Tutorial 7 - Reshaping DataFrames

Authors

Jose Storopoli

Kevin Bonham

Juan Oneto

Data comes in many forms, but some forms are more amenable to work with than others. This tutorial provides an introduction to some common patterns for reshaping data tables to make them easier to work with.

1 ↔︎️ Wide vs ↕️ Long form

One of the more common distinctions in tabular data, is between “wide” form, in which each row represents all related observations for a single specimen, site, or measurement period, or “long” form, where each row represents a single observation, and only enough columns as necessary to uniquely identify the observation type and the thing it’s tied to.

For example, imagine that you are collecting blood pressure measurements from a group of subjects over time. Your data table in wide form might look like:

Subject date systolic diastolic heart rate
1 2022-01-03 120 80 65
1 2022-01-05 135 90 68
2 2022-01-10 115 84 72

whereas the same data in long form would look like:

Subject date variable value
1 2022-01-03 systolic 120
1 2022-01-03 diastolic 80
1 2022-01-03 heart rate 65
1 2022-01-05 systolic 135
1 2022-01-05 diastolic 90
1 2022-01-05 heart rate 68
2 2022-01-10 systolic 115
2 2022-01-10 diastolic 84
2 2022-01-10 heart rate 72

If you collect additional types of data, in the wide table, you’d add additional columns, while in the long table you’d add additional rows.

Each form has its uses, and happily, you don’t have to pick just one - it’s easy to switch between them depending on your needs. Let’s load some actual data to see how this works.

using PharmaDatasets
using DataFramesMeta
wide = dataset("pumas_tutorials/wide_data")
15×6 DataFrame
Row IDS 0.0 30 min 1 hrs 2 hrs 4 hrs
String15 Float64 Float64 Float64 Float64 Float64
1 ID001_S001 17.9 16.5 15.5 10.2 17.4
2 ID001_S002 11.9 13.7 11.7 16.0 16.9
3 ID001_S003 14.5 11.0 17.4 10.4 14.1
4 ID002_S001 18.9 18.3 13.6 16.8 18.2
5 ID002_S002 12.1 11.8 11.5 12.6 17.8
6 ID002_S003 10.5 17.4 18.3 19.7 18.3
7 ID003_S001 18.4 18.4 14.9 13.4 18.0
8 ID003_S002 13.1 17.2 10.6 10.2 17.5
9 ID003_S003 18.5 10.1 19.1 10.1 15.2
10 ID004_S001 15.3 18.9 18.3 15.5 17.2
11 ID004_S002 10.5 18.9 10.3 15.6 15.9
12 ID004_S003 16.4 19.2 15.5 19.1 17.6
13 ID005_S001 11.1 16.1 18.0 19.2 17.7
14 ID005_S002 12.4 16.7 11.9 13.0 14.4
15 ID005_S003 10.2 11.4 15.3 19.9 13.8

And we’ll also split the “IDS” column into its component parts.

@rtransform! wide @astable begin
    (id, s) = split(:IDS, '_')
    :id = id
    :sid = s
end
first(wide, 5)
5×8 DataFrame
Row IDS 0.0 30 min 1 hrs 2 hrs 4 hrs id sid
String15 Float64 Float64 Float64 Float64 Float64 SubStrin… SubStrin…
1 ID001_S001 17.9 16.5 15.5 10.2 17.4 ID001 S001
2 ID001_S002 11.9 13.7 11.7 16.0 16.9 ID001 S002
3 ID001_S003 14.5 11.0 17.4 10.4 14.1 ID001 S003
4 ID002_S001 18.9 18.3 13.6 16.8 18.2 ID002 S001
5 ID002_S002 12.1 11.8 11.5 12.6 17.8 ID002 S002 
# reorder them...
select!(wide, :id, :sid, Not(:IDS))
15×7 DataFrame
Row id sid 0.0 30 min 1 hrs 2 hrs 4 hrs
SubStrin… SubStrin… Float64 Float64 Float64 Float64 Float64
1 ID001 S001 17.9 16.5 15.5 10.2 17.4
2 ID001 S002 11.9 13.7 11.7 16.0 16.9
3 ID001 S003 14.5 11.0 17.4 10.4 14.1
4 ID002 S001 18.9 18.3 13.6 16.8 18.2
5 ID002 S002 12.1 11.8 11.5 12.6 17.8
6 ID002 S003 10.5 17.4 18.3 19.7 18.3
7 ID003 S001 18.4 18.4 14.9 13.4 18.0
8 ID003 S002 13.1 17.2 10.6 10.2 17.5
9 ID003 S003 18.5 10.1 19.1 10.1 15.2
10 ID004 S001 15.3 18.9 18.3 15.5 17.2
11 ID004 S002 10.5 18.9 10.3 15.6 15.9
12 ID004 S003 16.4 19.2 15.5 19.1 17.6
13 ID005 S001 11.1 16.1 18.0 19.2 17.7
14 ID005 S002 12.4 16.7 11.9 13.0 14.4
15 ID005 S003 10.2 11.4 15.3 19.9 13.8

2 🥞 Stack

We use the stack() function to go from wide-form to long-form. The columns that represent particular observations are called “measurement variables” and those that uniquely identify each observation are the “ID variables.”

If we only pass the DataFrame, by default, stack() will treat any floating point column as a measurement var, and all other columns as id vars. In this case, that’s actually what we want:

long = stack(wide)
75×4 DataFrame
50 rows omitted
Row id sid variable value
SubStrin… SubStrin… String Float64
1 ID001 S001 0.0 17.9
2 ID001 S002 0.0 11.9
3 ID001 S003 0.0 14.5
4 ID002 S001 0.0 18.9
5 ID002 S002 0.0 12.1
6 ID002 S003 0.0 10.5
7 ID003 S001 0.0 18.4
8 ID003 S002 0.0 13.1
9 ID003 S003 0.0 18.5
10 ID004 S001 0.0 15.3
11 ID004 S002 0.0 10.5
12 ID004 S003 0.0 16.4
13 ID005 S001 0.0 11.1
⋮ ⋮ ⋮ ⋮ ⋮
64 ID002 S001 4 hrs 18.2
65 ID002 S002 4 hrs 17.8
66 ID002 S003 4 hrs 18.3
67 ID003 S001 4 hrs 18.0
68 ID003 S002 4 hrs 17.5
69 ID003 S003 4 hrs 15.2
70 ID004 S001 4 hrs 17.2
71 ID004 S002 4 hrs 15.9
72 ID004 S003 4 hrs 17.6
73 ID005 S001 4 hrs 17.7
74 ID005 S002 4 hrs 14.4
75 ID005 S003 4 hrs 13.8

Alternatively, we can explicitly pass the measurement vars and the id vars as the second and third positional arguments respectively.

stack(wide, [Symbol("0.0"), Symbol("30 min")], [:id, :sid])
30×4 DataFrame
5 rows omitted
Row id sid variable value
SubStrin… SubStrin… String Float64
1 ID001 S001 0.0 17.9
2 ID001 S002 0.0 11.9
3 ID001 S003 0.0 14.5
4 ID002 S001 0.0 18.9
5 ID002 S002 0.0 12.1
6 ID002 S003 0.0 10.5
7 ID003 S001 0.0 18.4
8 ID003 S002 0.0 13.1
9 ID003 S003 0.0 18.5
10 ID004 S001 0.0 15.3
11 ID004 S002 0.0 10.5
12 ID004 S003 0.0 16.4
13 ID005 S001 0.0 11.1
⋮ ⋮ ⋮ ⋮ ⋮
19 ID002 S001 30 min 18.3
20 ID002 S002 30 min 11.8
21 ID002 S003 30 min 17.4
22 ID003 S001 30 min 18.4
23 ID003 S002 30 min 17.2
24 ID003 S003 30 min 10.1
25 ID004 S001 30 min 18.9
26 ID004 S002 30 min 18.9
27 ID004 S003 30 min 19.2
28 ID005 S001 30 min 16.1
29 ID005 S002 30 min 16.7
30 ID005 S003 30 min 11.4

As you can see, stack() gives the new observation columns the names :variable and :value, but we can change this with the variable_name and value_name keyword arguments:

long_rename = stack(wide; variable_name = :original, value_name = :observed)
75×4 DataFrame
50 rows omitted
Row id sid original observed
SubStrin… SubStrin… String Float64
1 ID001 S001 0.0 17.9
2 ID001 S002 0.0 11.9
3 ID001 S003 0.0 14.5
4 ID002 S001 0.0 18.9
5 ID002 S002 0.0 12.1
6 ID002 S003 0.0 10.5
7 ID003 S001 0.0 18.4
8 ID003 S002 0.0 13.1
9 ID003 S003 0.0 18.5
10 ID004 S001 0.0 15.3
11 ID004 S002 0.0 10.5
12 ID004 S003 0.0 16.4
13 ID005 S001 0.0 11.1
⋮ ⋮ ⋮ ⋮ ⋮
64 ID002 S001 4 hrs 18.2
65 ID002 S002 4 hrs 17.8
66 ID002 S003 4 hrs 18.3
67 ID003 S001 4 hrs 18.0
68 ID003 S002 4 hrs 17.5
69 ID003 S003 4 hrs 15.2
70 ID004 S001 4 hrs 17.2
71 ID004 S002 4 hrs 15.9
72 ID004 S003 4 hrs 17.6
73 ID005 S001 4 hrs 17.7
74 ID005 S002 4 hrs 14.4
75 ID005 S003 4 hrs 13.8

3 🏠️ 🃏 Unstack

Of course, sometimes we may want to go the other way. Given a long-form DataFrame, the unstack() function can be used to convert to wide form.

unstack(long)
15×7 DataFrame
Row id sid 0.0 30 min 1 hrs 2 hrs 4 hrs
SubStrin… SubStrin… Float64? Float64? Float64? Float64? Float64?
1 ID001 S001 17.9 16.5 15.5 10.2 17.4
2 ID001 S002 11.9 13.7 11.7 16.0 16.9
3 ID001 S003 14.5 11.0 17.4 10.4 14.1
4 ID002 S001 18.9 18.3 13.6 16.8 18.2
5 ID002 S002 12.1 11.8 11.5 12.6 17.8
6 ID002 S003 10.5 17.4 18.3 19.7 18.3
7 ID003 S001 18.4 18.4 14.9 13.4 18.0
8 ID003 S002 13.1 17.2 10.6 10.2 17.5
9 ID003 S003 18.5 10.1 19.1 10.1 15.2
10 ID004 S001 15.3 18.9 18.3 15.5 17.2
11 ID004 S002 10.5 18.9 10.3 15.6 15.9
12 ID004 S003 16.4 19.2 15.5 19.1 17.6
13 ID005 S001 11.1 16.1 18.0 19.2 17.7
14 ID005 S002 12.4 16.7 11.9 13.0 14.4
15 ID005 S003 10.2 11.4 15.3 19.9 13.8

If your observation columns are named something other than :variable and :value, you’ll need to tell unstack() which columns to use.

unstack(long_rename, :original, :observed)
15×7 DataFrame
Row id sid 0.0 30 min 1 hrs 2 hrs 4 hrs
SubStrin… SubStrin… Float64? Float64? Float64? Float64? Float64?
1 ID001 S001 17.9 16.5 15.5 10.2 17.4
2 ID001 S002 11.9 13.7 11.7 16.0 16.9
3 ID001 S003 14.5 11.0 17.4 10.4 14.1
4 ID002 S001 18.9 18.3 13.6 16.8 18.2
5 ID002 S002 12.1 11.8 11.5 12.6 17.8
6 ID002 S003 10.5 17.4 18.3 19.7 18.3
7 ID003 S001 18.4 18.4 14.9 13.4 18.0
8 ID003 S002 13.1 17.2 10.6 10.2 17.5
9 ID003 S003 18.5 10.1 19.1 10.1 15.2
10 ID004 S001 15.3 18.9 18.3 15.5 17.2
11 ID004 S002 10.5 18.9 10.3 15.6 15.9
12 ID004 S003 16.4 19.2 15.5 19.1 17.6
13 ID005 S001 11.1 16.1 18.0 19.2 17.7
14 ID005 S002 12.4 16.7 11.9 13.0 14.4
15 ID005 S003 10.2 11.4 15.3 19.9 13.8

Any column in the long-form table that is not the variable or value column will be assumed to be one of the unique identifiers. If this is not the case, you may provide the unique id column(s) as an additional argument. But, if the column(s) supplied are not actually unique, unstack() will throw an error. You can enable duplicate rows using the allowduplicates keyword argument, but only the last values encountered will be retained.

unstack(long_rename, :id, :original, :observed)
ArgumentError: ArgumentError("Duplicate entries in unstack at row 2 for key (\"ID001\",) and variable 0.0. Pass `combine` keyword argument to specify how they should be handled.")
unstack(long_rename, :id, :original, :observed; allowduplicates = true)
5×6 DataFrame
Row id 0.0 30 min 1 hrs 2 hrs 4 hrs
SubStrin… Float64? Float64? Float64? Float64? Float64?
1 ID001 14.5 11.0 17.4 10.4 14.1
2 ID002 10.5 17.4 18.3 19.7 18.3
3 ID003 18.5 10.1 19.1 10.1 15.2
4 ID004 16.4 19.2 15.5 19.1 17.6
5 ID005 10.2 11.4 15.3 19.9 13.8

4 🥠 Split, Apply, Combine

A common pattern in data analysis is to split a dataset into groups, perform some calculations within those groups, and then combine the results into a new table. Combining reshaping and the transformations we learned earlier makes this trivial.

For example:

using Statistics
stack(wide, [:id, :sid])
30×7 DataFrame
5 rows omitted
Row 0.0 30 min 1 hrs 2 hrs 4 hrs variable value
Float64 Float64 Float64 Float64 Float64 String SubStrin…
1 17.9 16.5 15.5 10.2 17.4 id ID001
2 11.9 13.7 11.7 16.0 16.9 id ID001
3 14.5 11.0 17.4 10.4 14.1 id ID001
4 18.9 18.3 13.6 16.8 18.2 id ID002
5 12.1 11.8 11.5 12.6 17.8 id ID002
6 10.5 17.4 18.3 19.7 18.3 id ID002
7 18.4 18.4 14.9 13.4 18.0 id ID003
8 13.1 17.2 10.6 10.2 17.5 id ID003
9 18.5 10.1 19.1 10.1 15.2 id ID003
10 15.3 18.9 18.3 15.5 17.2 id ID004
11 10.5 18.9 10.3 15.6 15.9 id ID004
12 16.4 19.2 15.5 19.1 17.6 id ID004
13 11.1 16.1 18.0 19.2 17.7 id ID005
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
19 18.9 18.3 13.6 16.8 18.2 sid S001
20 12.1 11.8 11.5 12.6 17.8 sid S002
21 10.5 17.4 18.3 19.7 18.3 sid S003
22 18.4 18.4 14.9 13.4 18.0 sid S001
23 13.1 17.2 10.6 10.2 17.5 sid S002
24 18.5 10.1 19.1 10.1 15.2 sid S003
25 15.3 18.9 18.3 15.5 17.2 sid S001
26 10.5 18.9 10.3 15.6 15.9 sid S002
27 16.4 19.2 15.5 19.1 17.6 sid S003
28 11.1 16.1 18.0 19.2 17.7 sid S001
29 12.4 16.7 11.9 13.0 14.4 sid S002
30 10.2 11.4 15.3 19.9 13.8 sid S003 
@chain wide begin
    stack
    groupby(:variable)
    @combine(:μ = mean(:value), :σ = std(:value))
end
5×3 DataFrame
Row variable μ σ
String Float64 Float64
1 0.0 14.1133 3.21911
2 30 min 15.7067 3.2168
3 1 hrs 14.7933 3.04102
4 2 hrs 14.78 3.66688
5 4 hrs 16.6667 1.56464