Description Fields Methods Examples
Data rearranging and major data cleaning processes will be performed under the ccTable structre. It holds the original record (ccRecord), the dirty table (torigin) clean table (tclean) and various data quality information (dquality). Various data filters can also be found within the ccTable class.
record
ccRecord.
conf
the YAML style configuration.
torigin
the original wide data table.
tclean
the wide data table after cleaning processes.
dfilter
list contains data filtering information.
dquality
list contains data quality.
summary
list
base_cadence
the base cadence is specified in hours
apply.filters(warnings = T)
Apply all filters specified in the configuration to update the clean table (tclean)
create.table(freq)
Create a table contains the selected items in the conf with a given frequency (in hour)
export.csv(file = NULL)
Export the cleaned table to a CSV file.
filter.category()
Check individual entries if they are the in the categories specified in conf.
filter.missingness(recount = FALSE)
filter out the where missingness is too low.
filter.null(items = c("episode_id", "site"))
remove the entire episode when any of the selected items is NULL
imputation()
Filling missing data to a time series data by performing a given imputation method on a selected window period nearby the missing data.
reload.conf(file)
reload yaml configuration.
1 2 3 4 | rec <- ccRecord()
cctable <- create.cctable(rec, freq=1)
cctable <- cctable$clean()
#table <- cctable$tclean
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.