I'm using RStudio and the package Marmap to import my 3 columns txt file (latitude, longitude and depth values).
After the import all the depth values appear as NA.
I've tried different file format (csv) but the result was the same.
Can someone help me.
Thanks,
Paulo
Have you tried plot(praia)? It seems you have an enormous amount of NAs, but you also have a few (726!) non missing values.
Since the data you're trying to import are obviously not regularly spaced, you should check ?griddify()
Related
I have data from excel which should be a group data(the one I higlight in the picture), the problem is, when i import it to R, it won't consider those data as grouped, how can i fix the problem?
As I can see from the image you provide. They are 3 separated columns, so when you import the excel file to Rstudio. It will treat them as 3 different columns. However, if you want to unite 3 columns into 1. There are also solutions for that.
Recently I obtained a lot of RTF files that contain econ data for analysis I need to do. Unfortunately, this is how Bureau of Statistics of my country could help with time series data for a long time lapse. If there is a one time need to select particular indicator for 10 years or so I'm OK to find these values by hand using Word/Notepad/TestEdit(for Mac). But my problem is that I have 15 files with data that I need to combine somehow in one dataset for my work. But, before even start doing this I don't have a clue if it is possible to read those files in appropriate format (data.frame). Wanted to ask expert opinion on how to approach this task. An example of one of files could be downloaded from here:
[https://www.dropbox.com/s/863ikx6poid8unc/Export_for_SO.rtf?dl=0][1]
All values are in Russian. Dataset represents export of particular product (first column) across countries (second column) in US dollars for 2 periods.
Thank you.
Use code found on https://datascienceplus.com/how-to-import-multiple-csv-files-simultaneously-in-r-and-create-a-data-frame/
replace read_csv with read_rtf
You may want to manually convert your files to another format using an office suite or a text editor. You should be able to save as in another format.
While in R, you may want to give striprtf a try. I'm guessing you will still have to clean your data a bit afterward.
You can install the package like this:
install.packages("striprtf")
I'm having trouble extracting point data from a large shape file (916.2 Mb, 4618197 elements - from here: https://earthdata.nasa.gov/data/near-real-time-data/firms/active-fire-data) in R. I'm using readShapeSpatial in maptools to read in the shape file which takes a while but eventually works:
worldmap <- readShapeSpatial("shp_file_name")
I then have a data.frame of coordinates that I want extract data for. However R is really struggling with this and either loses connection or freezes, even with just one set of coordinates!
pt <-data.frame(lat=-64,long=-13.5)
pt<-SpatialPoints(pt)
e<-over(pt,worldmap)
Could anyone advise me on a more efficient way of doing this?
Or is it the case that I need to run this script on something more powerful (currently using a mac mini with 2.3 GHz processor)?
Many thanks!
By 'point data' do you mean the longitude and latitude coordinates? If that's the case, you can obtain the data underlying the shapefile with:
worldmap#data
You can view this in the same way you would any other data frame, for example:
View(worldmap#data)
You can also access columns in this data frame in the same way you normally would, except you don't need the #data, e.g.:
worldmap$LATITUDE
Finally, it is recommended to use readOGR from the rgdal package rather than maptools::readShapeSpatial as the former reads in the CRS/projection information.
I've dug around a lot for an answer to this and wasn't able to find anything, so here I am.
I have a whole bunch of ascii raster files corresponding to air temperature and dew point temperature of a certain area over 744 hourly time steps. (So I have 744 air temp and 744 dew point files corresponding to a 31-day month). The files are only about 45 kB each.
I want to stack them together so I can perform some analyses on them, and I also want to convert their units from K to deg F.
The file names air Tair1.txt, Tair2.txt, ... Tair744.txt and Eair1.txt, Eair2.txt, ... Eair744.txt.
Using the raster package, I can easily load all the files as rasters:
for (i in 1:744) {
assign(paste0("Tair",i),raster(paste0("Tair",i,".txt")))
assign(paste0("Eair",i),raster(paste0("Tair",i,".txt")))
}
I've tried to use ls() with pattern or glob2rx to define just the raster file names and
then do conversions on them, or to do something similar to join them in a stack, but to no avail. I also tried mget, values(mget(filename)) and things like that to get at the values in a loop.
I know R doesn't handle large datasets very well, but I'm thinking these aren't really that large so there should be something pretty simple?
I would appreciate any help and advice! Thank you.
The raster package's RasterStack is for this:
library(raster)
files <- paste0("Tair",1:744,".txt")
rs <- stack(files)
Why do you have these files in text format though? Who imposed this disaster on you? I suspect your individual layers have insufficient metadata, so try one and see if it's sensible. You can use extent(rs) <- and projection(rs) <- to fix:
r <- raster(files[1])
print(r)
Don't use assign() that's just creating a mess.
My goal is very simple, namely to add 1 column of statistical data to a shapefile so that I can use it for example to colour a geographical area. The data are a country file from gadm. To this end I usually use the foreign package in R thus:
library(foreign)
newdbf <- read.dbf("CHN_adm1.dbf") #original shape file
incrdata <- read.csv("CHN_test.csv") #.csv file with same region names column + new data column
mergedbf <- merge(newdbf,incrdata)
write.dbf(mergedbf,"CHN_New")
This achieves what I want in almost all circumstances, but one of the pieces of software I am dealing with external to R will only recognize .shp files and will not read .dbf (although clearly in a sense that statement is a slight contradiction). Not sure why it won't. Anyhow, essentially it leaves me needing to do the same thing as above, but with a shapefile. I think that according to notes on shapefiles package, the process should run something like this:
library(shapefiles)
shaper <- read.shp("CHN_adm1.shp")
simplified <- convert.to.simple(shaper)
simplified <- change.id(simplified,incrdata$DataNew) #DataNew being new column of data from the .csv
simpleAsList <- by(simplified,simplified[,1],function(x)x)
####This is where I hit problems####
backToShape <- convert.to.shapefile(simplified,
data.frame(index=c("20","30","40","50","60","70","80")),"index",5)
write.shapefile(backToShape,"CHN_TestShape")
I'm afraid that I can't get my head around shapefiles, since I can't unpick them or visualize them in a way I can with dataframes, and so the resultant shape has been screwed up when it goes back to the external charting package.
To be clear: in 'backToShape' I just want to add the column of data and reconstruct the shapefile. It so happens that the data I have appears as a factor, ie 20,30,40 etc, but the data could just as easily be continuous, and I'm sure I don't need to type in all possibilities, but it was the only way I could seem to get it to be accepted. Can somebody please put me on the right track, and if I'm missing a simpler way, I'd also be extremely grateful to hear a suggestion. Many thanks in advance.
Stop using the shapefiles package.
Install the sp and rgdal packages.
Read shapefile with:
chn = readOGR(".","CHN_adm1") # first arg is path, second is shapefile name w/o .shp
Now chn is like a data frame. In fact chn#data is a data frame. Do what you like to that data frame but keep it in the same order, and then you can save the updated shapefile with the new data by:
writeOGR(chn, ".", "CHN_new", driver="ESRI Shapefile")
Note you shouldn't really manipulate the chn#data data frame directly, you can work with chn like it is a data frame in many respects, for example chn$foo gets the column named foo, or chn$popden = chn$pop/chn$area would create a new column of population density if you have population and area columns.
spplot(chn, "popden")
will map by the popden column you just created, and:
head(as.data.frame(chn))
should show you the first few lines of the shapefile data.