Aggregate the index of a tsibble — aggregate

aggregate_index() allows you to aggregate the index of a tsibble object by applying a specific function to its measured variables.

Usage

aggregate_index(data, unit, fun = NULL, week_start = 1)

Arguments

data: A tsibble object.
unit: A string indicating at which time unit the index must be aggregated. Valid values are: “seconds”, “minutes”, “hours”, “days”, “weeks”, “months”, “quarters”, and “years”) (default: "minutes").
fun: (optional) A function to be applied to each measure variable of data. If NULL, aggregate_index() will apply its default function (see the Details section to learn more) (default: NULL).
week_start: (optional) An integer number indicating the day on which the week starts (1 for Monday and 7 for Sunday). This is only used when unit is set to "weeks" (default: 1).

Value

A tsibble object with the same columns as data, but with the index aggregated to the specified time unit.

Details

aggregate_index() is designed to simplify the regularization of tsibble objects by aggregating their index to a specified time unit. For more advanced or customized aggregation, consider using the index_by() function from the tsibble package, which offers greater flexibility and control.

Default function

If the fun argument is NULL, aggregate_index() will use the following function to transform each measured variable:

function(x) {
    if (is.numeric(x) && !all(nchar(x) == 1, na.rm = TRUE)) {
        mean(x, na.rm = TRUE)
    } else {
        y <- x[which(!is.na(x))]
        unique <- unique(y)
        unique[which.max(tabulate(match(y, unique)))]
    }
}

This function computes the mean for numeric variables and assigns the most frequent value (mode) for non-numeric variables. If no mode is found, it returns the first non-missing value of x.

Examples

acttrust
#> # A tsibble: 1,441 x 17 [1m] <America/Sao_Paulo>
#>    timestamp             pim   tat   zcm orientation wrist_temperature
#>    <dttm>              <dbl> <dbl> <dbl>       <dbl>             <dbl>
#>  1 2021-04-24 04:14:00  7815   608   228           0              26.9
#>  2 2021-04-24 04:15:00  2661   160    64           0              27.2
#>  3 2021-04-24 04:16:00  3402   243    80           0              27.7
#>  4 2021-04-24 04:17:00  4580   317   125           0              27.9
#>  5 2021-04-24 04:18:00  2624   255    33           0              28.0
#>  6 2021-04-24 04:19:00  3929   246   105           0              28.1
#>  7 2021-04-24 04:20:00  5812   369   171           0              28.2
#>  8 2021-04-24 04:21:00  3182   270    54           0              28.4
#>  9 2021-04-24 04:22:00  6362   373   189           0              28.6
#> 10 2021-04-24 04:23:00  2621   159    64           0              28.7
#> # ℹ 1,431 more rows
#> # ℹ 11 more variables: external_temperature <dbl>, light <dbl>,
#> #   ambient_light <dbl>, red_light <dbl>, green_light <dbl>, blue_light <dbl>,
#> #   ir_light <dbl>, uva_light <dbl>, uvb_light <dbl>, event <dbl>, state <dbl>

aggregate_index(acttrust, unit = "hour")
#> # A tsibble: 25 x 17 [1h] <America/Sao_Paulo>
#>    timestamp              pim    tat   zcm orientation wrist_temperature
#>    <dttm>               <dbl>  <dbl> <dbl>       <dbl>             <dbl>
#>  1 2021-04-24 04:00:00 2424.  182.   50.0            0              29.5
#>  2 2021-04-24 05:00:00 2631.  173.   64.1            0              30.0
#>  3 2021-04-24 06:00:00 1960.  139.   40.2            0              29.9
#>  4 2021-04-24 07:00:00 2453.  163.   56.7            0              29.9
#>  5 2021-04-24 08:00:00 1758.  128.   37.2            0              30.5
#>  6 2021-04-24 09:00:00 2008.  134.   45.3            0              31.0
#>  7 2021-04-24 10:00:00 3129.  192.   94.8            0              30.5
#>  8 2021-04-24 11:00:00  152.    9.72  4.48           0              32.3
#>  9 2021-04-24 12:00:00   72.9   4.18  2.1            0              29.6
#> 10 2021-04-24 13:00:00   79.8   6.22  1.33           0              31.4
#> # ℹ 15 more rows
#> # ℹ 11 more variables: external_temperature <dbl>, light <dbl>,
#> #   ambient_light <dbl>, red_light <dbl>, green_light <dbl>, blue_light <dbl>,
#> #   ir_light <dbl>, uva_light <dbl>, uvb_light <dbl>, event <dbl>, state <dbl>

aggregate_index(acttrust, unit = "day")
#> # A tsibble: 2 x 17 [1D]
#>   timestamp    pim   tat   zcm orientation wrist_temperature
#>   <date>     <dbl> <dbl> <dbl>       <dbl>             <dbl>
#> 1 2021-04-24 1657.  107.  43.8           0              30.8
#> 2 2021-04-25 2976.  198.  77.5           0              29.9
#> # ℹ 11 more variables: external_temperature <dbl>, light <dbl>,
#> #   ambient_light <dbl>, red_light <dbl>, green_light <dbl>, blue_light <dbl>,
#> #   ir_light <dbl>, uva_light <dbl>, uvb_light <dbl>, event <dbl>, state <dbl>

aggregate_index(acttrust, unit = "week")
#> # A tsibble: 1 x 17 [?]
#>   timestamp   pim   tat   zcm orientation wrist_temperature external_temperature
#>      <week> <dbl> <dbl> <dbl>       <dbl>             <dbl>                <dbl>
#> 1  2021 W16 1890.  123.  49.8           0              30.6                 29.6
#> # ℹ 10 more variables: light <dbl>, ambient_light <dbl>, red_light <dbl>,
#> #   green_light <dbl>, blue_light <dbl>, ir_light <dbl>, uva_light <dbl>,
#> #   uvb_light <dbl>, event <dbl>, state <dbl>

aggregate_index(acttrust, unit = "month")
#> # A tsibble: 1 x 17 [?]
#>   timestamp   pim   tat   zcm orientation wrist_temperature external_temperature
#>       <mth> <dbl> <dbl> <dbl>       <dbl>             <dbl>                <dbl>
#> 1  2021 Apr 1890.  123.  49.8           0              30.6                 29.6
#> # ℹ 10 more variables: light <dbl>, ambient_light <dbl>, red_light <dbl>,
#> #   green_light <dbl>, blue_light <dbl>, ir_light <dbl>, uva_light <dbl>,
#> #   uvb_light <dbl>, event <dbl>, state <dbl>

aggregate_index(acttrust, unit = "quarter")
#> # A tsibble: 1 x 17 [?]
#>   timestamp   pim   tat   zcm orientation wrist_temperature external_temperature
#>       <qtr> <dbl> <dbl> <dbl>       <dbl>             <dbl>                <dbl>
#> 1   2021 Q2 1890.  123.  49.8           0              30.6                 29.6
#> # ℹ 10 more variables: light <dbl>, ambient_light <dbl>, red_light <dbl>,
#> #   green_light <dbl>, blue_light <dbl>, ir_light <dbl>, uva_light <dbl>,
#> #   uvb_light <dbl>, event <dbl>, state <dbl>

aggregate_index(acttrust, unit = "year")
#> # A tsibble: 1 x 17 [?]
#>   timestamp   pim   tat   zcm orientation wrist_temperature external_temperature
#>       <dbl> <dbl> <dbl> <dbl>       <dbl>             <dbl>                <dbl>
#> 1      2021 1890.  123.  49.8           0              30.6                 29.6
#> # ℹ 10 more variables: light <dbl>, ambient_light <dbl>, red_light <dbl>,
#> #   green_light <dbl>, blue_light <dbl>, ir_light <dbl>, uva_light <dbl>,
#> #   uvb_light <dbl>, event <dbl>, state <dbl>

Aggregate the index of a `tsibble`