Assign an sf geometry for each R row with specific filter creteria: what's wrong with my current code?

Question

Suppose I have a dataframe test. What I want to do is assign a random geometry to the rows that cly == 8. For the other rows, I just leave it as NA

This is a reprex of my current code:

library(dplyr)
library(sf)
library(tmap)

tmap_mode("view")
#Step one=========================================
test <- mtcars %>% 
  mutate(geometry = NA)

shape <- st_read(system.file("shape/nc.shp", package="sf"))

for (i in 1:nrow(test)) {
  cylSelected <- test[i,]$cyl
  if (cylSelected == 8) {
   randomPoint <- st_sample(shape, 1)
   st_geometry(test[i,]) <- randomPoint
  }
}

I understand this code is definitely wrong because, during step one, the geometry is a logical variable and I transfer it to geometry in the for loop. However, this code can run smoothly on RStudio but not on a remote HPC. I am wondering what is the reason for this? How should I improve this code so that it can be run everywhere?

score 0 · Answer 1 · answered Feb 16 '22 at 18:09

If you are wanting it to run quicker, then you might want to use apply to avoid using a for loop and also filter the rows by the condition beforehand. Here, I first subset the dataframe to just cyl == 8. Then, I use apply to assign a random point for each row in the dataframe, which I have to turn into a dataframe and transpose (to make it a column). Then, I bind this back to the subset. Then, if we want to bind back to the other columns, then we create a subset and add a geometry column.

library(sf)

shape <- st_read(system.file("shape/nc.shp", package = "sf"))

test <- mtcars
test2 <- test[test$cyl == 8, ]

test_geom <-
  cbind(test2, t(data.frame(apply(test2, 1, function(x)
    st_sample(shape, 1)))))

colnames(test_geom)[12] <- "geometry"

# If we want to bind back to original dataframe.
test_orig <- test[test$cyl != 8, ]
test_orig$geometry <- NA

test_update <- rbind(test_orig, test_geom)

Output

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb            geometry
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4                  NA
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4                  NA
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1                  NA
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1                  NA
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1                  NA
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2                  NA
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2                  NA
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4                  NA
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4                  NA
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1                  NA
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2                  NA
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1                  NA
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1                  NA
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1                  NA
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2                  NA
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2                  NA
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6                  NA
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2                  NA
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 -78.19193, 35.71174
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 -79.49398, 36.40921
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3   -82.5198, 35.5307
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 -82.75768, 35.87617
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 -78.25614, 35.12009
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 -82.79324, 35.80735
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 -81.77563, 35.26631
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 -76.52665, 36.35889
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 -79.87681, 36.15730
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 -78.49396, 33.98547
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 -82.41442, 35.33043
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 -77.32198, 35.54528
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4 -78.82262, 35.95355
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 -78.66652, 36.45250

Or if you are wanting to have an actual geometry column, then you would need to filter out the NA values. So, you could do something like this:

library(tidyverse)
shape <- st_read(system.file("shape/nc.shp", package = "sf"))

test <- mtcars %>% 
  mutate(long = NA,
         lat = NA)

for (i in 1:nrow(test)) {
  cylSelected <- test[i,]$cyl
  
  if (cylSelected == 8) {
    randomPoint <- st_sample(shape, 1)
    test$lat[i] = unlist(map(randomPoint,2))
    test$long[i] = unlist(map(randomPoint,1))
  }
}

results <- test %>% 
  filter(!is.na(long)) %>% 
  st_as_sf(coords = c("long", "lat"), dim = "XY") %>% 
  st_set_crs(4326)

Output

Simple feature collection with 14 features and 11 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -82.97979 ymin: 34.76474 xmax: -76.12151 ymax: 36.36894
Geodetic CRS:  WGS 84
First 10 features:
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb                   geometry
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2 POINT (-80.26734 35.89567)
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4 POINT (-82.97979 35.46915)
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 POINT (-77.31967 34.76474)
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 POINT (-77.99282 34.84682)
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 POINT (-81.13808 36.15261)
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 POINT (-78.51336 34.88909)
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4  POINT (-76.12151 35.7968)
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 POINT (-81.68257 35.49511)
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 POINT (-76.37177 36.36894)
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 POINT (-77.49992 35.83624)

Hi Andrew, thanks for the answer. However, I would like to have the NA column as the sf objective which latitude and longitude are both NA (for that I can further fill them with other geometry features in the next step). I am wondering how should I achieve this? — Jingjun, Feb 16 '22 at 20:13

Assign an sf geometry for each R row with specific filter creteria: what's wrong with my current code?

1 Answers1