Daily routine: Each day will be split up into group discussion of the topics/assignments covered the previous day, introduction lectures of the day’s topics followed by practical assignments. Emphasis will be put on cooperative work and code sharing (including difficulties/stumbling blocks) among participants.
The R language is becoming the Lingua franca both in data science in general as well as within the ICES community. Recent advancements within R have resulted in that R can no longer be considered as a specific statistical programming language but as a general scientific working environment. This broader environment has resulted in the R has become a natural component of reproducible data analysis and document writing.
Various R packages (e.g. FLR, DATRAS, MSY, SURBAR, VMStools) have often been the backbone of ICES training course and/or workshops. These packages as well as courses are geared towards solving specific pending tasks that tend to come with requirements that the participants are reasonable proficient in basic R and that the input data are correctly formatted and available. Any of these requirements have been seen to pose problems.
The course is aimed at covering the fundamental/generic basis of the grammar of data and graphics as well reproducible document writing where R is used as the sole working medium. Recent developments in the R community that are of interest to fisheries science will also be described.
The objective of the course is to provide participants with a solid foundation in efficient use of the R environment using various typical and familiar fisheries data sets (landings data, catch data, survey data and tagging data) as case examples. Emphasis will be put on data munging and literate programming starting with “raw” data (individual stations, individual fish measurements) and culminating with deliverance of publishable output produced from a single coded document file.
By the end of the course, the participants:
The course is targeted at fisheries scientist with already have some basic experience in R but are yet not proficient enough to write fluently code for data manipulation, exploration and writing own functions. We believe that some part of the course would also be beneficial to those that are currently productively using R in fisheries science but may along the way have skipped the basics or are unaware of recent advancements in the R environment.