I have a large dataset with pressure data. I want to be able to create multiple sets of data that is filtered at various values (i.e. > 3500, > 3600, etc.) and then run a couple of analyses on each of these new sets of data that are cutoff as the specified value.
So for instance, this might be similar to what I currently do:
#making a reproducible example
pressure <- runif(30, min = 3750, max = 4500)
value <- runif(30, min = 0, max = 50)
stage <- rep(c(1, 2), each = 15)
raw.data <- data.frame(pressure, value, stage)
#set a cutoff point
cutoff.press <- 3750
#make a new dataset
cutoff <- raw.data[raw.data$pressure > cutoff.press,]
#run an analysis
analysis <- cutoff %>%
group_by(stage) %>%
summarize(
MinValue = min(value),
MaxValue = max(value)
)
Is there a way to do this without having to create multiple individual sets of data for each cut-off value of interest and then running each of the analyses individually?
Like if I wanted to test multiple pressure cutoff values, such as seq(3750, 4000, 50), I don't want to have to repeat the process above for each of the values generated in the sequence.
I've thought about using dplyr with the filter() function and setting a bunch of values by hand, but not only would that be time consuming, I am not sure that would allow me to have multiple datasets to do the analyses on.
If you have a lot of different iterations that you would like to run, then using purrr would be a good option too, as you could do it all in one pipe.
library(tidyverse)
purrr::pmap(data.frame(pressure = seq(3750, 4000, 50)),
~ dplyr::filter(raw.data, pressure > ..1)) %>%
purrr::map(. %>%
group_by(stage) %>%
summarize(MinValue = min(value),
MaxValue = max(value))) %>%
# If you want to set the names to the cutoff values.
setNames(seq(3750, 4000, 50))
Output
$`3750`
# A tibble: 2 × 3
stage MinValue MaxValue
<dbl> <dbl> <dbl>
1 1 3.52 46.6
2 2 0.575 49.3
$`3800`
# A tibble: 2 × 3
stage MinValue MaxValue
<dbl> <dbl> <dbl>
1 1 3.52 46.6
2 2 0.575 47.5
$`3850`
# A tibble: 2 × 3
stage MinValue MaxValue
<dbl> <dbl> <dbl>
1 1 3.52 46.6
2 2 0.575 47.5
$`3900`
# A tibble: 2 × 3
stage MinValue MaxValue
<dbl> <dbl> <dbl>
1 1 3.52 46.6
2 2 0.575 47.5
$`3950`
# A tibble: 2 × 3
stage MinValue MaxValue
<dbl> <dbl> <dbl>
1 1 3.52 46.6
2 2 0.575 47.5
$`4000`
# A tibble: 2 × 3
stage MinValue MaxValue
<dbl> <dbl> <dbl>
1 1 6.65 46.6
2 2 0.575 47.5
Data
raw.data <- structure(list(pressure = c(4160.41269886773, 4044.58961030468,
4336.48418885423, 3762.11064029485, 4235.55055609904, 3926.50744639104,
4086.0048676841, 4360.64667999744, 3850.74476944283, 3950.07681293646,
4347.61320002144, 3996.32209626725, 4262.53829378402, 3869.30528597441,
4252.7681372012, 4013.94325762521, 4275.64664371312, 4197.37908616662,
4231.71574808657, 4028.1643497292, 4407.9091984313, 4481.91399103962,
4353.40271308087, 4013.09538848, 4109.39885408152, 4195.05179609405,
4222.33691916335, 4316.15335500101, 3860.02388742054, 3772.72424055263
), value = c(46.6360261081718, 19.0778955002315, 9.46381011744961,
17.4791521392763, 6.64818733930588, 3.79822270479053, 17.0007253182121,
45.9705576649867, 39.6164933103137, 3.52405618177727, 29.9587145447731,
10.8624027809128, 45.8421137067489, 34.4845326268114, 17.0537169324234,
47.0035993610509, 29.5542735257186, 12.992845242843, 32.0275551988743,
21.112488291692, 12.7272683312185, 23.9938693121076, 18.5264392290264,
42.9235454765148, 0.575024168938398, 10.7687710318714, 0.992469629272819,
47.4592371145263, 40.4172958689742, 49.3020136258565),
stage = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2)), class = "data.frame", row.names = c(NA, -30L))