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So excited to try this out with students!

I hope they enjoy it. I have been on the lookout for anthropology/sociology datasets to use in the later chapters. We are trying to make it relevant to all sciences.

Elin Waring

@elinwaring.bsky.social

I was just thinking that the datasets in Salem might be good. We tend to use GSS a lot.

Marco Z

@ocramz.bsky.social

cool! looking forward to passing it on. Any plans to add a Python version?

Possibly but not in 2026.

Norm Matloff (你有冇諗清楚呀?)

@matloff.bsky.social

Really nice! But a suggestion: In Sec. 5.2. on pipes, you correctly note that computing intermediate variables aids debugging. But you say this adds too many vars. to the code. But there is no law that says you must give them different names! I just use 'tmp' for all of them.

Thank you for the suggestion. Since we recommend naming things with appropriate names (coming in Chapter 10), suggesting something like tmp would not be an option in our case. They don’t need to have different names technically but they do need to have appropriate names hence the different names.

Norm Matloff (你有冇諗清楚呀?)

@matloff.bsky.social

You can put meaning into comments, rather than names of the variables. BTW, wouldn't you agree that in many cases in which pipes are used in practice, each stage of the pipe itself consists of composition of functions, often rather complex. So the pipe is not avoiding the basic problem.

Norm Matloff (你有冇諗清楚呀?)

@matloff.bsky.social

stackoverflow.comdplyr filter with complex criteriaThere is dataframe raw_data ,how to filter the data ? The result as right part in attached image The filter criteria as below (maybe the image is more clear) 1)start amount >0 2)end amount<0 ...

Here is an example, from stackoverflow.com/questions/76... raw_data %>% mutate(groups = lag(cumsum(amount < 0))) %>% group_by(type, groups) %>% filter(sum(amount > 0) > 3) So we have mutate(lag(cumsum())).

🎓 Early version of this material is already tested. It is based on our intro to data science courses taught across a research university, a teaching university, and a community college. We are US based but we hope that the material works for a variety of learners around the world as well.

🖼️ In addition to text, you will find many figures explaining concepts. Here is one example with an explanation of how case_when() function works in #dplyr in #rstats

🧮 Learners get to work with real data solving real problems. Feel free to check out the R package hellodatascience for the datasets as well.