R Loop: Perform a Function for Every 3 Rows - r

Question

I have 2000 wheat plants, growing over the course of 40 days.
I'd like to perform the coeff function on each plant to find the coefficients of the quadratic equation the 3 time points make. (a, b, and c)
(1) The coef(lm(y~poly(x,2,raw=TRUE)) function works exactly the way I want it to.
(2) However, the way my data is presented, requires me to manually set x and y.
(3) Thus, I melted my data, and ordered it.
(4) I'd like to make a loop that will take the first three in column "Day" and set that as x. Then I'd like it to take the first three in column "Height" and set that as y.
Then I'd like to perform the coeff function.
Last I'd like it to present the coefficient outputs I need, preferably in a new data table.
Then repeat for every three rows, which represent each wheat ID, for all wheat plants.
1) This function works, giving me coefficients: a, b, c
x<-c(1,2,3)
y<-c(1,10,4)
coef(lm(y~poly(x,2,raw=TRUE)))
2) This is what my data originally looked like
A = matrix(c(5, 4, 2, 10, 10, 4, 5, 15, 6),nrow=3, ncol=3)
colnames(A)<-c("10", "25", "40")
rownames(A)<-c("Wheat 1", "Wheat 2", "Wheat 3")
A
3) This is my melted format
A.melted<-as.data.frame(melt(A, id.vars="ID"))
A.melted<-A.melted[with(A.melted,order(Var1)),]
colnames(A.melted) <- c("WheatID", "Day", "Height")
A.melted$Day<-as.numeric(as.character(A.melted$Day))
A.melted
#
4) This is what I am trying to do with my loop....
for every 3 rows,
x<-A.melted[,2]
y<-A.melted[,3]
coef(lm(y~poly(x,2,raw=TRUE)))
something to compile the coefficients: a, b, c
I am just not familiar with the syntax of loops, and I'd love any tips and suggestions. Perusing Google tells me that one should not do loops unless it is absolutely required since I may run into more problems- thus I am open to non loop techniques as well.

Answer 1

If you want to do it in a loop try this. The crucial part is to use seq together with a by = argument to let the index take the steps you need.
library(tibble)
df <- tibble(
WheatID = rep(NA_character_, nrow(A)),
Intercept = rep(NA_real_, nrow(A)),
poly1 = rep(NA_real_, nrow(A)),
poly2 = rep(NA_real_, nrow(A))
)
cnt <- 1
for (i in seq(1, nrow(A.melted), by = 3)) {
x <- A.melted$Day[i + 0:2]
y <- A.melted$Height[i + 0:2]
df$WheatID[cnt] <- as.character(A.melted$WheatID[i])
df[cnt, 2:4] <- coef(lm(y~poly(x,2,raw=TRUE)))
cnt <- cnt + 1
}
df
Note: I am not a data.table guy. Therefore, I present you with a tibble.

Answer 2

We can do this with the help of data.table, see ?data.table:
library(data.table)
A.models = A.melted[, model := list(.(lm(Height ~ poly(Day, 2),
data = list(.(.SD[WheatID == .BY[[1]]]))))),
by = WheatID]
A.models[, coefs := list(.(coefficients(model[[1]]))),
by = WheatID]
You can access each model like this:
A.models[WheatID == "Wheat 1", model[[1]]]
and even
A.models[WheatID == "Wheat 1", summary(model[[1]])]
The magic here happens because data.table takes in J expressions, not only functions.

Answer 3

This is something you can do with data.table package.
data.list <- split(A.melted, f = (1:nrow(A.melted) - 1) %/% 3)
coefs <- lapply(data.list, function(x) {
coefs <- coef(lm(Day ~ poly(Height, raw=TRUE), data = x))
data.table(
intercept = coefs[1],
poly.height = coefs[2]
)
})
coefs <- rbindlist(coefs)

Answer 4

Or you could perform apply() directly on the original matrix:
x <- as.numeric(colnames(A))
apply(A, 1, function(y) coef(lm(y~poly(x,2,raw=TRUE))))
Wheat 1 Wheat 2 Wheat 3
(Intercept) -3.88888889 -0.555555556 6.666667e-01
poly(x, 2, raw = TRUE)1 1.11111111 0.477777778 1.333333e-01
poly(x, 2, raw = TRUE)2 -0.02222222 -0.002222222 -2.417315e-18
Or you could transpose the data and use the coef(...) call directly:
x <- as.numeric(colnames(A))
coef(lm(t(A) ~ poly(x, 2, raw = TRUE)))