Moving average by multiple group - r

Question

I have a following DF (demo). I would like to find the previous 3 month moving average of Amount column per ID, Year and Month.
ID YEAR MONTH AMOUNT
1 ABC 2020 09 100
2 ABC 2020 11 200
3 ABC 2020 12 300
4 ABC 2021 01 400
5 ABC 2021 04 500
6 PQR 2020 10 100
7 PQR 2020 11 200
8 PQR 2020 12 300
9 PQR 2021 01 400
10 PQR 2021 03 500
Following is an attempt.
library(TTR)
library(dplyr)
DF %>% group_by(ID, YEAR, MONTH) %>% mutate(3MA = runMean(AMOUNT, 3))
resulting in error with n=3 is outside valid range.
Desired Output:
ID YEAR MONTH AMOUNT 3MA
1 ABC 2020 09 100 NA
2 ABC 2020 11 200 NA
3 ABC 2020 12 300 NA
4 ABC 2021 01 400 200 (100+200+300)/3
5 ABC 2021 04 500 300 (400+300+200)/3
6 PQR 2020 10 100 NA
7 PQR 2020 11 200 NA
8 PQR 2020 12 300 NA
9 PQR 2021 01 400 200 (100+200+300)/3
10 PQR 2021 03 500 300 (400+300+200)/3

Answer 1

You can use the following code:
library(dplyr)
arrange(DF,ID,YEAR) %>%
group_by(ID) %>%
mutate(lag1=lag(AMOUNT),
lag2=lag(AMOUNT,2),
lag3=lag(AMOUNT,3),
movave=(lag1+lag2+lag3)/3)
#> # A tibble: 10 × 8
#> # Groups: ID [2]
#> ID YEAR MONTH AMOUNT lag1 lag2 lag3 movave
#> <chr> <int> <int> <int> <int> <int> <int> <dbl>
#> 1 ABC 2020 9 100 NA NA NA NA
#> 2 ABC 2020 11 200 100 NA NA NA
#> 3 ABC 2020 12 300 200 100 NA NA
#> 4 ABC 2021 1 400 300 200 100 200
#> 5 ABC 2021 4 500 400 300 200 300
#> 6 PQR 2020 10 100 NA NA NA NA
#> 7 PQR 2020 11 200 100 NA NA NA
#> 8 PQR 2020 12 300 200 100 NA NA
#> 9 PQR 2021 1 400 300 200 100 200
#> 10 PQR 2021 3 500 400 300 200 300
Created on 2022-07-02 by the reprex package (v2.0.1)

Answer 2

An option using a sliding window:
library(tidyverse)
library(slider)
df <- tribble(
~id, ~year, ~month, ~amount,
"ABC", 2020, 09, 100,
"ABC", 2020, 11, 200,
"ABC", 2020, 12, 300,
"ABC", 2021, 01, 400,
"ABC", 2021, 04, 500,
"PQR", 2020, 10, 100,
"PQR", 2020, 11, 200,
"PQR", 2020, 12, 300,
"PQR", 2021, 01, 400,
"PQR", 2021, 03, 500
)
df |>
arrange(id, year, month) |>
group_by(id) |>
mutate(ma3 = slide_dbl(lag(amount), mean, .before = 2, complete = TRUE)) |>
ungroup() # if needed
#> # A tibble: 10 × 5
#> id year month amount ma3
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 ABC 2020 9 100 NA
#> 2 ABC 2020 11 200 NA
#> 3 ABC 2020 12 300 NA
#> 4 ABC 2021 1 400 200
#> 5 ABC 2021 4 500 300
#> 6 PQR 2020 10 100 NA
#> 7 PQR 2020 11 200 NA
#> 8 PQR 2020 12 300 NA
#> 9 PQR 2021 1 400 200
#> 10 PQR 2021 3 500 300
Created on 2022-07-02 by the reprex package (v2.0.1)

Answer 3

Here is a way.
suppressPackageStartupMessages({
library(dplyr)
library(TTR)
})
x <- ' ID YEAR MONTH AMOUNT
1 ABC 2020 09 100
2 ABC 2020 11 200
3 ABC 2020 12 300
4 ABC 2021 01 400
5 ABC 2021 04 500
6 PQR 2020 10 100
7 PQR 2020 11 200
8 PQR 2020 12 300
9 PQR 2021 01 400
10 PQR 2021 03 500 '
DF <- read.table(textConnection(x), header = TRUE)
DF %>%
arrange(ID, YEAR, MONTH) %>%
group_by(ID) %>%
mutate(`3MA` = lag(runMean(AMOUNT, 3)))
#> # A tibble: 10 × 5
#> # Groups: ID [2]
#> ID YEAR MONTH AMOUNT `3MA`
#> <chr> <int> <int> <int> <dbl>
#> 1 ABC 2020 9 100 NA
#> 2 ABC 2020 11 200 NA
#> 3 ABC 2020 12 300 NA
#> 4 ABC 2021 1 400 200
#> 5 ABC 2021 4 500 300
#> 6 PQR 2020 10 100 NA
#> 7 PQR 2020 11 200 NA
#> 8 PQR 2020 12 300 NA
#> 9 PQR 2021 1 400 200
#> 10 PQR 2021 3 500 300
Created on 2022-07-02 by the reprex package (v2.0.1)

Answer 4

Try this
DF |> arrange(ID , YEAR , MONTH) |> group_by(ID) |>
mutate(`3M` = (lag(AMOUNT) + lag(AMOUNT ,2) + lag(AMOUNT , 3)) / 3)
output
# A tibble: 10 × 5
# Groups: ID [2]
ID YEAR MONTH AMOUNT `3M`
<chr> <int> <int> <int> <dbl>
1 ABC 2020 9 100 NA
2 ABC 2020 11 200 NA
3 ABC 2020 12 300 NA
4 ABC 2021 1 400 200
5 ABC 2021 4 500 300
6 PQR 2020 10 100 NA
7 PQR 2020 11 200 NA
8 PQR 2020 12 300 NA
9 PQR 2021 1 400 200
10 PQR 2021 3 500 300