Hi everyone!
I have analysis to do on several data frames; I have:
10 countries
60 plants
60 traits
1 data frame per country containing 60 traits for 60 plants
1 data frame per country containing climatic data
example:
SWD_data
...1 bolting flowering `life span` colour
<chr> <chr> <chr> <chr> <chr>
1 rose 15 56 12 R
2 cabage 14 56 21 G
3 carrot 10 43 35 O
4 iris 52 12 62 B
FRA_data
...1 bolting flowering `life span` colour
<chr> <chr> <chr> <chr> <chr>
1 rose 15 54 12 Rg
2 cabage 14 65 84 G
3 carrot 10 43 35 P
4 iris 52 12 67 B
SWD_environment
...1 Rain PAR Temp Wind
<chr> <chr> <chr> <chr> <chr>
1 Day1 15 54 12 0
2 Day2 14 65 84 11
3 Day3 10 43 35 45
4 Day4 52 12 67 0
FRA_environment
...1 Rain PAR Temp Wind
<chr> <chr> <chr> <chr> <chr>
1 Day1 15 54 12 10
2 Day2 14 65 84 0
3 Day3 10 43 35 0
4 Day4 52 12 67 0
My goal is to cross all the data in multiple analysis:
- bolting of all roses depending on rain
- mean flowering of all roses regardless on the environment
- flowering of all plants depending on rain
- best plant to grow in Sweden
- most impacting environmental variable
- trait most sensitive to environmental fluctuation
- ...
To archive that I would need to ask R to draw graphics such as:
ggplot( y= (SWD_data$bolting, FRA_data$bolting), x= c(SWD_E$Rain, FRA_E$Rain))
Or anova functions that compute data from different tables.
So I am sorry this topic is a bit wide, but any advice and help would be strongly appreciated!
All the best!
