Mutate function in dplyr not working with Rolling Means/ Moving Averages - r

Question

First and foremost, regardless if you have input or not, thank you for taking your time to view my question.
Let me break down what I am doing, the sample dataset, and the error.
What I currently have is data for several different ID's that list the dispersion per day. (you will see below). I want to loop through the dates and add two columns to the data : Rolling Means columns & Rolling standard deviation column.
The code I have written out so far is this:
library(zoo)
Testing1 <- function(dataset, k) {
ops <- data.frame()
for (i in unique(dataset$Date)) {
ops <- dataset %>% mutate(rolling_mean = rollmean(dataset$Dispersion,k)) %>%
mutate(rolling_std = rollapply(dataset$Dispersion, width = k, FUN = sd))
}
Results <<- ops
}
however, i get the following error:
Error in mutate_impl(.data, dots) :
Column rolling_mean must be length 30 (the number of rows) or one, not 26
I am assuming that the row differential is due to me specifying a 5 day window for the rolling average, meaning it won't calculate it for the first 4 rows. But how do I go about telling R that it's ok to input NA's on those rows? Or If you guys have any other solution, that would work as well. Please do help.
Heres a sample of the data:
Identifier Date Dispersion
1000 2/15/2018 0.390
1000 2/16/2018 0.664
1000 2/17/2018 0.526
1000 2/18/2018 0.933
1000 2/19/2018 0.009
1000 2/20/2018 0.987
1000 2/21/2018 0.517
1000 2/22/2018 0.641
1000 2/23/2018 0.777
1000 2/24/2018 0.613
1001 2/15/2018 0.617
1001 2/16/2018 0.234
1001 2/17/2018 0.303
1001 2/18/2018 0.796
1001 2/19/2018 0.359
1001 2/20/2018 0.840
1001 2/21/2018 0.291
1001 2/22/2018 0.699
1001 2/23/2018 0.882
1001 2/24/2018 0.467
1002 2/15/2018 0.042
1002 2/16/2018 0.906
1002 2/17/2018 0.077
1002 2/18/2018 0.156
1002 2/19/2018 0.350
1002 2/20/2018 0.060
1002 2/21/2018 0.457
1002 2/22/2018 0.770
1002 2/23/2018 0.433
1002 2/24/2018 0.366

Answer 1

You get this error because the length of rolling means/stds does not match the legth of Dispersion. Simply add k - 1 NAs at the beginnig of your means/stds vectors.
Below is a working example. You can modify this based on your needs.
my_function <- function(df, k) {
df %>%
mutate(
rolling_mean = c(rep(NA, k - 1), rollmean(Dispersion, k)),
rolling_std = c(rep(NA, k - 1), rollapply(Dispersion, width = k, FUN = sd))
)
}
For example, you may want to add group_by to compute these values for each Identifier:
my_function <- function(df, k) {
df %>%
group_by(Identifier) %>%
mutate(
rolling_mean = c(rep(NA, k - 1), rollmean(Dispersion, k)),
rolling_std = c(rep(NA, k - 1), rollapply(Dispersion, width = k, FUN = sd))
)
}
Update following up #G. Grothendieck's comment:
It turns out the package zoo already has comprehensive features for NA handling, refactoring the above-given code as:
my_function <- function(df, k) {
df %>%
mutate(
rolling_mean = rollmeanr(Dispersion, k, fill = NA),
rolling_std = rollapplyr(Dispersion, width = k, FUN = sd, fill = NA)
)
}

Answer 2

I'd take a look at tibbletime.
Assuming your data frame is named mydata and the Date column is a character: first convert the Date, then convert to a time-aware tibble:
library(dplyr)
library(tibbletime)
mydata <- mydata %>%
mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
as_tbl_time(index = Date)
Now you can define functions for rolling mean and sd:
mean_5 <- rollify(mean, window = 5)
sd_5 <- rollify(sd, window = 5)
mydata %>%
mutate(rolling_mean = mean_5(Dispersion),
rolling_std = sd_5(Dispersion))
# A time tibble: 30 x 5
# Index: Date
Identifier Date Dispersion rolling_mean rolling_std
<int> <date> <dbl> <dbl> <dbl>
1 1000 2018-02-15 0.39 NA NA
2 1000 2018-02-16 0.664 NA NA
3 1000 2018-02-17 0.526 NA NA
4 1000 2018-02-18 0.933 NA NA
5 1000 2018-02-19 0.009 0.504 0.342
6 1000 2018-02-20 0.987 0.624 0.393
7 1000 2018-02-21 0.517 0.594 0.394
8 1000 2018-02-22 0.641 0.617 0.393
9 1000 2018-02-23 0.777 0.586 0.367
10 1000 2018-02-24 0.613 0.707 0.182
# ... with 20 more rows