The cleaningtools package focuses on cleaning, and has three
components:
1. Check, which includes a set of functions that flag values, such
as check_outliers and check_logical.
2. Create, which includes
a set of functions to create different items for use in cleaning, such
as the cleaning log from the checks, clean data, and enumerator
performance.
3. Review, which includes a set of functions to
review the cleaning, such as reviewing the cleaning.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("impact-initiatives/cleaningtools")You can use the R studio wizard to create project template.
check_pii() function takes raw data (input can be dataframe or list.
However incase of list, you must specify the element name in
element_name parameter!) and looks for potential PII in the dataset.
By default, the function will look for following words but you can also
add additional words to look by using words_to_look parameter.The
default words
are-c("telephone","contact","name","gps","neighbourhood","latitude","logitude","contact","nom","gps","voisinage").
The function will give a list with two element. One will be the data and
second one will be the list of potential PII
- Using dataframe as input
output_from_data <- cleaningtools::check_pii(dataset = cleaningtools::cleaningtools_raw_data, words_to_look = "date", uuid_column = "X_uuid")
output_from_data$potential_PII |> head()
#> # A tibble: 6 × 3
#> uuid question issue
#> <chr> <chr> <chr>
#> 1 all date_assessment Potential PII
#> 2 all neighbourhood Potential PII
#> 3 all return_date Potential PII
#> 4 all water_supply_rest_neighbourhood Potential PII
#> 5 all water_supply_other_neighbourhoods Potential PII
#> 6 all water_supply_other_neighbourhoods_why Potential PII- Using list as input
### from list
df_list <- list(raw_data = cleaningtools::cleaningtools_raw_data)
output_from_list <- cleaningtools::check_pii(dataset = df_list, element_name = "raw_data", words_to_look = "date", uuid_column = "X_uuid")
output_from_list$potential_PII |> head()
#> # A tibble: 6 × 3
#> uuid question issue
#> <chr> <chr> <chr>
#> 1 all date_assessment Potential PII
#> 2 all neighbourhood Potential PII
#> 3 all return_date Potential PII
#> 4 all water_supply_rest_neighbourhood Potential PII
#> 5 all water_supply_other_neighbourhoods Potential PII
#> 6 all water_supply_other_neighbourhoods_why Potential PIIIt will read only the compressed file.
my_audit_list <- cleaningtools::create_audit_list(audit_zip_path = "audit_for_tests_100.zip")Once you have read your audit file from the zip, you will get a list of audit. You can use this list to calculate and add the duration. You have 2 options with a start and end question or summing all the durations.
list_audit <- list(
uuid1 = data.frame(
event = c("form start", rep("question", 5)),
node = c("", paste0("/xx/question", 1:5)),
start = c(
1661415887295, 1661415887301,
1661415890819, 1661415892297,
1661415893529, 1661415894720
),
end = c(
NA, 1661415890790, 1661415892273,
1661415893506, 1661415894703,
1661415896452
)
),
uuid2 = data.frame(
event = c("form start", rep("question", 5)),
node = c("", paste0("/xx/question", 1:5)),
start = c(1661415887295, 1661415887301, 1661415890819, 1661415892297, 1661415893529, 1661415894720),
end = c(NA, 1661415890790, 1661415892273, 1661415893506, 1661415894703, 1661415896452)
)
)
some_dataset <- data.frame(
X_uuid = c("uuid1", "uuid2"),
question1 = c("a", "b"),
question2 = c("a", "b"),
question3 = c("a", "b"),
question4 = c("a", "b"),
question5 = c("a", "b")
)If you want to sum all the duration.
cleaningtools::add_duration_from_audit(some_dataset, uuid_column = "X_uuid", audit_list = list_audit)
#> X_uuid question1 question2 question3 question4 question5
#> 1 uuid1 a a a a a
#> 2 uuid2 b b b b b
#> duration_audit_sum_all_ms duration_audit_sum_all_minutes
#> 1 9058 0.2
#> 2 9058 0.2If you want to use calculate duration between 2 questions.
cleaningtools::add_duration_from_audit(some_dataset,
uuid_column = "X_uuid", audit_list = list_audit,
start_question = "question1",
end_question = "question3",
sum_all = F
)
#> X_uuid question1 question2 question3 question4 question5
#> 1 uuid1 a a a a a
#> 2 uuid2 b b b b b
#> duration_audit_start_end_ms duration_audit_start_end_minutes
#> 1 6205 0.1
#> 2 6205 0.1If you want to do both.
cleaningtools::add_duration_from_audit(some_dataset,
uuid_column = "X_uuid", audit_list = list_audit,
start_question = "question1",
end_question = "question3",
sum_all = T
)
#> X_uuid question1 question2 question3 question4 question5
#> 1 uuid1 a a a a a
#> 2 uuid2 b b b b b
#> duration_audit_sum_all_ms duration_audit_sum_all_minutes
#> 1 9058 0.2
#> 2 9058 0.2
#> duration_audit_start_end_ms duration_audit_start_end_minutes
#> 1 6205 0.1
#> 2 6205 0.1Once you have added the duration to the dataset, you can check if duration are between the threshold you are looking for.
testdata <- data.frame(
uuid = c(letters[1:7]),
duration_audit_start_end_ms = c(
2475353, 375491, 2654267, 311585, 817270,
2789505, 8642007
),
duration_audit_start_end_minutes = c(41, 6, 44, 5, 14, 46, 144)
)
cleaningtools::check_duration(testdata, column_to_check = "duration_audit_start_end_minutes") |> head()
#> $checked_dataset
#> uuid duration_audit_start_end_ms duration_audit_start_end_minutes
#> 1 a 2475353 41
#> 2 b 375491 6
#> 3 c 2654267 44
#> 4 d 311585 5
#> 5 e 817270 14
#> 6 f 2789505 46
#> 7 g 8642007 144
#>
#> $duration_log
#> uuid old_value question
#> 1 b 6 duration_audit_start_end_minutes
#> 2 d 5 duration_audit_start_end_minutes
#> 3 e 14 duration_audit_start_end_minutes
#> 4 g 144 duration_audit_start_end_minutes
#> issue
#> 1 Duration is lower or higher than the thresholds
#> 2 Duration is lower or higher than the thresholds
#> 3 Duration is lower or higher than the thresholds
#> 4 Duration is lower or higher than the thresholds
cleaningtools::check_duration(
testdata,
column_to_check = "duration_audit_start_end_ms",
lower_bound = 375490,
higher_bound = 8642000
) |> head()
#> $checked_dataset
#> uuid duration_audit_start_end_ms duration_audit_start_end_minutes
#> 1 a 2475353 41
#> 2 b 375491 6
#> 3 c 2654267 44
#> 4 d 311585 5
#> 5 e 817270 14
#> 6 f 2789505 46
#> 7 g 8642007 144
#>
#> $duration_log
#> uuid old_value question
#> 1 d 311585 duration_audit_start_end_ms
#> 2 g 8642007 duration_audit_start_end_ms
#> issue
#> 1 Duration is lower or higher than the thresholds
#> 2 Duration is lower or higher than the thresholds
testdata %>%
cleaningtools::check_duration(column_to_check = "duration_audit_start_end_minutes") %>%
check_duration(
column_to_check = "duration_audit_start_end_ms",
log_name = "duration_in_ms",
lower_bound = 375490,
higher_bound = 8642000
) |>
head()
#> $checked_dataset
#> uuid duration_audit_start_end_ms duration_audit_start_end_minutes
#> 1 a 2475353 41
#> 2 b 375491 6
#> 3 c 2654267 44
#> 4 d 311585 5
#> 5 e 817270 14
#> 6 f 2789505 46
#> 7 g 8642007 144
#>
#> $duration_log
#> uuid old_value question
#> 1 b 6 duration_audit_start_end_minutes
#> 2 d 5 duration_audit_start_end_minutes
#> 3 e 14 duration_audit_start_end_minutes
#> 4 g 144 duration_audit_start_end_minutes
#> issue
#> 1 Duration is lower or higher than the thresholds
#> 2 Duration is lower or higher than the thresholds
#> 3 Duration is lower or higher than the thresholds
#> 4 Duration is lower or higher than the thresholds
#>
#> $duration_in_ms
#> uuid old_value question
#> 1 d 311585 duration_audit_start_end_ms
#> 2 g 8642007 duration_audit_start_end_ms
#> issue
#> 1 Duration is lower or higher than the thresholds
#> 2 Duration is lower or higher than the thresholdscheck_outliers() takes raw data set and look for potential outlines.
It can both data frame or list. However you must specify the element
name (name of your data set in the given list) in element_name
parameter!
set.seed(122)
### from list
df_outlier <- data.frame(
uuid = paste0("uuid_", 1:100),
one_value = c(round(runif(90, min = 45, max = 55)), round(runif(5)), round(runif(5, 99, 100))),
expense = c(sample(200:500, replace = T, size = 95), c(600, 100, 80, 1020, 1050)),
income = c(c(60, 0, 80, 1020, 1050), sample(20000:50000, replace = T, size = 95)),
yy = c(rep(100, 99), 10)
)
outliers <- cleaningtools::check_outliers(dataset = df_outlier, uuid_column = "uuid")
#> [1] "checking_one_value"
#> [1] "checking_expense"
#> [1] "checking_income"
#> [1] "checking_yy"
outliers$potential_outliers |> head()
#> # A tibble: 6 × 4
#> uuid issue question old_value
#> <chr> <chr> <chr> <dbl>
#> 1 uuid_91 outlier (normal distribution) one_value 1
#> 2 uuid_92 outlier (normal distribution) one_value 0
#> 3 uuid_93 outlier (normal distribution) one_value 0
#> 4 uuid_94 outlier (normal distribution) one_value 0
#> 5 uuid_95 outlier (normal distribution) one_value 0
#> 6 uuid_96 outlier (normal distribution) one_value 100check_value() function look for specified value in the given data set
and return in a cleaning log format. The function can take a data frame
or a list as input.
set.seed(122)
df <- data.frame(
X_uuid = paste0("uuid_", 1:100),
age = c(sample(18:80, replace = T, size = 96), 99, 99, 98, 88),
gender = c("99", sample(c("male", "female"), replace = T, size = 95), "98", "98", "88", "888")
)
output <- cleaningtools::check_value(dataset = df, uuid_column = "X_uuid", element_name = "checked_dataset", values_to_look = c(99, 98, 88, 888))
output$flaged_value |> head()
#> # A tibble: 6 × 4
#> uuid question old_value issue
#> <chr> <chr> <chr> <chr>
#> 1 uuid_1 gender 99 Possible value to be changed to NA
#> 2 uuid_97 age 99 Possible value to be changed to NA
#> 3 uuid_97 gender 98 Possible value to be changed to NA
#> 4 uuid_98 age 99 Possible value to be changed to NA
#> 5 uuid_98 gender 98 Possible value to be changed to NA
#> 6 uuid_99 age 98 Possible value to be changed to NAcheck_logical() takes a regular expression, as it is how it will be
read from Excel check_logical_with_list().
test_data <- data.frame(
uuid = c(1:10) %>% as.character(),
today = rep("2023-01-01", 10),
location = rep(c("villageA", "villageB"), 5),
distance_to_market = c(rep("less_30", 5), rep("more_30", 5)),
access_to_market = c(rep("yes", 4), rep("no", 6)),
number_children_05 = c(rep(c(0, 1), 4), 5, 6)
)
cleaningtools::check_logical(test_data,
uuid_column = "uuid",
check_to_perform = "distance_to_market == \"less_30\" & access_to_market == \"no\"",
columns_to_clean = "distance_to_market, access_to_market",
description = "distance to market less than 30 and no access"
) |> head()
#> $checked_dataset
#> uuid today location distance_to_market access_to_market
#> 1 1 2023-01-01 villageA less_30 yes
#> 2 2 2023-01-01 villageB less_30 yes
#> 3 3 2023-01-01 villageA less_30 yes
#> 4 4 2023-01-01 villageB less_30 yes
#> 5 5 2023-01-01 villageA less_30 no
#> 6 6 2023-01-01 villageB more_30 no
#> 7 7 2023-01-01 villageA more_30 no
#> 8 8 2023-01-01 villageB more_30 no
#> 9 9 2023-01-01 villageA more_30 no
#> 10 10 2023-01-01 villageB more_30 no
#> number_children_05 logical_xx
#> 1 0 FALSE
#> 2 1 FALSE
#> 3 0 FALSE
#> 4 1 FALSE
#> 5 0 TRUE
#> 6 1 FALSE
#> 7 0 FALSE
#> 8 1 FALSE
#> 9 5 FALSE
#> 10 6 FALSE
#>
#> $logical_xx
#> # A tibble: 2 × 6
#> uuid question old_value issue check_id check_binding
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 5 distance_to_market less_30 distance to market … logical… logical_xx ~…
#> 2 5 access_to_market no distance to market … logical… logical_xx ~…test_data <- data.frame(
uuid = c(1:10) %>% as.character(),
distance_to_market = rep(c("less_30", "more_30"), 5),
access_to_market = c(rep("yes", 4), rep("no", 6)),
number_children_05 = c(rep(c(0, 1), 4), 5, 6),
number_children_618 = c(rep(c(0, 1), 4), 5, 6)
)
check_list <- data.frame(
name = c("logical_xx", "logical_yy", "logical_zz"),
check = c(
"distance_to_market == \"less_30\" & access_to_market == \"no\"",
"number_children_05 > 3",
"rowSums(dplyr::across(starts_with(\"number\")), na.rm = T) > 9"
),
description = c(
"distance to market less than 30 and no access",
"number of children under 5 seems high",
"number of children very high"
),
variables_to_clean = c(
"distance_to_market, access_to_market",
"number_children_05",
""
)
)
cleaningtools::check_logical_with_list(test_data,
uuid_column = "uuid",
list_of_check = check_list,
check_id_column = "name",
check_to_perform_column = "check",
columns_to_clean_column = "variables_to_clean",
description_column = "description"
) |> head()
#> Warning in check_logical(dataset = dataset, uuid_column = uuid_column,
#> information_to_add = information_to_add, : columns_to_clean not shared, results
#> may not be accurate
#> $checked_dataset
#> uuid distance_to_market access_to_market number_children_05
#> 1 1 less_30 yes 0
#> 2 2 more_30 yes 1
#> 3 3 less_30 yes 0
#> 4 4 more_30 yes 1
#> 5 5 less_30 no 0
#> 6 6 more_30 no 1
#> 7 7 less_30 no 0
#> 8 8 more_30 no 1
#> 9 9 less_30 no 5
#> 10 10 more_30 no 6
#> number_children_618 logical_xx logical_yy logical_zz
#> 1 0 FALSE FALSE FALSE
#> 2 1 FALSE FALSE FALSE
#> 3 0 FALSE FALSE FALSE
#> 4 1 FALSE FALSE FALSE
#> 5 0 TRUE FALSE FALSE
#> 6 1 FALSE FALSE FALSE
#> 7 0 TRUE FALSE FALSE
#> 8 1 FALSE FALSE FALSE
#> 9 5 TRUE TRUE TRUE
#> 10 6 FALSE TRUE TRUE
#>
#> $logical_all
#> # A tibble: 10 × 6
#> uuid question old_value issue check_id check_binding
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 5 distance_to_market less_30 dist… logical… logical_xx ~…
#> 2 5 access_to_market no dist… logical… logical_xx ~…
#> 3 7 distance_to_market less_30 dist… logical… logical_xx ~…
#> 4 7 access_to_market no dist… logical… logical_xx ~…
#> 5 9 distance_to_market less_30 dist… logical… logical_xx ~…
#> 6 9 access_to_market no dist… logical… logical_xx ~…
#> 7 9 number_children_05 5 numb… logical… logical_yy ~…
#> 8 10 number_children_05 6 numb… logical… logical_yy ~…
#> 9 9 unable to identify please check this uuid… numb… logical… logical_zz ~…
#> 10 10 unable to identify please check this uuid… numb… logical… logical_zz ~…testdata <- data.frame(
uuid = c(letters[1:4], "a", "b", "c"),
col_a = runif(7),
col_b = runif(7)
)
cleaningtools::check_duplicate(testdata)
#> $checked_dataset
#> uuid col_a col_b
#> 1 a 0.9166284 0.2958878
#> 2 b 0.3212423 0.6047783
#> 3 c 0.2052720 0.8932896
#> 4 d 0.1512732 0.1883179
#> 5 a 0.8096267 0.3822435
#> 6 b 0.5861553 0.6640918
#> 7 c 0.9902022 0.7932066
#>
#> $duplicate_log
#> uuid old_value question issue
#> 1 a a uuid duplicated uuid
#> 2 b b uuid duplicated uuid
#> 3 c c uuid duplicated uuidOr you can check duplicate for a specific variable or combination of variables.
testdata2 <- data.frame(
uuid = letters[c(1:7)],
village = paste("village", c(1:3, 1:3, 4)),
ki_identifier = paste0("xx_", c(1:5, 3, 4))
)
check_duplicate(testdata2, columns_to_check = "village")
#> $checked_dataset
#> uuid village ki_identifier
#> 1 a village 1 xx_1
#> 2 b village 2 xx_2
#> 3 c village 3 xx_3
#> 4 d village 1 xx_4
#> 5 e village 2 xx_5
#> 6 f village 3 xx_3
#> 7 g village 4 xx_4
#>
#> $duplicate_log
#> # A tibble: 3 × 4
#> uuid question old_value issue
#> <chr> <chr> <chr> <glue>
#> 1 d village village 1 duplicated village
#> 2 e village village 2 duplicated village
#> 3 f village village 3 duplicated village
check_duplicate(testdata2, columns_to_check = c("village", "ki_identifier"))
#> $checked_dataset
#> uuid village ki_identifier
#> 1 a village 1 xx_1
#> 2 b village 2 xx_2
#> 3 c village 3 xx_3
#> 4 d village 1 xx_4
#> 5 e village 2 xx_5
#> 6 f village 3 xx_3
#> 7 g village 4 xx_4
#>
#> $duplicate_log
#> # A tibble: 2 × 4
#> uuid question old_value issue
#> <chr> <chr> <chr> <glue>
#> 1 f village village 3 duplicated village ~/~ ki_identifier
#> 2 f ki_identifier xx_3 duplicated village ~/~ ki_identifierTo use it with the complete dataset:
soft_duplicates <- check_soft_duplicates(
dataset = cleaningtools_raw_data,
kobo_survey = cleaningtools_survey,
uuid_column = "X_uuid",
idnk_value = "dont_know",
sm_separator = ".",
log_name = "soft_duplicate_log",
threshold = 7
)
soft_duplicates[["soft_duplicate_log"]] %>% head()
#> [1] uuid issue
#> <0 rows> (or 0-length row.names)
soft_duplicates <- check_soft_duplicates(
dataset = cleaningtools_raw_data,
kobo_survey = cleaningtools_survey,
uuid_column = "X_uuid",
idnk_value = "dont_know",
sm_separator = ".",
log_name = "soft_duplicate_log",
threshold = 7,
return_all_results = TRUE
)
soft_duplicates[["soft_duplicate_log"]] %>% head()
#> uuid num_cols_not_NA total_columns_compared
#> 1 3370f726-395a-4675-94fe-9e745e0b36e9 75 148
#> 2 93095da3-5291-4d16-a19a-41bf13144bfe 85 148
#> 3 db5e05db-94e9-44aa-9206-3e1c17a7a233 85 148
#> 4 dc7bf25b-e18b-4b9e-bb34-5d7a1e762eb2 75 148
#> 5 0858486a-1d3d-492b-863f-b050cb9fe7af 74 148
#> 6 193d5f36-93b9-4c97-9205-13aa7e3a6c7f 75 148
#> num_cols_dont_know id_most_similar_survey
#> 1 0 dc7bf25b-e18b-4b9e-bb34-5d7a1e762eb2
#> 2 0 db5e05db-94e9-44aa-9206-3e1c17a7a233
#> 3 0 93095da3-5291-4d16-a19a-41bf13144bfe
#> 4 0 3370f726-395a-4675-94fe-9e745e0b36e9
#> 5 0 93893e39-9c82-4e19-b480-4dc78033157b
#> 6 0 ac42b381-4d3b-42b4-96fb-676d43a8c4e7
#> number_different_columns issue
#> 1 9 <NA>
#> 2 9 <NA>
#> 3 9 <NA>
#> 4 9 <NA>
#> 5 10 <NA>
#> 6 10 <NA>To use it grouping by enumerator:
group_by_enum_raw_data <- cleaningtools_raw_data %>%
dplyr::group_by(enumerator_num)
soft_per_enum <- group_by_enum_raw_data %>%
dplyr::group_split() %>%
purrr::map(~ check_soft_duplicates(
dataset = .,
kobo_survey = cleaningtools_survey,
uuid_column = "X_uuid", idnk_value = "dont_know",
sm_separator = ".",
log_name = "soft_duplicate_log",
threshold = 7,
return_all_results = TRUE
))
soft_per_enum %>%
purrr::map(~ .[["soft_duplicate_log"]]) %>%
purrr::map2(
.y = dplyr::group_keys(group_by_enum_raw_data) %>% unlist(),
~ dplyr::mutate(.x, enum = .y)
) %>%
do.call(dplyr::bind_rows, .) %>%
head()
#> uuid num_cols_not_NA total_columns_compared
#> 1 44614627-c152-4f24-a3ca-87a58b2f2e3f 65 118
#> 2 a537a7a3-468c-4661-8b7b-93e43e9b8a3b 63 118
#> 3 4cf2c1c2-75a9-4be1-ab1a-09e0b0bec0bd 87 118
#> 4 b6dc0988-15d1-49c6-859f-b0e63c18485b 86 118
#> 5 55e388ed-fdf2-4d53-96b8-8c406947cad3 64 118
#> 6 38d2f047-3036-458b-95fb-fc86f5b7924e 84 118
#> num_cols_dont_know id_most_similar_survey
#> 1 2 a537a7a3-468c-4661-8b7b-93e43e9b8a3b
#> 2 1 44614627-c152-4f24-a3ca-87a58b2f2e3f
#> 3 2 b6dc0988-15d1-49c6-859f-b0e63c18485b
#> 4 2 4cf2c1c2-75a9-4be1-ab1a-09e0b0bec0bd
#> 5 4 a537a7a3-468c-4661-8b7b-93e43e9b8a3b
#> 6 1 48f2f69f-4958-4180-8f31-e3eb3dedc28d
#> number_different_columns issue enum
#> 1 17 <NA> 1
#> 2 17 <NA> 1
#> 3 18 <NA> 1
#> 4 18 <NA> 1
#> 5 19 <NA> 1
#> 6 22 <NA> 1The check_fcs() function verifies whether all the food consumption
components have identical values or not. It will flag the UUIDs where
all the values are the same.
cleaningtools::check_fcs(
dataset = cleaningtools::cleaningtools_food_consumption_df,
uuid_column = "X_uuid",
cereals_column = "cereals_grains_roots_tubers",
pulses_column = "beans_legumes_pulses_nuts",
dairy_column = "milk_dairy_products",
meat_column = "meat_fish_eggs",
vegetables_column = "vegetables",
fruits_column = "fruite",
oil_column = "oil_fat_butter",
sugar_column = "sugar_sugary_food"
) |> head()
#> Warning in cleaningtools::check_fcs(dataset =
#> cleaningtools::cleaningtools_food_consumption_df, : Potential issue:: There are
#> 105 observations where all the variables of food consumption score are the
#> same.Check result.
#> X_uuid cereals_grains_roots_tubers
#> 1 e7da37c0-dd23-4d38-8cac-2e8e8a243b57 0
#> 2 f6b056c8-bda7-43d2-a723-582dc8001265 7
#> 3 c8a00acf-1626-42f8-8c8f-592aa3c9e688 1
#> 4 911204db-f11d-451b-9cd4-44cb76635611 3
#> 5 3f6ce5f7-5afe-4bd9-91ab-03c1b81a70ed 7
#> 6 cc002ebb-a3e5-4b6b-81ae-cbd9c9b047e8 1
#> beans_legumes_pulses_nuts milk_dairy_products meat_fish_eggs vegetables
#> 1 0 0 0 0
#> 2 7 7 7 7
#> 3 1 1 1 1
#> 4 3 3 3 3
#> 5 7 7 7 7
#> 6 1 1 1 1
#> fruite oil_fat_butter sugar_sugary_food
#> 1 0 0 0
#> 2 7 7 7
#> 3 1 1 1
#> 4 3 3 3
#> 5 7 7 7
#> 6 1 1 1
#> issue
#> 1 All the vlaues of of food consumption variables are the same
#> 2 All the vlaues of of food consumption variables are the same
#> 3 All the vlaues of of food consumption variables are the same
#> 4 All the vlaues of of food consumption variables are the same
#> 5 All the vlaues of of food consumption variables are the same
#> 6 All the vlaues of of food consumption variables are the sameThe check_others() function generate a log for other follow up
questions
output <- cleaningtools::check_others(
dataset = cleaningtools::cleaningtools_clean_data,
uuid_column = "X_uuid",
columns_to_check = names(cleaningtools::cleaningtools_clean_data |>
dplyr::select(ends_with("_other")) |>
dplyr::select(-contains(".")))
)
output$other_log |> head()
#> # A tibble: 6 × 4
#> uuid question old_value issue
#> <chr> <chr> <chr> <chr>
#> 1 630d0067-d84a-4fd0-8c36-029e87913c40 primary_livelihood_other عسكري ضم… reco…
#> 2 ac98cb2b-80a9-4ee9-ba67-6fd38b0247d8 problems_water_main_comp… عدم وصول… reco…
#> 3 c18fc8f9-c8b7-4ba3-8e61-4460c3604a9d problems_water_main_comp… عدم وصول… reco…
#> 4 816f41ec-603e-4d5d-a284-8bdb9cbf913d problems_water_main_comp… عدم وجود… reco…
#> 5 0be815d6-1f31-4d4d-8775-6043df8b9f03 problems_water_main_comp… عدم وجود… reco…
#> 6 b4588be7-aa42-47de-9d00-e85ec968c1fa problems_water_main_comp… عدم وجود… reco…The add_percentage_missing() adds the percentage of missing values per
row.
data_example <- data.frame(
uuid = letters[1:3],
col_1 = c(1, NA, 3),
col_2 = c(NA, NA, "expenditures"),
col_3 = c("with need", NA, "with need"),
col_4 = c("food health school", NA, "food"),
col_4.food = c(1, NA, 1),
col_4.health = c(1, NA, 0),
col_4.school = c(1, NA, 0)
)
data_example <- data_example %>% cleaningtools::add_percentage_missing()
data_example |> head()
#> uuid col_1 col_2 col_3 col_4 col_4.food col_4.health
#> 1 a 1 <NA> with need food health school 1 1
#> 2 b NA <NA> <NA> <NA> NA NA
#> 3 c 3 expenditures with need food 1 0
#> col_4.school percentage_missing
#> 1 1 0.125
#> 2 NA 0.875
#> 3 0 0.000The add_percentage_missing() function will flag if a survey for its
missing values. The missing values column can be created with
add_percentage_missing and the values are flagged with check_outliers.
data_example %>%
cleaningtools::check_percentage_missing() |>
head()
#> [1] "checking_percentage_missing"
#> $checked_dataset
#> uuid col_1 col_2 col_3 col_4 col_4.food col_4.health
#> 1 a 1 <NA> with need food health school 1 1
#> 2 b NA <NA> <NA> <NA> NA NA
#> 3 c 3 expenditures with need food 1 0
#> col_4.school percentage_missing
#> 1 1 0.125
#> 2 NA 0.875
#> 3 0 0.000
#>
#> $percentage_missing_log
#> # A tibble: 0 × 4
#> # ℹ 4 variables: uuid <chr>, issue <chr>, question <chr>, old_value <chr>The function create_combined_log() takes the cleaning logs as input
and returns a list with two elements: the dataset and the combined
cleaning log.
list_log <- cleaningtools::cleaningtools_raw_data |>
check_pii(uuid_column = "X_uuid") |>
check_duplicate(uuid_column = "X_uuid") |>
check_value(uuid_column = "X_uuid") |>
create_combined_log()
#> List of element to combine- checked_dataset, potential_PII, duplicate_log, flaged_value
list_log$cleaning_log |> head(6)
#> # A tibble: 6 × 7
#> uuid question issue old_value change_type new_value check_binding
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 all neighbo… Pote… <NA> <NA> <NA> neighbourhoo…
#> 2 all water_s… Pote… <NA> <NA> <NA> water_supply…
#> 3 all water_s… Pote… <NA> <NA> <NA> water_supply…
#> 4 all water_s… Pote… <NA> <NA> <NA> water_supply…
#> 5 all consent… Pote… <NA> <NA> <NA> consent_tele…
#> 6 ac26e24d-12be-47… X_index Poss… 88 <NA> <NA> X_index ~/~ …The function add_info_to_cleaning_log() is designed to add information
from the dataset into the cleaning log.
add_with_info <- list_log |> add_info_to_cleaning_log(dataset_uuid_column = "X_uuid")
add_with_info$cleaning_log |> head()
#> uuid question
#> 1 ac26e24d-12be-4729-bae7-21060ee00a28 X_index
#> 2 all neighbourhood
#> 3 all water_supply_rest_neighbourhood
#> 4 all water_supply_other_neighbourhoods
#> 5 all water_supply_other_neighbourhoods_why
#> 6 all consent_telephone_number
#> issue old_value change_type new_value
#> 1 Possible value to be changed to NA 88 <NA> <NA>
#> 2 Potential PII <NA> <NA> <NA>
#> 3 Potential PII <NA> <NA> <NA>
#> 4 Potential PII <NA> <NA> <NA>
#> 5 Potential PII <NA> <NA> <NA>
#> 6 Potential PII <NA> <NA> <NA>
#> check_binding enumerator_num
#> 1 X_index ~/~ ac26e24d-12be-4729-bae7-21060ee00a28 13
#> 2 neighbourhood ~/~ all NA
#> 3 water_supply_rest_neighbourhood ~/~ all NA
#> 4 water_supply_other_neighbourhoods ~/~ all NA
#> 5 water_supply_other_neighbourhoods_why ~/~ all NA
#> 6 consent_telephone_number ~/~ all NA
#> date_assessment
#> 1 2021-07-06
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 <NA>The function add_info_to_cleaning_log() is designed to add information
from the dataset into the cleaning log.
add_with_info |>
create_xlsx_cleaning_log(
kobo_survey = cleaningtools_survey,
kobo_choices = cleaningtools_choices,
use_dropdown = TRUE,
output_path = "mycleaninglog.xlsx"
)We are creating a dataset and cleaning log for the example
test_data <- data.frame(
uuid = paste0("uuid", 1:4),
age = c(180, 23, 45, 67),
gender = c("male", "female", "male", "female"),
pop_group = c("idp", "refugee", "host", "idp"),
strata = c("a", "b", "c", "d")
)
test_data
#> uuid age gender pop_group strata
#> 1 uuid1 180 male idp a
#> 2 uuid2 23 female refugee b
#> 3 uuid3 45 male host c
#> 4 uuid4 67 female idp dcleaning_log_test <- data.frame(
uuid = paste0("uuid", 1:4),
question = c("age", "gender", "pop_group", "strata"),
change_type = c("blank_response", "no_change", "Delete", "change_res"),
new_value = c(NA_character_, NA_character_, NA_character_, "st-a")
)
cleaning_log_test
#> uuid question change_type new_value
#> 1 uuid1 age blank_response <NA>
#> 2 uuid2 gender no_change <NA>
#> 3 uuid3 pop_group Delete <NA>
#> 4 uuid4 strata change_res st-aAfter obtaining both the cleaning log and dataset, it is considered good practice to utilize the review_cleaning_log() function to ensure the consistency between the cleaning log and the dataset. It is highly recommended to perform this check on a daily basis, enabling you to promptly identify any issues right from the outset.
cleaningtools::review_cleaning_log(
raw_dataset = test_data,
raw_data_uuid_column = "uuid",
cleaning_log = cleaning_log_test,
cleaning_log_change_type_column = "change_type",
change_response_value = "change_res",
cleaning_log_question_column = "question",
cleaning_log_uuid_column = "uuid",
cleaning_log_new_value_column = "new_value"
)
#> [1] "no issues in cleaning log found"Once you have a perfect cleaning log and the raw dataset, you can create
clean data by applyingcreate_clean_data() function.
cleaningtools::create_clean_data(
raw_dataset = test_data,
raw_data_uuid_column = "uuid",
cleaning_log = cleaning_log_test,
cleaning_log_change_type_column = "change_type",
change_response_value = "change_res",
NA_response_value = "blank_response",
no_change_value = "no_change",
remove_survey_value = "Delete",
cleaning_log_question_column = "question",
cleaning_log_uuid_column = "uuid",
cleaning_log_new_value_column = "new_value"
)
#> [1] "age"
#> [1] "strata"
#> uuid age gender pop_group strata
#> 1 uuid1 NA male idp a
#> 2 uuid2 23 female refugee b
#> 3 uuid4 67 female idp st-arecreate_parent_column() recreates the concerted columns for select
multiple questions
test_data <- dplyr::tibble(
uuid = paste0("uuid_", 1:6),
gender = rep(c("male", "female"), 3),
reason = c(
"xx,yy", "xx,zy",
"zy", "xx,xz,zy",
NA_character_, "xz"
),
reason.x.x. = c(0, 1, 0, 1, 0, 0),
reason.yy = c(1, 0, 0, 0, 1, 0),
reason.x.z = c(0, 0, 0, 1, 0, 1),
reason.zy = c(0, 1, 1, 1, 0, 0),
reason_zy = c(NA_character_, "A", "B", "C", NA_character_, NA_character_)
)
cleaningtools::recreate_parent_column(dataset = test_data, uuid_column = "uuid", sm_separator = ".") |> head()
#> Warning in cleaningtools::recreate_parent_column(dataset = test_data,
#> uuid_column = "uuid", : Column(s) names are renamed as multiple separators are
#> found in dataset column names. Please see the above table with the new name.
#> # A tibble: 2 × 2
#> old_name new_name
#> <chr> <chr>
#> 1 reason.x.x. reason.x_x_
#> 2 reason.x.z reason.x_z
#> gender
#> reason.yy
#> reason.zy
#> reason_zy
#> gender
#> reason.yy
#> reason.zy
#> reason_zy
#> gender
#> reason
#> reason.yy
#> reason.zy
#> reason_zy
#> $data_with_fix_concat
#> # A tibble: 6 × 8
#> uuid gender reason reason.x_x_ reason.yy reason.x_z reason.zy reason_zy
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 uuid_1 male yy 0 1 0 0 <NA>
#> 2 uuid_2 female x_x_ zy 1 0 0 1 A
#> 3 uuid_3 male zy 0 0 0 1 B
#> 4 uuid_4 female x_x_ x_z zy 1 0 1 1 C
#> 5 uuid_5 male yy 0 1 0 0 <NA>
#> 6 uuid_6 female x_z 0 0 1 0 <NA>
#>
#> $correction_parent_sm_log
#> uuid question change_type new_value old_value
#> 1 all reason.x.x. variable_removed <NA> <NA>
#> 2 all reason.x.z variable_removed <NA> <NA>
#> 3 all reason.x_x_ variable_added <NA> <NA>
#> 4 all reason.x_z variable_added <NA> <NA>
#> 5 uuid_1 reason change_response yy xx,yy
#> 6 uuid_2 reason change_response x_x_ zy xx,zy
#> 7 uuid_4 reason change_response x_x_ x_z zy xx,xz,zy
#> 8 uuid_6 reason change_response x_z xz
#> 9 uuid_5 reason change_response yy <NA>
#> comment
#> 1 variable removed from the clean dataset
#> 2 variable removed from the clean dataset
#> 3 variable added to the clean dataset
#> 4 variable added to the clean dataset
#> 5 Parent column changed to match children columns
#> 6 Parent column changed to match children columns
#> 7 Parent column changed to match children columns
#> 8 Parent column changed to match children columns
#> 9 NA changed to valuereview_cleaning function takes raw data, clean data and cleaning log
as inputs, and it first creates the cleaning log by comparing raw data
and clean data, then compares it with the user-provided cleaning log.
Finally, flagged the discrepancies between them (if any).
compared_df <- review_cleaning(
raw_dataset = cleaningtools::cleaningtools_raw_data,
raw_dataset_uuid_column = "X_uuid",
clean_dataset = cleaningtools::cleaningtools_clean_data,
clean_dataset_uuid_column = "X_uuid",
cleaning_log = cleaning_log2,
cleaning_log_uuid_column = "X_uuid",
cleaning_log_question_column = "questions",
cleaning_log_new_value_column = "new_value",
cleaning_log_old_value_column = "old_value",
deletion_log = deletion,
deletion_log_uuid_column = "X_uuid",
check_for_deletion_log = T
)
#> Warning: There were 2 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `numerical_check = as.numeric(df.new_value) ==
#> as.numeric(df.old_value)`.
#> Caused by warning:
#> ! NAs introduced by coercion
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
compared_df |> head()
#> # A tibble: 6 × 8
#> uuid df.question df.change_type df.new_value cl.new_value df.old_value
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1a80239e-a8… air_cooler… change_respon… <NA> 4 <NA>
#> 2 a37512fa-f4… air_cooler… change_respon… <NA> 2 <NA>
#> 3 bdcca634-6e… air_cooler… no_action <NA> <NA> <NA>
#> 4 dd3a9983-0b… connection… no_action <NA> <NA> <NA>
#> 5 9dac8c9e-a5… connection… no_action <NA> <NA> <NA>
#> 6 2b148a6e-90… days_avail… no_action <NA> <NA> <NA>
#> # ℹ 2 more variables: cl.old_value <chr>, comment <chr>4.2 Example:: The review_others() function reviews discrepancy between kobo relevancies and the dataset
review_others(
dataset = cleaningtools::cleaningtools_clean_data,
uuid_column = "X_uuid", kobo_survey = cleaningtools::cleaningtools_survey
) |> head()
#> uuid question old_value
#> 1 f58a7fda-27e8-4003-90b3-479bebbb99ab consent_telephone_number yes
#> 2 956b5ed0-5a62-41b7-aec3-af93fbc5b494 consent_telephone_number yes
#> 3 3413afd2-8f05-4a6e-8ec7-5d64dc8fea23 consent_telephone_number yes
#> 4 630d0067-d84a-4fd0-8c36-029e87913c40 consent_telephone_number yes
#> 5 2cd180e8-7f2b-460b-82b5-fb9d163f8e7b consent_telephone_number yes
#> 6 929d60f8-ed9c-4c42-9ee1-cb4a67e4ba27 consent_telephone_number yes
#> issue
#> 1 consent_telephone_number is selected but telephone_number is not found in the dataset
#> 2 consent_telephone_number is selected but telephone_number is not found in the dataset
#> 3 consent_telephone_number is selected but telephone_number is not found in the dataset
#> 4 consent_telephone_number is selected but telephone_number is not found in the dataset
#> 5 consent_telephone_number is selected but telephone_number is not found in the dataset
#> 6 consent_telephone_number is selected but telephone_number is not found in the dataset
#> check_id check_binding
#> 1 id- 27 id- 27 ~/~ f58a7fda-27e8-4003-90b3-479bebbb99ab
#> 2 id- 27 id- 27 ~/~ 956b5ed0-5a62-41b7-aec3-af93fbc5b494
#> 3 id- 27 id- 27 ~/~ 3413afd2-8f05-4a6e-8ec7-5d64dc8fea23
#> 4 id- 27 id- 27 ~/~ 630d0067-d84a-4fd0-8c36-029e87913c40
#> 5 id- 27 id- 27 ~/~ 2cd180e8-7f2b-460b-82b5-fb9d163f8e7b
#> 6 id- 27 id- 27 ~/~ 929d60f8-ed9c-4c42-9ee1-cb4a67e4ba27review_sample_frame_with_dataset() compares the sample frame with
dataset and provide the overview of completed and remaining surveys.
review_output <- cleaningtools::review_sample_frame_with_dataset(
sample_frame = cleaningtools::cleaningtools_sample_frame,
sampling_frame_strata_column = "Neighbourhood",
sampling_frame_target_survey_column = "Total.no.of.HH",
clean_dataset = cleaningtools::cleaningtools_clean_data,
clean_dataset_strata_column = "neighbourhood",
consent_column = "consent_remote",
consent_yes_value = "yes"
)
review_output |> head()
#> Managed.by Governorate Neighbourhood Total.no.of.HH Collected Remaining
#> 1 Talafar Ninewa A1 22 22 0
#> 2 Talafar Ninewa A2 19 19 0
#> 3 Talafar Ninewa A3 5 5 0
#> 4 Talafar Ninewa A4 6 6 0
#> 5 Talafar Ninewa B1 12 12 0
#> 6 Talafar Ninewa B2 15 15 0Please note that the cleaningtools project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
