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Train and analyze many models for #TidyTuesday crop yields | Julia Silge #30

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utterances-bot opened this issue May 24, 2021 · 8 comments

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@utterances-bot
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Train and analyze many models for #TidyTuesday crop yields | Julia Silge

Learn how to use tidyverse and tidymodels functions to fit and analyze many models at once.

https://juliasilge.com/blog/crop-yields/

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do you have a tutorial to check how models fits the dadta using glane?

@juliasilge
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Do you mean broom::glance()? You can check out this article on tidymodels.org, and maybe this one as well.

@ntihemuka
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ntihemuka commented May 24, 2021 via email

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Amazing work Julia especially for the data links. Do you have any expertise over Functional Data Analysis?

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poligabi commented Oct 3, 2021

Hello, it worked once with my data. But when I tried again with the same data.

serial_lm<- serialdt %>%
nest(yndvi = c(year, ndvi)) %>%
mutate(model = map(yndvi, ~ lm(ndvi ~ year, data = .x)))

Keep caming this error message:

Erro: Problem with mutate() column model.
i model = map(yndvi, ~lm(ndvi ~ year, data = .x)).
x 0 (non-NA) cases
Run rlang::last_error() to see where the error occurred.

But I already checked (several times and ways) and my data have not any NA.
Is there any other reason for this type of error?

@juliasilge
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@poligabi If you have some kind of problem where it worked one time before, I would suggest restarting R and rerunning in a fresh R session to get anything really messed up out of your environment.

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@juliasilge Even though this screencast have used a basic model but I still can learn new things in your data manipulation skill which its really great :)
Just one question in this: when we do EDA to visualize the crop yield of several countries, by eyes only we can see there is a obvious increase trend in overall, do you think in practical we still need to use model to confirm it?

@juliasilge
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@conlelevn HA yep, if you have an effect you can clearly see in visualization, you probably don't need to confirm it with a model. In this example, I think that using statistical modeling gives you a bit more, like which countries have the bigger/smaller increases in crop yields, and which have more uncertainty about.

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