Visualizing emoji data in R studio - ggplot2

This is a very general inquiry about working with emoji data in R studio.
I previously finished a project with emoji data, and the hardest thing I found was visualizing emojis in R studio. I tried to run a co-occurrence network analysis with emojis, but I couldn't visualize the result (emojis were shown as squares in the plot).
I tried emojifont and emo packages, and none of them worked.
Are there other packages that help visualize emojis or analyze emoji data?

Related

Problem with display when modifying edges of adjacent features

I am using ArcGIS Pro 3 to modify an existing layer to represent community boundaries as they existed in 1935. Everything displays just fine when I select the polygon I want to edit. But when I select Edges in the Modify Features pane the display changes to show wide magenta lines over every shape in the layer so I can't see anything. Hopefully a couple pictures will serve to clearly illustrate my dilemma.
I have talked to other GIS users at work and looked online at ESRI and Google, but I don't seem to be able to articulate the problem well enough to find any answers, or anyone else having the same problem.

Plotting and displaying a complex network with a react native component?

I have a rather large graph consisting of about 50 nodes and 100 vertices represented in DOT graph notation language which I want to display on a mobile device using React Native. Ideally, I would like to be able to fine-tune the layouting algorithm, e.g. the parameters of the Fruchterman–Reingold algorithm for force-directed graph drawing.
So far, I figured out two approaches:
Convert my input data such that it can be digested by react-graph-vis and plot my network on the fly. For adjusting the parameters of the drawing algorithm, things might get tricky.
Generate a SVG graphics on the server using graphviz and on the mobile device just display the SVG so that you can zoom and scroll. For this I found react-native-svg-pan-zoom whose last commit is 2 years ago (as of Dec 2019). This approach has the disadvantage that it does not work offline.
Which other alternatives do I have? Did I miss some out-of-the-box solution? Any help is appreciated!

Error using MODIS HDF files in ArcMap Raster Calculator

I have downloaded several HDF files from the MODIS database.
According to the documentation, the layers have to be multiplied by 0.1 to obtain the real values.
I get an error when I put the name of the HDF-layer in the Raster Calculator, however it does work when I export it as a new raster before. But after multiplication with 0.1, I still do not get a continuous scale image but only black and white areas. I excluded the seven highest values as indicated in the documentation, but still no change.
Another way of getting the MODIS files is to use the respective toolbox. Data imported with this tool does show up correctly, but I cannot import most of it even though it is available under the link indicated above:
Failed to execute (CreateCustomGeoTransformation)
Failed to execute (ImportEvapotranspiration)
Has anyone experienced something similar?

What tools are commonly used to visualize meteorological and climatological data?

I am interested in visualizing meteorological and climatological data.
Here we are talking about 2D/3D visualization for weather and climate elements:
Temperature
Pressure
Wind
Example
We have used some tools previously, such as:
GrADS
Surfer (commercial software)
GIS Meteo (commercial software)
What another tools (preferably open source) would you suggest for that purpose nowadays?
I know you mentioned GrADS, but it was the tool I used mostly for development of weather products, a little more intuitive and resource friendly than IDV when I coded, and generally pretty good rate of development. You mentioned Open Source... did you know there is an OpenGrADS (http://opengrads.org/)? Most friends involved in weather product development use a combination of GrADS\OpenGrADS for much of their work. But I agree it doesn't produce knock-your-socks-off graphics.
Another commonly used free program is Gempak, another Unidata product, which really seems to be becoming outdated in my personal opinion).
And then you can talk high end graphics, you're going to pay more. http://moe.met.fsu.edu/~hrw22/movies/WIND_Katrina_2005-08-28_00Z.gif is a great video of Katrina that was produced by someone I knew using Amira. According to Wikipedia, you're looking at
"Cost: $4,000 USD + $800/year support (2009)... although now has much more ugly/complex pricing structure where each feature is priced separately (eg: Amira Mesh Option $360). I believe at NCMIR we pay ~$9000/year for five user-license." Ouch!
I don't have an open source tool, but if you can get access to a Level-II data feed (Level-II is minimally post processed radar data), I and a meteorologist friend use GR2Analyst. I would assume you know enough about weather sources to be able to figure out how to set this up.
If you're looking for an open source (and free) tool that can do 2D and 3D, which also includes access to a wide variety of datasets (obs, model output, remote sensing - radar level 2 and 3, satellite, and more!), then you might want to check out the Unidata Integrated Data Viewer (IDV):
http://www.unidata.ucar.edu/software/idv/
Source code available here:
https://github.com/Unidata/IDV
The interface is a bit complex, but we have some youtube screencasts to help people get up and going:
http://www.youtube.com/user/unidatanews/videos
If you'd like to see a video for a specific thing, we are taking requests :-) (email support-idv#unidata.ucar.edu). We do yearly training workshops as well, and those materials are available online here:
http://www.unidata.ucar.edu/software/idv/docs/workshop/
Cheers!
Sean
Panoply is a multiplataform desktop option if data is available in formats such NetCDF, HDF or GRIB.
I extracted the following text from his site that describes some of the characteristics:
Slice and plot geo-gridded latitude-longitude, latitude-vertical, longitude-vertical, or time-latitude arrays from larger multidimensional variables.
Slice and plot "generic" 2D arrays from larger multidimensional variables.
Slice 1D arrays from larger multidimensional variables and create line plots.
Combine two geo-gridded arrays in one plot by differencing, summing or averaging.
Plot lon-lat data on a global or regional map using any of over 100 map projections or make a zonal average line plot.
Overlay continent outlines or masks on lon-lat map plots.
Use any of numerous color tables for the scale colorbar, or apply your own custom ACT, CPT, or RGB color table.
Save plots to disk GIF, JPEG, PNG or TIFF bitmap images or as PDF or PostScript graphics files.
Export lon-lat map plots in KMZ format.
Export animations as AVI or MOV video or as a collection of invididual frame images.
Explore remote THREDDS and OpenDAP catalogs and open datasets served from them.
If you are interested in interactive visualization over web, there are some options such as:
ncWMS: an webmapping server that reads NetCDF data and publish it using Web Mapping Service standard.
GeoServer: another webmapping server that has plugin to read NetCDF data.
Vtk (visualization Toolkit) is a C++ open source 2D and 3D visualization library that I use to visualize radar data in 3D.

how to extract data from plot produced by easy.py in libsvm-3.17

I just downloaded libsvm-3.17 abt two weeks ago. I tried heart_scale (dataset provided in the libsvm-3.17 package) with easy.py. An image or plot is produced (from gnuplot) to illustrate the best c and best gamma. I cannot post the image here because I am new here and do not have enough reputation.
I would like to ask from the many colors curves in the plot, how to extract from the plot that the best log2(c)=11 (which gives c=2048) and the best log2(gamma)=-13 (which gives gamma = 0.0001220703125).
Thank you very much.
the chosen parameters are reported by easy.py (cannot run it now, but you will find them). the plot is just a visual aid to manually verify the parameter neighborhood. with some experience you can interpret the diagram. without experience simply trust easy.py