Concerning @jmcvw's comment, please see the homework FAQ
# fake data using numerics; datetime objects will differ
Inp <- data.frame(date = seq(1:100))
# pre-allocate vector
Out <- rep(NA, 100)
# as written the loop only cycles the number (length) of columns, not rows
for (i in 1:length(Inp)) Out[i] <- max(Inp$date[i]:Inp$date[i + 10]) -> a
a
#> [1] 11
# corrected for that, it overshoots the range of Inp
for (i in 1:nrow(Inp)) Out[i] <- max(Inp$date[i]:Inp$date[i + 10]) -> b
#> Error in Inp$date[i]:Inp$date[i + 10]: NA/NaN argument
b
#> [1] 100
Created on 2020-10-23 by the [reprex package]
(https://reprex.tidyverse.org) (v0.3.0.9001)
The for statement is not the preferred approach for this class of problem in R, which is a functional language as presented to the user: f(x) = y.
x is the given, in this case a numeric column of a data frame, which can be simplified to a numeric vector, Inp.
y is the desired output, in this case another numeric vector representing the maxima of each sub-sequence of 10 elements of x.
f is the object that transforms x to y.
We say that f is a function, x is an argument and y is a value or return value.
Everything in R is an object, including functions, which can be arguments to other functions. Objects can be composed of other objects.
From that perspective, we have one piece in hand, max, which takes as its argument a vector. Now, we need a function that will provide a vector that represents a window of x.
At this point a toy example is helpful. Assume a x
Inp <- 1:10
and y that represents the maximum of each run of two elements. By inspection that's easy enough—it's simply the even integers. One of the possible ways of doing this is
Inp <- 1:10
Inp[which(Inp%%2 == 0)]
#> [1] 2 4 6 8 10
Created on 2020-10-23 by the reprex package (v0.3.0.9001)
This composition consists of the %% modulus function (a so-called primitive), which that finds even numbers (0 remainder) and the subset operators [ and ], primitives that filter for the elements that meet the modulus test.
Another way would be to split the toy vector into the number of pieces desired.
Finally, take a look at slider for a set of functions designed to deal with this type of windowing problem.