Summarize wind data by direction and intensity — summarize_wind

Generates a summary data.frame suitable for passing to plot_wind_rose() for plotting or wemo() for modeling. Input data should be a log of wind readings and must have columns wind_direction and wind_speed. Data generated from get_wind_data() is ready to be used as input. Input wind_direction values are coerced into values 0-359.9. The function can further coerce or snap input wind_directions to a user supplied vector of suitable directions (directions). This is often necessary with input data from public data sets that have occasional anomalous readings. If directions is not supplied the output is based on all unique wind_direction.

Usage

summarize_wind_data(
  wind_data,
  wind_percentile,
  directions = NULL,
  wind_speed_na.rm = TRUE
)

Arguments

wind_data: A data frame containing at least the columns wind_direction and wind_speed
wind_percentile: either 'mean' or a numeric between 0 and 1 specifying the percentile of wind_speed to calculate
directions: numeric vector of wind directions to snap input wind_direction to. This is useful if input wind_data$wind_data has anomolous wind_direction readings
wind_speed_na.rm: logical. indicating if wind_speed of NA indicates bad data (TRUE, the default) or calm wind (FALSE). If FALSE, the NA value will increase the n and proportion values but not effect the speed value in the returned object

Value

A tibble summarizing wind by direction and intensity, with columns:

direction: Wind direction (degrees, with 360 converted to 0)
n: Count of observations in each direction
proportion: Percentage of total observations in each direction
speed: Mean or percentile wind speed for each direction

Examples

set.seed(1) #for reproduceability
# generate some example data
wind_data <- data.frame(
  wind_direction = sample(c(90, 180, 270, 360), size = 25, replace = TRUE),
  wind_speed = runif(25, 0, 25)
)

# Summarize using the 95th percentile for wind speed
summarize_wind_data(wind_data, wind_percentile = 0.95)
#> # A tibble: 4 × 4
#>   direction     n proportion  speed
#>       <dbl> <int>      <dbl>  <dbl>
#> 1         0     1          4  0.335
#> 2        90    10         40 20.8  
#> 3       180     7         28 20.6  
#> 4       270     7         28 18.9  

# Summarize using the mean wind speed
summarize_wind_data(wind_data, wind_percentile = "mean")
#> # A tibble: 4 × 4
#>   direction     n proportion  speed
#>       <dbl> <int>      <dbl>  <dbl>
#> 1         0     1          4  0.335
#> 2        90    10         40 14.2  
#> 3       180     7         28 14.8  
#> 4       270     7         28 12.5  

# create some dummy data with NAs in the speed and direction columns
wind_data <- data.frame(
  wind_direction = c(runif(n = 5, min = 0, max = 315),rep(NA, 10)),
  wind_speed = c(runif(5, 0, 25), rep(NA, 5), rep(0, 5))
)

# remove the NAs in the speed column. perhaps these are data collection errors
summarize_wind_data(wind_data, wind_percentile = 'mean',
  directions = c(90, 180, 270, 360), wind_speed_na.rm = TRUE)
#> # A tibble: 4 × 4
#>   direction     n proportion speed
#>       <dbl> <int>      <dbl> <dbl>
#> 1        90     2         20 13.4 
#> 2       180     2         20  7.73
#> 3       270     1         10  7.91
#> 4        NA     5         50  0   

# keep the NAs in the speed column. perhaps these represent calm data
# (though normally 0 would indicate calm)
summarize_wind_data(wind_data, wind_percentile = 'mean',
  directions = c(90, 180, 270, 360), wind_speed_na.rm = FALSE)
#> # A tibble: 4 × 4
#>   direction     n proportion speed
#>       <dbl> <int>      <dbl> <dbl>
#> 1        90     2      13.3  13.4 
#> 2       180     2      13.3   7.73
#> 3       270     1       6.67  7.91
#> 4        NA    10      66.7   0