The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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:

Metadata

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

The imported meta data provide information for:

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.