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 # dataCleaning
 
-A place to store hints, tips and examples for data cleaning. We use a lot of very dirty data which often has outliers and missing observations. Since most of this data is large scale 'sensor' data with time stamps we make a lot of use of these R packages to process and visualise the data:
+A place to store hints, tips and examples for data cleaning. We use a lot of very dirty data which often has outliers and missing observations. Since most of this data is large scale 'sensor' data with time stamps we make a lot of use of these R packages to process and visualise the data so we can see what is odd and what is missing:
 
  * [data.table](https://rdatatable.gitlab.io/data.table/)
  * [lubridate](https://lubridate.tidyverse.org/)