library(tidyverse)
library(aqm)
library(mgcv)
data(arne_quads)
d<- arne_quads
str(d)
## 'data.frame':    184 obs. of  9 variables:
##  $ Year        : num  2019 2019 2019 2019 2019 ...
##  $ site        : chr  "Heath" "Heath" "Heath" "Heath" ...
##  $ npines      : num  11 7 12 3 4 8 5 8 7 6 ...
##  $ pine_density: num  0.875 0.557 0.955 0.239 0.318 0.637 0.398 0.637 0.557 0.477 ...
##  $ twi         : num  4.24 3.96 3.64 4.14 4.26 ...
##  $ sol         : num  0.873 0.906 0.86 0.84 0.878 ...
##  $ dtm         : num  12.6 12.8 13 13.3 13.2 ...
##  $ slope       : num  1.282 1.934 1.945 0.861 1.22 ...
##  $ min_dist    : num  14.02 9.11 5.79 11.8 21.26 ...

Advice on the write up

You should write up the analyses of these data in the style of a management report. This does not require using a large number of scientific references (3-6 is sufficient).

As this is a methods course you do not need to conduct too much additional research into the context of the study.

You are expected to demonstrate ability to produce interpretable figures in R that are appropriate given the characteristics of the variables that you are analysing.

You should report key elements of analyses such as regression in a formal manner. However do not over interprate the results.

If your analyses does not produce a statistically signficant result you should still include details of the analysis in the write up but you should interpret the result as lack of evidence rather than evidence of no effect.