con_contradictions: Checks user-defined contradictions in study data

Description Usage Arguments Details Value See Also Examples

View source: R/con_contradictions.R

Description

This approach considers a contradiction if impossible combinations of data are observed in one participant. For example, if age of a participant is recorded repeatedly the value of age is (unfortunately) not able to decline. Most cases of contradictions rest on comparison of two variables.

Important to note, each value that is used for comparison may represent a possible characteristic but the combination of these two values is considered to be impossible. The approach does not consider implausible or inadmissible values.

Usage

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con_contradictions(
  resp_vars = NULL,
  study_data,
  meta_data,
  label_col,
  threshold_value,
  check_table,
  summarize_categories = FALSE
)

Arguments

resp_vars

variable list the name of the measurement variables

study_data

data.frame the data frame that contains the measurements

meta_data

data.frame the data frame that contains metadata attributes of study data

label_col

variable attribute the name of the column in the metadata with labels of variables

threshold_value

numeric from=0 to=100. a numerical value ranging from 0-100

check_table

data.frame contradiction rules table. Table defining contractions. See details for its required structure.

summarize_categories

logical Needs a column 'tag' in the check_table. If set, a summary output is generated for the defined categories plus one plot per category.

Details

ALGORITHM OF THIS IMPLEMENTATION:

Value

If summarize_categories is FALSE: A list with:

if summarize_categories is TRUE, other objects are returned: one per category named by that category (e.g. "Empirical") containing a result for contradictions within that category only. Additionally, in the slot all_checks a result as it would have been returned with summarize_categories set to FALSE. Finally, a slot SummaryData is returned containing sums per Category and an according ggplot in SummaryPlot.

See Also

Online Documentation

Examples

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load(system.file("extdata", "meta_data.RData", package = "dataquieR"))
load(system.file("extdata", "study_data.RData", package = "dataquieR"))
check_table <- read.csv(system.file("extdata",
  "contradiction_checks.csv",
  package = "dataquieR"
),
header = TRUE, sep = "#"
)
check_table[1, "tag"] <- "Logical"
check_table[1, "Label"] <- "Becomes younger"
check_table[2, "tag"] <- "Empirical"
check_table[2, "Label"] <- "sex transformation"
check_table[3, "tag"] <- "Empirical"
check_table[3, "Label"] <- "looses academic degree"
check_table[4, "tag"] <- "Logical"
check_table[4, "Label"] <- "vegetarian eats meat"
check_table[5, "tag"] <- "Logical"
check_table[5, "Label"] <- "vegan eats meat"
check_table[6, "tag"] <- "Empirical"
check_table[6, "Label"] <- "non-veg* eats meat"
check_table[7, "tag"] <- "Empirical"
check_table[7, "Label"] <- "Non-smoker buys cigarettes"
check_table[8, "tag"] <- "Empirical"
check_table[8, "Label"] <- "Smoker always scrounges"
check_table[9, "tag"] <- "Logical"
check_table[9, "Label"] <- "Cuff didn't fit arm"
check_table[10, "tag"] <- "Empirical"
check_table[10, "Label"] <- "Very mature pregnant woman"
label_col <- "LABEL"
threshold_value <- 1
con_contradictions(
  study_data = study_data, meta_data = meta_data, label_col = label_col,
  threshold_value = threshold_value, check_table = check_table
)
check_table[1, "tag"] <- "Logical, Age-Related"
check_table[10, "tag"] <- "Empirical, Age-Related"
con_contradictions(
  study_data = study_data, meta_data = meta_data, label_col = label_col,
  threshold_value = threshold_value, check_table = check_table
)

dataquieR documentation built on Feb. 26, 2021, 5:08 p.m.