Package 'cleaningtools'

Title: cleaningtools package focuses on data cleaning
Description: The cleaningtools package focuses on cleaning, and has three components: **Check**, which includes a set of functions that flag values, such as check_outliers and check_logical. **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. **Review**, which includes a set of functions to review the cleaning.
Authors: Mehedi Khan [aut] , Yann Say [cre, aut]
Maintainer: Yann Say <[email protected]>
License: MIT + file LICENSE
Version: 0.0.0.9003
Built: 2024-10-17 05:32:49 UTC
Source: https://github.com/impact-initiatives/cleaningtools

Help Index

Creates a duration variable using the start and end time of the survey

Description

Creates a duration variable using the start and end time of the survey

Usage

add_duration(
  dataset,
  duration_column = "duration",
  uuid_column = "uuid",
  start_column = "start",
  end_column = "end"
)

Arguments

dataset

dataset to add the duration
duration_column

the name of the duration variable that will be created.
uuid_column

uuid column in the dataset. Default is uuid.
start_column

start time of the survey, in KOBO format (e.g "2021-07-14T14:02:24.955+03:00")
end_column

end time of the survey, in KOBO format (e.g "2021-07-14T14:03:28.955+03:00")

Value

the clean dataset with 6 new columns: duration, start_date, start_time, end_date, end_time, days_diff

Examples

test_data <- data.frame(
  start = c(
    "2021/07/13 11:25:49", "2021/07/13 12:36:16",
    "2021/07/13 10:21:10", "2021/07/13 10:54:07", "2021/07/13 11:18:45"
  ),
  end = c(
    "2021/07/13 12:02:39", "2021/07/13 13:20:17",
    "2021/07/13 10:53:42", "2021/07/13 11:28:58", "2021/07/13 11:55:24"
  ),
  uuid = 1:5
)

test_data_w_duration <- test_data %>%
  add_duration()

test_data_kobo_time <- data.frame(
  `X.U.FEFF.start` = c(
    "2021-07-13T11:25:49.543+03:00",
    "2021-07-13T12:36:16.316+03:00",
    "2021-07-13T10:21:10.337+03:00",
    "2021-07-13T10:54:07.394+03:00",
    "2021-07-13T11:18:45.521+03:00"
  ),
  end = c(
    "2021-07-14T12:02:39.269+03:00",
    "2021-07-13T13:20:17.815+03:00",
    "2021-07-13T10:53:42.662+03:00",
    "2021-07-13T11:28:58.295+03:00",
    "2021-07-13T11:55:24.366+03:00"
  ),
  uuid_column = 1:5
)
test_data_kobo_time_w_duration <- test_data_kobo_time %>%
  add_duration(start_column = "X.U.FEFF.start")

Adds duration from the audit file

Description

Wrapper around create_duration_from_audit_with_start_end and create_duration_from_audit_sum_all to add the duration to the dataset.

Usage

add_duration_from_audit(
  dataset,
  col_name_prefix = "duration_audit",
  uuid_column = "uuid",
  audit_list,
  start_question = NULL,
  end_question = NULL,
  sum_all = TRUE
)

Arguments

dataset

dataset to add the duration
col_name_prefix

string character to be used for the prefix of new columns
uuid_column

uuid column in the dataset. Default is uuid.
audit_list

list of dataframe that are the audit file with the uuid of each interview as name of the dataframe.
start_question

character vector use for the starting question (optional)
end_question

character vector use for the ending question (optional)
sum_all

TRUE or FALSE if to add the time with sum all duration

Value

return the dataset with durations column added

Examples

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")
)
add_duration_from_audit(some_dataset, uuid_column = "X_uuid", audit_list = list_audit)
add_duration_from_audit(some_dataset,
  uuid_column = "X_uuid", audit_list = list_audit,
  start_question = "question1",
  end_question = "question3",
  sum_all = FALSE
)
add_duration_from_audit(some_dataset,
  uuid_column = "X_uuid", audit_list = list_audit,
  start_question = "question1",
  end_question = "question3",
  sum_all = TRUE
)

Add information to the cleaning log

Description

Add information to the cleaning log

Usage

add_info_to_cleaning_log(
  list_of_log = NULL,
  dataset = "checked_dataset",
  cleaning_log = "cleaning_log",
  dataset_uuid_column = "uuid",
  cleaning_log_uuid_column = "uuid",
  information_to_add = c("enumerator_num", "date_assessment")
)

Arguments

list_of_log

A list file containing the cleaning_log and the dataset
dataset

If a list is provided, the element name represents the dataset; otherwise, it should be a dataframe.
cleaning_log

If a list is provided, the element name represents the cleaning log; otherwise, it is should be a dataframe.
dataset_uuid_column

uuid column in the cleaning dataset, it can take a vector of several binding key. Default is uuid.
cleaning_log_uuid_column

uuid column in the cleaning log, it can take a vectore of several binding key. Default is uuid.
information_to_add

Vector of name of the columns to be added.

Value

A list with cleaning log with added info and the dataset.

Examples

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() |>
  add_info_to_cleaning_log(dataset_uuid_column = "X_uuid")

Adds the percentage of missing values per row

Description

Adds the percentage of missing values per row

Usage

add_percentage_missing(
  dataset,
  column_name = "percentage_missing",
  kobo_survey = NULL,
  type_to_include = c("integer", "date", "text", "select_one", "select_multiple")
)

Arguments

dataset

A dataset to add the percentage of missing values
column_name

string variable with the name of the new column to be created, default is percentage_missing
kobo_survey

Kobo survey sheet.
type_to_include

Types (from KOBO) to be included in the columns default are integer, date, text, select_one and select_multiple.

Value

a dataset with the one additional column with the percentage of missing value

See Also

check_percentage_missing()

Examples

data_test <- data.frame(
  uuid = c(1:3),
  col_1 = c(1:3),
  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)
)
kobo_survey <- data.frame(
  type = c(
    "uuid",
    "integer",
    "select_one choice2",
    "select_one choice3",
    "select_multiple choice4"
  ),
  name = c("uuid", "col_1", "col_2", "col_3", "col_4")
)
data_test %>% add_percentage_missing(
  kobo_survey = kobo_survey,
  type_to_include = c("integer", "select_one", "select_multiple")
)
data_test %>% add_percentage_missing()

Detect select multiple parent columns

Description

auto_detect_sm_parents is mean to detect select multiple parent columns in a way that does not rely on the XLSForm as the input

Usage

auto_detect_sm_parents(dataset, sm_separator = ".")

Arguments

dataset

dataset to correct
sm_separator

Separator for choice multiple questions. The default is "." If using read_csv to read in data the separator will most likely be '/' where as if using read.csv it will likely be '.'

Value

a list of select multiple parent columns in data set.

detect and group together select multiple parent and children columns

Description

auto_sm_parent_children is mean to detect select multiple parent columns & children columns in a way that does not rely on the XLSForm as the input

Usage

auto_sm_parent_children(dataset, sm_separator = ".")

Arguments

dataset

dataset to correct
sm_separator

Separator for choice multiple questions. The default is "." If using read_csv to read in data the separator will most likely be '/' where as if using read.csv it will likely be '.'

Value

a data frame containing the the child select multiple columns alongside there parents and a log with all changes recorded.

Checks for duplicated values in columns

Description

Checks for duplicated values in columns

Usage

check_duplicate(
  dataset,
  uuid_column = "uuid",
  columns_to_check = NULL,
  log_name = "duplicate_log"
)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset".
uuid_column

uuid column in the dataset. Default is uuid.
columns_to_check

string character with the name of the columns to check. If NULL (default), it will check for the uuid_column
log_name

name of the log of flagged value

Value

return a list with the dataset checked stored as checked_dataset and a dataframe with the duplicate log

Examples

testdata <- data.frame(
  uuid = c(letters[1:4], "a", "b", "c"),
  col_a = runif(7),
  col_b = runif(7)
)

check_duplicate(testdata)

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")

check_duplicate(testdata2, columns_to_check = c("village", "ki_identifier"), uuid = "uuid")

Check if duration is outside of a range

Description

Check if value is strictly inferior of the lower threshold or strictly superior of the higher threshold.

Usage

check_duration(
  dataset,
  column_to_check,
  uuid_column = "uuid",
  log_name = "duration_log",
  lower_bound = 25,
  higher_bound = 90
)

Arguments

dataset

a dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
column_to_check

string character with the name of the duration column
uuid_column

uuid column in the dataset. Default is uuid.
log_name

character string with name to give to the log
lower_bound

lower value of the range (strictly inferior to)
higher_bound

higher value of the range (strictly superior to)

Value

return a list with the dataset checked stored as checked_dataset and a dataframe with the duration log

Examples

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)
)

check_duration(testdata,
  column_to_check = "duration_audit_start_end_minutes"
)

check_duration(
  testdata,
  column_to_check = "duration_audit_start_end_ms",
  lower_bound = 375490,
  higher_bound = 8642000
)

testdata %>%
  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
  )

FCS component checks

Description

FCS component checks

Usage

check_fcs(
  dataset,
  uuid_column = "uuid",
  cereals_column = "fs_fcs_cerealgrainroottuber",
  pulses_column = "fs_fcs_beansnuts",
  dairy_column = "fs_fcs_dairy",
  meat_column = "fs_fcs_meatfishegg",
  vegetables_column = "fs_fcs_vegetableleave",
  fruits_column = "fs_fcs_fruit",
  oil_column = "fs_fcs_fat",
  sugar_column = "fs_fcs_sugar"
)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
uuid_column

uuid column in the dataset. Default is uuid.
cereals_column

Variable name for- In the last 7 days, on how many days did your household eat cereals, grains, roots and tubers, including wild roots?
pulses_column

Variable name for- In the last 7 days, on how many days did your household eat any beans / legumes, pulses or nuts?
dairy_column

Variable name for- In the last 7 days, on how many days did your household drink milk or eat other dairy products?
meat_column

Variable name for- In the last 7 days, on how many days did your household eat meat, fish or eggs?
vegetables_column

Variable name for- In the last 7 days, on how many days did your household eat vegetables or leaves, including all wild vegetables and leaves?
fruits_column

Variable name for- In the last 7 days, on how many days did your household eat fruits, including all wild fruits?
oil_column

Variable name for- In the last 7 days, on how many days did your household eat oil, fat, or butter?
sugar_column

Variable name for- In the last 7 days, on how many days did your household eat sugar or sugary foods?

Value

dataset with potential issues

Check a logical test

Description

Check a logical test

Usage

check_logical(
  dataset,
  uuid_column = "uuid",
  information_to_add = NULL,
  check_id = "logical_xx",
  check_to_perform,
  columns_to_clean = NULL,
  description
)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
uuid_column

uuid column in the dataset. Default is "uuid".
information_to_add

string vector optional, if to add some information to the log (today, vilagge)
check_id

name of the check to perform
check_to_perform

test to perform as R code (in text format)
columns_to_clean

variables to be put in the log. if not provided, it will try to detect variables
description

description of the check performed

Value

return a list with the dataset checked stored as checked_dataset, it will have all a column added with the check_id and a dataframe with the logical check log.

Examples

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)
)

check_logical(test_data,
  uuid_column = "uuid",
  check_id = "my_test",
  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"
)

check_logical(test_data,
  uuid_column = "uuid",
  information_to_add = c("today", "location"),
  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"
)

Check several logical test

Description

This is a wrapper around check_logical to allow several checks to be performed.

Usage

check_logical_with_list(
  dataset,
  uuid_column = "uuid",
  information_to_add = NULL,
  list_of_check,
  check_id_column,
  check_to_perform_column,
  columns_to_clean_column = NULL,
  description_column,
  bind_checks = TRUE
)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
uuid_column

uuid column in the dataset. Default is "uuid".
information_to_add

string vector optional, if to add some information to the log (today, vilagge)
list_of_check

a dataframe with the checks to perform
check_id_column

the column name with the names of each test.
check_to_perform_column

the column name with the checks to perform as R code (in text format)
columns_to_clean_column

the column name with the variables to be put in the log.
description_column

the column name with the description
bind_checks

default is TRUE, to bind the checks into 1 log.

Value

return a list with the dataset checked stored as checked_dataset, it will have all a column added with the check_id and a dataframe with the logical check log.

Examples

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"
  ),
  columns_to_clean = c(
    "distance_to_market, access_to_market",
    "number_children_05",
    ""
  )
)
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 = "columns_to_clean",
  description_column = "description"
)

Generate a log for other follow up questions

Description

Generate a log for other follow up questions

Usage

check_others(dataset, uuid_column = "uuid", columns_to_check = NULL)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
uuid_column

uuid column in the dataset. Default is "uuid".
columns_to_check

Columns to include in the checks

Value

a log with all the values that may require recoding

Examples

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(".")))
)

Check if the input passed to the check_others function is correct

Description

Check if the input passed to the check_others function is correct

Usage

check_others_checks(dataset, uuid_column, columns_to_check)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
uuid_column

uuid column in the dataset. Default is uuid.
columns_to_check

Columns to include in the checks

Value

nothing

check outliers over the dataset

Description

check outliers over the dataset

Usage

check_outliers(
  dataset,
  uuid_column = "uuid",
  element_name = "checked_dataset",
  kobo_survey = NULL,
  kobo_choices = NULL,
  cols_to_add_cleaning_log = NULL,
  strongness_factor = 3,
  minimum_unique_value_of_variable = NULL,
  remove_choice_multiple = TRUE,
  sm_separator = ".",
  columns_not_to_check = NULL
)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
uuid_column

UUID. Default is uuid
element_name

name of the dataset in list
kobo_survey

Kobo survey sheet. Default is NULL.
kobo_choices

Kobo choices sheet. Default is NULL.
cols_to_add_cleaning_log

Variables those must be included in the output
strongness_factor

Strongness factor define how strong your outliers will be. The default is 3.
minimum_unique_value_of_variable

Default is NULL, mean this parameter won't be considered. For example 10 means for any variable where number of unique value is less than 10, then the variable won't be considered for outlier checking.
remove_choice_multiple

TRUE (default) will remove choice multiple questions from the output.
sm_separator

Separator for choice multiple questions. The default is "."
columns_not_to_check

Columns to exclude from the checks even if they are numeric values.

Value

return a list with the dataset checked stored as checked_dataset and a dataframe with the outliers log

Examples

dataset_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 = TRUE, size = 95), c(600, 100, 80, 1020, 1050)),
  income = c(c(60, 0, 80, 1020, 1050), sample(20000:50000, replace = TRUE, size = 95)),
  yy = c(rep(100, 99), 10)
)

check_outliers(dataset = dataset_outlier, uuid_column = "uuid")

Check the percentages of missing value

Description

The 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.

Usage

check_percentage_missing(
  dataset,
  uuid_column = "uuid",
  column_to_check = "percentage_missing",
  strongness_factor = 2,
  log_name = "percentage_missing_log"
)

Arguments

dataset

a dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset".
uuid_column

uuid column in the dataset. Default is "uuid".
column_to_check

string character with the name of the columns to check. Default is "percentage_missing"
strongness_factor

Strongness factor define how strong your outliers will be. The default is 3.
log_name

name of the log of flagged value, default is percentage_missing_log

Value

return a list with the dataset checked stored as checked_dataset and a dataframe with the flagged values log

See Also

add_percentage_missing(), check_outliers()

Examples

# Adding the percentage missing first
data_example <- data.frame(
  uuid = letters[1:3],
  col_1 = c(1:3),
  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 %>%
  add_percentage_missing()
data_example %>% check_percentage_missing()

# With a dataset that already has a percentage missing
data_example2 <- data.frame(
  uuid = letters,
  any_cols = LETTERS,
  any_number = 1:26,
  percentage_missing = c(rep(.05, 25), .99)
)
data_example2 %>% check_percentage_missing()

Checks for potential PII

Description

Checks for potential PII

Usage

check_pii(
  dataset,
  element_name = "checked_dataset",
  uuid_column = "uuid",
  words_to_look = NULL
)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
element_name

If the input is a list file, please specify the element name that contains the dataset
uuid_column

uuid column in the dataset. Default is uuid.
words_to_look

Specify the words that might be the PIIs

Value

dataset with potential PII

Examples

check_pii(cleaningtools_raw_data, uuid_column = "X_uuid")

Checks for survey similarities - Soft Duplicates

Description

Checks for survey similarities - Soft Duplicates

Usage

check_soft_duplicates(
  dataset,
  kobo_survey,
  uuid_column = "uuid",
  idnk_value = "idnk",
  sm_separator = ".",
  log_name = "soft_duplicate_log",
  threshold = 7,
  return_all_results = FALSE
)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset" stored as "checked_dataset"
kobo_survey

Kobo survey sheet.
uuid_column

uuid column in the dataset. Default is "uuid".
idnk_value

String character for the value of the "I don't know" value
sm_separator

Separator for choice multiple questions. The default is "."
log_name

Name of the log dataframe flagged in the end of the function
threshold

flag all entries less or equal a specified threshold. The default is 7.
return_all_results

By default, the function will return only the values that are under the threshold. Default is FALSE.

Value

return a list with the dataset checked stored as checked_dataset and a dataframe with the soft duplicate log

Examples

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
)

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, .)

Check for value(s) in the dataset

Description

Check for value(s) in the dataset

Usage

check_value(
  dataset,
  uuid_column = "uuid",
  element_name = "checked_dataset",
  values_to_look = c(99, 999, 999, 88, 888, 888)
)

Arguments

dataset

dataset to be check as a dataframe or a list with the dataframe stored as "checked_dataset"
uuid_column

uuid column in the dataset. Default is "uuid".
element_name

Name of the dataset in list
values_to_look

Values to look. Default are- 99,999,999,88,888,888

Value

A dataframe as cleaning log format

Examples

df <- data.frame(
  X_uuid = paste0("uuid_", 1:100),
  age = c(sample(18:80, replace = TRUE, size = 96), 99, 99, 98, 88),
  gender = c("99", sample(c("male", "female"),
    replace = TRUE, size = 95
  ), "98", "98", "88", "888")
)
check_value(
  dataset = df, uuid_column = "X_uuid",
  element_name = "checked_dataset",
  values_to_look = c(99, 98, 88, 888)
)

Analysis by population group

Description

Survey data

Usage

cleaningtools_analysis_by_group

Details

These data sets include HH data (raw and clean data) and analysis along with cleaning log.

Examples

cleaningtools_analysis_by_group
cleaningtools_raw_data
cleaningtools_cleaning_log
cleaningtools_clean_data
cleaningtools_survey
cleaningtools_choices
cleaningtools_overall_analysis
cleaningtools_sample_frame

Choices tab of kobo tool

Description

Choices tab of kobo tool

Usage

cleaningtools_choices

Clean data

Description

Clean data

Usage

cleaningtools_clean_data

Cleaning log

Description

Cleaning log

Usage

cleaningtools_cleaning_log

Dataset with food consumption, household hunger Score component

Description

Dataset with food consumption, household hunger Score component

Usage

cleaningtools_food_consumption_df

Nation/all population level analysis.

Description

Nation/all population level analysis.

Usage

cleaningtools_overall_analysis

Raw data

Description

Raw data

Usage

cleaningtools_raw_data

Sample frame

Description

Sample frame

Usage

cleaningtools_sample_frame

Survey tab of kobo tool

Description

Survey tab of kobo tool

Usage

cleaningtools_survey

Coerce numeric values to character, without scientific noting and NA are kept as NA.

Description

Coerce numeric values to character, without scientific noting and NA are kept as NA.

Usage

coerce_to_character(x)

Arguments

x

a value to convert

Value

x as character

Examples

coerce_to_character(c("a", NA))
coerce_to_character(c(1, 10000000, NA))

Read all audit files from a zip

Description

Read all audit files from a zip

Usage

create_audit_list(audit_zip_path, dataset = NULL, uuid_column = "uuid")

Arguments

audit_zip_path

location to .zip file. It must contain .zip
dataset

dataset to (optional)
uuid_column

uuid column if a dataset is provided. It will only read the uuid present in the dataset. It will only be used if a dataset is provided.

Value

A list with all the audits file read as they are.

Examples

## Not run: 
create_audit_list("audit_path.zip")

## End(Not run)

implement cleaning log on raw data set.

Description

implement cleaning log on raw data set.

Usage

create_clean_data(
  raw_dataset,
  raw_data_uuid_column = "uuid",
  cleaning_log,
  cleaning_log_uuid_column = "uuid",
  cleaning_log_question_column,
  cleaning_log_new_value_column,
  cleaning_log_change_type_column,
  change_response_value = "change_response",
  NA_response_value = "blank_response",
  no_change_value = "no_action",
  remove_survey_value = "remove_survey"
)

Arguments

raw_dataset

Raw dataset
raw_data_uuid_column

uuid column in the raw dataset. Default is uuid.
cleaning_log

cleaning + deletion log (data.frame).
cleaning_log_uuid_column

uuid column in the cleaning log. Default is "uuid".
cleaning_log_question_column

column in cleaning log which specifies data set column to change
cleaning_log_new_value_column

cleaning log column specifying the new correct value
cleaning_log_change_type_column

column in cleaning log which specifies change type to be made
change_response_value

values in change type column which should be changed to a new value.
NA_response_value

values in change type column which should be blank (NA).
no_change_value

values in change type column which should NOT be changed to a new value.
remove_survey_value

values in change type column which should be deleted from the data.

Value

clean data set

Examples

cleaning_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")
)
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")
)


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"
)

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"
)

Compares 2 dataset and logs differences

Description

Compares 2 dataset and logs differences

Usage

create_cleaning_log(
  raw_dataset,
  raw_dataset_uuid_column = "uuid",
  clean_dataset = clean_dataset,
  clean_dataset_uuid_column = "uuid",
  check_for_deletion_log = T,
  columns_not_to_check = NULL,
  check_for_variable_name = T
)

Arguments

raw_dataset

Raw dataset
raw_dataset_uuid_column

uuid column in the raw dataset. Default is "uuid".
clean_dataset

Clean dataset
clean_dataset_uuid_column

uuid column in the raw dataset. Default is "uuid".
check_for_deletion_log

TRUE to flag the removed survey
columns_not_to_check

Columns to exclude from the checks
check_for_variable_name

TRUE to flag the removed variables

Value

Cleaning log

Examples

## Not run: 
create_cleaning_log(
  raw_dataset = cleaningtools::cleaningtools_raw_data, raw_dataset_uuid_column = "X_uuid",
  clean_dataset = cleaningtools::cleaningtools_clean_data, clean_dataset_uuid_column = "X_uuid",
  check_for_deletion_log = TRUE, check_for_variable_name = TRUE
)

## End(Not run)

Create a project folder with a cleaning template

Description

Create a project folder with a cleaning template

Usage

create_cleaning_template(folder_path, write_template_files, ...)

Arguments

folder_path

Path for the new folder
write_template_files

TRUE/FALSE if to write template files
...

Extra information collected from the RStudio wizard

Value

Folder with a copy of the cleaning_template_folder

Examples

## Not run: 
create_cleaning_template("path/to/folder")

## End(Not run)

Generate excel range to be used for the data validation formula in excel

Description

This function returns an Excel cell range for a given column in a dataframe. The range is formatted for use in Excel functions or formulas and is prefixed with the sheet name 'validation_rules'.

Usage

create_col_range(variable, data.val)

Arguments

variable

The name of the column/question for which the Excel range is to be generated.
data.val

A dataframe containing the column specified by variable.

Value

A string representing the Excel cell range containing all choices for the column variable in the sheet 'validation_rules'.

Examples

# Assume df is a sample dataframe with a column named 'consent'.
# range_string <- create_col_range('consent', df)

Merging the cleaning logs

Description

Merging the cleaning logs

Usage

create_combined_log(list_of_log, dataset_name = "checked_dataset")

Arguments

list_of_log

A list file containig all the logs
dataset_name

The dataset's name, if it exists in the list_of_log

Value

Combined cleaning log

Examples

list <- 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_log = list)

Calculate duration from audit summing all time

Description

Duration is the difference between the sum of all start and the sum of all end. It ignores all node that are empty such as form_start, end screen, form save, etc.

Usage

create_duration_from_audit_sum_all(audit_file)

Arguments

audit_file

a dataframe with a single audit file, it needs start, end, node column

Value

A dataframe with the duration in ms and duration in minutes.

Examples

some_audit <- 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
  )
)
create_duration_from_audit_sum_all(some_audit)

Calculate duration from audit between 2 questions

Description

The function will calculate time difference between the start of the start_question and the end of the end_question.

Usage

create_duration_from_audit_with_start_end(
  audit_file,
  start_question,
  end_question
)

Arguments

audit_file

a dataframe with a single audit file, it needs start, end, node column
start_question

character vector use for the starting question
end_question

character vector use for the ending question

Details

In case the node appear several time (if the value was changed or with a select multiple) it will take the minimum for the start and the maximum for the end.

If a value is missing (skip logic or question not found), it will return -Inf

Value

A dataframe with the duration in ms and duration in minutes.

Examples

some_audit <- 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
  )
)
create_duration_from_audit_with_start_end(some_audit, "question1", "question3")

Creates formatted workbook with openxlsx

Description

Creates formatted workbook with openxlsx

Usage

create_formated_wb(
  write_list,
  column_for_color = NULL,
  header_front_size = 12,
  header_front_color = "#FFFFFF",
  header_fill_color = "#ee5859",
  header_front = "Arial Narrow",
  body_front = "Arial Narrow",
  body_front_size = 11
)

Arguments

write_list

List of dataframe
column_for_color

Column name in the dataframe which should be used for colorizing the cell. The default is null.
header_front_size

Header front size (default is 12)
header_front_color

Hexcode for header front color (default is white)
header_fill_color

Hexcode for header fill color (default is red)
header_front

Front name for header (default is Arial Narrow)
body_front

Front name for body (default is Arial Narrow)
body_front_size

Front size for body (default is 11)

Value

A workbook

Examples

checks_list <- 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_log = checks_list) |>
  create_formated_wb()

Format and filter Choices for 'select_one' Questions

Description

This function returns a dataframe containing 'select_one' questions from the tool dataframe with their corresponding choices in the choices dataframe.

Usage

create_formatted_choices(choices, tool)

Arguments

choices

A dataframe containing the choices sheet from the Kobo tool Expected to have at least the columns list_name and name.
tool

A dataframe containing the survey sheet from the Kobo tool Expected to have at least the columns type and name.

Value

A dataframe containing 'select_one' questions with their corresponding choices. The dataframe has columns from both the tool and choices dataframes.

Examples

# formatted_choices <- create_formatted_choices(choices, tool)

Create logical checks for "other" values.

Description

Create logical checks for "other" values.

Usage

create_logic_for_other(
  kobo_survey,
  sm_separator = ".",
  compare_with_dataset = FALSE,
  dataset = NULL
)

Arguments

kobo_survey

Kobo survey sheet.
sm_separator

Separator for choice multiple questions. The default is "."
compare_with_dataset

Default FALSE will not compare the applicability of logics with the dataset whereas TRUE will check the applicability of logics.
dataset

dataset to be checked.

Value

A dataframe will all logical tests to check "others" value. It should be used in combination of review_others

Examples

create_logic_for_other(
  kobo_survey = cleaningtools::cleaningtools_survey,
  sm_separator = ".",
  dataset = cleaningtools::cleaningtools_clean_data,
  compare_with_dataset = TRUE
)

Create a Validation List for Data Entry

Description

This function generates a validation list to be used for data entry validation. It combines predefined validation lists with dynamically formatted choices derived from the choices and tool parameters. The resulting dataframe is intended to provide structured and valid choices for various question types.

Usage

create_validation_list(choices, tool)

Arguments

choices

A dataframe representing the Kobo choices sheet. Expected to have at least the columns list_name and name.
tool

A dataframe representing the Kobo survey sheet. Expected to have at least the columns type and name.

Value

A dataframe where each column corresponds to a choice list for a specific question. Each row contains a valid choice for the question.

Examples

# Assume choices_df and tool_df are sample dataframes that fit the expected structure.
# validation_list <- create_validation_list(choices, tool)

Creates formatted excel for cleaning log

Description

Creates formatted excel for cleaning log

Usage

create_xlsx_cleaning_log(
  write_list,
  cleaning_log_name = "cleaning_log",
  change_type_col = "change_type",
  column_for_color = "check_binding",
  header_front_size = 12,
  header_front_color = "#FFFFFF",
  header_fill_color = "#ee5859",
  header_front = "Arial Narrow",
  body_front = "Arial Narrow",
  body_front_size = 11,
  use_dropdown = F,
  sm_dropdown_type = NULL,
  kobo_survey = NULL,
  kobo_choices = NULL,
  output_path = NULL
)

Arguments

write_list

List of dataframe
cleaning_log_name

Name for cleaning log from write_list
change_type_col

Change type column name in the cleaning log
column_for_color

Column name in the dataframe which should be used for colorizing the cell. The default is null.
header_front_size

Header front size (default is 12)
header_front_color

Hexcode for header front color (default is white)
header_fill_color

Hexcode for header fill color (default is red)
header_front

Front name for header (default is Arial Narrow)
body_front

Front name for body (default is Arial Narrow)
body_front_size

Front size for body (default is 11)
use_dropdown

Use drop down lists for data validation in the cleaning log output (default is FALSE)
sm_dropdown_type

Dropdown list options for select multiple questions: numerical (1/0) or logical (TRUE/FALSE) - default is logical
kobo_survey

Kobo survey dataframe
kobo_choices

Kobo choices dataframe
output_path

Output path. Default is NULL which will return a workbook instead of an excel file.

Value

save a .xlsx file or return a workbook object

Examples

## Not run: 

checks_list <- 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_log = checks_list) |>
  create_xlsx_cleaning_log()

logical_check_example <- cleaningtools::cleaningtools_raw_data |>
  check_logical(
    check_to_perform = 'treat_cook_water == "always_treat"',
    uuid_column = "X_uuid",
    description = "description",
    check_id = "check_4",
    columns_to_clean = "treat_cook_water"
  )
create_combined_log(logical_check_example) |>
  create_xlsx_cleaning_log(
    output_path = paste0(tempdir(check = TRUE), "/cleaning_log.xlsx"),
    cleaning_log_name = "cleaning_log",
    change_type_col = "change_type",
    kobo_survey = cleaningtools::cleaningtools_survey,
    kobo_choices = cleaningtools::cleaningtools_choices,
    use_dropdown = TRUE,
    sm_dropdown_type = "logical"
  )

## End(Not run)

detects variables names in code

Description

detects variables names in code

Usage

detect_variable(codeintext)

Arguments

codeintext

a string vector to look for variables

Value

a vector with strings

Examples

string_to_check <- c(
  "ki_age> 1",
  "noo==\"mar\"",
  "sum(var1, var2, na.rm = T)",
  "oks != \"mar2\"",
  "oknospace!=\"mar2\""
)
detect_variable(string_to_check)

This function recreates the columns for select multiple questions

Description

This function recreates the columns for select multiple questions

Usage

recreate_parent_column(
  dataset,
  uuid_column = "uuid",
  kobo_survey = NULL,
  kobo_choices = NULL,
  sm_separator = ".",
  cleaning_log_to_append = NULL
)

Arguments

dataset

data frame
uuid_column

uuid column in the dataset. Default is "uuid".
kobo_survey

Kobo survey sheet.
kobo_choices

Kobo choices sheet.
sm_separator

Separator for choice multiple questions. The default is "."
cleaning_log_to_append

A cleaning log where to add the changes from this functions. Names of the log from this function are "uuid", "question", "change_type", "new_value", "old_value", "comment". If the cleaning_log_to_append names are not matching, the only way is to create without a cleaning_log_to_append, and rename the columns and then bind.

Examples

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.xx = c(0, 1, 0, 1, 0, 0),
  reason.yy = c(1, 0, 0, 0, 1, 0),
  reason.xz = 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_)
)
recreate_parent_column(dataset = test_data, uuid_column = "uuid", sm_separator = ".")

Review cleaning

Description

It will compare the raw dataset, clean dataset, cleaning log and deletion log. Possible flags are :

  • UUID found in deletion log.

  • No action with different value in new value column.

  • Changes were not applied

  • This survey should be deleted from the clean dataset but it was not deleted

  • This survey should be removed from deletion log as it doesn't exist in the raw data.

  • Duplicated entry with different value, please recheck and keep one

  • Entry missing in cleaning log

  • New value in cleaning log and value in clean dataset not matching

  • Survey missing in the raw data

Usage

review_cleaning(
  raw_dataset,
  raw_dataset_uuid_column = "uuid",
  clean_dataset,
  clean_dataset_uuid_column = "uuid",
  cleaning_log = cleaning_log_only,
  cleaning_log_uuid_column = "uuid",
  cleaning_log_change_type_column = "change_type",
  cleaning_log_question_column = "question",
  cleaning_log_new_value_column = "new_value",
  cleaning_log_old_value_column = "old_value",
  cleaning_log_added_survey_value = "added_survey",
  cleaning_log_no_change_value = c("no_action", "no_change"),
  deletion_log = NULL,
  deletion_log_uuid_column = NULL,
  check_for_deletion_log = T
)

Arguments

raw_dataset

Raw dataset
raw_dataset_uuid_column

uuid column in the raw dataset. Default is "uuid".
clean_dataset

Clean dataset
clean_dataset_uuid_column

uuid column in the raw dataset. Default is "uuid".
cleaning_log

Cleaning log
cleaning_log_uuid_column

uuid column in the raw dataset. Default is "uuid".
cleaning_log_change_type_column

column in cleaning log which specifies which change to be made
cleaning_log_question_column

column in cleaning log which specifies which column to change
cleaning_log_new_value_column

cleaning log column specifying the new correct value
cleaning_log_old_value_column

cleaning log column specifying the old value
cleaning_log_added_survey_value

Value for change type column which defines for new surveys
cleaning_log_no_change_value

Value for change type column which defines for no action needed
deletion_log

deletion log
deletion_log_uuid_column

Unique ID column name of deletion log
check_for_deletion_log

TRUE to flag the removed survey

Value

Discrepancy in cleaning log

Examples

## Not run: 
deletion_log <- cleaningtools::cleaningtools_cleaning_log |>
  dplyr::filter(change_type == "remove_survey")
cleaning_log <- cleaningtools::cleaningtools_cleaning_log |>
  dplyr::filter(change_type != "remove_survey")

review_cleaning(
  raw_dataset = cleaningtools::raw_dataset, raw_dataset_uuid_column = "X_uuid",
  clean_dataset = cleaningtools::clean_dataset, 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 = deletaion_log,
  deletion_log_uuid_column = "X_uuid",
  check_for_deletion_log = T
)

## End(Not run)

Review cleaning log

Description

Review the cleaning log. It can be used before create_clean_data to check cleaning log. Possible flags are:

  • Value in change_response_value not found

  • question_does_not_exist: question does not exist

  • uuid_does_not_exist: cannot find a UUID

  • na_in_change_type: change_type is NA

Usage

review_cleaning_log(
  raw_dataset,
  raw_data_uuid_column = "uuid",
  cleaning_log,
  cleaning_log_uuid_column = "uuid",
  cleaning_log_question_column,
  cleaning_log_new_value_column,
  cleaning_log_change_type_column,
  change_response_value = "change_response"
)

Arguments

raw_dataset

Raw dataset
raw_data_uuid_column

uuid column in the raw dataset. Default is "uuid".
cleaning_log

cleaning log (data.frame)
cleaning_log_uuid_column

uuid column in the cleaning log. Default is "uuid".
cleaning_log_question_column

column in cleaning log which specifies data set column to change
cleaning_log_new_value_column

cleaning log column specifying the new correct value
cleaning_log_change_type_column

column in cleaning log which specifies change type to be made
change_response_value

values in change type column which should be changed to a new value.

Value

review of the cleaning log with flagged entries and note explaining the flags.

Review discrepancy between kobo relevancies and the dataset.

Description

Review discrepancy between kobo relevancies and the dataset.

Usage

review_others(
  dataset,
  uuid_column = "uuid",
  kobo_survey,
  sm_separator = ".",
  information_to_add = NULL,
  columns_not_to_check = NULL
)

Arguments

dataset

dataset to be check
uuid_column

uuid column in the dataset. Default is "uuid".
kobo_survey

Kobo survey sheet.
sm_separator

Separator for choice multiple questions. The default is "."
information_to_add

string vector optional, if to add some information to the log (today,village etc.)
columns_not_to_check

Columns to exclude from the checks

Value

Any discrepancy between kobo relevancies and the dataset.

Examples

review_others(
  dataset = cleaningtools::cleaningtools_clean_data,
  uuid_column = "X_uuid", kobo_survey = cleaningtools_survey
)

Compares the sample frame with the clean data

Description

Compares the sample frame with the clean data

Usage

review_sample_frame_with_dataset(
  sample_frame,
  sampling_frame_strata_column,
  sampling_frame_target_survey_column = "Total.no.of.HH",
  clean_dataset,
  clean_dataset_strata_column,
  consent_column = "consent",
  consent_yes_value = "yes"
)

Arguments

sample_frame

A sample frame. It mush have a column for strata and another column defining the number of needed survey per strata.
sampling_frame_strata_column

Column name for strata in the sample frame
sampling_frame_target_survey_column

Column name defining number of survey per strata in sample frame.
clean_dataset

Clean data
clean_dataset_strata_column

Column name for strata in clean data
consent_column

Dataset consent column
consent_yes_value

Choice for consent yes

Value

a dataset with the two additional columns with the number of completed surveys and number of remaining survey.

Examples

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"
)

Verify if the Kobo choices dataframe is valid

Description

This function checks whether the provided Kobo choices dataframe meets certain criteria:

  1. It is a dataframe.

  2. It is not empty.

  3. It contains columns named "list_name" and "name".

Usage

verify_valid_choices(kobo_choices)

Arguments

kobo_choices

A dataframe representing the Kobo choices sheet.

Value

Logical. Returns TRUE if the dataframe meets all the criteria. Otherwise, it returns FALSE.

Examples

# Assume df_valid_choices is a valid Kobo choices dataframe
df_valid_choices <- data.frame(list_name = c("ChoiceA", "ChoiceB"), name = c("Option1", "Option2"))
verify_valid_choices(df_valid_choices) # should return TRUE

# Assume df_invalid_choices lacks the required columns
df_invalid_choices <- data.frame(column1 = c("ChoiceA", "ChoiceB"),
                                 column2 = c("Option1", "Option2"))
verify_valid_choices(df_invalid_choices) # should return FALSE

Verify if the Kobo survey dataframe is valid

Description

This function checks whether the provided Kobo survey dataframe meets certain criteria:

  1. It is a dataframe.

  2. It is not empty.

  3. It contains columns named "type" and "name".

Usage

verify_valid_survey(kobo_survey)

Arguments

kobo_survey

A dataframe representing the Kobo survey sheet.

Value

Logical. Returns TRUE if the dataframe meets all the criteria. Otherwise, it returns FALSE.

Examples

# Assume df_valid is a valid Kobo survey dataframe
df_valid <- data.frame(type = c("integer", "selecte_one yesno"), name = c("age", "consent"))
verify_valid_survey(df_valid) # should return TRUE

# Assume df_invalid lacks the required columns
df_invalid <- data.frame(column1 = c("integer", "selecte_one yesno"), column2 = c("age", "consent"))
verify_valid_survey(df_invalid) # should return FALSE