Symphony of Structures: A Journey through List-Columns and Nested Data Frames with purrr
Overture: Introduction
Just as a symphony’s overture sets the tone for the entire performance, so too does our introduction provide an overview of what’s to come. Much like an orchestra is composed of different sections — each with their unique characteristics — data in R can be complex, having different layers and structures. Today, we’ll be delving into the magic of list-columns and nested data frames, two aspects of the purrr package that can sometimes seem as intricate and detailed as a beautifully crafted symphony.
Whether you’re just starting to compose your first few notes in R, or you’re a seasoned conductor of data analysis, navigating these structures is crucial. When data is layered within itself, like a melody within a melody, it can become a bit daunting. But fear not — by the end of this post, you will have the necessary knowledge to conduct your way through even the most complex data structures in R with the baton of the purrr package!
Harmony in Chaos: Understanding List-Columns
Picture an orchestra where each musician brings their unique skillset and instrument to create a harmonious symphony, striking the perfect balance between order and chaos. This mirrors the concept of list-columns in our data-orchestra. Each cell in a list-column can house a list, rather than a single value as in traditional data frame columns. This unique structure allows for a richer, more layered dataset, much like the harmonious complexity of an orchestra’s melody.
library(tidyverse)
# create a list-column
df <- tibble(
x = 1:3,
y = list(1:2, 1:3, 1:4)
)
print(df)
# A tibble: 3 × 2
x y
<int> <list>
1 1 <int [2]>
2 2 <int [3]>
3 3 <int [4]>With this code snippet, we’ve composed the first few bars of our data-symphony, introducing a data frame with a list-column. In the ‘y’ column, rather than seeing individual notes (or single data values), we see miniature symphonies — lists of values, all housed within a single cell.
But remember, just as an orchestra is not composed in a day, so too does understanding list-columns take time and practice. Each musician, each instrument, adds to the overall melody, and each new note of knowledge brings us closer to understanding the grand symphony of list-columns. It may seem chaotic at first glance, but as we delve deeper into the layers of this data structure, we’ll uncover the order within the chaos, the harmony within the cacophony.
It’s crucial to acknowledge that complexity isn’t a deterrent — it’s a challenge that promises a greater depth of understanding. As we journey through list-columns, remember that their complexity is their strength, allowing for intricate compositions of data that bring new perspectives to your analysis. So, let’s embrace this unique element of our data orchestra, wielding the baton of purrr with a renewed sense of purpose.
Conducting the Orchestra: Mapping Functions on List-Columns
Our exploration of the composition of list-columns would be incomplete without the magic wand that every maestro needs — mapping functions. Mapping functions are to a conductor as bow is to a violinist, they help to extract the desired notes, or in our case, data, from our instruments.
Mapping functions are a cornerstone of purrr, allowing us to apply functions to each element of a list or a list-column in a systematic way. They can be seen as the conductor guiding the different sections of the orchestra to play in unison, each producing their unique sound but contributing to a harmonious melody.
In the case of list-columns, mapping functions can help us uncover and manipulate the data hidden within these nested structures. Let’s look at an example with the mtcars dataset:
library(dplyr)
library(purrr)
# Creating a list-column of data frames
mtcars_nested <- mtcars %>%
split(.$cyl)
# Applying a function to each data frame using map
mtcars_nested %>%
map(~ summary(.))
$`4`
mpg cyl disp hp drat wt
Min. :21.40 Min. :4 Min. : 71.10 Min. : 52.00 Min. :3.690 Min. :1.513
1st Qu.:22.80 1st Qu.:4 1st Qu.: 78.85 1st Qu.: 65.50 1st Qu.:3.810 1st Qu.:1.885
Median :26.00 Median :4 Median :108.00 Median : 91.00 Median :4.080 Median :2.200
Mean :26.66 Mean :4 Mean :105.14 Mean : 82.64 Mean :4.071 Mean :2.286
3rd Qu.:30.40 3rd Qu.:4 3rd Qu.:120.65 3rd Qu.: 96.00 3rd Qu.:4.165 3rd Qu.:2.623
Max. :33.90 Max. :4 Max. :146.70 Max. :113.00 Max. :4.930 Max. :3.190
qsec vs am gear carb
Min. :16.70 Min. :0.0000 Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:18.56 1st Qu.:1.0000 1st Qu.:0.5000 1st Qu.:4.000 1st Qu.:1.000
Median :18.90 Median :1.0000 Median :1.0000 Median :4.000 Median :2.000
Mean :19.14 Mean :0.9091 Mean :0.7273 Mean :4.091 Mean :1.545
3rd Qu.:19.95 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:2.000
Max. :22.90 Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :2.000
$`6`
mpg cyl disp hp drat wt
Min. :17.80 Min. :6 Min. :145.0 Min. :105.0 Min. :2.760 Min. :2.620
1st Qu.:18.65 1st Qu.:6 1st Qu.:160.0 1st Qu.:110.0 1st Qu.:3.350 1st Qu.:2.822
Median :19.70 Median :6 Median :167.6 Median :110.0 Median :3.900 Median :3.215
Mean :19.74 Mean :6 Mean :183.3 Mean :122.3 Mean :3.586 Mean :3.117
3rd Qu.:21.00 3rd Qu.:6 3rd Qu.:196.3 3rd Qu.:123.0 3rd Qu.:3.910 3rd Qu.:3.440
Max. :21.40 Max. :6 Max. :258.0 Max. :175.0 Max. :3.920 Max. :3.460
qsec vs am gear carb
Min. :15.50 Min. :0.0000 Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:16.74 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.500 1st Qu.:2.500
Median :18.30 Median :1.0000 Median :0.0000 Median :4.000 Median :4.000
Mean :17.98 Mean :0.5714 Mean :0.4286 Mean :3.857 Mean :3.429
3rd Qu.:19.17 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :20.22 Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :6.000
$`8`
mpg cyl disp hp drat wt
Min. :10.40 Min. :8 Min. :275.8 Min. :150.0 Min. :2.760 Min. :3.170
1st Qu.:14.40 1st Qu.:8 1st Qu.:301.8 1st Qu.:176.2 1st Qu.:3.070 1st Qu.:3.533
Median :15.20 Median :8 Median :350.5 Median :192.5 Median :3.115 Median :3.755
Mean :15.10 Mean :8 Mean :353.1 Mean :209.2 Mean :3.229 Mean :3.999
3rd Qu.:16.25 3rd Qu.:8 3rd Qu.:390.0 3rd Qu.:241.2 3rd Qu.:3.225 3rd Qu.:4.014
Max. :19.20 Max. :8 Max. :472.0 Max. :335.0 Max. :4.220 Max. :5.424
qsec vs am gear carb
Min. :14.50 Min. :0 Min. :0.0000 Min. :3.000 Min. :2.00
1st Qu.:16.10 1st Qu.:0 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.25
Median :17.18 Median :0 Median :0.0000 Median :3.000 Median :3.50
Mean :16.77 Mean :0 Mean :0.1429 Mean :3.286 Mean :3.50
3rd Qu.:17.55 3rd Qu.:0 3rd Qu.:0.0000 3rd Qu.:3.000 3rd Qu.:4.00
Max. :18.00 Max. :0 Max. :1.0000 Max. :5.000 Max. :8.00 In this example, we’re applying the summary function to each data frame in our list-column using the map() function. The ~ is a shorthand for defining a function in purrr, so ~ summary(.) is equivalent to function(x) summary(x). Like a conductor guiding the orchestra to play a particular section of the score, the map function applies the summary function to each nested data frame in our list-column.
This is just a glimpse of what mapping functions can do. They are capable of orchestrating complex transformations and analyses on list-columns and other list-like structures, making them indispensable in our data analysis symphony.
Exploring the Soundscapes: Working with Nested Data Frames using purrr
Just as an explorer ventures into new lands, it’s time for us to journey through the intriguing landscapes of nested data frames using purrr.
Nested data frames can be considered as multilevel compositions in our symphony, each bearing their unique tunes yet blending harmoniously to create a beautiful melody. They add an additional layer of complexity by nesting data frames within each row of another data frame. However, with the potent power of purrr, this complexity can be tackled gracefully.
Let’s take a look at how we can utilize purrr functions with nested data frames:
# Load the tidyr package
library(tidyr)
# Creating a nested data frame
mtcars_nested <- mtcars %>%
group_by(cyl) %>%
nest()
# Display the nested data frame
print(mtcars_nested)
# A tibble: 3 × 2
# Groups: cyl [3]
cyl data
<dbl> <list>
6 <tibble [7 × 10]>
4 <tibble [11 × 10]>
8 <tibble [14 × 10]>
# Applying a function to the nested data frame using map
mtcars_nested %>%
mutate(mean_mpg = map_dbl(data, ~ mean(.$mpg)))
# A tibble: 3 × 3
# Groups: cyl [3]
cyl data mean_mpg
<dbl> <list> <dbl>
6 <tibble [7 × 10]> 19.7
4 <tibble [11 × 10]> 26.7
8 <tibble [14 × 10]> 15.1In this example, we’ve created a nested data frame with nest() function by nesting all columns except cyl in mtcars. Then, using mutate() combined with map_dbl(), we computed the mean of mpg for each nested data frame.
You can imagine this as focusing on each individual section of the orchestra, understanding their specific rhythm, and then integrating that knowledge into the entire symphony.
The ability to traverse these nested data frames opens up new possibilities for data analysis, enabling us to uncover deeper insights within our data. Like the various sections of the orchestra uniting to create a harmonious performance, the different layers of a nested data frame can be collectively leveraged to tell a comprehensive data story.
With the power of purrr at our fingertips, we are well-equipped to conduct our data orchestra through these complex soundscapes.
Symphony Rehearsals: Iterating over List-Columns and Nested Data Frames
You’ve tuned your instruments, studied the sheet music, and the conductor has just given the downbeat. But how do you make your orchestra play in unison? The answer lies in iterating over these list-columns and nested data frames using purrr.
Consider a situation where you need to perform multiple operations on different columns in each nested data frame. Imagine each player in the orchestra playing their own instrument, but in harmony with the whole ensemble. That’s where purrr's iterate functions like map(), map2(), and pmap() shine.
For instance, let’s compute the mean and standard deviation of mpg within each cyl group:
mtcars_nested %>%
mutate(mean_mpg = map_dbl(data, ~ mean(.$mpg)),
sd_mpg = map_dbl(data, ~ sd(.$mpg)))
# A tibble: 3 × 4
# Groups: cyl [3]
cyl data mean_mpg sd_mpg
<dbl> <list> <dbl> <dbl>
6 <tibble [7 × 10]> 19.7 1.45
4 <tibble [11 × 10]> 26.7 4.51
8 <tibble [14 × 10]> 15.1 2.56Here, map_dbl() elegantly steps in, repeating the operations for each nested data frame (or list-item in the data column), and returns a double vector. The result is an augmented data frame where the mean and standard deviation of mpg for each cyl group have been calculated and added as new columns.
This ability to iterate over list-columns and nested data frames is akin to a conductor ensuring that each instrument plays its part at the right time, contributing to the harmony of the whole performance. The resulting music is as beautiful as our tidily handled complex data structure.
But remember, each piece of music has its tricky passages and potential pitfalls. In our next section, we will explore some of these challenges and strategies to overcome them in the context of complex data structures.
Cacophonies and Solutions: Dealing with Complex Structures
Any musician can tell you that perfect harmony is a combination of practice and overcoming hurdles, and our journey with complex data structures in R is no different. With list-columns and nested data frames, we’re weaving intricate musical phrases and occasionally, cacophonies will emerge.
One common issue you might encounter with these structures is their resistance to the usual data frame operations. For instance, if you try to use dplyr::filter() or dplyr::select() directly on a nested data frame, you'll run into problems.
Consider this:
mtcars_nested %>%
filter(mean_mpg > 20)
Error in `filter()`:
ℹ In argument: `mean_mpg > 20`.
ℹ In group 1: `cyl = 4`.
Caused by error:
! object 'mean_mpg' not foundIf you run this, R will throw an error because it doesn’t know how to compare a list-column to a single number. It’s like trying to compare the volume of a whole orchestra to a single violin — it doesn’t quite work.
In this situation, you’d want to un-nest the data, perform the filtering, and then re-nest if necessary. Alternatively, you can use the purrr::map() function to apply the filter within each list-item of the list-column. It's like adjusting the sheet music for each individual musician.
mtcars_nested %>%
mutate(data = map(data, ~ filter(.x, mpg > 20)))
# A tibble: 1 × 4
# Groups: cyl [1]
cyl data mean_mpg sd_mpg
<dbl> <list> <dbl> <dbl>
4 <tibble [11 × 10]> 26.7 4.51The above code will return the rows in each nested data frame where mpg is greater than 20.
Remember, the key to dealing with these complex structures is to think of them as collections of smaller pieces that you can manipulate independently. Just as a symphony is comprised of individual notes that together create a harmonious piece, your data structure is a collection of components that can be handled one at a time. With practice, your understanding of these structures will be music to your ears!
In this performance, we’ve attuned ourselves to the harmonious rhythms of list-columns and nested data frames, conducting complex structures in our R orchestration. We’ve demonstrated how the purrr package and its various functions, like our virtuoso violinists, are instrumental in navigating the symphony of nested data structures.
In many ways, working with list-columns and nested data frames is like directing an orchestra. Each musician has a specific part to play, but they all contribute to the overall melody. Just as each instrument in an orchestra adds depth and richness to the music, each element in a list-column or nested data frame adds complexity and granularity to our data.
But, as with any musical masterpiece, it requires practice to perfect. By understanding these structures and how to manipulate them, we’ve acquired an important skill in data science. The ability to manage complex data structures can open up new possibilities for your data analysis, allowing you to work more efficiently and handle more intricate datasets.
Continue to practice and explore these concepts. Every new dataset is a fresh sheet of music waiting for your interpretation. Remember that the more comfortable you are with the tools at your disposal, the more effectively you can turn your data dissonance into a harmonious data symphony. Let’s continue to make beautiful music together with R and purrr!
