![[Experimental]](figures/lifecycle-experimental.svg)
aggregate_index() allows you to aggregate the index of a
tsibble object by applying a specific function to its
measured variables.
aggregate_index(data, unit, fun = NULL, week_start = 1)Arguments
- data
A
tsibbleobject.- 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) The
functionto be applied to each measure variable ofdata. IfNULL,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 (
1for Monday and7forSunday). This is only used whenunit == "weeks(default:1).
Value
A tsibble object.
Details
aggregate_index() was created to easily regularize
tsibble objects. If you need more control while doing
this operation, check the index_by() function
provided by the tsibble package.
Default function
If fun == NULL, aggregate_index() will use the following function to
transform each measured variable:
function(x) {
checkmate::assert_atomic_vector(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 average values for numeric variables and assigning the most
frequent value (mode) for single integer or other type of variables. If no
mode can be found, the function will return the first value of x.
See also
Other utility functions:
find_epoch(),
raw_data(),
read_acttrust(),
write_acttrust()
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>