# dataquieR example report

#### Elisa Kasbohm, Joany Marino, Elena Salogni, Adrian Richter, Stephan Struckmann, Carsten Oliver Schmidt

# Preface

This is a brief example report using `dataquieR`’s functions. Also, all outputs are disabled to avoid big files and long runtimes on CRAN. For a longer and better elaborated example, please also consider our online example with data from SHIP.

Please, also consider the `dq_report2` function for creating interactive reports, that can be viewed using a web browser.

# INTEGRITY

## Study data

    load(system.file("extdata", "study_data.RData", package = "dataquieR"))
    sd1 <- study_data

The imported study data consist of:

- N = `dim(sd1)[1]` observations and
- P = `dim(sd1)[2]` study variables

## Metadata

    load(system.file("extdata", "meta_data.RData", package = "dataquieR"))
    md1 <- meta_data

The imported meta data provide information for:

- P = `dim(md1)[1]` study variables and
- Q = `dim(md1)[2]` attributes

## Applicability

The call of this R-function requires two inputs only:

    appmatrix <- pro_applicability_matrix(
      study_data = sd1,
      meta_data = md1,
      label_col = LABEL
    )

Heatmap-like plot:

    appmatrix$ApplicabilityPlot

# COMPLETENESS

## Unit missingness

    my_unit_missings2 <- com_unit_missingness(
      study_data = sd1,
      meta_data = md1,
      id_vars = c("CENTER_0", "PSEUDO_ID"),
      strata_vars = "CENTER_0",
      label_col = "LABEL"
    )

    my_unit_missings2$SummaryData

## Segment missingness

    MissSegs <- com_segment_missingness(
      study_data = sd1,
      meta_data = md1,
      label_col = "LABEL",
      threshold_value = 5,
      direction = "high",
      exclude_roles = c("secondary", "process")
    )

    MissSegs$SummaryPlot

### Adding variables for stratification

For some analyses adding new and transformed variable to the study data is necessary.

    # use the month function of the lubridate package to extract month of exam date
    require(lubridate)
    # apply changes to copy of data
    sd2 <- sd1
    # indicate first/second half year
    sd2$month <- month(sd2$v00013)

Static metadata of the variable must be added to the respective metadata.

    MD_TMP <- prep_add_to_meta(
      VAR_NAMES = "month",
      DATA_TYPE = "integer",
      LABEL = "EXAM_MONTH",
      VALUE_LABELS = "1 = January | 2 = February | 3 = March |
                                              4 = April | 5 = May | 6 = June | 7 = July |
                                              8 = August | 9 = September | 10 = October |
                                              11 = November | 12 = December",
      meta_data = md1
    )

Subsequent call of the R-function may include the new variable.

    MissSegs <- com_segment_missingness(
      study_data = sd2,
      meta_data = MD_TMP,
      group_vars = "EXAM_MONTH",
      label_col = "LABEL",
      threshold_value = 1,
      direction = "high",
      exclude_roles = c("secondary", "process")
    )

    MissSegs$SummaryPlot

## Item missingness

The following implementation considers also labeled missing codes. The use of such a table is optional but recommended. Missing code labels used in the simulated study data are loaded as follows:

    code_labels <- prep_get_data_frame("meta_data_v2|missing_table")

    item_miss <- com_item_missingness(
      study_data = sd1,
      meta_data = meta_data,
      label_col = "LABEL",
      show_causes = TRUE,
      cause_label_df = code_labels,
      include_sysmiss = TRUE,
      threshold_value = 80
    )

The function call above sets the analyses of causes for missing values to TRUE, includes system missings with an own code, and sets the threshold to 80%.

    item_miss$SummaryTable

#### Summary plot of item missingness

    item_miss$SummaryPlot

# CONSISTENCY

## Limit deviations

    MyValueLimits <- con_limit_deviations(
      resp_vars = NULL,
      label_col = "LABEL",
      study_data = sd1,
      meta_data = md1,
      limits = "HARD_LIMITS"
    )

### Summary table

    MyValueLimits$SummaryTable

### Summary plot

    # select variables with deviations
    whichdeviate <- unique(as.character(MyValueLimits$SummaryData$Variables)[
      MyValueLimits$SummaryData$Number > 0 & MyValueLimits$SummaryData$Section !=
        "within"])

    patchwork::wrap_plots(plotlist = MyValueLimits$SummaryPlotList[whichdeviate], ncol = 2)

## Inadmissible levels

    IAVCatAll <- con_inadmissible_categorical(
      study_data = sd1,
      meta_data = md1,
      label_col = "LABEL"
    )

## Contradictions

    checks <- read.csv(system.file("extdata",
      "contradiction_checks.csv",
      package = "dataquieR"
    ),
    header = TRUE, sep = "#"
    )

    AnyContradictions <- con_contradictions(
      study_data = sd1,
      meta_data = md1,
      label_col = "LABEL",
      check_table = checks,
      threshold_value = 1
    )

    AnyContradictions$SummaryTable

    AnyContradictions$SummaryPlot

# ACCURACY

    robust_univariate_outlier(study_data = sd1, meta_data = md1, label_col = LABEL)

    c(
    #  head(ruol$SummaryPlotList, 2),
      tail(ruol$SummaryPlotList, 2)
    )

    myloess <- dataquieR::acc_loess(
      resp_vars = "SBP_0",
      group_vars = "USR_BP_0",
      time_vars = "EXAM_DT_0",
      label_col = "LABEL",
      study_data = sd1,
      meta_data = md1
    )

    myloess$SummaryPlotList