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Calculate mean daily metrics from Time Series

Source: R/mean_daily.R
mean_daily_metric.Rd

mean_daily_metric is a convenience wrapper around mean_daily that summarizes data imported with LightLogR per weekday and calculates mean daily values for a specific metric. Examples include duration_above_threshold() (the default), or durations().

Usage

mean_daily_metric(
  data,
  Variable,
  Weekend.type = Date,
  Datetime.colname = Datetime,
  metric_type = duration_above_threshold,
  prefix = "average_",
  filter.empty = FALSE,
  Datetime2Time = TRUE,
  ...
)

Arguments

data

A dataframe containing light logger data imported with LightLogR

Variable

The variable column to analyze. Expects a symbol. Needs to be part of the dataset.

Weekend.type

A (new) column in the dataframe that specifies the day of the week as a factor

Datetime.colname

Column name containing datetime values. Defaults to Datetime

metric_type

The metric function to apply, default is duration_above_threshold()

prefix

String that is the prefix on summarized values

filter.empty

Filter out empty rows. Default is FALSE

Datetime2Time

Logical of whether POSIXct columns should be transformed into hms(time) columns, which is usually sensible for averaging (default is TRUE). Calls Datetime2Time() with default settings (all POSIXct are affected).

...

Additional arguments passed to the metric function

Value

A dataframe with three rows representing average weekday, weekend, and mean daily values for the specified metric

Examples


# Calculate mean daily duration above threshold. As the data only contains
# data for two days, Weekend and Mean daily will throw NA
sample.data.irregular |> 
aggregate_Datetime(unit = "1 min") |> 
mean_daily_metric(
  Variable = lux,
  threshold = 100
)
#> # A tibble: 3 × 3
#> # Groups:   Id [1]
#>   Id    Date       average_duration_above_100
#>   <chr> <chr>      <Duration>                
#> 1 P1    Mean daily NA                        
#> 2 P1    Weekday    17220s (~4.78 hours)      
#> 3 P1    Weekend    NA                        

# again with another dataset
sample.data.environment |> 
  mean_daily_metric(
  Variable = MEDI,
  threshold = 250)
#> # A tibble: 6 × 3
#> # Groups:   Id [2]
#>   Id          Date       average_duration_above_250
#>   <fct>       <chr>      <Duration>                
#> 1 Environment Mean daily 48710s (~13.53 hours)     
#> 2 Environment Weekday    48712s (~13.53 hours)     
#> 3 Environment Weekend    48705s (~13.53 hours)     
#> 4 Participant Mean daily 15716s (~4.37 hours)      
#> 5 Participant Weekday    11495s (~3.19 hours)      
#> 6 Participant Weekend    26270s (~7.3 hours)       

# by default, datetime columns are converted to time
sample.data.environment |> 
  mean_daily_metric(
  Variable = MEDI,
  metric_type = timing_above_threshold,
  threshold = 250)
#> # A tibble: 6 × 5
#> # Groups:   Id [2]
#>   Id          Date       average_mean_timing_above_250 average_first_timing_ab…¹
#>   <fct>       <chr>      <time>                        <time>                   
#> 1 Environment Mean daily 13:24:00                      06:38:19                 
#> 2 Environment Weekday    13:24:12                      06:38:30                 
#> 3 Environment Weekend    13:23:30                      06:37:53                 
#> 4 Participant Mean daily 15:15:33                      09:58:06                 
#> 5 Participant Weekday    15:11:08                      09:58:09                 
#> 6 Participant Weekend    15:26:37                      09:57:59                 
#> # ℹ abbreviated name: ¹​average_first_timing_above_250
#> # ℹ 1 more variable: average_last_timing_above_250 <time>