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Convert windscape objects to data frames for ggplot2

fortify.windscape.Rd

fortify() methods that convert windscape objects to tidy data frames, so they can be passed directly to ggplot2::ggplot() or a layer's data argument. They can also be called directly, to inspect or modify the data before plotting.

Usage

# S3 method for class 'wind_rose'
fortify(model, data, na.rm = TRUE, ...)

# S3 method for class 'wind_field'
fortify(model, data, na.rm = TRUE, ...)

# S3 method for class 'wind_series'
fortify(model, data, na.rm = TRUE, ...)

# S3 method for class 'random_walk'
fortify(model, data, na.rm = TRUE, ...)

Arguments

model

A windscape object: a wind_rose, wind_field, wind_series, or the result of random_walk().

data

Not used.

na.rm

Logical: drop grid cells with missing values? Default TRUE.

...

Not used.

Value

A data frame with grid cell center coordinates x and y, plus:

  • wind_rose: one column of conductance per direction, SW, W, NW, N, NE, E, SE, S, and total, their sum. For longitude/latitude roses, also per-cell flow summaries matching those computed by geom_wind_rose(): speed, the mean wind speed in km/h (the sum of the flows toward the eight neighbors, if trans = 1 and wind speeds are in m/s); net, the speed of net flow in km/h; bearing, its direction in degrees clockwise from north; and consistency, net / speed, the steadiness of wind direction over time (near 1 where wind blows predominantly one way, near 0 where it has no net direction).

  • wind_field: wind components u and v; speed, in the units of u and v; and bearing, the direction the wind blows toward, in degrees clockwise from north.

  • wind_series: one row per grid cell and time step, with step (the time step's index), time (parsed from layer names where possible, otherwise NA), and u, v, speed, and bearing as for a wind_field.

  • random_walk() result, pulse mode: one row per grid cell and recorded iteration, with iteration, hours (elapsed time), airborne, and deposition.

  • random_walk() result, stream mode: residence, deposition, and for upwind walks, origin (the flux element is a wind_field; fortify it separately).

Examples

rose <- windscape_example("wind_rose")
head(ggplot2::fortify(rose))
#>           x  y          SW          W         NW          N         NE
#> 1 -120.0000 50 0.001837510 0.01303786 0.04689814 0.03452373 0.02867673
#> 2 -119.6842 50 0.003325526 0.01249226 0.04783026 0.04005871 0.02012151
#> 3 -119.3684 50 0.003556589 0.01323038 0.04368901 0.04622269 0.01887328
#> 4 -119.0526 50 0.003929742 0.01285706 0.03766154 0.05081339 0.02046587
#> 5 -118.7368 50 0.002442006 0.01549426 0.03567446 0.05171587 0.02091837
#> 6 -118.4211 50 0.002181489 0.02034521 0.03644594 0.05170206 0.01930627
#>            E         SE           S     total    speed      net  bearing
#> 1 0.13035671 0.02295375 0.006273328 0.2845578 8.895296 3.901069 44.44692
#> 2 0.11763944 0.02839319 0.004975851 0.2748367 8.714891 3.427445 42.85106
#> 3 0.10748533 0.03674658 0.003969136 0.2737730 8.817199 3.257523 45.75774
#> 4 0.09756345 0.04342743 0.003780435 0.2704989 8.848722 3.172106 50.06727
#> 5 0.08117093 0.04646795 0.005902223 0.2597861 8.643859 2.865233 48.87757
#> 6 0.06930302 0.04561561 0.007976120 0.2528757 8.475258 2.498716 43.23467
#>   consistency
#> 1   0.4385542
#> 2   0.3932861
#> 3   0.3694510
#> 4   0.3584819
#> 5   0.3314762
#> 6   0.2948248