Moving average and moving slope in R - r

Question

I am looking to separately calculate a 7-day moving average and 7-day moving slope of 'oldvar'.
My sincere apologies that I didn't add the details below in my original post. These are repeated observations for each id which can go from a minimum of 3 observations per id to 100 observations per id. The start day can be different for different IDs, and to make things complicated, the days are not equally spaced, so some IDs have missing days.
Here is the data structure. Please note that 'average' is the variable that I am trying to create as moving 7-day average for each ID:
id day outcome average
1 1 15 100 NA
2 1 16 110 NA
3 1 17 190 NA
4 1 18 130 NA
5 1 19 140 NA
6 1 20 150 NA
7 1 21 160 140
8 1 22 100 140
9 1 23 180 150
10 1 24 120 140
12 2 16 90 NA
13 2 17 110 NA
14 2 18 120 NA
12 2 20 130 NA
15 3 16 110 NA
16 3 18 200 NA
17 3 19 180 NA
18 3 21 170 NA
19 3 22 180 168
20 3 24 210 188
21 3 25 160 180
22 3 27 200 184
Also, would appreciate advice on how to calculate a moving 7-day slope using the same.
Thank you and again many apologies for being unclear the first time around.

Answer 1

The real challenge is to create a data.frame after completing the missing rows. One solution could be using zoo library. The rollapply function will provide a way to assign NA value for the initial rows.
Using data from OP as is, the solution could be:
library(zoo)
library(dplyr)
# Data from OP
df <- structure(list(id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L),
day = c(15L,16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 16L, 17L, 18L, 20L,
16L, 18L, 19L, 21L, 22L, 24L, 25L, 27L),
outcome = c(100L, 110L,190L, 130L, 140L, 150L, 160L, 100L, 180L, 120L, 90L, 110L, 120L,
130L, 110L, 200L, 180L, 170L, 180L, 210L, 160L, 200L)),
.Names = c("id", "day", "outcome"), row.names = c(NA, -22L), class = "data.frame")
# Make a list without missing day for each id
df_complete <- merge(
expand.grid(id=unique(df$id), day=min(df$day):max(df$day)),
df, all=TRUE)
# Valid range of day for each ID group
df_id_wise_range <- df %>% group_by(id) %>%
summarise(min_day = min(day), max_day = max(day)) %>% as.data.frame()
# id min_day max_day
# 1 1 15 24
# 2 2 16 20
# 3 3 16 27
# Join original df and df_complete and then use df_id_wise_range to
# filter it for valid range of day for each group
df_final <- df_complete %>%
left_join(df, by=c("id","day")) %>%
select(-outcome.y) %>%
inner_join(df_id_wise_range, by="id") %>%
filter(day >= min_day & day <= max_day) %>%
mutate(outcome = outcome.x) %>%
select( id, day, outcome) %>%
as.data.frame()
# Now apply mean to get average
df_average <- df_final %>% group_by(id) %>%
mutate(average= rollapply(outcome, 7, mean, na.rm = TRUE, by = 1,
fill = NA, align = "right", partial = 7)) %>% as.data.frame()
df_average
# The result
# id day outcome average
#1 1 15 100 NA
#2 1 16 110 NA
#3 1 17 190 NA
#4 1 18 130 NA
#5 1 19 140 NA
#6 1 20 150 NA
#7 1 21 160 140.0
#8 1 22 100 140.0
#9 1 23 180 150.0
#10 1 24 120 140.0
#11 2 16 90 NA
#12 2 17 110 NA
#13 2 18 120 NA
#....
#....
#19 3 19 180 NA
#20 3 20 NA NA
#21 3 21 170 NA
#22 3 22 180 168.0
#23 3 23 NA 182.5
#24 3 24 210 188.0
#25 3 25 160 180.0
#26 3 26 NA 180.0
#27 3 27 200 184.0
The steps to calculate moving slope are:
First create a function to return slope
Use function as as part of rollapplyr
#Function to calculate slope
slop_e <- function(z) coef(lm(b ~ a, as.data.frame(z)))[[2]]
#Apply function
z2$slope <- rollapplyr(zoo(z2), 7, slop_e , by.column = FALSE, fill = NA, align = "right")
z2
a b mean_a slope
1 1 21 NA NA
2 2 22 NA NA
3 3 23 NA NA
4 4 24 NA NA
5 5 25 NA NA
6 6 26 NA NA
7 7 27 4 1
8 8 28 5 1
9 9 29 6 1
10 10 30 7 1
11 11 31 8 1
12 12 32 9 1
13 13 33 10 1
14 14 34 11 1
15 15 35 12 1
16 16 36 13 1
17 17 37 14 1
18 18 38 15 1
19 19 39 16 1
20 20 40 17 1