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Hadley Wickham: Managing many models with R

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Visualisation alone is not enough to solve most data analysis challenges. The data may be too big or too messy to show in a single plot. In this talk, Hadley outlines his current thinking about how the synthesis of visualisation, modelling, and data manipulation allows you to effectively explore and understand large and complex datasets. There are three key ideas:
1. Using tidyr to make nested data frame, where one column is a list of data frames.
2. Using purrr to use function programming tools instead of writing for loops 3. Visualising models by converting them to tidy data with broom, by David Robinson.
This work is embedded in R so Hadley not only talks about the ideas, but shows concrete code for working with large sets of models. You'll see how you can combine the dplyr and purrr packages to fit many models, then use tidyr and broom to convert to tidy data which can be visualised with ggplot2.
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Visualisation alone is not enough to solve most data analysis challenges. The data may be too big or too messy to show in a single plot. In this talk, Hadley outlines his current thinking about how the synthesis of visualisation, modelling, and data manipulation allows you to effectively explore and understand large and complex datasets. There are three key ideas:
1. Using tidyr to make nested data frame, where one column is a list of data frames.
2. Using purrr to use function programming tools instead of writing for loops 3. Visualising models by converting them to tidy data with broom, by David Robinson.
This work is embedded in R so Hadley not only talks about the ideas, but shows concrete code for working with large sets of models. You'll see how you can combine the dplyr and purrr packages to fit many models, then use tidyr and broom to convert to tidy data which can be visualised with ggplot2.
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