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.
The imported study data consist of:
dim(sd1)[1]
observations anddim(sd1)[2]
study variablesThe imported meta data provide information for:
dim(md1)[1]
study variables anddim(md1)[2]
attributesMissSegs <- com_segment_missingness(
study_data = sd1,
meta_data = md1,
label_col = "LABEL",
threshold_value = 5,
direction = "high",
exclude_roles = c("secondary", "process")
)
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.
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:
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%.