This half-day course explains why pipeline tools such as make, snakemake and targets are indispensible tools in (reproducible) data analyses.

If you have taken this course and are willing to provide a answers to a short (1-2 minute) survey, please fill these in here.

There is a short lecture here.

Applied example: there are two folders:

• this contains a blank R project along with the data in a subdirectory, which can be a starting point for developing a reproducible data analysis
• this contains a completed R project along with the required _targets.R file

1. get and clean data
2. fit a model: ozone ~ temperature
3. plot the model's fit versus data
4. diagnose any issues with model fit; if necessary, change the model and rerun the above steps

1. clone the repo and double click on the r_project_blank_slate.Rproj icon to launch RStudio
2. install targets package via install.packages("targets")
3. type use_targets() in the console, which should create an _targets.R file
4. add tidyverse as a package under the package item in the _targets.R file
5. comment out tar_source()
6. remove the list of example targets that have been generated in the _targets.R file (but keep the list which had these targets within it)
7. create a folder called scripts that has in it a file called clean_data.R
8. in clean_data.R write a function that takes the data\raw\airquality.csv file, renames the columns using only lowercase letters and removes any rows that have NA values in them
9. in _targets.R add in the preamble source(scripts\clean_data.R) to ensure that targets has access to your function
10. in the _targets.R file, add a target for the cleaned data via tar_target(data_airquality_cleaned, clean_data(filename))
11. in the _targets.R file, create a leaf target for the file itself via tar_target(filename, "data\raw\airquality.csv", format="file")
12. visualise the network via tar_visnetwork(names=data_airquality_cleaned)
13. clean the data using tar_make()
14. revisualise the network to check that everything is up to date
15. type tar_read(data_airquality_cleaned) in the console to view the cleaned data
16. continue with this methodology to create a full data analysis

• basic R skills (although not essential)
• some experience of having done data analysis