with $, and with c() giving us fig <- plt$figure(figsize = c(15, 5)). In Python, we would specify the size of a figure using fig = plt.figure(figsize = ), but when we use this command in R with reticulate we must use types that R recognizes, so we replace the = with <.
#R STUDIO PYTHON CODE#
One example of where this is used is in the first line in the following code block. However, the equivalent R type to this is a multi-element vector, so we must define it as such. Python users define lists with square brackets. This is a map of air temperature over the United States for any day in 2013 specified in the plotting command. air_temperature <- xr$tutorial$open_dataset("air_temperature.nc") We can now use all the usual Python commands, substituting. To import it, we will use the xarray open_dataset command. We are going to use the air temperature tutorial dataset here. Xarray comes with some tutorial datasets. We are assigning these to some common nicknames for these packages so they are easier to reference later on. Open a new R script and import these packages using the `import` command.
We can now load our newly installed Python libraries in R.
#R STUDIO PYTHON INSTALL#
We are going to install three different packages here, () (for making maps), () (a common plotting package), and () (to import data). We specify `"r-reticulate"` because that is the Python environment we want to install the packages into. The `conda_install` function uses Miniconda to install Python packages. Python_variable = 4 print(python_variable) To edit the interactive environment Repl_python() We can do some simple Python commands Library(reticulate) Open an interactive Python environment Type this in the console to open an interactive environment To do this, we will use the repl_python() command. We can use Python interactively within the console in R studio. Python code blocks will run as Python code when you knit your document. Once you have a code block you can code using typical Python syntax. When in an RMarkdown document you can either manually create a code block or click on the `insert` dropdown list in R Studio and select Python. () can contain both Python and R code blocks. You do not need to use this environment but I will be using it for the rest of this post. This will create a new Python environment on your machine called `r-reticulate`. When you install reticulate you are also installing (), a lightweight package manager for Python.
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