Commit 5cfba908 authored by Ben Anderson's avatar Ben Anderson
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Update README.md

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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/)
* [hms](https://hms.tidyverse.org/)
* [ggplot2](https://ggplot2.tidyverse.org/)'s [geom_tile()](https://ggplot2.tidyverse.org/reference/geom_tile.html) with time of day on the date on the x axis, time on the y and 'fill' set to the sensor value that _should_ be there. This shows up non-random (and random) data holes like [these](https://git.soton.ac.uk/SERG/datacleaning/-/blob/master/rmd/cleaningFeederData_files/figure-latex/missingVis-1.pdf) very nicely.
* [data.table](https://rdatatable.gitlab.io/data.table/) - very fast data loading and wrangling
* [lubridate](https://lubridate.tidyverse.org/) - _the_ way to do dates and dateTimes in R
* [hms](https://hms.tidyverse.org/) - deals with time (HH:MM:SS)
* [ggplot2](https://ggplot2.tidyverse.org/) - plots, especially using [geom_tile()](https://ggplot2.tidyverse.org/reference/geom_tile.html) with time of day on the date on the x axis, time on the y and 'fill' set to the sensor value that _should_ be there. This shows up non-random (and random) data holes like [these](https://git.soton.ac.uk/SERG/datacleaning/-/blob/master/rmd/cleaningFeederData_files/figure-latex/missingVis-1.pdf) very nicely.
This repo is an R package. This means:
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