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Calculate mean daily metrics from daily summary

Source: R/mean_daily.R
mean_daily.Rd

mean_daily calculates a three-row summary of metrics showing average weekday, weekend, and mean daily values of all non-grouping numeric columns. The basis is a dataframe that contains metrics per weekday, or per date (with calculate.from.Date = Datetime). The function requires a column specifying the day of the week as a factor (with Monday as the weekstart), or it can calculate this from a date column if provided.

Usage

mean_daily(
  data,
  Weekend.type = Date,
  na.rm = TRUE,
  calculate.from.Date = NULL,
  prefix = "average_",
  filter.empty = FALSE,
  sub.zero = FALSE,
  Datetime2Time = TRUE
)

Arguments

data

A dataframe containing the metrics to summarize

Weekend.type

A column in the dataframe that specifies the day of the week as a factor, where weekstart is Monday (so weekends are 6 and 7 in numeric representation). If it is a date, it will be converted to this factor

na.rm

Logical, whether to remove NA values when calculating means. Default is TRUE.

calculate.from.Date

Optional. A column in the dataframe containing dates from which to calculate the Weekend.type. If provided, Weekend.type will be generated from this column.

prefix

String that is the prefix on summarized values

filter.empty

Filter out empty rows. Default is FALSE

sub.zero

Logical. Should missing values be replaced by zero? Defaults to FALSE. Will throw an error, if it happens on a type other than double.

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).

Value

A dataframe with three rows representing average weekday, weekend, and mean daily values of all numeric columns

Details

Summary values for type POSIXct are calculated as the mean, which can be nonsensical at times (e.g., the mean of Day1 18:00 and Day2 18:00, is Day2 6:00, which can be the desired result, but if the focus is on time, rather then on datetime, it is recommended that values are converted to times via hms::as_hms() before applying the function (the mean of 18:00 and 18:00 is still 18:00, not 6:00).

Examples

# Create sample data
sample_data <- data.frame(
  Date = factor(c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"),
               levels = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")),
  lux = c(250, 300, 275, 280, 290, 350, 320),
  duration = lubridate::as.duration(c(120, 130, 125, 135, 140, 180, 160))
)

# Calculate mean daily metrics
mean_daily(sample_data)
#> # A tibble: 3 × 3
#>   Date       average_lux average_duration    
#>   <chr>            <dbl> <Duration>          
#> 1 Mean daily         295 141s (~2.35 minutes)
#> 2 Weekday            279 130s (~2.17 minutes)
#> 3 Weekend            335 170s (~2.83 minutes)

# With a Date column
sample_data_with_date <- data.frame(
  Date = seq(as.Date("2023-05-01"), as.Date("2023-05-07"), by = "day"),
  lux = c(250, 300, 275, 280, 290, 350, 320),
  duration = lubridate::as.duration(c(120, 130, 125, 135, 140, 180, 160))
)

mean_daily(sample_data_with_date)
#> # A tibble: 3 × 3
#>   Date       average_lux average_duration    
#>   <chr>            <dbl> <Duration>          
#> 1 Mean daily         295 141s (~2.35 minutes)
#> 2 Weekday            279 130s (~2.17 minutes)
#> 3 Weekend            335 170s (~2.83 minutes)