inst/doc/DQ-report-example.R

In dataquieR: Data Quality in Epidemiological Research

## ----include=FALSE------------------------------------------------------------
library(knitr)
library(DT)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, dpi = 10)
if (rmarkdown::pandoc_available(version = "1.12.3")) {
  knit_print.data.frame <- function(x, ...) {
    knit_print(DT::datatable(head(x, 10)), ...)
  }
  registerS3method("knit_print", "data.frame", knit_print.data.frame)
}
library(dataquieR)

## ----echo=TRUE, warning=FALSE, message=FALSE----------------------------------
load(system.file("extdata", "study_data.RData", package = "dataquieR"))
sd1 <- study_data

## ----echo=TRUE, warning=FALSE, message=FALSE----------------------------------
load(system.file("extdata", "meta_data.RData", package = "dataquieR"))
md1 <- meta_data

## ----message=FALSE, warning=FALSE---------------------------------------------
appmatrix <- pro_applicability_matrix(
  study_data = sd1,
  meta_data = md1,
  label_col = LABEL
)

## ----message=FALSE, warning=FALSE, fig.height = 10, fig.width = 6-------------
appmatrix$ApplicabilityPlot

## ----message = FALSE, warning = FALSE-----------------------------------------
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

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

## ----message=FALSE, echo=TRUE, warning=FALSE, results = 'hide', fig.keep = 'all',  fig.align="center", fig.height = 3, fig.width = 4----
MissSegs$SummaryPlot

## ----message=FALSE, warning=FALSE---------------------------------------------
# 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)

## ----message=FALSE, warning=FALSE---------------------------------------------
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
)

## ----message=FALSE, warning=FALSE---------------------------------------------
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")
)

## ----message=FALSE, echo=TRUE, warning=FALSE, results = 'hide', fig.keep = 'all',  fig.align="center", fig.height = 6, fig.width = 4----
MissSegs$SummaryPlot

## ----message=FALSE, warning=FALSE---------------------------------------------
code_labels <- prep_get_data_frame("meta_data_v2|missing_table")

## ----message = FALSE, warning = FALSE-----------------------------------------
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
)

## ----message=FALSE, echo=TRUE, warning=FALSE----------------------------------
item_miss$SummaryTable

## ----message=FALSE, echo=TRUE, warning=FALSE, fig.height=5, fig.width = 5-----
item_miss$SummaryPlot

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

## ----message=FALSE, echo=TRUE, warning=FALSE----------------------------------
MyValueLimits$SummaryTable

## -----------------------------------------------------------------------------
# select variables with deviations
whichdeviate <- as.character(MyValueLimits$SummaryTable$Variables)[MyValueLimits$SummaryTable$GRADING == 1]

## ----message=FALSE, echo=TRUE, warning=FALSE, results = 'hide', fig.keep = 'all', fig.align="center", fig.height = 5, fig.width = 7----
patchwork::wrap_plots(plotlist = MyValueLimits$SummaryPlotList[whichdeviate], ncol = 2) 

## ----message=FALSE, warning=FALSE---------------------------------------------
IAVCatAll <- con_inadmissible_categorical(
  study_data = sd1,
  meta_data = md1,
  label_col = "LABEL"
)

## ----message=FALSE, warning=FALSE---------------------------------------------
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
)

## ----message=FALSE, echo=TRUE, warning=FALSE----------------------------------
AnyContradictions$SummaryTable

## ----message=FALSE, echo=TRUE, warning=FALSE, fig.height = 4, fig.width = 6----
AnyContradictions$SummaryPlot

## ----echo = TRUE--------------------------------------------------------------
ruol <- dataquieR:::acc_robust_univariate_outlier(study_data = sd1, meta_data = md1, label_col = LABEL)

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

## ---- fig.height = 3, fig.width = 4-------------------------------------------
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