I have a CSV with 3 columns: time, longitude, latitude. I need to extract the time at every 10 meters (0.01km). I've managed to calculate cumulative distance for each row:
gps <- read.csv("SP1ST1.csv")
gps_sp <- SpatialPoints(cbind(gps$lng,gps$lat))
test <- spDistsN1(gps_sp, gps_sp[1,], longlat=TRUE)
So the output looks like this:
[1] 0.000000000 0.001586483 0.004574098 0.004493954 0.004887035 0.005405389 0.005930999 0.006443206 0.006991742 0.007595466 0.009693191
[12] 0.010654023 0.010231435 0.010082614 0.012005496 0.012905777 0.013896484 0.014873557 0.015857558 0.016905208 0.013991941 0.017441699
[23] 0.017797154 0.018539821 0.019254225 0.019914940 0.020634398 0.021411878 0.022246358 0.023037314 0.023832587 0.024608449 0.023977990
I can see just by looking at the output that my first approximately 0.01km increase is between row 1 and row 11, and the second one is between row 11 and 26.
I need to write a code in R that will find all of these jumps for me, but it's not a jump of exactly 0.01 and it's not evenly distributed through the rows. I also need to link this back to the original "gps" object so I can extract the times that are associated with the ~0.01 increase.
How do I do this?
Edit: Added a data sample below.
sample <- dput(head(gps,30))
filename taken_at lng lat gps_altitude
1 20230718_GSL_SP1ST1_4k_01.MOV 14:11:05 -65.36897 49.95216 -31.625
2 20230718_GSL_SP1ST1_4k_01.MOV 14:11:08 -65.36898 49.95218 -31.373
3 20230718_GSL_SP1ST1_4k_01.MOV 14:11:12 -65.36899 49.95220 -31.254
4 20230718_GSL_SP1ST1_4k_01.MOV 14:11:13 -65.36898 49.95220 -31.604
5 20230718_GSL_SP1ST1_4k_01.MOV 14:11:14 -65.36897 49.95221 -31.419
6 20230718_GSL_SP1ST1_4k_01.MOV 14:11:15 -65.36897 49.95221 -31.432
7 20230718_GSL_SP1ST1_4k_01.MOV 14:11:16 -65.36896 49.95222 -31.445
8 20230718_GSL_SP1ST1_4k_01.MOV 14:11:17 -65.36896 49.95222 -31.459
9 20230718_GSL_SP1ST1_4k_01.MOV 14:11:18 -65.36895 49.95222 -31.472
10 20230718_GSL_SP1ST1_4k_01.MOV 14:11:19 -65.36895 49.95223 -31.485
11 20230718_GSL_SP1ST1_4k_01.MOV 14:11:20 -65.36900 49.95225 -31.328
12 20230718_GSL_SP1ST1_4k_01.MOV 14:11:21 -65.36899 49.95226 -31.322
13 20230718_GSL_SP1ST1_4k_01.MOV 14:11:22 -65.36901 49.95225 -31.462
14 20230718_GSL_SP1ST1_4k_01.MOV 14:11:23 -65.36903 49.95224 -31.614
15 20230718_GSL_SP1ST1_4k_01.MOV 14:11:24 -65.36899 49.95227 -31.272
16 20230718_GSL_SP1ST1_4k_01.MOV 14:11:25 -65.36898 49.95228 -31.189
17 20230718_GSL_SP1ST1_4k_01.MOV 14:11:26 -65.36897 49.95229 -31.102
18 20230718_GSL_SP1ST1_4k_01.MOV 14:11:27 -65.36896 49.95230 -31.015
19 20230718_GSL_SP1ST1_4k_01.MOV 14:11:28 -65.36895 49.95230 -30.927
20 20230718_GSL_SP1ST1_4k_01.MOV 14:11:29 -65.36894 49.95231 -30.838
21 20230718_GSL_SP1ST1_4k_01.MOV 14:11:30 -65.36899 49.95229 -32.265
22 20230718_GSL_SP1ST1_4k_01.MOV 14:11:31 -65.36901 49.95232 -31.533
23 20230718_GSL_SP1ST1_4k_01.MOV 14:11:32 -65.36901 49.95232 -31.781
24 20230718_GSL_SP1ST1_4k_01.MOV 14:11:33 -65.36900 49.95233 -31.921
25 20230718_GSL_SP1ST1_4k_01.MOV 14:11:34 -65.36899 49.95234 -32.056
26 20230718_GSL_SP1ST1_4k_01.MOV 14:11:35 -65.36898 49.95234 -32.188
27 20230718_GSL_SP1ST1_4k_01.MOV 14:11:36 -65.36897 49.95235 -32.320
28 20230718_GSL_SP1ST1_4k_01.MOV 14:11:37 -65.36896 49.95236 -32.452
29 20230718_GSL_SP1ST1_4k_01.MOV 14:11:38 -65.36901 49.95236 -31.729
30 20230718_GSL_SP1ST1_4k_01.MOV 14:11:39 -65.36901 49.95237 -31.705
We can set thresholds of 0.01 and then calculate the rows where the output exceeds that threshold. Then we can filter just those rows of interest:
# Load libraries.
library(tidyverse)
library(sp)
# Using your sample data as `gps` find the cumulative distance.
gps_sp <- SpatialPoints(cbind(gps$lng,gps$lat))
test <- spDistsN1(gps_sp, gps_sp[1,], longlat=TRUE)
# Add output values to dataframe.
gps$test <- test
# Find rows that pass the 0.01 value thresholds.
thresholds <- seq(0.01, max(gps$test), by = 0.01)
threshold_indices <- as.data.frame(thresholds) %>%
mutate(index = map(thresholds, ~which(gps$test >= .x)[1])) %>%
unnest(cols = c(index))
# Add threshold column.
final_gps <- gps %>%
mutate(row_id = row_number()) %>%
mutate(passes_threshold = row_id %in% threshold_indices$index) %>%
select(-row_id)
Now we have a column with TRUE where it hits the threshold value and FALSE otherwise. Then you could do:
final_gps %>%
filter(passes_threshold == TRUE)
For output:
| filename | taken_at | lng | lat | gps_altitude | test | passes_threshold |
|-----------------------------------|-----------|-----------|----------|--------------|-----------|------------------|
| 20230718_GSL_SP1ST1_4k_01.MOV | 14:11:20 | -65.36900 | 49.95225 | -31.328 | 0.01023953| TRUE |
| 20230718_GSL_SP1ST1_4k_01.MOV | 14:11:34 | -65.36899 | 49.95234 | -32.056 | 0.02007263| TRUE |