Looking for "training wheel" ideas to explore R / Python with iNat sample data

[grmorrison]

I also strongly agree if you’re looking to become a programmer more generally Python all the way.

[jtklein]

If you can, then build in public so we can follow. Post your code / repository here.

[scarletskylight]

Briefly, I wanted to thank everybody for the great ideas on getting started and provide an update. (Also apologize for not being more responsive earlier on, I have an unfortunate habit of getting overwhelmed sometimes with social things and this happened here. A reason why I love being in nature I suppose!)

I’ve had a great start to learning R and now am going to delve into these responses more and more to keep learning.

I was able to:

• Download data for one of my favorite species, Desmodium tweedyi, and make charts to show prevalence based on country, then within the U.S. by state, and further compare which areas had the highest percentage of research grade data. From this, I discovered a similar species in Mexico has nearly identical leaves but appears to have more hispid stems, and I suspect there have been a number of incorrect IDs as a result. iNat tends to suggest D. tweedyi for Desmodiums with variegated leaves. It’s hard to know how many of the observations are accurate. It’s a tricky species.
• Download data for Desmodium genus in Texas, and try out things I know how to do in Excel (great suggestion, just doing some basic things)… filter by species and county, then make charts comparing species by county. I limited to counties near me, as I live just east of the dry line… hoping I could see species change in counties that split the dry line… and indeed! I could.
• MAPPING! I loved the mapping suggestion and just finished my list of things I wanted to do while learning today. I was able to map a gall species vs. it’s host to see how the range of the two overlapped. It was really interesting to see and I am not sure if this can be done on iNat.

A few things I want to learn to do still:

• Bring in elevations along with lat and long to better compare galls vs. host ranges; I want to understand if galls are specialized to elevations in addition to host plants
• Look at the flowering times of certain species over a year-by-year basis to see how much earlier things are trending. Claytonia is one I’d like to map. I volunteer at an herbarium and have access to historical data from this, so I could potentially go back by decades.

Here is how I went about learning:

• Your thoughts and ideas helped ground my ideas and push my passion forward. I created a list of things I wanted to accomplish from it.
• I watched videos on Linked In Learning:
  – Barton Paulson’s “R Essential training” was a fast but good intro. I abandoned ship after starting his “Modeling Data” course. My Dunning Kruger syndrome wore off at this point. (It was bound to happen!)
  – Mike Chapple’s “Data Visualization in R with ggplot2” was particularly helpful once I got the basics and helped connect the dots… this is when I really “got” what I was learning and could apply it. I recommend it for anybody reading this in the future if you are interested in charting and mapping data, as the sample dataset he uses has concepts that are easy to apply to iNat.

I also supplemented my initial learning with reading on W3, especially to ensure I got the concept of the data types and vocabulary.

At this point, I am going to do a deeper dive into the comments and examples from this thread and may respond a bit more to individual comments as I go through them and continue learning.

Thank you all for giving me the time, ideas, and encouragement to move forward on this. I am really excited about what I can continue to do.