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Variable assignment

name <- "John Doe"

name = "John Doe"

R does accept the = syntax for assignment, but it’s non-idiomatic and can lead to unexpected errors.

For example,

my_function <- function(y = 3, x = 5) {
  print(paste(y, x))
}

my_function(x = 10)   # Passes 10 to the x parameter, and outputs "3 10"

my_function(x <- 20)  # Assigns 20 to the global x variable, which it passes to the y parameter. Outputs "20 5"

To avoid confusion, always use <- for assignment, and use = for passing arguments within function calls.

Declaring a Range

Ouch. Python’s code is not only longer, but it excludes the final number.
range(1, 11) creates a range object in Python, which represents the numbers 1 through 10.

list(range(1, 11)) creates a list object in Python, which represents [1, 2, 3, … 10]
Both are similar to the 1:10 vector in R. But you need to change a range to a list in order to output it.

Loads a library for handling geospatial data, including working with shapefiles.

In the Python code, gpd is an alias for the geopandas library, which we use to reduce typing and improve code readability.

RStudio defaults to a global working directory, which means file paths must be specified explicitly.

Python defaults to the folder where the source code is located.

Jupyter Notebook defaults to where you launch the notebook server.

• You could launch it from the command line within the source folder:

  Code Block
  cd \psa\MDA_2023-24\python jupyter notebook

• Or you could specify the folder directly when launching Jupyter:

  Code Block
  jupyter notebook \psa\MDA_2023-24\python

The original file path was "E:/Eastern_Shore/MD_enrollment_data_2023_24/fields/", but I don't have access to that location.

To avoid referencing "E:/Eastern_Shore..." explicitly, I saved all the necessary project files to the same folder as the source code, and I added a setwd() statement to the R source. I then removed all references to "E:/Eastern_Shore..."

This way, the R program can run without requiring the full path in filenames.

This code:

• Reads a shapefile into a spatial data frame (R) or a GeoDataFrame (Python).

• Converts the coordinate reference system (CRS) to EPSG 32618 (WGS 84).

• Applies a negative buffer of 30 meters to shrink the geometries.
  Note that sf objects in R have a designated geometry column, but in GeoPandas you need to explicitly reference the geometry column.

• Filters out any empty geometries resulting from the buffering.
  In Python, ~ is a bitwise NOT operator.

R uses %>% for chaining operations, while Python uses . (dot notation).

In Python, a backslash (\) means line continuation. I used it here to align the code with the R version. But I could have also written it as:
mda = gpd.read_file("MDAEnrollment2023-2024.shp").to_crs(epsg=32618)

Also, note that the dots in st_transform(., crs = 32618) and st_buffer(., dist = -30) are optional but may improve readability.

Writes the buffered data frame to a new shapefile.

I added append=FALSE to the original source so the file would be overwritten if it exists. GeoPandas automatically overwrites existing files.

Syntax is easy. Knowing which libraries to use – not so much!

Explained previously.

Reads the buffered shapefile.

Plots the shapefile.

edgecolor isn’t required in the Python code. It simply makes the output consistent with the R output.