vpts
) to a data frameR/vpts.R
as.data.frame.vpts.Rd
Converts vertical profile time series (objects of class vpts
) to a
data Frame, and optionally adds information on sunrise/sunset, day/night
and derived quantities like migration traffic rates.
# S3 method for vpts as.data.frame( x, row.names = NULL, optional = FALSE, quantities = names(x$data), suntime = TRUE, geo = TRUE, elev = -0.268, lat = NULL, lon = NULL, ... )
x | An object of class |
---|---|
row.names |
|
optional | If |
quantities | An optional character vector with the names of the quantities to include as columns in the data frame. |
suntime | Logical, when TRUE, adds sunrise/sunset and day/night information to each row. |
geo | Logical, when TRUE, adds latitude, longitude and antenna height of the radar to each row. |
elev | |
lat | Radar latitude in decimal degrees. When set, overrides the
latitude stored in |
lon | Radar longitude in decimal degrees. When set, overrides the
longitude stored in |
... | Additional arguments to be passed to or from methods. |
An object of class data.frame.
Note that only the 'dens' quantity is thresholded for radial velocity standard deviation by sd_vvp_threshold. Note that this is different from the default plot.vp, plot.vpts and get_quantity.vp functions, where quantities "eta", "dbz", "ff", "u", "v", "w", "dd" are all thresholded by sd_vvp_threshold.
#> Irregular time series of vertical profiles (class vpts) #> #> radar: KBGM #> # profiles: 1934 #> time range (UTC): 2016-09-01 00:02:00 - 2016-09-10 11:56:00 #> time step (s): min: 180 max: 16320# convert the object to a data.frame df <- as.data.frame(example_vpts) # do not compute sunrise/sunset information df <- as.data.frame(example_vpts, suntime = FALSE) # override the latitude/longitude information stored in the object # when calculating sunrise / sunset df <- as.data.frame(example_vpts, suntime = TRUE, lat = 50, lon = 4) # print first then rows of data.frame to console: df[1:10, ]#> radar datetime height u v w ff dd sd_vvp gap #> 1 KBGM 2016-09-01 00:02:00 0 NaN NaN NaN NaN NaN NaN TRUE #> 2 KBGM 2016-09-01 00:02:00 200 NaN NaN NaN NaN NaN NaN TRUE #> 3 KBGM 2016-09-01 00:02:00 400 NaN NaN NaN NaN NaN 2.81 TRUE #> 4 KBGM 2016-09-01 00:02:00 600 4.14 3.84 12.17 5.65 47.2 2.80 FALSE #> 5 KBGM 2016-09-01 00:02:00 800 5.06 0.24 15.17 5.07 87.2 2.42 FALSE #> 6 KBGM 2016-09-01 00:02:00 1000 4.74 -2.69 18.48 5.45 119.6 2.38 FALSE #> 7 KBGM 2016-09-01 00:02:00 1200 4.41 -4.09 -2.49 6.02 132.9 2.29 FALSE #> 8 KBGM 2016-09-01 00:02:00 1400 4.78 -3.55 -2.96 5.96 126.6 2.34 FALSE #> 9 KBGM 2016-09-01 00:02:00 1600 3.75 -3.03 -7.32 4.82 128.9 2.62 FALSE #> 10 KBGM 2016-09-01 00:02:00 1800 4.14 -2.03 -5.69 4.61 116.1 3.06 FALSE #> dbz eta dens DBZH n n_dbz n_all n_dbz_all lat lon #> 1 NaN NaN NaN NaN 0 0 0 0 50 4 #> 2 NaN NaN NaN NaN 0 0 0 0 50 4 #> 3 1.54 30.8 2.800000000 3.77 326 356 22485 28416 50 4 #> 4 3.36 46.9 4.263636364 0.50 9006 13442 65947 104455 50 4 #> 5 -7.89 3.5 0.318181818 -10.33 2313 11145 21321 63542 50 4 #> 6 -16.17 0.5 0.045454545 -15.58 652 8851 11789 38006 50 4 #> 7 -19.61 0.2 0.018181818 -16.93 990 8026 8434 25583 50 4 #> 8 -22.72 0.1 0.009090909 -19.60 407 9945 2718 20376 50 4 #> 9 -24.65 0.1 0.009090909 -24.28 247 11786 442 14716 50 4 #> 10 -25.29 0.1 0.009090909 -25.38 154 10878 163 11482 50 4 #> height_antenna day sunrise sunset #> 1 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 2 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 3 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 4 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 5 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 6 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 7 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 8 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 9 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38 #> 10 519 FALSE 2016-09-01 05:03:05 2016-09-01 18:23:38