Summarize Daily Precipitation by 4 and 6 days

#1

image

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#2

What have you tried so far? what is your specific problem?
Could you please turn this into a self-contained REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

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#3

I have daily data in csv format and the data frame is below. I wanted to find the sum and maximum of four and six days. However, I tried the commands below. When I use tapply i get the error and when I applied the rollapply command the data is rolled for four and six days like stepping one day ahead.
Can you help on this?

library(hydroTSM)
data.frame(
        Year = c(1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
                 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
                 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
                 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
                 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
                 1904L, 1904L, 1904L, 1904L, 1904L),
       Month = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
                 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
                 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
                 2L, 2L, 2L, 2L),
         Day = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
                 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
                 27L, 28L, 29L, 30L, 31L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
                 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L),
        Rain = c(0, 0, 1, 1, 1, 1, 0, 2, 2, 0, 4, 0, 0, 5, 0, 0, 3, 0, 10, 4,
                 10, 7, 10, 11, 0, 1, 5, 0, 0, 2, 0, 0, 0, 5, 0, 0, 1, 6, 0, 3,
                 0, 1, 0, 0, 1, 0, 0, 0, 0, 0)
)

day4=tapply(b$Rain,b$Year,b$Month,b$Day, width=4,sum,na.rm=TRUE)
Error in match.fun(FUN) : 
  'b$Month' is not a function, character or symbol
day4=rollapply(Rain,width=4, FUN=sum)
day4max=tapply(b$Rain,b$Year,b$Month,b$Day,width=4,max,na.rm=TRUE)
day4=rollapply(Rain,width=4, FUN=max)
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#4

Its not clear to me what are you looking for, is a roll sum or you want to aggregate by 4 days periods? see examples below

df <- data.frame(
    Year = c(1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
             1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
             1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
             1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
             1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L, 1904L,
             1904L, 1904L, 1904L, 1904L, 1904L),
    Month = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
              1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
              1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
              2L, 2L, 2L, 2L),
    Day = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
            15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
            27L, 28L, 29L, 30L, 31L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
            11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L),
    Rain = c(0, 0, 1, 1, 1, 1, 0, 2, 2, 0, 4, 0, 0, 5, 0, 0, 3, 0, 10, 4,
             10, 7, 10, 11, 0, 1, 5, 0, 0, 2, 0, 0, 0, 5, 0, 0, 1, 6, 0, 3,
             0, 1, 0, 0, 1, 0, 0, 0, 0, 0)
)

library(zoo)
library(dplyr)
library(tibbletime)

# Rollsum
df %>% 
    mutate(roll_sum = rollsum(Rain, 4, fill = NA, align = "right"))
#>    Year Month Day Rain roll_sum
#> 1  1904     1   1    0       NA
#> 2  1904     1   2    0       NA
#> 3  1904     1   3    1       NA
#> 4  1904     1   4    1        2
#> 5  1904     1   5    1        3
#> 6  1904     1   6    1        4
#> 7  1904     1   7    0        3
#> 8  1904     1   8    2        4
#> 9  1904     1   9    2        5
#> 10 1904     1  10    0        4
#> 11 1904     1  11    4        8
#> 12 1904     1  12    0        6
#> 13 1904     1  13    0        4
#> 14 1904     1  14    5        9
#> 15 1904     1  15    0        5
#> 16 1904     1  16    0        5
#> 17 1904     1  17    3        8
#> 18 1904     1  18    0        3
#> 19 1904     1  19   10       13
#> 20 1904     1  20    4       17
#> 21 1904     1  21   10       24
#> 22 1904     1  22    7       31
#> 23 1904     1  23   10       31
#> 24 1904     1  24   11       38
#> 25 1904     1  25    0       28
#> 26 1904     1  26    1       22
#> 27 1904     1  27    5       17
#> 28 1904     1  28    0        6
#> 29 1904     1  29    0        6
#> 30 1904     1  30    2        7
#> 31 1904     1  31    0        2
#> 32 1904     2   1    0        2
#> 33 1904     2   2    0        2
#> 34 1904     2   3    5        5
#> 35 1904     2   4    0        5
#> 36 1904     2   5    0        5
#> 37 1904     2   6    1        6
#> 38 1904     2   7    6        7
#> 39 1904     2   8    0        7
#> 40 1904     2   9    3       10
#> 41 1904     2  10    0        9
#> 42 1904     2  11    1        4
#> 43 1904     2  12    0        4
#> 44 1904     2  13    0        1
#> 45 1904     2  14    1        2
#> 46 1904     2  15    0        1
#> 47 1904     2  16    0        1
#> 48 1904     2  17    0        1
#> 49 1904     2  18    0        0
#> 50 1904     2  19    0        0

# Aggregation by 4 days
df %>% 
    mutate(date = as.Date(paste(Year, Month, Day, sep = "-"))) %>% 
    select(date, Rain) %>% 
    as_tbl_time(index = date) %>% 
    collapse_by("4 days", side = "start", clean = TRUE) %>%
    group_by(date) %>% 
    summarise(days4 = sum(Rain))
#> # A time tibble: 13 x 2
#> # Index: date
#>    date       days4
#>    <date>     <dbl>
#>  1 1904-01-01     2
#>  2 1904-01-05     4
#>  3 1904-01-09     6
#>  4 1904-01-13     5
#>  5 1904-01-17    17
#>  6 1904-01-21    38
#>  7 1904-01-25     6
#>  8 1904-01-29     2
#>  9 1904-02-02     5
#> 10 1904-02-06    10
#> 11 1904-02-10     1
#> 12 1904-02-14     1
#> 13 1904-02-18     0

Created on 2019-03-19 by the reprex package (v0.2.1)

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