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

dataquieR example report