TY - ICOMM
T1 - Reorganisation following disturbance: multi trait-based methods in R
AU - Richardson, Laura
AU - Magneville, Camille
AU - Grange, Laura
AU - Shepperson, Jennifer
AU - Skov, Martin
AU - Hoey, Andrew
AU - Heenan, Adel
PY - 2024/6
Y1 - 2024/6
N2 - Teaching material - This practical is designed for third-year undergraduate students, as part of a wider module on "marine ecosystems and processes" in the UK. This equates to junior level on a bachelor degree in the United States. Students will need to have done an "Introduction to R" course ahead of this practical. For example, at Bangor University, all students taking this practical course would have completed module ONS-1001 "Environmental Data and Analysis" in year 1, where they receive training in basic R coding, data wrangling, graphing, common statistical tests and simple linear models, and an introduction to mapping in R. These students then use R to analyse data in several other modules during their first and second year, so would approach this practical with prior experience of some required tasks in R (e.g., creating boxplot graphs, implementing a t-test). If instructors wish to implement this practical with students who do not have any prior background using R, we suggest running two 3-hour workshops where students are introduced to R, covering basic data wrangling, graphing, and statistical tests.
AB - Teaching material - This practical is designed for third-year undergraduate students, as part of a wider module on "marine ecosystems and processes" in the UK. This equates to junior level on a bachelor degree in the United States. Students will need to have done an "Introduction to R" course ahead of this practical. For example, at Bangor University, all students taking this practical course would have completed module ONS-1001 "Environmental Data and Analysis" in year 1, where they receive training in basic R coding, data wrangling, graphing, common statistical tests and simple linear models, and an introduction to mapping in R. These students then use R to analyse data in several other modules during their first and second year, so would approach this practical with prior experience of some required tasks in R (e.g., creating boxplot graphs, implementing a t-test). If instructors wish to implement this practical with students who do not have any prior background using R, we suggest running two 3-hour workshops where students are introduced to R, covering basic data wrangling, graphing, and statistical tests.
UR - http://tiee.esa.org/vol/v20/issues/data_sets/richardson/abstract.html
M3 - Net publication - Internet publication
VL - 20
ER -