Data Science for Agriculture
How to manage and manipulate data for agricultural research
This is a follow-up to R Markdown Basics.
You now have all the data read into R and you are ready to begin your analysis. You consult with a colleague who has done this kind of work for Dr. Raun before and are delighted to discover that she uses R, too. She shares a function with you that she wrote to automate parts of the analysis:
analyze <- function(formula,data){
library(agricolae,quietly=TRUE)
fit.lm <- lm(formula,data=data)
fit.anova <- anova(fit.lm)
fit.test <- LSD.test(y=data[,as.character(formula[[2]])],
trt=data[,as.character(formula[[3]])],
DFerror=tail(fit.anova$Df,1),
MSerror=tail(fit.anova$`Mean Sq`,1))
return(fit.test$groups)
}
You notice that the function loads the agricolae package, which you have not
used before. You make a note that you’ll need to install it before you can use
the function.
Add a section to your R Markdown file entitled Results and copy the function code into a new R code chunk with the name “define_analysis_function”. Knit your document and inspect the output. Set the code chunk options so that neither R code nor any output are displayed for this code chunk.
Create a new code chunk named “run_analysis” and add R code to use the
analyze() function to calculate treatment means and mean groupings using
Fisher’s LSD for post-freeze decline in NDVI and harvested yield. Assign each of
these to a separate object. Set the code chunk options so that neither R code
nor any output are displayed for this code chunk.
Use in-line R code chunks to write a sentence for each variable that states the minimum and maximum mean values for NDVI decline, rounded to the 2^nd^ decimal place, and for harvested yield, rounded to the nearest bushel.
