NA,"unit5")))
crop_unit<- crop_unit %>% dplyr::mutate_all(as.character)
crop_price <- tibble::as_tibble(list(banana=c(NA,NA,NA,NA),
cassava=c(NA,NA,NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,NA,NA),
maize=c(NA,NA,NA,NA)))
actual_result <- map_to_wide_format(data, name_column, column_prefixes, types)
# Expected result
crop_variable <- tibble::as_tibble(list(banana=c("ex1",NA,NA,NA),
cassava=c("ex3","ex2",NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,"ex4",NA),
maize=c(NA,NA,NA,"ex5")))
crop_variable<- crop_variable %>% dplyr::mutate_all(as.character)
crop_unit <- tibble::as_tibble(list(banana=c("unit1",NA,NA,NA),
cassava=c(NA,"unit2",NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,"unit4",NA),
maize=c(NA,NA,NA,"unit5")))
crop_unit<- crop_unit %>% dplyr::mutate_all(as.character)
crop_price <- tibble::as_tibble(list(banana=c(NA,NA,NA,NA),
cassava=c(NA,NA,NA,NA),
millet=c(NA,NA,NA,NA),
melon=c(NA,NA,NA,NA),
maize=c(NA,NA,NA,NA)))
crop_price<- crop_price %>% dplyr::mutate_all(as.character)
expected_result <- list(crop_variable=crop_variable,
crop_unit=crop_unit,
crop_price=crop_price)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Gender splitting of information works",{
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_control_1=c("male_adult female_adult","male_adult",NA,"female_youth"),
crop_control_2=c("male_youth",NA,"female_youth","female_adult")))
wide_data <- map_to_wide_format(data, "crop_name", c("crop_control"), c("chr"))
gender_data <- wide_data$crop_control
female_youth <- tibble::as_tibble(list(banana=c(0,NA,NA,NA),
cassava=c(0,0,NA,NA),
millet=c(NA,NA,NA,1),
melon=c(NA,NA,1,NA),
maize=c(NA,NA,NA,0)))
female_adult <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,0,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,1)))
male_youth <- tibble::as_tibble(list(banana=c(0,NA,NA,NA),
cassava=c(1,0,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
male_adult <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,1,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
female_adult <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,0,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,1)))
male_youth <- tibble::as_tibble(list(banana=c(0,NA,NA,NA),
cassava=c(1,0,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
male_adult <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,1,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
expected_result <- list(female_youth=female_youth,
female_adult=female_adult,
male_youth=male_youth,
male_adult=male_adult)
actual_result <- split_gender_data(gender_data)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Test that can work out gender proportion scores for a single column",{
column <- c("male_adult female_adult","male_adult",NA,"female_youth")
actual_result <- proportion_control_per_person(column)
expected_result <- c(0.5,1,NA,1)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Test that can correctly identify the categories in a column", {
item <- c("male_adult female_adult","male_adult",NA,"female_youth")
category <- "female_youth"
actual_result <-check_val_in_list(item, category)
expected_result <- c(0,0,0,1)
expect_equal(actual_result,expected_result)
item <- c("male_adult female_adult","male_adult",NA,"female_youth")
category <- "male_adult"
actual_result <-check_val_in_list(item, category)
expected_result <- c(1,1,0,0)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Test that can identify gender control proportions for individual categories",{
data <- tibble::as_tibble(list(crop_name_1=c("banana", "cassava", NA, "millet"),
crop_name_2=c("cassava",NA, "melon", "maize"),
random_crop_name_2=c("blue", "green", "red",NA),
crop_name=c("orange", "purple", NA, "black"),
crop_control_1=c("male_adult female_adult","male_adult",NA,"female_youth"),
crop_control_2=c("male_youth",NA,"female_youth","female_adult")))
# Converting the dataset to the wide format (i.e crops as header, and genders controlling for each crop)
wide_data <- map_to_wide_format(data, "crop_name", c("crop_control"), c("chr"))
genderdf <- wide_data$crop_control
numberControllingDF <- genderdf %>% dplyr::mutate(across(.cols=everything(),~proportion_control_per_person(.x)))
category <- "male_adult"
actual_result <- gender_control_props(genderdf, numberControllingDF, category)
expected_result <- tibble::as_tibble(list(banana=c(0.5,NA,NA,NA),
cassava=c(0,1,NA,NA),
millet=c(NA,NA,NA,0),
melon=c(NA,NA,0,NA),
maize=c(NA,NA,NA,0)))
expect_equal(actual_result,expected_result)
})
testthat::test_that("Gender split for individual column works",{
data <- c("male_adult","male_adult female_adult",NA,"female_youth male_adult female_adult")
actual_result <- split_gender_columns(data)
expected_result <- structure(list(female_youth = c(0, 0, NA, 0.333333333333333),
female_adult = c(0, 0.5, NA, 0.333333333333333),
male_youth = c(0, 0, NA, 0),
male_adult = c(1, 0.5, NA, 0.333333333333333)),
row.names = c(NA, -4L),
class = c("tbl_df", "tbl", "data.frame"))
expect_equal(actual_result,expected_result)
})
testthat::test_that("Can reformat ODK central loops into RHoMIS format",{
central_core <- tibble::as_tibble(list("random_col_1"=c("x","y"),
"random_col_2"=c("a","b"),
"KEY"=c("uuid:3dbc6436-45a5-438e-9213-e13fefa40eac",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb")))
loop_data <- tibble::as_tibble(list("hh_pop_rep"=c(1,2,3,4,5,1,2),
"hh_pop_repeat_grp-hh_intro"=c("","","","","","",""),
"hh_pop_repeat_grp-person_number"=c("","","","","","",""),
"hh_pop_repeat_grp-person_gender"=c("M","F","F","F","M","M","F"),
"hh_pop_repeat_grp-person_age"=c(58,25,12,45,12,56,22),
"hh_pop_repeat_grp-head_person"=c("Y","N","N","N","N","Y","N"),
"PARENT_KEY"=c("uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb",
"uuid:3dbc6436-45a5-438e-9213-e13fefa40eac",
"uuid:3dbc6436-45a5-438e-9213-e13fefa40eac"),
"KEY"=c("uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[1]",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[2]",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[3]",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[4]",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[5]",
"uuid:3dbc6436-45a5-438e-9213-e13fefa40eac/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[1]",
"uuid:3dbc6436-45a5-438e-9213-e13fefa40eac/survey_grp/SECTION_HOUSEHOLD_INFO/householdpopulation/hhpoprep[2]")))
expected_result <- tibble::as_tibble(list("random_col_1"=c("x","y"),
"random_col_2"=c("a","b"),
"KEY"=c("uuid:3dbc6436-45a5-438e-9213-e13fefa40eac",
"uuid:076bb740-3542-49ab-a6b2-e5efb70d76eb"),
"hh_pop_rep_1"=c(1,1),
"hh_pop_repeat_grp-hh_intro_1"=c("",""),
"hh_pop_repeat_grp-person_number_1"=c("",""),
"hh_pop_repeat_grp-person_gender_1"=c("M","M"),
"hh_pop_repeat_grp-person_age_1"=c(56,58),
"hh_pop_repeat_grp-head_person_1"=c("Y","Y"),
"hh_pop_rep_2"=c(2,2),
"hh_pop_repeat_grp-hh_intro_2"=c("",""),
"hh_pop_repeat_grp-person_number_2"=c("",""),
"hh_pop_repeat_grp-person_gender_2"=c("F","F"),
"hh_pop_repeat_grp-person_age_2"=c(22,25),
"hh_pop_repeat_grp-head_person_2"=c("N","N"),
"hh_pop_rep_3"=c(NA,3),
"hh_pop_repeat_grp-hh_intro_3"=c(NA,""),
"hh_pop_repeat_grp-person_number_3"=c(NA,""),
"hh_pop_repeat_grp-person_gender_3"=c(NA,"F"),
"hh_pop_repeat_grp-person_age_3"=c(NA,12),
"hh_pop_repeat_grp-head_person_3"=c(NA,"N"),
"hh_pop_rep_4"=c(NA,4),
"hh_pop_repeat_grp-hh_intro_4"=c(NA,""),
"hh_pop_repeat_grp-person_number_4"=c(NA,""),
"hh_pop_repeat_grp-person_gender_4"=c(NA,"F"),
"hh_pop_repeat_grp-person_age_4"=c(NA,45),
"hh_pop_repeat_grp-head_person_4"=c(NA,"N"),
"hh_pop_rep_5"=c(NA,5),
"hh_pop_repeat_grp-hh_intro_5"=c(NA,""),
"hh_pop_repeat_grp-person_number_5"=c(NA,""),
"hh_pop_repeat_grp-person_gender_5"=c(NA,"M"),
"hh_pop_repeat_grp-person_age_5"=c(NA,12),
"hh_pop_repeat_grp-head_person_5"=c(NA,"N")
))
actual_result <- central_loops_to_rhomis_loops(central_core, loop_data)
expect_equal(actual_result,expected_result)
})
testthat::test_that("Gendered split of cropping information", {
data <- structure(list(crop_name_1 = c("maize", "cassava", "wheat"),
crop_yield_1 = c(50, 20, 10),
crop_yield_units_1 = c("kg", "sacks_100kg", "tonnes"),
crop_harvest_kg_per_year_1 = c(50, 2000, 10000),
crop_use_1 = c("eat", "eat sell", "eat sell feed_livestock"),
crop_consumed_prop_1 = c(NA, "most", "half"),
crop_consumed_prop_numeric_1 = c(1, 0.7, 0.5),
crop_consumed_kg_per_year_1 = c(50, 1400, 5000),
crop_sold_prop_1 = c(NA, "little", "little"),
crop_sold_prop_numeric_1 = c(NA, 0.1, 0.1),
crop_sold_kg_per_year_1 = c(NA, 200, 1000),
crop_sold_income_1 = c(NA, 500, 800),
crop_sold_price_quantityunits_1 = c("other bla_unir", "price_per_bag_50kg", "price_per_quintal"),
crop_sold_units_numeric_1 = c(NA, "0.02", "0.01"),
crop_income_per_year_1 = c(NA, 2000, 8000),
crop_price_1 = c(NA, 10, 8),
crop_who_control_revenue_1=c(NA,"female_adult male_adult","female_youth male_youth"),
crop_consume_control_1=c("male_adult female_youth female_adult","female_adult","female_youth"),
crop_name_2 = c("wheat", "maize", "millet"),
crop_yield_2 = c(12, 8, NA),
crop_yield_units_2 = c("kg", "other_randome_unit", "unit_not_registered"),
crop_harvest_kg_per_year_2 = c(12, NA, NA),
crop_use_2 = c("eat sell", "eat sell", "eat sell"),
crop_consumed_prop_2 = c("most", "half", "underhalf"),
crop_consumed_prop_numeric_2 = c(0.7, 0.5, 0.2),
crop_consumed_kg_per_year_2 = c(8.4, NA, NA),
crop_sold_prop_2 = c("little", "half", "most"),
crop_sold_prop_numeric_2 = c(0.1, 0.5, 0.7),
crop_sold_kg_per_year_2 = c(1.2, NA, NA),
crop_sold_income_2 = c(800, 1000, 800),
crop_sold_price_quantityunits_2 = c("total_income_per_year", "price_per_kg", "price_per_bag_100kg" ),
crop_sold_units_numeric_2 = c("total_income_per_year", "1", "0.01"),
crop_income_per_year_2 = c(800, NA, NA),
crop_price_2 = c(666.666666666667, NA, NA),
crop_who_control_revenue_2=c("male_adult female_adult",NA,NA),
crop_consume_control_2=c("male_adult female_youth",NA,NA),
crop_name_3 = c("rice", "other_vegetables", NA),
crop_yield_3 = c(25, 100, NA),
crop_yield_units_3 = c("kg", "cart_225kg", NA),
crop_harvest_kg_per_year_3 = c(25, 22500, NA),
crop_use_3 = c("sell", "eat", NA),
crop_consumed_prop_3 = c(NA, NA, NA),
crop_consumed_prop_numeric_3 = c(NA, 1, NA),
crop_consumed_kg_per_year_3 = c(NA, 22500, NA),
crop_sold_prop_3 = c(NA, NA, NA),
crop_sold_prop_numeric_3 = c(1, NA, NA),
crop_sold_kg_per_year_3 = c(25, NA, NA),
crop_sold_income_3 = c(800, 600, NA),
crop_sold_price_quantityunits_3 = c("total_income_per_year", "price_per_bag_45kg", NA),
crop_sold_units_numeric_3 = c("total_income_per_year", "0.022", NA),
crop_income_per_year_3 = c(800, NA, NA),
crop_price_3 = c(32, NA, NA),
crop_who_control_revenue_3=c("female_adult",NA,NA),
crop_consume_control_3=c(NA,"male_adult",NA)),
row.names = c(NA, -3L),
class = c("tbl_df", "tbl", "data.frame"))
expected_result <- structure(list(crop_name_1 = c("maize", "cassava", "wheat"),
crop_yield_1 = c(50, 20, 10),
crop_yield_units_1 = c("kg", "sacks_100kg", "tonnes"),
crop_harvest_kg_per_year_1 = c(50, 2000, 10000),
crop_use_1 = c("eat", "eat sell", "eat sell feed_livestock"),
crop_consumed_prop_1 = c(NA, "most", "half"),
crop_consumed_prop_numeric_1 = c(1, 0.7, 0.5),
crop_consumed_kg_per_year_1 = c(50, 1400, 5000),
crop_sold_prop_1 = c(NA, "little", "little"),
crop_sold_prop_numeric_1 = c(NA, 0.1, 0.1),
crop_sold_kg_per_year_1 = c(NA, 200, 1000),
crop_sold_income_1 = c(NA, 500, 800),
crop_sold_price_quantityunits_1 = c("other bla_unir", "price_per_bag_50kg", "price_per_quintal"),
crop_sold_units_numeric_1 = c(NA, "0.02", "0.01"),
crop_income_per_year_1 = c(NA, 2000, 8000),
crop_price_1 = c(NA, 10, 8),
crop_who_control_revenue_1=c(NA,"female_adult male_adult","female_youth male_youth"),
male_adult_crop_sold_kg_per_year_1=c(NA,100,0),
male_youth_crop_sold_kg_per_year_1=c(NA,0,500),
female_adult_crop_sold_kg_per_year_1=c(NA,100,0),
female_youth_crop_sold_kg_per_year_1=c(NA,0,500),
crop_consume_control_1=c("male_adult female_youth female_adult","female_adult","female_youth"),
male_adult_crop_consumed_kg_per_year_1=c(16.66667,0,0),
male_youth_crop_consumed_kg_per_year_1=c(0,0,0),
female_adult_crop_consumed_kg_per_year_1=c(16.66667,1400,0),
female_youth_crop_consumed_kg_per_year_1=c(16.66667,0,5000),
crop_name_2 = c("wheat", "maize", "millet"),
crop_yield_2 = c(12, 8, NA),
crop_yield_units_2 = c("kg", "other_randome_unit", "unit_not_registered"),
crop_harvest_kg_per_year_2 = c(12, NA, NA),
crop_use_2 = c("eat sell", "eat sell", "eat sell"),
crop_consumed_prop_2 = c("most", "half", "underhalf"),
crop_consumed_prop_numeric_2 = c(0.7, 0.5, 0.2),
crop_consumed_kg_per_year_2 = c(8.4, NA, NA),
crop_sold_prop_2 = c("little", "half", "most"),
crop_sold_prop_numeric_2 = c(0.1, 0.5, 0.7),
crop_sold_kg_per_year_2 = c(1.2, NA, NA),
crop_sold_income_2 = c(800, 1000, 800),
crop_sold_price_quantityunits_2 = c("total_income_per_year", "price_per_kg", "price_per_bag_100kg" ),
crop_sold_units_numeric_2 = c("total_income_per_year", "1", "0.01"),
crop_income_per_year_2 = c(800, NA, NA),
crop_price_2 = c(666.666666666667, NA, NA),
crop_who_control_revenue_2=c("male_adult female_adult",NA,NA),
male_adult_crop_sold_kg_per_year_2=c(0.6,NA,NA),
male_youth_crop_sold_kg_per_year_2=c(0,NA,NA),
female_adult_crop_sold_kg_per_year_2=c(0.6,NA,NA),
female_youth_crop_sold_kg_per_year_2=c(0,NA,NA),
crop_consume_control_2=c("male_adult female_youth",NA,NA),
male_adult_crop_consumed_kg_per_year_2=c(4.2,NA,NA),
male_youth_crop_consumed_kg_per_year_2=c(0,NA,NA),
female_adult_crop_consumed_kg_per_year_2=c(0,NA,NA),
female_youth_crop_consumed_kg_per_year_2=c(4.2,NA,NA),
crop_name_3 = c("rice", "other_vegetables", NA),
crop_yield_3 = c(25, 100, NA),
crop_yield_units_3 = c("kg", "cart_225kg", NA),
crop_harvest_kg_per_year_3 = c(25, 22500, NA),
crop_use_3 = c("sell", "eat", NA),
crop_consumed_prop_3 = c(NA, NA, NA),
crop_consumed_prop_numeric_3 = c(NA, 1, NA),
crop_consumed_kg_per_year_3 = c(NA, 22500, NA),
crop_sold_prop_3 = c(NA, NA, NA),
crop_sold_prop_numeric_3 = c(1, NA, NA),
crop_sold_kg_per_year_3 = c(25, NA, NA),
crop_sold_income_3 = c(800, 600, NA),
crop_sold_price_quantityunits_3 = c("total_income_per_year", "price_per_bag_45kg", NA),
crop_sold_units_numeric_3 = c("total_income_per_year", "0.022", NA),
crop_income_per_year_3 = c(800, NA, NA),
crop_price_3 = c(32, NA, NA),
crop_who_control_revenue_3=c("female_adult",NA,NA),
male_adult_crop_sold_kg_per_year_3=c(0,NA,NA),
male_youth_crop_sold_kg_per_year_3=c(0,NA,NA),
female_adult_crop_sold_kg_per_year_3=c(25,NA,NA),
female_youth_crop_sold_kg_per_year_3=c(0,NA,NA),
crop_consume_control_3=c(NA,"male_adult",NA),
male_adult_crop_consumed_kg_per_year_3=c(NA,22500,NA),
male_youth_crop_consumed_kg_per_year_3=c(NA,0,NA),
female_adult_crop_consumed_kg_per_year_3=c(NA,0,NA),
female_youth_crop_consumed_kg_per_year_3=c(NA,0,NA)),
row.names = c(NA, -3L),
class = c("tbl_df", "tbl", "data.frame"))
actual_result<-insert_gender_columns_in_core_data(data, original_column="crop_consumed_kg_per_year", control_column="crop_consume_control", loop_structure=T)
actual_result<-insert_gender_columns_in_core_data(actual_result, original_column="crop_sold_kg_per_year", control_column="crop_who_control_revenue", loop_structure=T)
expect_equal(actual_result, expected_result, tolerance=0.001)
})
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