## Moving mean as a function in dplyr

Question

I'd like to create a function that can calculate the moving mean for a variable number of last observations and different variables. Take this as mock data:

``````df = expand.grid(site = factor(seq(10)),
year = 2000:2004,
day = 1:50)
df\$temp = rpois(dim(df)[1], 5)
``````

Calculating for 1 variable and a fixed number of last observations works. E.g. this calculates the average of the temperature of the last 5 days:

``````library(dplyr)
library(zoo)

df <- df %>%
group_by(site, year) %>%
arrange(site, year, day) %>%
mutate(almost_avg = rollmean(x = temp, 5, align = "right", fill = NA)) %>%
mutate(avg = lag(almost_avg, 1))
``````

So far so good. Now trying to functionalize fails.

``````avg_last_x <- function(dataframe, column, last_x) {

dataframe <- dataframe %>%
group_by(site, year) %>%
arrange(site, year, day) %>%
mutate(almost_avg = rollmean(x = column, k = last_x, align = "right", fill = NA)) %>%
mutate(avg = lag(almost_avg, 1))

return(dataframe) }

avg_last_x(dataframe = df, column = "temp", last_x = 10)
``````

I get this error:

``````Error in mutate_impl(.data, dots) : k <= n is not TRUE
``````

I understand this is probably related to the evaluation mechanism in dplyr, but I don't get it fixed.

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## Answers to Moving mean as a function in dplyr ( 1 )

1. This should fix it.

``````library(lazyeval)

avg_last_x <- function(dataframe, column, last_x) {
dataframe %>%
group_by(site, year) %>%
arrange(site, year, day) %>%
mutate_(almost_avg = interp(~rollmean(x = c, k = last_x, align = "right",
fill = NA), c = as.name(column)),
avg = ~lag(almost_avg, 1))
}
``````