Obtain moving average map series

Source: R/functions.R

Obtain a moving average for monthly, annual or seasonal resolution data from monthly map data.

get_ma(x, type, season = NULL, size = 10, format = "table",
  use_mclapply = FALSE, mc.cores = 32)

Arguments

x

a data frame (or data table) or a list of these.
type

character, one of "monthly", "annual", or "seasonal".
season

NULL or character, one of "winter", "spring", "summer", or "autumn". Default is NULL.
size

number of years for the moving average window. Default 10.
format

return results as a single data frame with format="table" (default) or a as a list split either on unique year for seasonal and annual moving averages or on unique year and month combination for monthly moving averages.
use_mclapply

use mclapply from the parallel package (Linux). Defaults to FALSE.
mc.cores

number of CPUs for parallel processing when use_mclapply=TRUE.

Value

returns a single data frame or a list of data frames.

Details

get_ma takes a single data frame or a list of data frames. A list is useful for example if the table is very large and is pre-split into a list of smaller data frames for parallel processing on a Linux cluster with many CPU cores available. If x is a data frame rather than a data table, it will be converted to a data table.

For each data frame get_ma computes and returns the moving or rolling average, after first summarizing monthly data to seasonal or annual averages if applicable. Winter begins in December and each season is three consecutive months (DJF, MAM, JJA, SON). Whether x is a data frame or list of data frames, the output can be returned as a single data frame or list of data frames

Parallel processing is Linux-only (uses mclapply) and the default of 32 CPU cores will have to be changed if you don't have that many. It is convenient for my usage and this package version is not intended for general use. Most other users will probably not be using parallel processing at all, in which case n.cores is ignored.

Examples

data(monthlytemps)
res <- "seasonal" # annual, seasonal, monthly
season <- "winter" # winter, spring, summer, autumn
idx <- switch(season, winter=c(12,1,2), spring=3:5, summer=6:8, autumn=9:11)
if(res=="seasonal") monthlytemps <- dplyr::filter(monthlytemps, Month %in% idx)
get_ma(monthlytemps, res, season)
#> # A tibble: 80 x 4
#>      lon   lat  Year     z
#>    <dbl> <dbl> <dbl> <dbl>
#>  1 -177.  53.7  2015 0.526
#>  2 -177.  53.7  2016 0.410
#>  3 -177.  53.7  2017 0.522
#>  4 -177.  53.7  2018 0.460
#>  5 -177.  53.7  2019 0.547
#>  6 -177.  53.7  2020 0.477
#>  7 -177.  53.7  2021 0.598
#>  8 -177.  53.7  2022 0.702
#>  9 -177.  53.7  2023 0.665
#> 10 -177.  53.7  2024 0.713
#> # ... with 70 more rows
get_ma(list(monthlytemps, monthlytemps), res, season)
#> # A tibble: 160 x 4
#>      lon   lat  Year     z
#>    <dbl> <dbl> <dbl> <dbl>
#>  1 -177.  53.7  2015 0.526
#>  2 -177.  53.7  2016 0.410
#>  3 -177.  53.7  2017 0.522
#>  4 -177.  53.7  2018 0.460
#>  5 -177.  53.7  2019 0.547
#>  6 -177.  53.7  2020 0.477
#>  7 -177.  53.7  2021 0.598
#>  8 -177.  53.7  2022 0.702
#>  9 -177.  53.7  2023 0.665
#> 10 -177.  53.7  2024 0.713
#> # ... with 150 more rows
get_ma(monthlytemps, res, season, format="list")
#> $`2015`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2015 0.526
#> 
#> $`2016`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2016 0.410
#> 
#> $`2017`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2017 0.522
#> 
#> $`2018`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2018 0.460
#> 
#> $`2019`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2019 0.547
#> 
#> $`2020`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2020 0.477
#> 
#> $`2021`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2021 0.598
#> 
#> $`2022`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2022 0.702
#> 
#> $`2023`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2023 0.665
#> 
#> $`2024`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2024 0.713
#> 
#> $`2025`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2025 0.668
#> 
#> $`2026`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2026 0.826
#> 
#> $`2027`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2027 0.869
#> 
#> $`2028`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2028 0.937
#> 
#> $`2029`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2029 0.963
#> 
#> $`2030`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2030  1.04
#> 
#> $`2031`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2031  1.07
#> 
#> $`2032`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2032 0.980
#> 
#> $`2033`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2033 0.992
#> 
#> $`2034`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2034 0.942
#> 
#> $`2035`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2035  1.09
#> 
#> $`2036`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2036 0.995
#> 
#> $`2037`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2037 0.992
#> 
#> $`2038`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2038 0.989
#> 
#> $`2039`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2039 0.936
#> 
#> $`2040`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2040 0.886
#> 
#> $`2041`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2041 0.852
#> 
#> $`2042`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2042 0.960
#> 
#> $`2043`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2043  1.01
#> 
#> $`2044`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2044  1.03
#> 
#> $`2045`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2045  0.98
#> 
#> $`2046`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2046  1.04
#> 
#> $`2047`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2047  1.18
#> 
#> $`2048`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2048  1.18
#> 
#> $`2049`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2049  1.28
#> 
#> $`2050`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2050  1.24
#> 
#> $`2051`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2051  1.25
#> 
#> $`2052`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2052  1.28
#> 
#> $`2053`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2053  1.43
#> 
#> $`2054`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2054  1.54
#> 
#> $`2055`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2055  1.52
#> 
#> $`2056`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2056  1.55
#> 
#> $`2057`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2057  1.45
#> 
#> $`2058`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2058  1.52
#> 
#> $`2059`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2059  1.50
#> 
#> $`2060`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2060  1.65
#> 
#> $`2061`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2061  1.72
#> 
#> $`2062`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2062  1.79
#> 
#> $`2063`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2063  1.74
#> 
#> $`2064`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2064  1.82
#> 
#> $`2065`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2065  1.94
#> 
#> $`2066`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2066  2.08
#> 
#> $`2067`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2067  2.14
#> 
#> $`2068`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2068  2.16
#> 
#> $`2069`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2069  2.14
#> 
#> $`2070`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2070  2.09
#> 
#> $`2071`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2071  2.01
#> 
#> $`2072`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2072  1.98
#> 
#> $`2073`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2073  2.01
#> 
#> $`2074`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2074  1.89
#> 
#> $`2075`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2075  1.77
#> 
#> $`2076`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2076  1.65
#> 
#> $`2077`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2077  1.60
#> 
#> $`2078`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2078  1.57
#> 
#> $`2079`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2079  1.71
#> 
#> $`2080`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2080  1.77
#> 
#> $`2081`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2081  1.83
#> 
#> $`2082`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2082  1.80
#> 
#> $`2083`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2083  1.79
#> 
#> $`2084`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2084  1.84
#> 
#> $`2085`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2085  1.95
#> 
#> $`2086`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2086  1.98
#> 
#> $`2087`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2087  1.95
#> 
#> $`2088`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2088  1.97
#> 
#> $`2089`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2089  1.82
#> 
#> $`2090`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2090  1.76
#> 
#> $`2091`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2091  1.77
#> 
#> $`2092`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2092  1.84
#> 
#> $`2093`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2093  1.79
#> 
#> $`2094`
#> # A tibble: 1 x 4
#>     lon   lat  Year     z
#>   <dbl> <dbl> <dbl> <dbl>
#> 1 -177.  53.7  2094  1.82
#> 
# NOT RUN {
get_ma(list(monthlytemps, monthlytemps), res, season, use_mclapply=T, mc.cores=2)
# }