vp
) or time series of vertical profiles
(vpts
) to a data frameR/as.data.frame.R
as.data.frame.vp.Rd
Converts a vertical profile (vp
) or a time series of vertical profiles
(vpts
) to a data frame containing all quantities per datetime and height.
Has options to include latitude/longitude/antenna height (parameter geo
)
and day/sunrise/sunset (parameter suntime
).
# S3 method for vp as.data.frame( x, row.names = NULL, optional = FALSE, geo = TRUE, suntime = TRUE, lat = NULL, lon = NULL, elev = -0.268, ... ) # S3 method for vpts as.data.frame( x, row.names = NULL, optional = FALSE, geo = TRUE, suntime = TRUE, lat = NULL, lon = NULL, elev = -0.268, ... )
x | A |
---|---|
row.names |
|
optional | Logical. If |
geo | Logical. When |
suntime | Logical. When |
lat | Numeric. Radar latitude in decimal degrees. When set, overrides
the latitude stored in |
lon | Numeric. Radar longitude in decimal degrees. When set, overrides
the longitude stored in |
elev | Numeric. Sun elevation in degrees, used for
|
... | Additional arguments to be passed to or from methods. |
A data.frame
object, containing radar, datetime and height as rows
and all profile quantities as columns, complemented with some oft-used
additional information (columns lat
, lon
, height_antenna
, day
,
sunrise
, sunset
).
Note that only the dens
quantity is thresholded for radial velocity
standard deviation by sd_vvp_threshold()
. This is different from the
default plot.vp()
, plot.vpts()
and get_quantity()
functions, where
quantities eta
, dbz
, ff
, u
, v
, w
, dd
are all thresholded by
sd_vvp_threshold()
.
# Load the example vertical profile vp <- example_vp # Convert to a data.frame vp_df <- as.data.frame(vp) # Print data.frame vp_df#> radar datetime ff dbz dens u #> 1 seang 2015-10-18 18:00:00 NA NA NA NA #> 2 seang 2015-10-18 18:00:00 11.53461 4.3039665 85.0995178 -3.860118 #> 3 seang 2015-10-18 18:00:00 13.35705 5.4355612 110.4298782 -5.824306 #> 4 seang 2015-10-18 18:00:00 13.77482 4.6322823 91.7822418 -6.317519 #> 5 seang 2015-10-18 18:00:00 12.37280 0.3914067 34.5677528 -5.464484 #> 6 seang 2015-10-18 18:00:00 11.62856 -1.8672314 20.5497875 -5.435530 #> 7 seang 2015-10-18 18:00:00 12.26567 -1.3853970 22.9609985 -5.871046 #> 8 seang 2015-10-18 18:00:00 12.44552 -2.0301955 19.7929668 -5.042855 #> 9 seang 2015-10-18 18:00:00 12.72011 -2.0336432 19.7772617 -4.089148 #> 10 seang 2015-10-18 18:00:00 13.16166 -2.5901840 17.3985100 -4.711092 #> 11 seang 2015-10-18 18:00:00 13.24782 -4.2063093 11.9922190 -4.695442 #> 12 seang 2015-10-18 18:00:00 12.97676 -6.4061351 7.2263165 -4.695178 #> 13 seang 2015-10-18 18:00:00 13.96152 -10.1189432 3.0735207 -5.040771 #> 14 seang 2015-10-18 18:00:00 NaN -14.6137285 0.0000000 NaN #> 15 seang 2015-10-18 18:00:00 18.53717 -16.1167755 0.7724188 -6.448045 #> 16 seang 2015-10-18 18:00:00 NaN -16.8988857 0.0000000 NaN #> 17 seang 2015-10-18 18:00:00 NaN -28.0253792 0.0000000 NaN #> 18 seang 2015-10-18 18:00:00 NaN -37.8717766 0.0000000 NaN #> 19 seang 2015-10-18 18:00:00 NaN -38.7156448 0.0000000 NaN #> 20 seang 2015-10-18 18:00:00 NaN -38.6279716 0.0000000 NaN #> 21 seang 2015-10-18 18:00:00 NaN -39.0613213 0.0000000 NaN #> 22 seang 2015-10-18 18:00:00 NaN -41.9553452 0.0000000 NaN #> 23 seang 2015-10-18 18:00:00 NaN -39.9685364 0.0000000 NaN #> 24 seang 2015-10-18 18:00:00 NaN -44.7469788 0.0000000 NaN #> 25 seang 2015-10-18 18:00:00 NaN -40.6572495 0.0000000 NaN #> v gap w n_dbz dd n DBZH height n_dbz_all #> 1 NA 1 NA 0 NA 0 NA 0 0 #> 2 -10.86953 0 -8.255996 9786 199.5516 4384 4.853236 200 27593 #> 3 -12.02032 0 -3.870171 10807 205.8520 5680 4.977803 400 24154 #> 4 -12.24070 0 -15.429576 10874 207.2986 5456 4.296990 600 18282 #> 5 -11.10070 0 -7.646388 9343 206.2094 4067 0.749139 800 13321 #> 6 -10.28000 0 2.433591 9025 207.8676 3628 -1.486296 1000 10471 #> 7 -10.76928 0 3.606835 6743 208.5977 3126 -1.220092 1200 7308 #> 8 -11.37807 0 2.602238 6873 203.9033 3180 -2.030195 1400 6873 #> 9 -12.04492 0 1.397334 6516 198.7519 2962 -2.033643 1600 6516 #> 10 -12.28962 0 5.478257 5871 200.9738 2284 -2.590184 1800 5871 #> 11 -12.38780 0 6.864256 2960 200.7586 1034 -4.206309 2000 2960 #> 12 -12.09759 0 4.974006 3640 201.2117 859 -6.406135 2200 3640 #> 13 -13.01978 0 0.743979 3989 201.1645 374 -10.118943 2400 3989 #> 14 NaN 1 NaN 2998 NaN 125 -14.613729 2600 2998 #> 15 -17.37957 0 -2.458026 3656 200.3555 72 -16.116776 2800 3656 #> 16 NaN 1 NaN 3660 NaN 51 -16.898886 3000 3660 #> 17 NaN 1 NaN 3995 NaN 2 -28.025379 3200 3995 #> 18 NaN 1 NaN 3656 NaN 0 -37.871777 3400 3656 #> 19 NaN 1 NaN 2989 NaN 0 -38.715645 3600 2989 #> 20 NaN 1 NaN 2654 NaN 0 -38.627972 3800 2654 #> 21 NaN 1 NaN 1991 NaN 0 -39.061321 4000 1991 #> 22 NaN 1 NaN 1991 NaN 0 -41.955345 4200 1991 #> 23 NaN 1 NaN 2326 NaN 0 -39.968536 4400 2326 #> 24 NaN 1 NaN 2319 NaN 0 -44.746979 4600 2319 #> 25 NaN 1 NaN 1991 NaN 0 -40.657249 4800 1991 #> eta sd_vvp n_all lat lon height_antenna day #> 1 NA NA 0 56.3675 12.8517 209 FALSE #> 2 9.360947e+02 4.349111 11472 56.3675 12.8517 209 FALSE #> 3 1.214729e+03 4.015257 9936 56.3675 12.8517 209 FALSE #> 4 1.009605e+03 3.286927 8026 56.3675 12.8517 209 FALSE #> 5 3.802453e+02 3.546942 5282 56.3675 12.8517 209 FALSE #> 6 2.260477e+02 3.821268 4078 56.3675 12.8517 209 FALSE #> 7 2.525710e+02 3.926216 3304 56.3675 12.8517 209 FALSE #> 8 2.177226e+02 3.797443 3180 56.3675 12.8517 209 FALSE #> 9 2.175499e+02 3.670397 2962 56.3675 12.8517 209 FALSE #> 10 1.913836e+02 3.618254 2284 56.3675 12.8517 209 FALSE #> 11 1.319144e+02 3.535195 1034 56.3675 12.8517 209 FALSE #> 12 7.948948e+01 3.134931 859 56.3675 12.8517 209 FALSE #> 13 3.380873e+01 2.994742 374 56.3675 12.8517 209 FALSE #> 14 1.201020e+01 NaN 125 56.3675 12.8517 209 FALSE #> 15 8.496607e+00 4.066769 72 56.3675 12.8517 209 FALSE #> 16 7.096342e+00 NaN 51 56.3675 12.8517 209 FALSE #> 17 5.475014e-01 NaN 2 56.3675 12.8517 209 FALSE #> 18 5.672123e-02 NaN 0 56.3675 12.8517 209 FALSE #> 19 4.670447e-02 NaN 0 56.3675 12.8517 209 FALSE #> 20 4.765693e-02 NaN 0 56.3675 12.8517 209 FALSE #> 21 4.313115e-02 NaN 0 56.3675 12.8517 209 FALSE #> 22 2.215075e-02 NaN 0 56.3675 12.8517 209 FALSE #> 23 3.500013e-02 NaN 0 56.3675 12.8517 209 FALSE #> 24 1.164730e-02 NaN 0 56.3675 12.8517 209 FALSE #> 25 2.986746e-02 NaN 0 56.3675 12.8517 209 FALSE #> sunrise sunset #> 1 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 2 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 3 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 4 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 5 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 6 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 7 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 8 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 9 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 10 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 11 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 12 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 13 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 14 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 15 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 16 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 17 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 18 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 19 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 20 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 21 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 22 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 23 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 24 2015-10-18 05:50:07 2015-10-18 15:56:28 #> 25 2015-10-18 05:50:07 2015-10-18 15:56:28# Load the example time series of vertical profiles vpts <- example_vpts # Convert to a data.frame vpts_df <- as.data.frame(vpts) # Print the first 5 rows of the data.frame vpts_df[1:5, ]#> radar datetime height u v w ff dd sd_vvp gap dbz #> 1 KBGM 2016-09-01 00:02:00 0 NaN NaN NaN NaN NaN NaN TRUE NaN #> 2 KBGM 2016-09-01 00:02:00 200 NaN NaN NaN NaN NaN NaN TRUE NaN #> 3 KBGM 2016-09-01 00:02:00 400 NaN NaN NaN NaN NaN 2.81 TRUE 1.54 #> 4 KBGM 2016-09-01 00:02:00 600 4.14 3.84 12.17 5.65 47.2 2.80 FALSE 3.36 #> 5 KBGM 2016-09-01 00:02:00 800 5.06 0.24 15.17 5.07 87.2 2.42 FALSE -7.89 #> eta dens DBZH n n_dbz n_all n_dbz_all lat lon #> 1 NaN NaN NaN 0 0 0 0 42.19972 -75.98472 #> 2 NaN NaN NaN 0 0 0 0 42.19972 -75.98472 #> 3 30.8 2.8000000 3.77 326 356 22485 28416 42.19972 -75.98472 #> 4 46.9 4.2636364 0.50 9006 13442 65947 104455 42.19972 -75.98472 #> 5 3.5 0.3181818 -10.33 2313 11145 21321 63542 42.19972 -75.98472 #> height_antenna day sunrise sunset #> 1 519 FALSE 2016-09-01 10:32:55 2016-09-01 23:33:48 #> 2 519 FALSE 2016-09-01 10:32:55 2016-09-01 23:33:48 #> 3 519 FALSE 2016-09-01 10:32:55 2016-09-01 23:33:48 #> 4 519 FALSE 2016-09-01 10:32:55 2016-09-01 23:33:48 #> 5 519 FALSE 2016-09-01 10:32:55 2016-09-01 23:33:48# Do not add lat/lon/height_antenna information vpts_df <- as.data.frame(vpts, geo = FALSE) # Do not add day/sunrise/sunset information vpts_df <- as.data.frame(vpts, suntime = FALSE) # Override the latitude/longitude information stored in the object when # calculating sunrise/sunset information vpts_df <- as.data.frame(vpts, lat = 50, lon = 4)