I've imported some GPS points from my Sports watch into R:
library(plotKML)
route <- readGPX("Move_Cycling.gpx")
str(route)
The data looks like this:
List of 5
$ metadata : NULL
$ bounds : NULL
$ waypoints: NULL
$ tracks :List of 1
..$ :List of 1
.. ..$ Move:'data.frame': 677 obs. of 5 variables:
.. .. ..$ lon : num [1:677] -3.8 -3.8 -3.8 -3.8 -3.8 ...
.. .. ..$ lat : num [1:677] 52.1 52.1 52.1 52.1 52.1 ...
.. .. ..$ ele : chr [1:677] "152" "151" "153" "153" ...
.. .. ..$ time : chr [1:677] "2014-06-08T09:17:08.050Z" "2014-06-08T09:17:18.680Z" "2014-06-08T09:17:23.680Z" "2014-06-08T09:17:29.680Z" ...
.. .. ..$ extensions: chr [1:677] "7627.7999992370605141521101800" "7427.6000003814697141511.7000000476837210180.8490009442642210" "9127.523.13003521531.7000000476837210181.799999952316280" "10027.534.96003841534.1999998092651410181.88300029210510" ...
$ routes : NULL
I've managed to transform to get the data points into a SpatialPointsDataFrame and to plot it over Google Earth with:
SPDF <- SpatialPointsDataFrame(coords=route$tracks[[1]]$Move[1:2],
data=route$tracks[[1]]$Move[1:2],
proj4string = CRS("+init=epsg:4326"))
plotKML(SPDF)
What I really want is the cycling track, i.e. a SpatialLinesDataFrame, but I can't work out how to set the ID field correctly to match the SpatialLines object with the data.
This is how far I've got:
tmp <- Line(coords=route$tracks[[1]]$Move[1:2])
tmp2 <- Lines(list(tmp), ID=c("coord"))
tmp3 <- SpatialLines(list(tmp2), proj4string = CRS("+init=epsg:4326"))
# result should be something like,
# but the ID of tmp3 and data don't match at the moment
SPDF <- SpatialLinesDataFrame(tmp3, data)
You can read the GPX file straight into a SpatialLinesDataFrame object with readOGR from the rgdal package. A GPX file can contain tracks, waypoints, etc and these are seen by OGR as layers in the file. So simply:
> track = readOGR("myfile.gpx","tracks")
> plot(track)
should work. You should see lines.
In your last line you've not said what your data is, but it needs to be a data frame with one row per track if you are trying to construct a SpatialLinesDataFrame from some SpatialLines and a data frame, and you can tell it not to bother matching the IDs because you don't actually have any real per-track data you are merging. So:
> SPDF = SpatialLinesDataFrame(tmp3, data.frame(who="me"),match=FALSE)
> plot(SPDF)
But if you use readOGR you don't need to go through all that. It will also read in a bit of per-track metadata from the GPX file.
Happy cycling!
As an update, here's my final solution
library(rgdal)
library(plotKML)
track <- readOGR("Move_Cycling.gpx","tracks")
plotKML(track, colour='red', width=2, labels="Cwm Rhaeadr Trail")
Related
I have some polygon data as an csv file with this format:
"ID","name","geometry"
"1","A","list(c(-0.25291548373789, -0.251828087559847, -0.251065989114876, -0.24944850900495, 51.4652163826438, 51.4652280432439, 51.4652040920587, 51.4650720752044))"
"2","B","list(c(-0.26291548373789, -0.211828087559847, -0.231065989114876, -0.26944850900495, 51.1652163826438, 51.2652280432439, 51.3652040920587, 51.5650720752044))"
And, when I import it to Rstudio looks like this:
Which is what I'm looking for, but the type of data is not what I need:
$ ID : num [1:2] 1 2
$ name : chr [1:2] "A" "B"
$ geometry: chr [1:2] ""list(c(-0.25291548373789, -0.2518280875598..."
What I need is this geometry column to an Spatial Object as Polygon, to be able to use it on Leaflet as:
addPolygons(data=test$geometry)
Any ideas how to achieve this?
Thanks!
test <- st_as_sf(test, wkt = 'geometry')
I'd like to convert a sf object to a dataframe and restore it to its original state. But when I make the conversion of st_as_text(st_sfc(stands_sel$geometry)) is shows very difficult to retrieve it again. In my example:
library(sf)
# get AOI in shapefile
download.file(
"https://github.com/Leprechault/trash/raw/main/sel_stands_CMPC.zip",
zip_path <- tempfile(fileext = ".zip")
)
unzip(zip_path, exdir = tempdir())
# Open the file
setwd(tempdir())
stands_sel <- st_read("sel_stands_CMPC.shp")
st_crs(stands_sel) = 4326
# Extract geometry as text
geom <- st_as_text(st_sfc(stands_sel$geometry))
# Add the features
features <- st_drop_geometry(stands_sel)
str(features)
# Joining feature + geom
geo_df <- cbind(features, geom)
str(geo_df)
# 'data.frame': 2 obs. of 17 variables:
# $ CD_USO_SOL: num 2433 9053
# $ ID_REGIAO : num 11 11
# $ ID_PROJETO: chr "002" "344"
# $ PROJETO : chr "BARBA NEGRA" "CAMPO SECO"
# $ CD_TALHAO : chr "159A" "016A"
# $ CARACTERIS: chr "Plantio Comercial" "Plantio Comercial"
# $ CARACTER_1: chr "Produtivo" "Produtivo"
# $ CICLO : int 2 1
# $ ROTACAO : int 1 1
# $ DATA_PLANT: chr "2008/04/15" "2010/04/15"
# $ LOCALIDADE: chr "BARRA DO RIBEIRO" "DOM FELICIANO"
# $ ESPACAMENT: chr "3.00 x 2.50" "3.5 x 2.14"
# $ ESPECIE : chr "SALIGNA" "DUNNI"
# $ SISTEMA_PR: chr "MACRO ESTACA - EUCALIPTO" "SEMENTE - EUCALIPTO"
# $ VLR_AREA : num 8.53 28.07
# $ ID_UNIQUE : chr "BARBANEGRA159A" "CAMPOSECO016A"
# $ geom : chr "MULTIPOLYGON (((-51.21423 -30.35172, -51.21426 -30.35178, -51.2143 -30.35181, -51.21432 -30.35186, -51.21433 -3"| __truncated__
# Return to original format again
stands_sf <- geo_df %>%
st_geometry(geom) %>%
sf::st_as_sf(crs = 4326)
#Error in UseMethod("st_geometry") :
Please, any help to restore my stands_sf object to the orinal state?
I think geom isn't in a format st_geometry is expecting. st_as_text converted your geometry into WKT as discussed in the help:
The returned WKT representation of simple feature geometry conforms to the simple features access specification and extensions, known as EWKT, supported by PostGIS and other simple features implementations for addition of SRID to a WKT string.
https://r-spatial.github.io/sf/reference/st_as_text.html
Instead, use st_as_sf(wkt=) to set the new (old) geometry.
st_as_sf(geo_df, wkt = "geom", crs = 4326)
I'm using ggmap library in R. I'm trying to download a rectangular map with it, but I know it'll give me a square. I only need the bounding box of the returned square.
library(ggmap)
map <- get_map(c(-65.7,-3.1,-64.4,-2.3),maptype="satellite",filename="map.png")
str(map)
chr [1:1280, 1:1280] "#294829" "#294829" "#2D512D" "#264425" ...
- attr(*, "class")= chr [1:2] "ggmap" "raster"
- attr(*, "bb")='data.frame': 1 obs. of 4 variables:
..$ ll.lat: num -3.14
..$ ll.lon: num -65.5
..$ ur.lat: num -2.26
..$ ur.lon: num -64.6
Object map have two classes "ggmap" and "raster". I can't use # or $ in it. How then can I access the ll.lat and other attributes from "bb" sub-object?
You can do this:
> attr(map, "bb")
ll.lat ll.lon ur.lat ur.lon
1 -3.139567 -65.48877 -2.261646 -64.60986
I get monthly price value for the two assets below from Yahoo:
if(!require("tseries") | !require(its) ) { install.packages(c("tseries", 'its')); require("tseries"); require(its) }
startDate <- as.Date("2000-01-01", format="%Y-%m-%d")
MSFT.prices = get.hist.quote(instrument="msft", start= startDate,
quote="AdjClose", provider="yahoo", origin="1970-01-01",
compression="m", retclass="its")
SP500.prices = get.hist.quote(instrument="^gspc", start=startDate,
quote="AdjClose", provider="yahoo", origin="1970-01-01",
compression="m", retclass="its")
I want to put these two into a single data frame with specified columnames (Pandas allows this now - a bit ironic since they take the data.frame concept from R). As below, I assign the two time series with names:
MSFTSP500.prices <- data.frame(msft = MSFT.prices, sp500= SP500.prices )
However, this does not preserve the column names [msft, snp500] I have appointed. I need to define column names in a separate line of code:
colnames(MSFTSP500.prices) <- c("msft", "sp500")
I tried to put colnames and col.names inside the data.frame() call but it doesn't work. How can I define column names while creating the data frame?
I found ?data.frame very unhelpful...
The code fails with an error message indicating no availability of as.its. So I added the missing code (which appears to have been successful after two failed attempts.) Once you issue the missing require() call you can use str to see what sort of object get.hist.quote actually returns. It is neither a dataframe nor a zoo object, although it resembles a zoo-object in many ways:
> str(SP500.prices)
Formal class 'its' [package "its"] with 2 slots
..# .Data: num [1:180, 1] 1394 1366 1499 1452 1421 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:180] "2000-01-02" "2000-01-31" "2000-02-29" "2000-04-02" ...
.. .. ..$ : chr "AdjClose"
..# dates: POSIXct[1:180], format: "2000-01-02 16:00:00" "2000-01-31 16:00:00" ...
If you run cbind on those two objects you get a regular matrix with dimnames:
> str(cbind(SP500.prices, MSFT.prices) )
num [1:180, 1:2] 1394 1366 1499 1452 1421 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:180] "2000-01-02" "2000-01-31" "2000-02-29" "2000-04-02" ...
..$ : chr [1:2] "AdjClose" "AdjClose"
You will still need to change the column names since there does not seem to be a cbind.its that lets you assign column-names. I would caution about using the data.frame method, since the object is might get confusing in its behavior:
> str( MSFTSP500.prices )
'data.frame': 180 obs. of 2 variables:
$ AdjClose :Formal class 'AsIs', 'its' [package ""] with 1 slot
.. ..# .S3Class: chr "AsIs" "its"
$ AdjClose.1:Formal class 'AsIs', 'its' [package ""] with 1 slot
.. ..# .S3Class: chr "AsIs" "its"
The columns are still S4 objects. I suppose that might be useful if you were going to pass them to other its-methods but could be confusing otherwise. This might be what you were shooting for:
> MSFTSP500.prices <- data.frame(msft = as.vector(MSFT.prices),
sp500= as.vector(SP500.prices) ,
row.names= as.character(MSFT.prices#dates) )
> str( MSFTSP500.prices )
'data.frame': 180 obs. of 2 variables:
$ msft : num 35.1 32 38.1 25 22.4 ...
$ sp500: num 1394 1366 1499 1452 1421 ...
> head(rownames(MSFTSP500.prices))
[1] "2000-01-02 16:00:00" "2000-01-31 16:00:00" "2000-02-29 16:00:00"
[4] "2000-04-02 17:00:00" "2000-04-30 17:00:00" "2000-05-31 17:00:00"
MSFT.prices is a zoo object, which seems to be a data-frame-alike, with its own column name which gets transferred to the object. Confer
tmp <- data.frame(a=1:10)
b <- data.frame(lost=tmp)
which loses the second column name.
If you do
MSFTSP500.prices <- data.frame(msft = as.vector(MSFT.prices),
sp500=as.vector(SP500.prices))
then you will get the colnames you want (though you won't get zoo-specific behaviours). Not sure why you object to renaming columns in a second command, though.
I have downloaded a nc file from
f=open.ncdf("file.nc")
[1] "file Lfile.nc has 2 dimensions:"
[1] "Longitude Size: 1440"
[1] "Latitude Size: 720"
[1] "------------------------"
[1] "file filr.nc has 8 variables:"
[1] "short ts[Latitude,Longitude] Longname:Skin Temperature (2mm) Missval:NA"
I then wanted to work with the variable soil_moisture_c
A = get.var.ncdf(nc=f,varid="soil_moisture_c",verbose=TRUE)
I then plot A with image(A). I got the map shown below,I even transposed it image(t(a)) but that was changed to other direction,and not how it should be. Anyway,in order to know what is wrong,I used the netcdf viewer Panoply and the map was correctly plotted as you can see below.
The reason is that the NetCDF interface you are using is very low-level, and all you have done is read out the variable without any of its dimension information. The orientation of the grid is really arbitrary, and the coordinate information needs to be understood in a particular context.
library(raster) ## requires ncdf package for this file
d <- raster("LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T185959Z_20040114.nc", varname = "soil_moisture_c")
(I used a different file to yours, but it should work the same).
It turns out that even raster does not get this right without work, but it does make it easy to rectify:
d <- flip(t(d), direction = "x")
That transposed the data and flipped around "x" (longitude), keeping the georeferencing from the original context.
Plot it up with a map from maptools to check:
plot(d)
library(maptools)
data(wrld_simpl)
plot(wrld_simpl, add = TRUE)
There are many other ways to achieve this by reading the dimension information from the file, but this is at least a shortcut to do most of the hard work for you.
Just as a complement to #mdsumner far better solution, here is a way to do that using library ncdf only.
library(ncdf)
f <- open.ncdf("LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040101.nc")
A <- get.var.ncdf(nf,"soil_moisture_c")
All you need is to find your dimensions in order to have a coherent x and y-axis. If you look at your netCDF objects dimensions, here what you see:
str(f$dim)
List of 2
$ Longitude:List of 8
..$ name : chr "Longitude"
..$ len : int 1440
..$ unlim : logi FALSE
..$ id : int 1
..$ dimvarid : num 2
..$ units : chr "degrees_east"
..$ vals : num [1:1440(1d)] -180 -180 -179 -179 -179 ...
..$ create_dimvar: logi TRUE
..- attr(*, "class")= chr "dim.ncdf"
$ Latitude :List of 8
..$ name : chr "Latitude"
..$ len : int 720
..$ unlim : logi FALSE
..$ id : int 2
..$ dimvarid : num 1
..$ units : chr "degrees_north"
..$ vals : num [1:720(1d)] 89.9 89.6 89.4 89.1 88.9 ...
..$ create_dimvar: logi TRUE
..- attr(*, "class")= chr "dim.ncdf"
Hence your dimensions are:
f$dim$Longitude$vals -> Longitude
f$dim$Latitude$vals -> Latitude
Now your Latitude goes from 90 to -90 instead of the opposite, which image prefers, so:
Latitude <- rev(Latitude)
A <- A[nrow(A):1,]
Finally, as you noticed, the x and y of your object A are flipped so you need to transpose it, and your NA values are represented for some reasons by value -32767:
A[A==-32767] <- NA
A <- t(A)
And finally the plot:
image(Longitude, Latitude, A)
library(maptools)
data(wrld_simpl)
plot(wrld_simpl, add = TRUE)
Edit: To do that on your 31 files, let's call your vector of file names ncfiles and yourpath the directory where you stored them (for simplicity i'm going to assume your variable is always called soil_moisture_c and your NAs are always -32767):
ncfiles
[1] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040101.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040102.nc"
[3] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040103.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040104.nc"
[5] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040105.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040106.nc"
[7] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040107.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040108.nc"
[9] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040109.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040110.nc"
[11] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040111.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040112.nc"
[13] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040113.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040114.nc"
[15] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040115.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040116.nc"
[17] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040117.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040118.nc"
[19] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040119.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040120.nc"
[21] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040121.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040122.nc"
[23] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040123.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040124.nc"
[25] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040125.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040126.nc"
[27] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040127.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040128.nc"
[29] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040129.nc" "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040130.nc"
[31] "LPRM-AMSR_E_L3_D_SOILM3_V002-20120520T173800Z_20040131.nc"
yourpath
[1] "C:\\Users"
library(ncdf)
library(maptools)
data(wrld_simpl)
for(i in 1:length(ncfiles)){
f <- open.ncdf(paste(yourpath,ncfiles[i], sep="\\"))
A <- get.var.ncdf(f,"soil_moisture_c")
f$dim$Longitude$vals -> Longitude
f$dim$Latitude$vals -> Latitude
Latitude <- rev(Latitude)
A <- A[nrow(A):1,]
A[A==-32767] <- NA
A <- t(A)
close.ncdf(f) # this is the important part
png(paste(gsub("\\.nc","",ncfiles[i]), "\\.png", sep="")) # or any other device such as pdf, jpg...
image(Longitude, Latitude, A)
plot(wrld_simpl, add = TRUE)
dev.off()
}
You can also simply invert the latitudes from the command line using CDO:
cdo invertlat file.nc file_inverted.nc