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Distance to a point in space

I want to align the distance from the origin to all points where the points are given by a 2-coordinate dataframe.

I have all points like:

           x      y
  1      0.0    0.0
  2     -4.0   -2.8
  3     -7.0   -6.5
  4     -9.0  -11.1
  5     -7.7  -16.9
  6     -4.2  -22.4
  7     -0.6  -27.7
  8      3.0  -32.5
  9      5.6  -36.7
  10     8.4  -40.8

To get the distance, I am applying Euclidean distance to the vector. I've tried this:

distance <- function(trip) {
     distance = lapply(trip, function (x) sqrt( (trip[x,]-trip[1,] )^2+ trip[,x]-trip[,1] )^2))
     return(distance)
 }

and this also:

distance = apply(trip,1, function (x) sqrt( (trip[x,]-trip[1,] )^2+ (trip[,x]-trip[,1] )^2))
return(distance)
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2 answers


There is no need to iterate over individual lines of your data with a function apply

. You can calculate all distances in one shot using vectorized arithmetic in R:

(distance <- sqrt((trip$x - trip$x[1])^2 + (trip$y - trip$y[1])^2))
#  [1]  0.000000  4.882622  9.552487 14.290206 18.571484 22.790349 27.706497 32.638168 37.124790 41.655732


Calculating all distances at once using vectorized operations will be much faster in cases where you have many points.

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There is a function for calculating the distance between matrices:

dist(trip, method = "euclidean")


If you are not expecting a distance matrix, but only the distance from each point to the origin, you can multiply the 1st column as.matrix(dist(mat, method = "euclidean"))[1,]

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