About size of data frames
Rui, et. al. : "real results probably depend on the functions you want to apply to the data." Indeed! I would presume that one would want to analyze such data as time series of some sort, for which I think long form is inherently "more sensible". If so, I also would think that you would want columns for data, sensor*, season, and day, as Rui suggested. However, note that this presumes no missing data, which is usually wrong. To handle this, within each day the rows would need to be in order of the hour the data was recorded (I assume twice per hour) with a missing code when data was missing. *As an aside, whether <data, season, day> data is in the form of a single data frame with an additional sensor ID column or a list of 70 frames, one for each sensor, is not really much of an issue these days, where gigabytes and gigaflops are cheap and available, as it is trivial to convert from one form to another as needed. Feel free to disagree -- I am just amplifying Rui's comment above; "what I would presume" and "what I think" doesn't matter. What matters is Stefano's response to his comment. Cheers, Bert
On Thu, Aug 14, 2025 at 10:54?AM Rui Barradas <ruipbarradas at sapo.pt> wrote:
On 8/14/2025 12:27 PM, Stefano Sofia via R-help wrote:
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.sofia at regione.marche.it
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______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide
https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Hello, First of all, 48 * 181 * 28 = 243,264, not 234,576. And 243264 * 70 = 17,028,480. As for the question, why don't you try it with smaller data sets? In the test bellow I have tested with the sizes you have posted and the many columns (wide format) is fastest. Then the df's list, then the 4 columns (long format). 4 columns because it's sensor, day, season and data. And the wide format df is only 72 columns wide, one for day, one for season and one for each sensor. The test computes mean values aggregated by day and season. When the data is in the long format it must also include the sensor, so there is an extra aggregation column. The test is very simple, real results probably depend on the functions you want to apply to the data. # create the test data makeDataLong <- function(sensor, x) { x[["data"]] <- rnorm(nrow(df1)) cbind.data.frame(sensor, x) } makeDataWide <- function(sensor, x) { x[[sensor]] <- rnorm(nrow(x)) x } set.seed(2025) n_per_day <- 48 n_days <- 181 n_seasons <- 28 n_sensors <- 70 day <- rep(1:n_days, each = n_per_day) season <- 1:n_seasons sensor_names <- sprintf("sensor_%02d", 1:n_sensors) df1 <- expand.grid(day = day, season = season, KEEP.OUT.ATTRS = FALSE) df_list <- lapply(1:n_sensors, makeDataLong, x = df1) names(df_list) <- sensor_names df_long <- lapply(1:n_sensors, makeDataLong, x = df1) |> do.call(rbind, args = _) df_wide <- df1 for(s in sensor_names) { df_wide <- makeDataWide(s, df_wide) } # test functions f <- function(x) aggregate(data ~ season + day, data = x, mean) g <- function(x) aggregate(data ~ sensor + season + day, data = x, mean) h <- function(x) aggregate(. ~ season + day, x, mean) # timings bench::mark( list_base = lapply(df_list, f), long_base = g(df_long), wide_base = h(df_wide), check = FALSE ) Hope this helps, Rui Barradas ______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.