Apply conditional function in every row of a data frame - r

Question

I'm new in R and I'm struggling with this df that looks like this:
Date Group Factor 1 Factor 2 Spread
2019-04-01 a 1.01 1.011 0.01
2019-04-02 a 1.02 1.012 0.02
2019-04-03 a 1.03 1.013 0.03
2019-04-01 b 1.005 1.004 0.01
2019-04-02 b 1.0051 1.0041 0.02
2019-04-03 b 1.0052 1.0042 0.03
I would like do verify each group in each row and if the results are Group "a" do Factor1/Factor1(1 day lag) * Factor2 + spread, and if the group it's not "a" do not add the spread.

Answer 1

Since you are conditioning on the group, this is a good example of by (base R), dplyr::group_by, or data.table's x[,,by=].
The equation is effectively the same in all three, capitalizing on the fact that (Group[1] == "a") will be coerced from a logical to numeric when multipled by a number; since FALSE translates to a 0, then effectively disabled adding Spread.
Base
I use within here to make the internals a little more readable, but this is not a requirement (in which case you'd need to prepend x$ in front of all of the variable names).
The lagging can be done using dplyr::lag (even if you don't use the rest of the package for this) or many other techniques. I don't find stats::lag to be the most intuitive in applications like this, but I'm sure somebody will suggest a way to incorporate it into an answer. The use of c(NA, ...) ensures that we don't bring in a different group's data or impute data we don't have, since we have no value to bring in on the first row of a group. Finally, head(..., n = 1) returns the first element of a vector/list, while head(..., n = -1) (negative) returns all but the last.
newx <- by(x, x$Group, function(y) {
within(y, {
NewVal = Factor2 * Factor1 / c(NA, head(Factor1, n=-1)) + (Group[1] == "a") * Spread
})
})
newx
# x$Group: a
# Date Group Factor1 Factor2 Spread NewVal
# 1 2019-04-01 a 1.01 1.011 0.01 NA
# 2 2019-04-02 a 1.02 1.012 0.02 1.042020
# 3 2019-04-03 a 1.03 1.013 0.03 1.052931
# -------------------------------------------------------
# x$Group: b
# Date Group Factor1 Factor2 Spread NewVal
# 4 2019-04-01 b 1.0050 1.0040 0.01 NA
# 5 2019-04-02 b 1.0051 1.0041 0.02 1.0042
# 6 2019-04-03 b 1.0052 1.0042 0.03 1.0043
This is really just a list with some fancy by-specific formatting, so you can treat it as such as combine them in an efficient base-R way:
do.call("rbind.data.frame", c(newx, stringsAsFactors = FALSE))
# Date Group Factor1 Factor2 Spread NewVal
# a.1 2019-04-01 a 1.0100 1.0110 0.01 NA
# a.2 2019-04-02 a 1.0200 1.0120 0.02 1.042020
# a.3 2019-04-03 a 1.0300 1.0130 0.03 1.052931
# b.4 2019-04-01 b 1.0050 1.0040 0.01 NA
# b.5 2019-04-02 b 1.0051 1.0041 0.02 1.004200
# b.6 2019-04-03 b 1.0052 1.0042 0.03 1.004300
dplyr
Many find the tidyverse line of packages to read intuitively.
library(dplyr)
x %>%
group_by(Group) %>%
mutate(NewVal = Factor2 * Factor1 / lag(Factor1) + (Group[1] == "a") * Spread) %>%
ungroup()
# # A tibble: 6 x 6
# Date Group Factor1 Factor2 Spread NewVal
# <chr> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 2019-04-01 a 1.01 1.01 0.01 NA
# 2 2019-04-02 a 1.02 1.01 0.02 1.04
# 3 2019-04-03 a 1.03 1.01 0.03 1.05
# 4 2019-04-01 b 1.00 1.00 0.01 NA
# 5 2019-04-02 b 1.01 1.00 0.02 1.00
# 6 2019-04-03 b 1.01 1.00 0.03 1.00
data.table
On a different note, many find data.table better because of efficiencies gained from in-place modification (most of R's operations are copy-on-write, meaning some operations re-copy the object or a portion of it with each change).
library(data.table)
X <- as.data.table(x)
X[, NewVal := Factor2 * Factor1 / shift(Factor1) + (Group[1] == "a") * Spread, by = "Group"]
X
# Date Group Factor1 Factor2 Spread NewVal
# 1: 2019-04-01 a 1.0100 1.0110 0.01 NA
# 2: 2019-04-02 a 1.0200 1.0120 0.02 1.042020
# 3: 2019-04-03 a 1.0300 1.0130 0.03 1.052931
# 4: 2019-04-01 b 1.0050 1.0040 0.01 NA
# 5: 2019-04-02 b 1.0051 1.0041 0.02 1.004200
# 6: 2019-04-03 b 1.0052 1.0042 0.03 1.004300
The "in-place" part is evident on the second line here, where it appears as if the [ operation should just return a subset or something of the data ... but in this case using := causes the columns to be created (or changed) in-place.