Hadley Wickham: Managing many models with R
286 1 21234
Hadley Wickham is Chief Scientist at RStudio, and an Adjunct Professor of Statistics at the University of Auckland. This talk has been organised by EdinbR (The Edinburgh R User Group, http://www.edinbR.org, represented at the event by Caterina Constantinescu, Psychology PhD candidate at the University of Edinburgh), and was kindly supported by The Data Lab, MBN Solutions and the School of Philosophy, Psychology and Language Sciences at the University of Edinburgh. The talk summary is presented below:
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.
By anonymous 2017-09-20
i would recommend watching this video from hadley wickham when you have the time. it relates very much to your challenge.
this also seems like a classic split-apply-combine problem, so my first thought is to consider the
tidyverse. here is some code that might help you:
library(tidyverse) library(randomForest) df2 <- df %>% group_by(cl) %>% mutate(rfcol=list(randomForest(x=., formula=.$cl~.$Work+.$Age)))
basically a new column has been created that contains the randomforest algorithm appropriate for that row based on its value in
cl. you can explore the details of each model by looking at
to summarize what's going on, the
group_by function gets you started with creating dataframes based on
cl values. the
. within the
randomForest function nested within
mutate is a way of referencing each grouped dataframe.
hope this helps. but as noted, try watching that video from hadley wickham if you have the time. it will really explain how to think about these types of problems in detail.
By anonymous 2017-09-20
This is a really good application of the
tidyr::nest() function in conjunction with
broom. What you do is:
- Group the data frame
- Apply a model with
mutate(mod = map(data, model)
- summarize the model using
- extract the relevant statistics.
For more on this here's a great talk by Hadley on the subject: https://www.youtube.com/watch?v=rz3_FDVt9eg
In your case I think you can do something like this:
library(tidyverse) library(broom) diamonds %>% group_by(cut) %>% nest() %>% mutate( model1 = map(data, ~lm(price~carat, data=.)), model2 = map(data, ~lm(price~carat+depth, data=.)) ) %>% mutate(anova = map2(model1, model2, ~anova(.x,.y))) %>% mutate(tidy_anova = map(anova, broom::tidy)) %>% mutate(p_val = map_dbl(tidy_anova, ~.$p.value)) %>% select(p_val)
By anonymous 2018-01-01
It helped me to look at this post http://omaymas.github.io/Climate_Change_ExpAnalysis/, and this video https://www.youtube.com/watch?v=rz3_FDVt9eg to understand how to best use purrr and broom together. As G. Grothendeik points out, I can add a column with models to a data frame (where each cell is a full model). The way to do this with the map function is
duneJ %>% group_by(Species) %>% nest %>% mutate(Mod = map(data, my_lm0)) -> test
nest is a key function that makes a column that is a list of data frames, each of which contain the data about each species and saves it to a default column named "data". I run
map inside of the mutate funciton to save the models to yet another column, where each cell is a new model.
If I want to look at model results, I can combine them into a list of data frames with map and broom, select the relevant data, and then
unnest them, like so:
test %>% mutate(Glance = map(Mod, glance)) %>% select(Species, Glance) %>% unnest
This gets me a new data frame that has model results for each species, which was what I was ultimately aiming for, even if I didn't fully explain that in the question.
Popular Videos 151
Submit Your Video