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Perform common cleaning tasks for ISS/eSURV data

clean_iss_data.Rd

ISS/eSURV data often needs to be cleaned and standardized before analysis. Because these datasets may vary from country to country, reviewing the data first and its columns is the first step. In general, there are eight required columns. These are the parameters with a suffix _col. Modify the passed arguments as necessary so the function can successfully run. Priority levels are set to automatically detect high, medium, low, and not a focal site. Ensure that priority level column categories have these specification:

  • High: begins with "h".

  • Medium: begins with "m".

  • Low: begins with "l".

  • Not Focal Site: begins with "n" or "x".

Usage

clean_iss_data(
  iss_data,
  start_date,
  end_date,
  priority_col = "priority_level",
  start_time_col = "starttime",
  unreported_cases_col = "num_unreportedcases",
  prov_col = "states",
  dist_col = "districts",
  hf_col = "name_of_facility_visited",
  today_col = "today",
  date_of_visit_col = "date_of_visit",
  ctry.data = lifecycle::deprecated()
)

Arguments

iss_data

tibble ISS data.

start_date

str Start date of desk review.

end_date

str End date of desk review.

priority_col

str Column representing priority level.

start_time_col

str Column representing start time.

unreported_cases_col

str Column representing unreported cases.

prov_col

str Column representing province.

dist_col

str Column representing district.

hf_col

str Column representing the health facility name.

today_col

str Column representing when info was recorded.

date_of_visit_col

str Column representing date of visit.

ctry.data

list [Deprecated] Please pass the ISS data directly to the iss.data parameter.

Value

tibble Cleaned eSurv/ISS data.

Examples

if (FALSE) { # \dontrun{
iss_path <- "C:/Users/ABC1/Desktop/iss_data.csv"
ctry.data <- init_dr("somalia", iss_data_path = iss_path)
ctry.data$iss.data <- clean_iss_data(ctry.data$iss.data, start_date, end_date)
} # }