when to use map() function and when to use summarise_at()/mutate_at()

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Can anyone give a suggestion regarding when to use the map() (all map_..() functions) and when to use summarise_at()/mutate_at()?

E.g. if we are doing some modification to the column of vectors then we do not need to think map() ? If we have a df / have a column has a list in it then we need to use map()?

Does map() function always need to be used with nest() function? Anyone could suggest some learning videos regarding this. And also how to put lists in df and modeling multiple lists at the same time then store the model results in another column ?

Thank you so much!


The biggest difference between {dplyr} and {purrr} is that {dplyr} is designed to work on data.frames only, and {purrr} is designed to work on every kind of lists. Data.frames being lists, you can also use {purrr} for iterating on a data.frame.

map_chr(iris, class) Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species     "numeric"    "numeric"    "numeric"    "numeric"     "factor"  

summarise_at and map_at do not exactly behave the same: summarise_at just return the summary you're looking for, map_at return all the data.frame as a list, with the modification done where you asked it :

> library(purrr) > library(dplyr) > small_iris <- sample_n(iris, 5) > map_at(small_iris, c("Sepal.Length", "Sepal.Width"), mean) $Sepal.Length [1] 6.58  $Sepal.Width [1] 3.2  $Petal.Length [1] 6.7 1.3 5.7 4.3 4.7  $Petal.Width [1] 2.0 0.4 2.1 1.3 1.5  $Species [1] virginica  setosa     virginica  versicolor versicolor Levels: setosa versicolor virginica  > summarise_at(small_iris, c("Sepal.Length", "Sepal.Width"), mean)   Sepal.Length Sepal.Width 1         6.58         3.2 

map_at always return a list, mutate_at always a data.frame :

> map_at(small_iris, c("Sepal.Length", "Sepal.Width"), ~ .x / 10) $Sepal.Length [1] 0.77 0.54 0.67 0.64 0.67  $Sepal.Width [1] 0.28 0.39 0.33 0.29 0.31  $Petal.Length [1] 6.7 1.3 5.7 4.3 4.7  $Petal.Width [1] 2.0 0.4 2.1 1.3 1.5  $Species [1] virginica  setosa     virginica  versicolor versicolor Levels: setosa versicolor virginica  > mutate_at(small_iris, c("Sepal.Length", "Sepal.Width"), ~ .x / 10)   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species 1         0.77        0.28          6.7         2.0  virginica 2         0.54        0.39          1.3         0.4     setosa 3         0.67        0.33          5.7         2.1  virginica 4         0.64        0.29          4.3         1.3 versicolor 5         0.67        0.31          4.7         1.5 versicolor 

So to sum up on your first question, if you are thinking about doing operation "column-wise" on a non-nested df and want to have a data.frame as a result, you should go for {dplyr}.

Regarding nested column, you have to combine group_by(), nest() from {tidyr}, mutate() and map(). What you're doing here is creating a smaller version of your dataframe that will contain a column which is a list of data.frames. Then, you're going to use map() to iterate over the elements inside this new column.

Here is an example with our beloved iris:

library(tidyr)  iris_n <- iris %>%    group_by(Species) %>%    nest() iris_n # A tibble: 3 x 2   Species    data                <fct>      <list>            1 setosa     <tibble [50 × 4]> 2 versicolor <tibble [50 × 4]> 3 virginica  <tibble [50 × 4]> 

Here, the new object is a data.frame with the colum data being a list of smaller data.frames, one by Species (the factor we specified in group_by()). Then, we can iterate on this column by simply doing :

map(iris_n$data, ~ lm(Sepal.Length ~ Sepal.Width, data = .x)) [[1]]  Call: lm(formula = Sepal.Length ~ Sepal.Width, data = .x)  Coefficients: (Intercept)  Sepal.Width        2.6390       0.6905     [[2]]  Call: lm(formula = Sepal.Length ~ Sepal.Width, data = .x)  Coefficients: (Intercept)  Sepal.Width        3.5397       0.8651     [[3]]  Call: lm(formula = Sepal.Length ~ Sepal.Width, data = .x)  Coefficients: (Intercept)  Sepal.Width        3.9068       0.9015   

But the idea is to keep everything inside a data.frame, so we can use mutate to create a column that will keep this new list of lm results:

iris_n %>%   mutate(lm = map(data, ~ lm(Sepal.Length ~ Sepal.Width, data = .x))) # A tibble: 3 x 3   Species    data              lm         <fct>      <list>            <list>   1 setosa     <tibble [50 × 4]> <S3: lm> 2 versicolor <tibble [50 × 4]> <S3: lm> 3 virginica  <tibble [50 × 4]> <S3: lm> 

So you can run several mutate() to get the r.squared for e.g:

iris_n %>%   mutate(lm = map(data, ~ lm(Sepal.Length ~ Sepal.Width, data = .x)),           lm = map(lm, summary),           r_squared = map_dbl(lm, "r.squared"))  # A tibble: 3 x 4   Species    data              lm               r_squared   <fct>      <list>            <list>               <dbl> 1 setosa     <tibble [50 × 4]> <S3: summary.lm>     0.551 2 versicolor <tibble [50 × 4]> <S3: summary.lm>     0.277 3 virginica  <tibble [50 × 4]> <S3: summary.lm>     0.209 

But a more efficient way is to use compose() from {purrr} to build a function that will do it once, instead of repeating the mutate().

get_rsquared <- compose(as_mapper("r.squared"), summary, lm)  iris_n %>%   mutate(lm = map_dbl(data, ~ get_rsquared(Sepal.Length ~ Sepal.Width, data = .x))) # A tibble: 3 x 3   Species    data                 lm   <fct>      <list>            <dbl> 1 setosa     <tibble [50 × 4]> 0.551 2 versicolor <tibble [50 × 4]> 0.277 3 virginica  <tibble [50 × 4]> 0.209 

If you know you'll always be using Sepal.Length ~ Sepal.Width, you can even prefill lm() with partial():

pr_lm <- partial(lm, formula = Sepal.Length ~ Sepal.Width) get_rsquared <- compose(as_mapper("r.squared"), summary, pr_lm)  iris_n %>%   mutate(lm = map_dbl(data, get_rsquared)) # A tibble: 3 x 3   Species    data                 lm   <fct>      <list>            <dbl> 1 setosa     <tibble [50 × 4]> 0.551 2 versicolor <tibble [50 × 4]> 0.277 3 virginica  <tibble [50 × 4]> 0.209 

Regarding the resources, I've written a series of blogpost on {purrr} you can check: https://colinfay.me/tags/#purrr

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