Using leaflet, I'm trying to plot some lines and set their color based on a 'speed' variable. My data start at an encoded polyline level (i.e. a series of lat/long points, encoded as an alphanumeric string) with a single speed value for each EPL.
I'm able to decode the polylines to get lat/long series of (thanks to Max, here) and I'm able to create segments from those series of points and format them as a SpatialLines object (thanks to Kyle Walker, here).
My problem: I can plot the lines properly using leaflet, but I can't join the SpatialLines object to the base data to create a SpatialLinesDataFrame, and so I can't code the line color based on the speed var. I suspect the issue is that the IDs I'm assigning SL segments aren't matching to those present in the base df.
The objects I've tried to join, with SpatialLinesDataFrame():
"sl_object", a SpatialLines object with ~140 observations, one for each segment; I'm using Kyle's code, linked above, with one key change - instead of creating an arbitrary iterative ID value for each segment, I'm pulling the associated ID from my base data. (Or at least I'm trying to.) So, I've replaced:
id <- paste0("line", as.character(p))
with
lguy <- data.frame(paths[[p]][1])
id <- unique(lguy[,1])
"speed_object", a df with ~140 observations of a single speed var and row.names set to the same id var that I thought I created in the SL object above. (The number of observations will never exceed but may be smaller than the number of segments in the SL object.)
My joining code:
splndf <- SpatialLinesDataFrame(sl = sl_object, data = speed_object)
And the result:
row.names of data and Lines IDs do not match
Thanks, all. I'm posting this in part because I've seen some similar questions - including some referring specifically to changing the ID output of Kyle's great tool - and haven't been able to find a good answer.
EDIT: Including data samples.
From sl_obj, a single segment:
print(sl_obj)
Slot "ID":
[1] "4763655"
[[151]]
An object of class "Lines"
Slot "Lines":
[[1]]
An object of class "Line"
Slot "coords":
lon lat
1955 -74.05228 40.60397
1956 -74.05021 40.60465
1957 -74.04182 40.60737
1958 -74.03997 40.60795
1959 -74.03919 40.60821
And the corresponding record from speed_obj:
row.names speed
... ...
4763657 44.74
4763655 34.8 # this one matches the ID above
4616250 57.79
... ...
To get rid of this error message, either make the row.names of data and Lines IDs match by preparing sl_object and/or speed_object, or, in case you are certain that they should be matched in the order they appear, use
splndf <- SpatialLinesDataFrame(sl = sl_object, data = speed_object, match.ID = FALSE)
This is documented in ?SpatialLinesDataFrame.
All right, I figured it out. The error wasn't liking the fact that my speed_obj wasn't the same length as my sl_obj, as mentioned here. ("data =
object of class data.frame; the number of rows in data should equal the number of Lines elements in sl)
Resolution: used a quick loop to pull out all of the unique lines IDs, then performed a left join against that list of uniques to create an exhaustive speed_obj (with NAs, which seem to be OK).
ids <- data.frame()
for (i in (1:length(sl_obj))) {
id <- data.frame(sl_obj#lines[[i]]#ID)
ids <- rbind(ids, id)
}
colnames(ids)[1] <- "linkId"
speed_full <- join(ids, speed_obj)
speed_full_short <- data.frame(speed_obj[,c(-1)])
row.names(speed_full_short) <- speed_full$linkId
splndf <- SpatialLinesDataFrame(sl_obj, data = speed_full_short, match.ID = T)
Works fine now!
I may have deciphered the issue.
When I am pulling in my spatial lines data and I check the class it reads as
"Spatial Lines Data Frame" even though I know it's a simple linear shapefile, I'm using readOGR to bring the data in and I believe this is where the conversion is occurring. With that in mind the speed assignment is relatively easy.
sl_object$speed <- speed_object[ match( sl_object$ID , row.names( speed_object ) ) , "speed" ]
This should do the trick, as I'm willing to bet your class(sl_object) is "Spatial Lines Data Frame".
EDIT: I had received the same error as OP, driving me to check class()
I am under the impression that the error that was populated for you is because you were trying to coerce a data frame into a data frame and R wasn't a fan of that.
Related
There are R native datasets, such as the Nile dataset, that are time series. However, if I actually look at the data set, be it as it was, after as_tibble(), after as.data.frame() – it doesn't matter –, there is only one column: x (which, in this specific case, is the "measurement of anual flow of the river"). However, if I plot() the data, in any of the three formats (raw, tibble or data.frame), I plots with the dates:
(Technically, the x axis label changes, but that's not the point).
Where are these dates stored? How can I access them (to use ggplot(), for example), or even – how can I see them?
If you use str(Nile) or print(Nile), you'll see that the Nile data set is store in a Time-Series object. You can use the start(), end() and frequency() functions to extract those attribute then create a new column to store those informations.
data(Nile)
new_df = data.frame(Nile)
new_df$Time = seq(from = start(Nile)[[1]], to = end(Nile)[[1]], by = frequency(Nile))
The simplest description of what I am trying to do is that I have a column in a data.frame like 1,2,3,..., n, 1,2,3,...n,.... and I want group the first 1...n as 1 the second 1...n as 2 and so on.
The full context is; I am using the R spcosa package to do equal area stratification composite sampling on parcels of land. I start with a shape file from a GIS that contains a number of polygons (land parcels). The end result I want is a GIS file with each of the strata and sample locations in a GIS file format with each stratum and sample location labeled by land parcel, stratum and sample id. So far I can do all this except one bit which is identifying the stratum that the samples belongs too and including it in the sample label. The sample label needs to look like "parcel#-strata#-composite# (where # is the number). In practice I don't need this actual label but as separate attributes in GIS file.
The basic work flow is a follows
For each individual polygon using spcosa::stratify I divide it into a number of equal area strata like
strata.CSEA <- stratify(poly[i,], nStrata = n, nTry = 1, equalArea = TRUE, nGridCells = x)
Note spcosa::stratify generates a CompactStratificationEqualArea object. I cocerce this to a SpatialPixelData then use rasterToPolygon to be able to output it as a GIS file.
I then generate the sample locations as follows:
samples.SPRC <- spsample(strata.CSEA, n = n, type = "composite")
spcosa::spsample creates a SamplingPatternRandomComposite object. I coerce this to a SpatialPointsDataFrame
samples.SPDF <- as(samples.SPRC, "SpatialPointsDataFrame")
and add two columns to the #data slot
samples.SPDF#data$Strata <- "this is the bit I can't do yet"
samples.SPDF#data$CEA <- poly[i,]$name
I can then write samples.SPDF as a GIS file ( ie writeOGE) with all the wanted attributes.
As above the part I can't sort out is how the sample ids relate to the strata ids. The sample points are a vector like 1,2,3...n, 1,2,3...n,.... How do I extract which sample goes with which strata? As actual strata number are arbitrary, I can just group ( as per my simple question above) but ideally I would like to use the numbering of the actual strata so everything lines up.
To give any contributors access to a hands on example I copy below the code from the spcosa documentation slightly modified to generate the correct objects.
# Note: the example below requires the 'rgdal'-package You may consider the 'maptools'-package as an alternative
if (require(rgdal)) {
# read a vector representation of the `Farmsum' field
shpFarmsum <- readOGR(
dsn = system.file("maps", package = "spcosa"),
layer = "farmsum"
)
# stratify `Farmsum' into 50 strata
# NB: increase argument 'nTry' to get better results
set.seed(314)
myStratification <- stratify(shpFarmsum, nStrata = 50, nTry = 1, equalArea = TRUE)
# sample two sampling units per stratum
mySamplingPattern <- spsample(myStratification, n = 2 type = "composite")
# plot the resulting sampling pattern on
# top of the stratification
plot(myStratification, mySamplingPattern)
}
Maybe order() function can help you
n <- 10
dat <- data.frame(col1 = rep(1:n, 2), col2 = rnorm(2*n))
head(dat)
dat[order(dat$col1), ]
I did not get where the "ID" (1,2,3...n) is to be found; so let's assume you have your SpatialPolygonsDataFrame called shpFarmsum with a attribute data column "ID". You can access this column via shpFarmsum$ID. Therefore, if you want to create individual subsets for each ID this is one way to go:
for (i in unique(shpFarmsum$ID)) {
tempSubset shpFarmsum[shpFarmsum$ID == i,]
writeOGR(tempSubset, ".", paste0("subset_", i), driver = "ESRI Shapefile")
}
I added the line writeOGR(... so all subsets are written to your working direktory. However, you can change this line or add further analysis into the for-loop.
How it works
unique(shpFarmsum$ID) extracts all occuring IDs (compareable to your 1,2,3...n).
In each repetition of the for loop, another value of this IDs will be used to create a subset of the whole SpatialPolygonsDataFrame, which you can use for further analysis.
I am relatively new to R so i apologize if I have trouble expressing what I'm attempting to do. I have a 'spatial' panel dataset in long form and a shapefile. The long form table is a data.frame and it includes a column of dates (that have been converted to dates using 'as.date') and an ID column that is the same as that in the shapefile used to identify the different polygons (thus my long form dataset has no long lat values just an ID field that corresponds to the polygon features in the shapefile). I want to construct a spatiotemporal object of class ST out of these two objects (the shapefile and the long form dataset). To do this I have tried using stcontruct() and STFDF() but with absolutely not luck. stcontruct() gives me this error:
stConstruct(x, x$ID, x$date, SpatialObj = pol, TimeObj = NULL, interval=FALSE)
Error in stConstruct(x, x$ID, x$date, SpatialObj = pol, TimeObj = NULL, :
unknown parameter combination
and STFDF() gives me this error:
STFDF(shapefile, x$date, x)
Error: nrow(object#data) == length(object#sp) * nrow(object#time) is not TRUE
I've been stuck on this for days reading everything I can about the spacetime package in forums, etc. but to no avail. Any help is greatly appreciated.
thanks!
About STFDF error
From:
http://r-sig-geo.2731867.n2.nabble.com/Error-with-STFDF-td7584461.html
"If you don't have every time value at every spatial point, then you can't
have an STFDF object, as by definition STFDF is a full space by time grid.
The equation in the error is part of the definition/requirement for an
STFDF object.
SDIDF objects don't have that requirement of all times at all locations..."
I have successfully added information to shapefiles before (see my post on http://rusergroup.swansea.ac.uk/Healthmap.ashx?HL=map ).
However, I just tried to do it again with a slightly different shapefile (new local health boards for Wales) and the code fails at spCbind with a "row names not identical error"
o <- match(wales.lonlat$NEW_LABEL, wds$HB_CD)
wds.xtra <- wds[o,]
wales.ncchd <- spCbind(wales.lonlat, wds.xtra)
My rows did have different names before and that didn't cause any problems. I relabeled the column in wds.xtra to match "NEW_LABEL" and that doesn't help.
The labels and order of labels do match exactly between wales.lonlat and wds.xtra.
(I'm using Revolution R 5.0, which is built on R 2.13.2)
I use match to merge data to the sp data slot based on rownames (or any other common ID). This avoids the necessity of maptools for the spCbind function.
# Based on rownames
sdata#data=data.frame(sdata#data, new.df[match(rownames(sdata#data), rownames(new.df)),])
# Based on common ID
sdata#data=data.frame(sdata#data, new.df[match(sdata#data$ID, new.df$ID),])
# where; sdata is your sp object and new.df is a data.frame object that you want to merge to sdata.
I had the same error and could resolve it by deleting all other data, which were not actually to be added. I suppose, they confused spCbind because the matching wanted to match all row-elements, not only the one given. In my example, I used
xtra2 <- data.frame(xtra$ID_3, xtra$COMPANY)
to extract the relevant fields and fed them to spCbind afterwards
gadm <- spCbind(gadm, xtra2)
I want to use ChemoSpec with a mass spectra of about 60'000 datapoint.
I have them already in one txt file as a matrix (X + 90 samples = 91 columns; 60'000 rows).
How may I adapt this file as spectra data without exporting again each single file in csv format (which is quite long in R given the size of my data)?
The typical (and only?) way to import data into ChemoSpec is by way of the getManyCsv() function, which as the question indicates requires one CSV file for each sample.
Creating 90 CSV files from the 91 columns - 60,000 rows file described, may be somewhat slow and tedious in R, but could be done with a standalone application, whether existing utility or some ad-hoc script.
An R-only solution would be to create a new method, say getOneBigCsv(), adapted from getManyCsv(). After all, the logic of getManyCsv() is relatively straight forward.
Don't expect such a solution to be sizzling fast, but it should, in any case, compare with the time it takes to run getManyCsv() and avoid having to create and manage the many files, hence overall be faster and certainly less messy.
Sorry I missed your question 2 days ago. I'm the author of ChemoSpec - always feel free to write directly to me in addition to posting somewhere.
The solution is straightforward. You already have your data in a matrix (after you read it in with >read.csv("file.txt"). So you can use it to manually create a Spectra object. In the R console type ?Spectra to see the structure of a Spectra object, which is a list with specific entries. You will need to put your X column (which I assume is mass) into the freq slot. Then the rest of the data matrix will go into the data slot. Then manually create the other needed entries (making sure the data types are correct). Finally, assign the Spectra class to your completed list by doing something like >class(my.spectra) <- "Spectra" and you should be good to go. I can give you more details on or off list if you describe your data a bit more fully. Perhaps you have already solved the problem?
By the way, ChemoSpec is totally untested with MS data, but I'd love to find out how it works for you. There may be some changes that would be helpful so I hope you'll send me feedback.
Good Luck, and let me know how else I can help.
many years passed and I am not sure if anybody is still interested in this topic. But I had the same problem and did a little workaround to convert my data to class 'Spectra' by extracting the information from the data itself:
#Assumption:
# Data is stored as a numeric data.frame with column names presenting samples
# and row names including domain axis
dataframe2Spectra <- function(Spectrum_df,
freq = as.numeric(rownames(Spectrum_df)),
data = as.matrix(t(Spectrum_df)),
names = paste("YourFileDescription", 1:dim(Spectrum_df)[2]),
groups = rep(factor("Factor"), dim(Spectrum_df)[2]),
colors = rainbow(dim(Spectrum_df)[2]),
sym = 1:dim(Spectrum_df)[2],
alt.sym = letters[1:dim(Spectrum_df)[2]],
unit = c("a.u.", "Domain"),
desc = "Some signal. Describe it with 'desc'"){
features <- c("freq", "data", "names", "groups", "colors", "sym", "alt.sym", "unit", "desc")
Spectrum_chem <- vector("list", length(features))
names(Spectrum_chem) <- features
Spectrum_chem$freq <- freq
Spectrum_chem$data <- data
Spectrum_chem$names <- names
Spectrum_chem$groups <- groups
Spectrum_chem$colors <- colors
Spectrum_chem$sym <- sym
Spectrum_chem$alt.sym <- alt.sym
Spectrum_chem$unit <- unit
Spectrum_chem$desc <- desc
# important step
class(Spectrum_chem) <- "Spectra"
# some warnings
if (length(freq)!=dim(data)[2]) print("Dimension of data is NOT #samples X length of freq")
if (length(names)>dim(data)[1]) print("Too many names")
if (length(names)<dim(data)[1]) print("Too less names")
if (length(groups)>dim(data)[1]) print("Too many groups")
if (length(groups)<dim(data)[1]) print("Too less groups")
if (length(colors)>dim(data)[1]) print("Too many colors")
if (length(colors)<dim(data)[1]) print("Too less colors")
if (is.matrix(data)==F) print("'data' is not a matrix or it's not numeric")
return(Spectrum_chem)
}
Spectrum_chem <- dataframe2Spectra(Spectrum)
chkSpectra(Spectrum_chem)