Convert to coordinates over a list of data frames

Question

I am trying to convert point values to coordinates using the sp package to perform operations similar to this question. I have a list of data frames (hundreds in the full data set, 2 short ones here).

> dput(df)
list(structure(list(group = c(22, 43, 43, 36, 9, 20, 35, 18, 
32, 2), mean_x_m = c(-2578373.61904762, -2082265, -1853701.875, 
-2615961.89189189, -1538829.07815509, -1753235.6200847, -1690679.5, 
-1694763.64583333, -1700343.15217391, -1416060), mean_y_m = c(3242738.76190476, 
2563892.5, 1945883.125, 3130074.86486486, 1373724.65001039, 1468737.97186933, 
2123413.5, 1442167.01388889, 2144261.73913043, 1352573.33333333
)), .Names = c("group", "mean_x_m", "mean_y_m"), row.names = c(72L, 
140L, 142L, 121L, 27L, 66L, 114L, 60L, 105L, 5L), class = "data.frame"), 
    structure(list(group = c(12, 12, 47, 30, 39, 34, 47, 22, 
    10, 1), mean_x_m = c(-1830635.68663753, -2891058.33333333, 
    -1637448.59886202, -1974773.67400716, -1571853.24324324, 
    -2723090.33333333, -2704594.92760618, -2240863.49122807, 
    -1940748.88253242, -2176724.69924812), mean_y_m = c(2324222.49926225, 
    3261997.5, 2057096.55049787, 2411733.29933653, 1447883.78378379, 
    3406879.26666667, 3291053.77606178, 2788255.49473684, 2176919.6882151, 
    2920168.77443609)), .Names = c("group", "mean_x_m", "mean_y_m"
    ), row.names = c(67L, 68L, 243L, 155L, 202L, 173L, 244L, 
    114L, 61L, 3L), class = "data.frame"))

I can pull one data frame out at a time and convert to a SpatialPointsDataFrame without issue.

df1 = df[[1]]
coordinates(df1) = ~mean_x_m+mean_y_m

My problem is I can't get this to iterate over the entire list using a function, or even get the function to work for a single dataframe.

c = function(f){coordinates(f) = ~mean_x_m+mean_y_m}
df2 = c(df1)
c(df1)
df3 = lapply(df,c)

Would a for loop work better? I'm still learning about working with lists of data frames and matrices so any help on apply or for in this context would be appreciated. Thank you.

score 2 · Accepted Answer · answered Oct 24 '15 at 02:40

This is how you can use lapply:

fc <- function(f){coordinates(f) = ~mean_x_m + mean_y_m; f}
lapply(df, fc)

The problem was that your function did not return anything.

To make a single object:

 x <- lapply(1:length(df), function(i) cbind(id=i, df[[i]]))
 x <- do.call(rbind, x)
 coordinates(x) <- ~mean_x_m+mean_y_m

score 1 · Answer 2 · answered Oct 23 '15 at 14:21

1

If your dataframes have a consistent structure, it would be better to put them all into one dataframe.

library(dplyr)
library(sp)

result = 
  df %>% 
  bind_rows(.id = "list_number") %>%
  as.data.frame %>%
  `coordinates<-`(~mean_x_m+mean_y_m)

answered Oct 23 '15 at 14:21

bramtayl

4,004
2
11
18

Thanks. That may work if I can add a column with a unique identifier for each original dataframe that I can then aggregate/sort/split by later. Any quick tips on using dplyr to do that in the same code as you provide? – tjr Oct 23 '15 at 18:07
It should already be there, in a column called "list_number". You can change the name as necessary. – bramtayl Oct 23 '15 at 18:42

score 1 · Answer 3 · answered Oct 23 '15 at 19:43

If you are working with geographic data I find it is easiest to use Spatial Points and SpatialPointsDataFrame classes to store data. To convert all elements of a list containing dataframes with the same column headings you could adapt this code:

library(sp)
# toy dataset X
X<-list(
x1 = data.frame(group =c("a","b","c"), X = c(-110.1,-110.2,-110), Y = c(44,44.2,44.3)),
x2 = data.frame(group =c("a","b","c"), X = c(-110.1,-110.2,-110), Y = c(44,44.2,44.3)))
# write a function based on the structure of your dfs 
spdf_fxn<-function(df){
SpatialPointsDataFrame(coords= cbind(df$X,df$Y), data= data.frame(group = df$group),
                     proj4string=CRS("+proj=longlat +datum=WGS84"))
}
#apply this function over the list
Out_List<-lapply(X,spdf_fxn)

Write a function to convert the generic dataframe structure to a SpatialPointsDataframe, with group as the data appended to each point, then apply that function to the list. Note you will have to adapt the column names and use the appropriate proj4string (in this example it is longitude and latitude in WGS 84).

Convert to coordinates over a list of data frames

3 Answers3