I feel like I started my #stats journey wanting to do lots of fancy multi-level, spatial, and Bayesian models. Then I'm now happy if I can produce a useful count, that I understand and can clearly explain.

In many ways knowing the complex stuff just gave me permission to do simple things. #rstats

I was chatting with a colleague about their #PhD viva on #spatialstats and I showed them this diagram that I made as as part of my thesis to help me understand the building blocks of #spatial models, especially for #inla. #statssky. pg 254 tinyurl.com/3ymp26p3

qtab: Similar but you can do tables with one, two, or even three variables. if_ and in_ work the same but we also have by_ to make 2x2 tables for each category of a third variable. The input is a data.frame and the output is a tabyl. This means it can be combined with #gt and #flex.table

qlist: Tell the function what variables you want and then filter using the if_ arg and pick specific rows using the in_ arg. Input and output are data.frames so it's tidyverse compatible. The if_ command should be compatible with anything you could send to filter in dplyr.

I get you so something like this where I used patchwork is a simple compound plot? I bet you did, I feel like network plots are impossible to get to a place where I'm happy with them.

Fun! And the magritte version yeah?

Nice map. Reminds me of the population density by James Cheshire. #maps

Really cool, We did a lot of automated reporting in rmd and we really benefited from the access to env objects when rendering but in #quarto you can't because it renders in a fresh env each time. Have you used this to make lots of graphs that then feed them into a single qmd to make many reports?

Honestly I love #tidyverse but using data.table to quickly filter and print a subset of columns or create summary tables. It helps me interrogate data really quickly. #Rstats #data.table

It reminds me of the list command in #stata which is one of the main things I missed after moving to R.

Something I find useful from base #R is the dput command.

When I have an object but I want to recreate it and copy into my script I chuck it into dput and it gives me the code that could be used to create it.
#Rstats #Datascience

I love this! Makes me think about stuff by Paul Graham and how clutter might relate to energy and perhaps (big perhaps!) kids have coarser perception so less drained. Maybe some of these high-energy rulers are Peter Pan like and never grew up? 😂 paulgraham.com/stuff.html