great question, and one that touches on both performance and usability in R.
Here's a breakdown of the trade-offs and recommendations:
You're comparing three data structure strategies for handling ~16.5 million
observations:
- Single long data frame, ~16.5M rows × 3 columns, Simple to manage, easy to
filter/group, tidyverse-friendly, May require more memory; slower row-wise
operations
- Wide data frame, ~235K rows × 141 columns, Fast column-wise operations;
good for matrix-style analysis, to reshape/filter; less tidy
- List of 70 data frames, Each ~235K rows × 3 columns, Parallel processing
possible; modular, Complex to manage; harder to aggregate or compare
Performance Considerations
- Memory Efficiency: A single long data frame is generally more
memory-efficient than a list of data frames, especially if column types are
consistent.
- Vectorization: R is optimized for vectorized operations. A long format works
well with dplyr, data.table, and tidyverse tools.
- Parallelism: If you plan to process each sensor independently, a list of data
frames could allow parallel computation using future, furrr, or parallel.
- Reshaping Costs: Wide formats are fast for matrix-style operations but can be
cumbersome when filtering by time, sensor, or value.
I'd stick with the single long-format data frame:
- It aligns with tidy data principles.
- It's easier to filter, group, and summarize.
- It integrates seamlessly with packages like ggplot2, dplyr, and data.table.
If performance becomes an issue:
- Consider converting to a data.table object (setDT(df)), which is highly
optimized for large datasets.
- Use indexing and keys for faster filtering.
- Use arrow::read_parquet() or fst::write_fst() for fast disk I/O if you need
to save/load frequently.
If you're doing seasonal analysis, consider adding a season column. That way,
you can easily group by sensor, season, and day without needing to split the
data.
-----Original Message-----
From: R-help <r-help-boun...@r-project.org> On Behalf Of Stefano Sofia via
R-help
Sent: Thursday, August 14, 2025 6:27 AM
To: r-help@R-project.org
Subject: [R] About size of data frames
Dear R-list users,
let me ask you a very general question about performance of big data frames.
I deal with semi-hourly meteorological data of about 70 sensors during 28
winter seasons.
It means that for each sensor I have 48 data for each day, 181 days for each
winter season (182 in case of leap year): 48 * 181 * 28 = 234,576
234,576 * 70 = 16420320
>From the computational point of view it is better to deal with a single data
>frame of approximately 16.5 M rows and 3 columns (one for data, one for sensor
>code and one for value), with a single data frame of approximately 235,000
>rows and 141 rows or 70 different data frames of approximately 235,000 rows
>and 3 rows? Or it doesn't make any difference?
I personally would prefer the first choice, because it would be easier for me
to deal with a single data frame and few columns.
Thank you for your usual help
Stefano
(oo)
--oOO--( )--OOo--------------------------------------
Stefano Sofia MSc, PhD
Civil Protection Department - Marche Region - Italy Meteo Section Snow Section
Via Colle Ameno 5
60126 Torrette di Ancona, Ancona (AN)
Uff: +39 071 806 7743
E-mail: stefano.so...@regione.marche.it
---Oo---------oO----------------------------------------
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