Re: [R] [External] Re: Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute
В Wed, 9 Nov 2022 02:38:15 +
"Duhl, Tiffany R." пишет:
> MinConc <- function(x, lst, pts) {
>Concs <- lapply(lst, function(p) {
> pts$Conc[p]
>})
>return(min(Concs[[1]]))
> }
This function doesn't seem to use its first argument, x. Moreover,
its return value seems to only depend on the first element of its
second argument, lst.
> ###But I need the length of the list the function is applied to
> ###to be variable, depending on the length of the input csv file
> ###unlike the dummy variable dataframe that Eric used with a set
> ###length
> ###So I changed the "x" argument in lapply to "pts$X" but
> ###that generates an empty list
>
> Conc_min <- lapply(pts$X, function(i){
>MinConc(i, dist_matrix[i], pts)
> })
So instead of doing extra work to obtain the list indices (use
seq_along if you do need them) and to subset the one-element lists
returned by dist_matrix[i] (use dist_matrix[[i]] to get the value
instead of one-element list containing the value), it should be
possible to perform
Conc_min <- sapply(
dist_matrix,
function(neighbours) min(pts$Conc[neighbours])
)
This assumes that dist_matrix is of the same length as pts$X.
Admittedly, the example above can't consider each point by itself,
which isn't mentioned in dist_matrix (as far as I'm aware), so to
include the original point, we can run
mapply(
function(point, neighbours) min(pts$Conc[point], pts$Conc[neighbours]),
seq_along(pts$X), dist_matrix
)
--
Best regards,
Ivan
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Re: [R] [External] Re: Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute
First off, thanks SO much Eric and Micha for your help on this problem! I
think Micha's spatially-oriented solution with Eric's slight modifications will
work best for my application but there is one snag (see the commented section
near the end of the following code)-- basically I don't know how to apply the
lapply operator to a list with a variable length (namely the length of my input
csv files) rather than the fixed length that Eric used:
library(sf)
library(spdep)
data<- read.csv("R_find_pts_testdata.csv")
MAX_DIST <- 0.05 #50 m in km, the units of dnearneigh when coords are in
degrees
pts <- st_as_sf(data, coords=c('LON', 'LAT'), crs=4326)
dist_matrix <- dnearneigh(pts, 0, MAX_DIST, use_s2=TRUE)
#Micha's function to get the minimum Conc value among all points
#within the buffer distance to a given single point:
# Function to get minimum Conc values for one row of distance matrix
MinConc <- function(x, lst, pts) {
Concs <- lapply(lst, function(p) {
pts$Conc[p]
})
return(min(Concs[[1]]))
}
# above, x is an index to a single point, lst is a list of point indices
#from distance matrix within the buffer distance
#Next run function on all points to get list of minimum Conc
#values for all points, and merge back to pts.
#...modified by Eric to include original point
return(min(c(Concs[[1]], pts$Conc[x])))
# Now apply this function to all points in pts
###This is where the problem is, I think:
###Eric had used N <- 1000L and later
###Conc_min <- lapply(1:N, function(i) {
### MinConc(i, dist_matrix[i], pts)})
###But I need the length of the list the function is applied to
###to be variable, depending on the length of the input csv file
###unlike the dummy variable dataframe that Eric used with a set length
###So I changed the "x" argument in lapply to "pts$X" but that generates an
empty list
Conc_min <- lapply(pts$X, function(i){
MinConc(i, dist_matrix[i], pts)
#})
Conc_min <- data.frame("Conc_min" = as.integer(Conc_min))
# Add back as new attrib to original points sf object
pts_with_min <- do.call(cbind, c(pts, Conc_min))
Many thanks again for your help on this!
Best regards,
-Tiffany
From: Micha Silver
Sent: Monday, November 7, 2022 8:11 AM
To: Duhl, Tiffany R. ; Eric Berger
Cc: R-help
Subject: Re: [R] [External] Re: Selecting a minimum value of an attribute
associated with point values neighboring a given point and assigning it as a
new attribute
Eric's solution notwithstanding, here's a more "spatial" approach.
I first create a fictitious set of 1000 points (and save to CSV to
replicate your workflow)
library(sf)
library(spdep)
# Prepare fictitious data
# Create a data.frame with 1000 random points, and save to CSV
LON <- runif(1000, -70.0, -69.0)
LAT <- runif(1000, 42.0, 43.0)
Conc <- runif(1000, 9, 10)
df <- data.frame(LON, LAT, Conc)
csv_file = "/tmp/pts_testdata.csv"
write.csv(df, csv_file)
Now read that CSV back in directly as an sf object (No need for the old
SpatialPointsDataFrame). THen create a distance matrix for all points,
which contains indicies to those points within a certain buffer
distance, just as you did in your example.
# Read back in as sf object, including row index
pts <- st_as_sf(read.csv(csv_file), coords=c('LON', 'LAT'), crs=4326)
dist_matrix <- dnearneigh(pts, 0, 100, use_s2=TRUE) # use_s2 since these
are lon/lat
Now I prepare a function to get the minimum Conv value among all points
within the buffer distance to a given single point:
# Function to get minimum Conc values for one row of distance matrix
MinConc <- function(x, lst, pts) {
# x is an index to a single point,
# lst is a list of point indices from distance matrix
# that are within the buffer distance
Concs <- lapply(lst, function(p) {
pts$Conc[p]
})
return(min(Concs[[1]]))
}
Next run that function on all points to get a list of minimum Conv
values for all points, and merge back to pts.
# Now apply this function to all points in pts
Conc_min <- lapply(pts$X, function(i){
MinConc(i, dist_matrix[i], pts)
})
Conc_min <- data.frame("Conc_min" = as.integer(Conc_min))
# Add back as new attrib to original points sf object
pts_with_min <- do.call(cbind, c(pts, Conc_min))
HTH,
Micha
On 06/11/2022 18:40, Duhl, Tiffany R. wrote:
> Thanks so much Eric!
>
> I'm going to play around with your toy code (pun intended) & see if I can
> make it work for my application.
>
> Cheers,
> -Tiffany
>
> From: Eric Berger
> Sent: Sunday, November 6, 2022 10:27 AM
> To: Bert Gunter
> Cc: Duhl, Tiffany R. ; R-help
> Subject: [External] Re: [R] Selecting a minimum value of an attribute
> associated with point values neighboring a given point and assigning it as a
> new attribute
>
> Whoops ... left out a line in Part 2. Resending with the correction
>
> ## PART 2: You can use this code on the real data with f() defined
> appropriately
> A <- matrix(0,N,N)
> v <- 1:N
> ## get the
Re: [R] [External] Re: Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute
Hi Tiffany,
You can replace
Conc_min <- lapply(1:N, function(i) { ...
by
Conc_min <- lapply(seq_len(nrow(pts)), function(i) { ..
Best,
Eric
On Wed, Nov 9, 2022 at 4:38 AM Duhl, Tiffany R. wrote:
>
> First off, thanks SO much Eric and Micha for your help on this problem! I
> think Micha's spatially-oriented solution with Eric's slight modifications
> will work best for my application but there is one snag (see the commented
> section near the end of the following code)-- basically I don't know how to
> apply the lapply operator to a list with a variable length (namely the length
> of my input csv files) rather than the fixed length that Eric used:
>
> library(sf)
> library(spdep)
> data<- read.csv("R_find_pts_testdata.csv")
>
> MAX_DIST <- 0.05 #50 m in km, the units of dnearneigh when coords are in
> degrees
> pts <- st_as_sf(data, coords=c('LON', 'LAT'), crs=4326)
> dist_matrix <- dnearneigh(pts, 0, MAX_DIST, use_s2=TRUE)
>
> #Micha's function to get the minimum Conc value among all points
> #within the buffer distance to a given single point:
> # Function to get minimum Conc values for one row of distance matrix
>
> MinConc <- function(x, lst, pts) {
>Concs <- lapply(lst, function(p) {
> pts$Conc[p]
>})
>return(min(Concs[[1]]))
> }
>
> # above, x is an index to a single point, lst is a list of point indices
> #from distance matrix within the buffer distance
>
> #Next run function on all points to get list of minimum Conc
> #values for all points, and merge back to pts.
> #...modified by Eric to include original point
>
> return(min(c(Concs[[1]], pts$Conc[x])))
>
> # Now apply this function to all points in pts
> ###This is where the problem is, I think:
> ###Eric had used N <- 1000L and later
> ###Conc_min <- lapply(1:N, function(i) {
> ### MinConc(i, dist_matrix[i], pts)})
> ###But I need the length of the list the function is applied to
> ###to be variable, depending on the length of the input csv file
> ###unlike the dummy variable dataframe that Eric used with a set length
> ###So I changed the "x" argument in lapply to "pts$X" but that generates an
> empty list
>
> Conc_min <- lapply(pts$X, function(i){
>MinConc(i, dist_matrix[i], pts)
> #})
> Conc_min <- data.frame("Conc_min" = as.integer(Conc_min))
>
> # Add back as new attrib to original points sf object
> pts_with_min <- do.call(cbind, c(pts, Conc_min))
>
>
>
> Many thanks again for your help on this!
> Best regards,
> -Tiffany
>
> From: Micha Silver
> Sent: Monday, November 7, 2022 8:11 AM
> To: Duhl, Tiffany R. ; Eric Berger
>
> Cc: R-help
> Subject: Re: [R] [External] Re: Selecting a minimum value of an attribute
> associated with point values neighboring a given point and assigning it as a
> new attribute
>
> Eric's solution notwithstanding, here's a more "spatial" approach.
>
>
> I first create a fictitious set of 1000 points (and save to CSV to
> replicate your workflow)
>
> library(sf)
> library(spdep)
>
> # Prepare fictitious data
> # Create a data.frame with 1000 random points, and save to CSV
> LON <- runif(1000, -70.0, -69.0)
> LAT <- runif(1000, 42.0, 43.0)
> Conc <- runif(1000, 9, 10)
> df <- data.frame(LON, LAT, Conc)
> csv_file = "/tmp/pts_testdata.csv"
> write.csv(df, csv_file)
>
>
> Now read that CSV back in directly as an sf object (No need for the old
> SpatialPointsDataFrame). THen create a distance matrix for all points,
> which contains indicies to those points within a certain buffer
> distance, just as you did in your example.
>
>
> # Read back in as sf object, including row index
> pts <- st_as_sf(read.csv(csv_file), coords=c('LON', 'LAT'), crs=4326)
> dist_matrix <- dnearneigh(pts, 0, 100, use_s2=TRUE) # use_s2 since these
> are lon/lat
>
> Now I prepare a function to get the minimum Conv value among all points
> within the buffer distance to a given single point:
> # Function to get minimum Conc values for one row of distance matrix
> MinConc <- function(x, lst, pts) {
># x is an index to a single point,
># lst is a list of point indices from distance matrix
># that are within the buffer distance
>Concs <- lapply(lst, function(p) {
> pts$Conc[p]
>})
>return(min(Concs[[1]]))
> }
>
> Next run that function on all points to get a list of minimum Conv
> values for all points, and merge back to pts.
>
>
> # Now apply this function to all points in pts
> Conc_min <- lapply(pts$X, function(i){
>MinConc(i, dist_matrix[i], pts)
> })
> Conc_min <- data.frame("Conc_min" = as.integer(Conc_min))
>
> # Add back as new attrib to original points sf object
> pts_with_min <- do.call(cbind, c(pts, Conc_min))
>
> HTH,
>
> Micha
>
>
>
> On 06/11/2022 18:40, Duhl, Tiffany R. wrote:
> > Thanks so much Eric!
> >
> > I'm going to play around with your toy code (pun intended) & see if I can
> > make it work for my application.
> >
> > Cheers,
> > -Tiffany
> >
> > From: Eric Berger
> > Sent:
Re: [R] [External] Re: Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute
## Some further comments and approaches, divided into 3 sections
##
## Section 1: Micha's modified code
## Section 2: Eric's modified code:
##a) uses Micha's dist_matrix from dnearneigh instead of the f() function
##b) add check that it gets the same solution as Micha's modified code
## Section 3: Solution explicitly using a graph (via package igraph)
## ###
## Section 1: Micha's modified code
##
## Major change: OP wanted the Min Conc value
## to include the Conc of the point itself
## Minor Changes: no need to write/read csv file;
##change MAX_DIST to 50 for fewer neighbors
library(sf)
library(spdep)
## Prepare fictitious data
## Create a data.frame with n random points
set.seed(234) ## for reproducibility
N <- 1000L
LON <- runif(N, -70.0, -69.0)
LAT <- runif(N, 42.0, 43.0)
Conc <- runif(N, 9, 10)
df <- data.frame(LON, LAT, Conc)
## Create a distance matrix for all points,
## which contains indices to those points within
## a certain buffer distance, just as you did in your example.
MAX_DIST <- 50
pts <- st_as_sf(df, coords=c('LON', 'LAT'), crs=4326)
dist_matrix <- dnearneigh(pts, 0, MAX_DIST, use_s2=TRUE)
cat("Average number of neighbors within cutoff = ",
mean(unlist(lapply(dist_matrix,length))),"\n")
## Function to get minimum Conc values for one row of distance matrix
MinConc <- function(x, lst, pts) {
## x is an index to a single point,
## lst is a list of point indices from distance matrix
## that are within the buffer distance
Concs <- lapply(lst, function(p) {
pts$Conc[p]
})
## return(min(c(Concs[[1]])) - ## original code - forgets to
include the point itself
return(min(c(Concs[[1]], pts$Conc[x]))) ## modified code
}
## Now apply this function to all points in pts
Conc_min <- lapply(1:N, function(i) {
MinConc(i, dist_matrix[i], pts)
})
Conc_min <- data.frame("Conc_min" = as.numeric(Conc_min))
# Add back as new attrib to original points sf object
pts_with_min <- do.call(cbind, c(pts, Conc_min))
## ###
## Section 2: Eric's modified code:
A <- matrix(0,N,N)
z <- sapply(1:N, \(i) A[i, dist_matrix[[i]]] <<- 1) ## z is ignored
B <- A + diag(N)
C <- diag(Conc)
D <- B %*% C
D[D==0] <- NA
Conc_min.eric <- apply(D,MAR=1,\(v) min(v,na.rm=TRUE) )
test <- identical(Conc_min$Conc_min, Conc_min.eric)
cat("test = ", test, "\n")
## ###
## Section 3: Solution explicitly using a graph (via package igraph)
## For those with some familiarity with graphs, the matrix 'B' is
## an adjacency matrix. This suggests using graphs explicitly to solve
## the problem. Here is how to rewrite my code using graphs.
library(igraph)
g <- graph_from_adjacency_matrix(B,"undirected")
g <- set_vertex_attr(g, "Conc", 1:N, Conc )
Conc_min.igraph <- sapply(1:N, \(i) min(vertex_attr(g,"Conc",neighbors(g,i
test.igraph <- identical(Conc_min$Conc_min, Conc_min.igraph)
cat("test.igraph = ", test.igraph, "\n")
Eric
On Mon, Nov 7, 2022 at 3:11 PM Micha Silver wrote:
>
> Eric's solution notwithstanding, here's a more "spatial" approach.
>
>
> I first create a fictitious set of 1000 points (and save to CSV to
> replicate your workflow)
>
> library(sf)
> library(spdep)
>
> # Prepare fictitious data
> # Create a data.frame with 1000 random points, and save to CSV
> LON <- runif(1000, -70.0, -69.0)
> LAT <- runif(1000, 42.0, 43.0)
> Conc <- runif(1000, 9, 10)
> df <- data.frame(LON, LAT, Conc)
> csv_file = "/tmp/pts_testdata.csv"
> write.csv(df, csv_file)
>
>
> Now read that CSV back in directly as an sf object (No need for the old
> SpatialPointsDataFrame). THen create a distance matrix for all points,
> which contains indicies to those points within a certain buffer
> distance, just as you did in your example.
>
>
> # Read back in as sf object, including row index
> pts <- st_as_sf(read.csv(csv_file), coords=c('LON', 'LAT'), crs=4326)
> dist_matrix <- dnearneigh(pts, 0, 100, use_s2=TRUE) # use_s2 since these
> are lon/lat
>
> Now I prepare a function to get the minimum Conv value among all points
> within the buffer distance to a given single point:
> # Function to get minimum Conc values for one row of distance matrix
> MinConc <- function(x, lst, pts) {
># x is an index to a single point,
># lst is a list of point indices from distance matrix
># that are within the buffer distance
>Concs <- lapply(lst, function(p) {
> pts$Conc[p]
>})
>return(min(Concs[[1]]))
> }
>
> Next run that function on all points to get a list of minimum Conv
> values for all points, and merge back to pts.
>
>
> # Now apply this function to all points in pts
> Conc_min <- lapply(pts$X, function(i){
>MinConc(i, dist_matrix[i], pts)
> })
> Conc_min <- data.frame("Conc_min" = as.integer(Conc_min))
>
> # Add back as new attrib to original points sf object
>
Re: [R] [External] Re: Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute
Eric's solution notwithstanding, here's a more "spatial" approach.
I first create a fictitious set of 1000 points (and save to CSV to
replicate your workflow)
library(sf)
library(spdep)
# Prepare fictitious data
# Create a data.frame with 1000 random points, and save to CSV
LON <- runif(1000, -70.0, -69.0)
LAT <- runif(1000, 42.0, 43.0)
Conc <- runif(1000, 9, 10)
df <- data.frame(LON, LAT, Conc)
csv_file = "/tmp/pts_testdata.csv"
write.csv(df, csv_file)
Now read that CSV back in directly as an sf object (No need for the old
SpatialPointsDataFrame). THen create a distance matrix for all points,
which contains indicies to those points within a certain buffer
distance, just as you did in your example.
# Read back in as sf object, including row index
pts <- st_as_sf(read.csv(csv_file), coords=c('LON', 'LAT'), crs=4326)
dist_matrix <- dnearneigh(pts, 0, 100, use_s2=TRUE) # use_s2 since these
are lon/lat
Now I prepare a function to get the minimum Conv value among all points
within the buffer distance to a given single point:
# Function to get minimum Conc values for one row of distance matrix
MinConc <- function(x, lst, pts) {
# x is an index to a single point,
# lst is a list of point indices from distance matrix
# that are within the buffer distance
Concs <- lapply(lst, function(p) {
pts$Conc[p]
})
return(min(Concs[[1]]))
}
Next run that function on all points to get a list of minimum Conv
values for all points, and merge back to pts.
# Now apply this function to all points in pts
Conc_min <- lapply(pts$X, function(i){
MinConc(i, dist_matrix[i], pts)
})
Conc_min <- data.frame("Conc_min" = as.integer(Conc_min))
# Add back as new attrib to original points sf object
pts_with_min <- do.call(cbind, c(pts, Conc_min))
HTH,
Micha
On 06/11/2022 18:40, Duhl, Tiffany R. wrote:
Thanks so much Eric!
I'm going to play around with your toy code (pun intended) & see if I can
make it work for my application.
Cheers,
-Tiffany
From: Eric Berger
Sent: Sunday, November 6, 2022 10:27 AM
To: Bert Gunter
Cc: Duhl, Tiffany R. ; R-help
Subject: [External] Re: [R] Selecting a minimum value of an attribute
associated with point values neighboring a given point and assigning it as a
new attribute
Whoops ... left out a line in Part 2. Resending with the correction
## PART 2: You can use this code on the real data with f() defined appropriately
A <- matrix(0,N,N)
v <- 1:N
## get the indices (j,k) where j < k (as columns in a data.frame)
idx <- expand.grid(v,v) |> rename(j=Var1,k=Var2) |> filter(j < k)
u <- sapply(1:nrow(idx),
\(i){ j <- idx$j[i]; k <- idx$k[i]; A[j,k] <<- f(j,k,myData) })
B <- A + t(A) + diag(N)
C <- diag(myData$Conc)
D <- B %*% C
D[D==0] <- NA
myData$Conc_min <- apply(D,MAR=1,\(v){min(v,na.rm=TRUE)})
print(head(myData))
On Sun, Nov 6, 2022 at 5:19 PM Eric Berger wrote:
Hi Tiffany,
Here is some code that might help with your problem. I solve a "toy"
problem that is conceptually the same.
Part 1 sets up my toy problem. You would have to replace Part 1 with
your real case. The main point is to define
a function f(i, j, data) which returns 0 or 1 depending on whether the
observations in rows i and j in your dataset 'data'
are within your cutoff distance (i.e. 50m).
You can then use Part 2 almost without changes (except for changing
'myData' to the correct name of your data).
I hope this helps,
Eric
library(dplyr)
## PART 1: create fake data for minimal example
set.seed(123) ## for reproducibility
N <- 5 ## replace by number of locations (approx 9000 in your case)
MAX_DISTANCE <- 2 ## 50 in your case
myData <- data.frame(x=rnorm(N),y=rnorm(N),Conc=sample(1:N,N))
## The function which you must re-define for your actual case.
f <- function(i,j,a) {
dist <- sqrt(sum((a[i,1:2] - a[j,1:2])^2)) ## Euclidean distance
as.integer(dist < MAX_DISTANCE)
}
## PART 2: You can use this code on the real data with f() defined appropriately
A <- matrix(0,N,N)
## get the indices (j,k) where j < k (as columns in a data.frame)
idx <- expand.grid(v,v) |> rename(j=Var1,k=Var2) |> filter(j < k)
u <- sapply(1:nrow(idx),\(i){ j <- idx$j[i]; k <- idx$k[i]; A[j,k] <<-
f(j,k,myData) })
B <- A + t(A) + diag(N)
C <- diag(myData$Conc)
D <- B %*% C
D[D==0] <- NA
myData$Conc_min <- apply(D,MAR=1,\(v){min(v,na.rm=TRUE)})
print(head(myData))
On Sat, Nov 5, 2022 at 5:14 PM Bert Gunter wrote:
Probably better posted on R-sig-geo.
-- Bert
On Sat, Nov 5, 2022 at 12:36 AM Duhl, Tiffany R.
wrote:
Hello,
I have sets of spatial points with LAT, LON coords (unprojected, WGS84
datum) and several value attributes associated with each point, from
numerous csv files (with an average of 6,000-9,000 points in each file) as
shown in the following example:
data<- read.csv("R_find_pts_testdata.csv")
data
ID Date TimeLATLON Conc
Leg.SpeedCO2 H2O BC61 Hr Min Sec
1 76 4/19/2021
Re: [R] [External] Re: Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute
Thanks so much Eric!
I'm going to play around with your toy code (pun intended) & see if I can make
it work for my application.
Cheers,
-Tiffany
From: Eric Berger
Sent: Sunday, November 6, 2022 10:27 AM
To: Bert Gunter
Cc: Duhl, Tiffany R. ; R-help
Subject: [External] Re: [R] Selecting a minimum value of an attribute
associated with point values neighboring a given point and assigning it as a
new attribute
Whoops ... left out a line in Part 2. Resending with the correction
## PART 2: You can use this code on the real data with f() defined appropriately
A <- matrix(0,N,N)
v <- 1:N
## get the indices (j,k) where j < k (as columns in a data.frame)
idx <- expand.grid(v,v) |> rename(j=Var1,k=Var2) |> filter(j < k)
u <- sapply(1:nrow(idx),
\(i){ j <- idx$j[i]; k <- idx$k[i]; A[j,k] <<- f(j,k,myData) })
B <- A + t(A) + diag(N)
C <- diag(myData$Conc)
D <- B %*% C
D[D==0] <- NA
myData$Conc_min <- apply(D,MAR=1,\(v){min(v,na.rm=TRUE)})
print(head(myData))
On Sun, Nov 6, 2022 at 5:19 PM Eric Berger wrote:
>
> Hi Tiffany,
> Here is some code that might help with your problem. I solve a "toy"
> problem that is conceptually the same.
> Part 1 sets up my toy problem. You would have to replace Part 1 with
> your real case. The main point is to define
> a function f(i, j, data) which returns 0 or 1 depending on whether the
> observations in rows i and j in your dataset 'data'
> are within your cutoff distance (i.e. 50m).
>
> You can then use Part 2 almost without changes (except for changing
> 'myData' to the correct name of your data).
>
> I hope this helps,
> Eric
>
> library(dplyr)
>
> ## PART 1: create fake data for minimal example
> set.seed(123) ## for reproducibility
> N <- 5 ## replace by number of locations (approx 9000 in your case)
> MAX_DISTANCE <- 2 ## 50 in your case
> myData <- data.frame(x=rnorm(N),y=rnorm(N),Conc=sample(1:N,N))
>
> ## The function which you must re-define for your actual case.
> f <- function(i,j,a) {
> dist <- sqrt(sum((a[i,1:2] - a[j,1:2])^2)) ## Euclidean distance
> as.integer(dist < MAX_DISTANCE)
> }
>
> ## PART 2: You can use this code on the real data with f() defined
> appropriately
> A <- matrix(0,N,N)
> ## get the indices (j,k) where j < k (as columns in a data.frame)
> idx <- expand.grid(v,v) |> rename(j=Var1,k=Var2) |> filter(j < k)
> u <- sapply(1:nrow(idx),\(i){ j <- idx$j[i]; k <- idx$k[i]; A[j,k] <<-
> f(j,k,myData) })
> B <- A + t(A) + diag(N)
> C <- diag(myData$Conc)
> D <- B %*% C
> D[D==0] <- NA
> myData$Conc_min <- apply(D,MAR=1,\(v){min(v,na.rm=TRUE)})
> print(head(myData))
>
>
> On Sat, Nov 5, 2022 at 5:14 PM Bert Gunter wrote:
> >
> > Probably better posted on R-sig-geo.
> >
> > -- Bert
> >
> > On Sat, Nov 5, 2022 at 12:36 AM Duhl, Tiffany R.
> > wrote:
> >
> > > Hello,
> > >
> > > I have sets of spatial points with LAT, LON coords (unprojected, WGS84
> > > datum) and several value attributes associated with each point, from
> > > numerous csv files (with an average of 6,000-9,000 points in each file) as
> > > shown in the following example:
> > >
> > > data<- read.csv("R_find_pts_testdata.csv")
> > >
> > > > data
> > > ID Date TimeLATLON Conc
> > > Leg.SpeedCO2 H2O BC61 Hr Min Sec
> > > 1 76 4/19/2021 21:25:38 42.40066 -70.98802 99300 0.0 mph 428.39 9.57
> > > 578 21 25 38
> > > 2 77 4/19/2021 21:25:39 42.40066 -70.98802 96730 0.0 mph 428.04 9.57
> > > 617 21 25 39
> > > 3 79 4/19/2021 21:25:41 42.40066 -70.98802 98800 0.2 mph 427.10 9.57
> > > 1027 21 25 41
> > > 4 80 4/19/2021 21:25:42 42.40066 -70.98802 96510 2 mph 427.99 9.58
> > > 1381 21 25 42
> > > 5 81 4/19/2021 21:25:43 42.40067 -70.98801 95540 3 mph 427.99 9.58
> > > 1271 21 25 43
> > > 6 82 4/19/2021 21:25:44 42.40068 -70.98799 94720 4 mph 427.20 9.57
> > > 910 21 25 44
> > > 7 83 4/19/2021 21:25:45 42.40069 -70.98797 94040 5 mph 427.18 9.57
> > > 652 21 25 45
> > > 8 84 4/19/2021 21:25:46 42.40072 -70.98795 95710 7 mph 427.07 9.57
> > > 943 21 25 46
> > > 9 85 4/19/2021 21:25:47 42.40074 -70.98792 96200 8 mph 427.44 9.56
> > > 650 21 25 47
> > > 10 86 4/19/2021 21:25:48 42.40078 -70.98789 9375010 mph 428.76 9.57
> > > 761 21 25 48
> > > 11 87 4/19/2021 21:25:49 42.40081 -70.98785 9336011 mph 429.25 9.56
> > > 1158 21 25 49
> > > 12 88 4/19/2021 21:25:50 42.40084 -70.98781 9434012 mph 429.56 9.57
> > > 107 21 25 50
> > > 13 89 4/19/2021 21:25:51 42.40087 -70.98775 9278012 mph 428.62 9.56
> > > 720 21 25 51
> > >
> > >
> > > What I want to do is, for each point, identify all points within 50m of
> > > that point, find the minimum value of the "Conc" attribute of each nearby
> > > set of points (including the original point) and then create a new
> > > variable
> > > ("Conc_min") and assign this minimum value to a new variable added to
> > > "data".
> > >
> > > So far, I have the following
