I'm clearly struggling with this problem for a day now and can't seem to find a nice solution to it. I would really appreciate some help and I'm really a novice in R (since last week).
Problem 1:
I have a set CSV representing grid points which I can parse into a data frame (pointname, latitude, longitude).
Eg:
name,latitude,longitude
x0y0,35.9767,-122.605
x1y0,35.9767,-122.594
x2y0,35.9767,-122.583
x0y1,35.9857,-122.605
x1y1,35.9857,-122.594
x2y1,35.9857,-122.583
x0y2,35.9947,-122.605
x1y2,35.9947,-122.594
x2y2,35.9947,-122.583
The points in this file represent the lower left corner and are arranged in row major format, meaning lowest horizontal grid points first. Each point is a certain great circle distance away from its neighbors (1km). I want to create a grid overlay on a map which I've plotted using ggmap.
What I've tried or considered:
map.grid() - this is really not useful to me as I'm not looking for any kind of projection.
geom_vline() and geom_hline(). These look good but I don't have constant x and y intercepts on a plane. Moreover, once I create a grid, I'd like to use the grid to color against a density.
geom_rect() and geom_tile(). These look really promising and may be what I want. But I'm not able to find a good way of working with these.
I'd like to fill these grid boxes later with another parameter. Any suggestions on how I can create such a grid? This may be a trivial question but I don't know a lot of R yet.
Problem 2:
How can I store or hold such a grid so that I given a point (lat,lon), I can quickly get to that grid. In fact my whole back end is in C++ and can directly output the grid name x<n>y<n> directly against a given search point. I somehow am finding it difficult to count such points against grid points so that I can fill grid with a representative color.
I'm not sure if everything of what I'm saying is clear. Please tell me if I've to clarify something.
Also note that I've Googled quite a lot and not found relevant answers although some looked close.
Eg: This, ThisToo
Thanks for the help!
Related
I would like to create a ggplot2 layer that includes multiple geom_bspline(), or something similar, to point to regions on different plots after combining them into a single figure. A feature in the data seen in one plot appears in another plot after a transformation. However, it may not be clear to a non-expert they are due to the same phenomenon. The plots are to be combined into a single figure using ggarrange(), cowplot(), patchwork() or something similar.
I can get by using ggforce::geom_ellipse() on each plot but it's not as clean. Any suggestions?
Of course, after asking the question and staring at the figure in question, it came to me that I simply need to add a geom_bspline() to the combined figure. Tried that earlier but didn't give enough thought to the coordinates on the new layer. The coordinates of the spline are given in the range of 0 to 1 for both the x and y values on this new layer. Simple and obvious.
I made a heatmap on R and most of it is one colour. I have two columns of data which showed up as various colours, but the rest of it is red.
Does anyone know how to increase the "resolution" of this? I don't mean anything about how to make the image more clear (which is why I think I'm having trouble searching for info on it). I mean, how do I make my heatmap more meaningful and not all mostly one colour.
Thanks and sorry if this has been answered somewhere else. I think I don't know the key term I need to search properly.
Edit:
Here is the code I used so far (heatdata is my matrix):
heatmap <- heatmap(heatdata,Rowv=NA,Colv=NA,col=cm.colors(256),scale="row")
So I got some kind of cross section picture in jpg format I want to work with. For better understanding I just drew a picture, hopefully symbolising well enough kinda how the real pictures will look like:
At the top of the picture is material A, at the bottom material B.
Goal: I want to get the Pixels of the boundary line between both materials.
My way so far:
I already know how to read pictures with package called EBImage
I also know, that this will result in a matrix with a color value for
every pixel.
I thought it would be better to convert the jpeg into a binary picture with only black and white colors.
I thought filling up the black part below (Material B) and reducing the noise would be nice, so I could use column sums (a sum of 1's) to find the row number where material A touches material B, which should be my searched boundary line (right?).
Problems:
I don't find filters which fill up the black parts intelligently, in the real pictures, there will be much more noise, which will complicate things even further...
I am not sure if all this is even necessary, and there is a more efficient way to reach my goal of finding the boundary line
Thank you very much for every tip in advance!
Answers will always be vague when there's no example to work with. I would normally use ImageJ for a task like this but EBImage has the commands that I would use.
From EBImage I would make binary and then erode , dilate, and fill holes (fillHull).
Your picture looks like it might be a candidate for a support vector machine. There are a couple of packages for R with svm functions, one is e1071.
I'm getting familiar with Graphviz and wonder if it's doable to generate a diagram/graph like the one below (not sure what you call it). If not, does anyone know what's a good open source framework that does it? (pref, C++, Java or Python).
According to Many Eyes, this is a bubble chart. They say:
It is especially useful for data sets with dozens to hundreds of values, or with values that differ by several orders of magnitude.
...
To see the exact value of a circle on the chart, move your mouse over it. If you are charting more than one dimension, use the menu to choose which dimension to show. If your data set has multiple numeric columns, you can choose which column to base the circle sizes on by using the menu at the bottom of the chart.
Thus, any presentation with a lot of bubbles in it (especially with many small bubbles) would have to be dynamic to respond to the mouse.
My usual practice with bubble charts is to show three or four variables (x, y and another variable through the size of the bubble, and perhaps another variable with the color or shading of the bubble). With animation, you can show development over time too - see GapMinder. FlowingData provides a good example with a tutorial on how to make static bubble charts in R.
In the example shown in the question, though, the bubbles appear to be located somewhat to have similar companies close together. Even then, the exact design criteria are unclear to me. For example, I'd have expected Volkswagen to be closer to General Motors than Pfizer is (if some measure of company similarity is used to place the bubbles), but that isn't so in this diagram.
You could use Graphviz to produce a static version of a bubble chart, but there would be quite a lot of work involved to do so. You would have to preprocess the data to calculate a similarity matrix, obtain edge weights from that matrix, assign colours and sizes to each bubble and then have the preprocessing script write the Graphviz file with all edges hidden and run the Graphviz file through neato to draw it.
I have a scanned map from which i would like to extract the data into form of Long Lat and the corresponding value. Can anyone please tell me about how i can extract the data from the map. Is there any packages in R that would enable me to extract data from the scanned map. Unfortunately, i cannot find the person who made this map.
Thanks you very much for your time and help.
Take a look at OCR. I doubt you'll find anything for R, since R is primarily a statistical programming language.
You're better off with something like opencv
Once you find the appropriate OCR package, you will need to identify the x and y positions of your characters which you can then use to classify them as being on the x or y axis of your map.
This is not trivial, but good luck
Try this:
Read in the image file using the raster package
Use the locator() function to click on all the lat-long intersection points.
Use the locator data plus the lat-long data to create a table of lat-long to raster x-y coordinates
Fit a radial (x,y)->(r,theta) transformation to the data. You'll be assuming the projected latitude lines are circular which they seem to be very close to but not exact from some overlaying I tried earlier.
To sample from your image at a lat-long point, invert the transformation.
The next hard problem is trying to get from an image sample to the value of the thing being mapped. Maybe take a 5x5 grid of pixels and average, leaving out any gray pixels. Its even harder than that because some of the colours look like they are made from combining pixels of two different colours to make a new shade. Is this the best image you have?
I'm wondering what top-secret information has been blanked out from the top left corner. If it did say what the projection was that would help enormously.
Note you may be able to do a lot of the process online with mapwarper:
http://mapwarper.net
but I'm not sure if it can handle your map's projection.