sf VS dplyr

sf

Before learning about geostatistics, do you feel comfortable working with and exploring geospatial data? If not, I'd highly recommend getting comfortable with the sf package in R. It's an implementation of the OpenGIS standard in R tidyverse. The OpenGIS standard defines specific data types and functions for geospatial data, which means that you can read e.g. PostGIS documentation and use the same functions in R.

Tools used: various packages in R (tidycensus, dplyr, ggplot2, sf)

R supports working with spatial data really well; you should look into the sf-package: https://r-spatial.github.io/sf/

A lot of the books are out if date because geospatial has been rewritten from the ground up to be dramatically improved. I would focus on the sf package: https://r-spatial.github.io/sf/. I would also find a PostGIS book which sf shares many functions and learn to the the database when appropriate.

Are you using the sf package? I envision running a loop that creates a vector feature for each cell of the raster grid, intersects that feature with the underlying shapefile, multiplies the area of each intersected portion by its value, and assigns the raster the mean of those values. Kind of a lot to set up, but I'm not a master at this so maybe someone else knows a more straightforward method

I found this: https://github.com/r-spatial/sf/issues/1595

Now that I explained the concept, I will show some R code using the sf library to achieve this. sf stands for simple features and it's a very nice library for working with geospatial data.

That's great feedback, thanks!

This tool definitely comes from a place of personal need - beyond just handling large files, I've also never really gelled well with the Excel/Google Sheet model of changing data in place as if you were editing text. I'm a Data Scientist and always preferred the chained data transforms you see in things like dplyr (https://dplyr.tidyverse.org/) or Polars (https://pola.rs/) and I feel this tool maps very closely to the chained model.

Also, thank you for the feature requests! Those would all be very useful - we'll put them on the roadmap.

There's an example of how to use zzz.R with a .onload() function to set options in the dplyr code base: https://github.com/tidyverse/dplyr/blob/bbcfe99e29fe737d456b0d7adc33d3c445a32d9d/R/zzz.r

Look at the tidyverse, especially the case_when or mutate functions.

Before diving into advanced machine learning algorithms or statistical models, we need to start with the basics: collecting and organizing data. Fortunately, both Python and R offer a wealth of libraries that make it easy to collect data from a variety of sources, including web scraping, APIs, and reading from files. Key libraries in Python include requests, BeautifulSoup, and pandas, while R has httr, rvest, and dplyr.

It looks like your syntax is wrong. I think you’re trying to calculate a new variables in your data frame, or alter an existing column in a data frame. Have a look at the select() function in this reference for the proper syntax to use. https://dplyr.tidyverse.org/ Does that help?

A lot of populat libraries have their own logos. Maybe one of them would be good. Check out dplyr for example: https://dplyr.tidyverse.org/

I hope you give it a try when you get a chance: https://dplyr.tidyverse.org/

You can read up on the dplyr-verbs here, which I strongly suggest for your exam! In the code examples, you can simply click on any function you don't understand and it will take you directly to the documentation. Good Luck!