使用 spark_apply 计算纬度/经度之间的距离

如何解决使用 spark_apply 计算纬度/经度之间的距离

我尝试使用 spark_apply 函数来计算 R 中某些经度和纬度坐标之间的一些距离。我可以在基数 R 中计算它们，但我想使用 {{1} } 函数。

如何在 spark_apply() 函数中复制 distm(latLong,distanceFrom) 计算？

数据：

spark_apply

解决方法

由于 sparklyr::spark_apply 作用于一个 spark 数据帧，因此一种策略是通过“crossjoin”将所有数据放到单个 spark 数据帧上。然后，可以使用 geodist::geodist 计算距离。

library("data.table")
library("sparklyr")
#> 
#> Attaching package: 'sparklyr'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library("geosphere")
library("tidyverse")

# setup
conf <- spark_config()
conf$spark.dynamicAllocation.enabled <- "true"
sc <- spark_connect(master = "local")

# create data
df <- data_frame(
  place=c("Finland","Canada","Tanzania","Bolivia","France"),longitude=c(27.472918,-90.476303,34.679950,-65.691146,4.533465),latitude=c(63.293001,54.239631,-2.855123,-13.795272,48.603949),crs="+proj=longlat +datum=WGS84")
#> Warning: `data_frame()` was deprecated in tibble 1.1.0.
#> Please use `tibble()` instead.

df
#> # A tibble: 5 x 4
#>   place    longitude latitude crs                       
#>   <chr>        <dbl>    <dbl> <chr>                     
#> 1 Finland      27.5     63.3  +proj=longlat +datum=WGS84
#> 2 Canada      -90.5     54.2  +proj=longlat +datum=WGS84
#> 3 Tanzania     34.7     -2.86 +proj=longlat +datum=WGS84
#> 4 Bolivia     -65.7    -13.8  +proj=longlat +datum=WGS84
#> 5 France        4.53    48.6  +proj=longlat +datum=WGS84

# compute distance from the "distanceFrom" data
latLong <- df %>% 
  dplyr::select(c(longitude,latitude))

distanceFrom <- rbind(c(34.20,-3.67),c(30.56,-2.50))

distm(latLong,distanceFrom)
#>            [,1]       [,2]
#> [1,]  7448355.4  7302060.8
#> [2,] 12520695.4 12197620.9
#> [3,]   104712.2   459812.3
#> [4,] 10987001.5 10626916.8
#> [5,]  6466454.9  6196687.9

# create df_1 from df (5 row dataframe)
df_1 = df %>% 
    select(longitude,latitude)

# create df_2 from 'distanceFrom' (2 row matrix)
df_2 = as_tibble(distanceFrom)
#> Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
#> Using compatibility `.name_repair`.
colnames(df_2) = c("longitude_2","latitude_2")
df_2
#> # A tibble: 2 x 2
#>   longitude_2 latitude_2
#>         <dbl>      <dbl>
#> 1        34.2      -3.67
#> 2        30.6      -2.5

# copy both of them to spark
df_1_sdf = df_1 %>% 
    copy_to(sc,.,overwrite = TRUE)

df_1_sdf
#> # Source: spark<?> [?? x 2]
#>   longitude latitude
#>       <dbl>    <dbl>
#> 1     27.5     63.3 
#> 2    -90.5     54.2 
#> 3     34.7     -2.86
#> 4    -65.7    -13.8 
#> 5      4.53    48.6

df_2_sdf = df_2 %>% 
    copy_to(sc,overwrite = TRUE)

df_2_sdf
#> # Source: spark<?> [?? x 2]
#>   longitude_2 latitude_2
#>         <dbl>      <dbl>
#> 1        34.2      -3.67
#> 2        30.6      -2.5

# define distance function using geodist package
get_geodesic_distance = function(x){
    
    dist_vec = 
        geodist::geodist(dplyr::select(x,c(latitude,longitude)),dplyr::select(x,c(latitude_2,longitude_2)),paired = TRUE,measure = "geodesic"
                         )
    res = dplyr::mutate(x,distance = dist_vec)
    res
}

# create all pairs of points
full_join(df_1_sdf,df_2_sdf,by = character(0)) %>% 
    glimpse() %>% 
    spark_apply(get_geodesic_distance)
#> Rows: ??
#> Columns: 4
#> Database: spark_connection
#> $ longitude   <dbl> 27.472918,27.472918,3…
#> $ latitude    <dbl> 63.293001,63.293001,-2.…
#> $ longitude_2 <dbl> 34.20,30.56,34.20,34…
#> $ latitude_2  <dbl> -3.67,-2.50,-3.67,-3…
#> # Source: spark<?> [?? x 5]
#>    longitude latitude longitude_2 latitude_2  distance
#>        <dbl>    <dbl>       <dbl>      <dbl>     <dbl>
#>  1     27.5     63.3         34.2      -3.67  7448355.
#>  2     27.5     63.3         30.6      -2.5   7302061.
#>  3    -90.5     54.2         34.2      -3.67 12520695.
#>  4    -90.5     54.2         30.6      -2.5  12197621.
#>  5     34.7     -2.86        34.2      -3.67   104712.
#>  6     34.7     -2.86        30.6      -2.5    459812.
#>  7    -65.7    -13.8         34.2      -3.67 10987002.
#>  8    -65.7    -13.8         30.6      -2.5  10626917.
#>  9      4.53    48.6         34.2      -3.67  6466455.
#> 10      4.53    48.6         30.6      -2.5   6196688.

PS：考虑在 spark 上进行地理空间工作的 geospark 包。