compute location of coordinates after great circle projection

This function provides a convienant wrapper to destPoint

et_projection(x, ..., method = "GC")

# S3 method for data.frame
et_projection(.data, latitude, longitude, bearing,
  distance, method = "GC")

# S3 method for numeric
et_projection(latitude, longitude, bearing, distance,
  output_type = "data.table", method = "GC")

Arguments

x	An object used to determine which implementation to use
...	It's an S3 thing. You wouldn't understand.
method	Either "GC" [default] or "rhumb". Used to declare either a great circle calculation or rhumb line calculation
.data	An object that inherits from data.frame. In general this will be on of data.frame, data.table, or tbl_df
latitude	Either a numeric vector of latitudes [degrees] or the column of .data which contains latitudes. This maybe quoted or unquoted; see examples.
longitude	Either a numeric vector of longitudes [degrees] corresponding with latitude or the column of .data which contains longitudes. This maybe quoted or unquoted; see examples.
bearing	Either a numeric vector of bearings [degrees] or the column of .data which contains bearings/headings. This maybe quoted or unquoted see examples.
distance	Either a numeric vector of projection distance [nautical miles] or the column of .data which contains projection distances. This maybe quoted or unquoted see examples.
output_type	string in c("matrix", "data.table", "data.frame", "list")

Value

If .data is supplied, an object of the same type and with the same columns as .data plus two more, end_latitude and end_longitude. Otherwise, an object of type determined by output_type which will generally have two columns, latitude and longitude. If the input coordinates have length 1, then a named numeric vector is returned.

Examples

# basic use
et_projection(39.86167, -104.6732, 90, 15)
#>    latitude longitude
#> 1: 39.86121 -104.3475
et_projection(39.86167, -104.6732, 86:90, 1:15)
#>     latitude longitude
#>  1: 39.86283 -104.6515
#>  2: 39.86341 -104.6298
#>  3: 39.86340 -104.6081
#>  4: 39.86280 -104.5864
#>  5: 39.86162 -104.5646
#>  6: 39.86857 -104.5432
#>  7: 39.86768 -104.5214
#>  8: 39.86619 -104.4996
#>  9: 39.86412 -104.4778
#> 10: 39.86147 -104.4561
#> 11: 39.87421 -104.4349
#> 12: 39.87185 -104.4130
#> 13: 39.86889 -104.3911
#> 14: 39.86535 -104.3693
#> 15: 39.86121 -104.3475

# use inside a data.table
library(data.table)
apts <- data.table(airport=c("ATL", "DEN", "ORD", "SEA"),
                   latitude=c(33.63670, 39.86167, 41.97933, 47.44989),
                   longitude=c(-84.42786, -104.67317, -87.90739, -122.31178))
apts[, c("platitude", "plongitude"):=et_projection(latitude, longitude, 90, 15)]
#>    airport latitude  longitude platitude plongitude
#> 1:     ATL 33.63670  -84.42786  33.63634  -84.12762
#> 2:     DEN 39.86167 -104.67317  39.86121 -104.34752
#> 3:     ORD 41.97933  -87.90739  41.97884  -87.57113
#> 4:     SEA 47.44989 -122.31178  47.44930 -121.94213

# use with magrittr
library(magrittr)
apts %>% et_projection(latitude, longitude, 90, 15)
#>    airport latitude  longitude platitude plongitude end_latitude end_longitude
#> 1:     ATL 33.63670  -84.42786  33.63634  -84.12762     33.63634     -84.12762
#> 2:     DEN 39.86167 -104.67317  39.86121 -104.34752     39.86121    -104.34752
#> 3:     ORD 41.97933  -87.90739  41.97884  -87.57113     41.97884     -87.57113
#> 4:     SEA 47.44989 -122.31178  47.44930 -121.94213     47.44930    -121.94213

# columns as strings
lat_col <- names(apts)[2]
apts %>% et_projection(lat_col, "longitude", 90, 15)
#> Error in eval(val): object 'lat_col' not found

# predict next position
tracks <- data.frame(id = c("a","b","c"),
                     lat = 0,
                     lon = 0,
                     heading = 30,
                     ground_speed = seq(300,360, 30))
time_step <- 1/60 #one minute

tracks %>% et_projection(lat, lon, heading, tracks$ground_speed*time_step)
#> Error in eval(val): object 'tracks' not found