Create subset of data using conditions from another data frame - r

Question

I would like to use something like dplyr to create a subset of data from one data frame using conditions from another data frame. So in one data frame I have a set of data with minimum and maximum years and other sea-level data lsp , and in another frame I have a time series of ocean dynamics. For each row in the lsp dataframe, I would like to extract every year between the minimum and maximum ages in the dynamics data frame and create a sub set of data. I think this will require a for loop. Does anyone have any idea if this is possible?
Desired output using row 1 of LSP as an example:
Row 1 LSP (simplified) is:
Age min
Age max
1997
2007
I want to use this information to create a data frame like this from the dynamics file:
Subset
Year
Dynamics
1997
125
1998
109
1999
152
2000
161
2001
106
2002
120
2003
58
2004
68
2005
110
2006
144
2007
100
Many thanks
## LSP data
structure(list(Depth = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5,
8.5, 10.5, 13.5, 14.5, 18.5, 19.5, 27.5, 28.5, 32, 35.5, 40.5,
41.5), RSL = c(0.03, 0.03, 0.01, 0.01, -0.04, -0.01, -0.03, 0,
0.04, 0.03, 0, -0.01, -0.05, -0.07, -0.19, -0.24, -0.31, -0.31,
-0.27, -0.29), RSL_err_1sig = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1), Age_mean = c(2001.754499, 1994.278776, 1987.678949, 1980.805889,
1973.270485, 1965.018421, 1957.442729, 1952.134369, 1949.031929,
1945.148184, 1939.132213, 1936.957531, 1927.311071, 1924.379033,
1897.26123, 1892.977317, 1876.1995, 1858.135589, 1825.967544,
1820.605298), Age.min = c(1996.752238, 1985.111654, 1977.483594,
1968.26211, 1961.886124, 1958.219318, 1947.496532, 1943.084044,
1941.761439, 1935.843414, 1923.952516, 1920.057048, 1906.228232,
1902.242998, 1875.327613, 1869.925103, 1834.992176, 1811.928966,
1784.998245, 1767.524866), Age.max = c(2006.75676, 2003.445898,
1997.874304, 1993.349668, 1984.654846, 1971.817524, 1967.388926,
1961.184694, 1956.302419, 1954.452954, 1954.31191, 1953.858014,
1948.39391, 1946.515068, 1919.194847, 1916.029531, 1917.406824,
1904.342212, 1866.936843, 1873.68573)), class = "data.frame", row.names = c(NA,
-20L))
## Dynamics (only head)
structure(list(Year = 1815:1820, dynamics = c(-76.01893261, -64.50519732,
-66.06270761, -76.22822397, -72.35960029, -77.34157443)), row.names = c(NA,
6L), class = "data.frame")

Answer 1

Here is a base R option with Map and subset -
Map(function(x, y) subset(dynamics, Year >= x & Year <= y),
LSP$Age.min, LSP$Age.max)
The same logic can be implemented using tidyverse functions as well.
library(dplyr)
library(purrr)
map2(LSP$Age.min, LSP$Age.max, ~dynamics %>% filter(Year >= .x & Year <= .y))

Answer 2

As long as your dataset isn't huge, I would take something like the following approach.
Add the (nested) dynamics dataset to each row of your lsp dataset
Unnest the dynamics dataset to get one row per year
Filter out years that aren't relevant
(Optional)
Renest the dynamics columns to you have one row per lsp record with a tibble for all relevant years from the dynamics set.
lsp %>%
add_column(dynamics %>% nest(data = everything())) %>%
unnest(data) %>%
filter(year >= min & year <= max) %>%
nest(filtered = c(year, value))

Answer 3

I guess this does what you want to do. First assign names to your input data, so later you know what my codes mean.
lsp <- structure(list(Depth = c(0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5,
8.5, 10.5, 13.5, 14.5, 18.5, 19.5, 27.5, 28.5, 32, 35.5, 40.5,
41.5), RSL = c(0.03, 0.03, 0.01, 0.01, -0.04, -0.01, -0.03, 0,
0.04, 0.03, 0, -0.01, -0.05, -0.07, -0.19, -0.24, -0.31, -0.31,
-0.27, -0.29), RSL_err_1sig = c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1), Age_mean = c(2001.754499, 1994.278776, 1987.678949, 1980.805889,
1973.270485, 1965.018421, 1957.442729, 1952.134369, 1949.031929,
1945.148184, 1939.132213, 1936.957531, 1927.311071, 1924.379033,
1897.26123, 1892.977317, 1876.1995, 1858.135589, 1825.967544,
1820.605298), Age.min = c(1996.752238, 1985.111654, 1977.483594,
1968.26211, 1961.886124, 1958.219318, 1947.496532, 1943.084044,
1941.761439, 1935.843414, 1923.952516, 1920.057048, 1906.228232,
1902.242998, 1875.327613, 1869.925103, 1834.992176, 1811.928966,
1784.998245, 1767.524866), Age.max = c(2006.75676, 2003.445898,
1997.874304, 1993.349668, 1984.654846, 1971.817524, 1967.388926,
1961.184694, 1956.302419, 1954.452954, 1954.31191, 1953.858014,
1948.39391, 1946.515068, 1919.194847, 1916.029531, 1917.406824,
1904.342212, 1866.936843, 1873.68573)), class = "data.frame", row.names = c(NA,
-20L))
dynamics <- structure(list(Year = 1815:1820, dynamics = c(-76.01893261, -64.50519732,
-66.06270761, -76.22822397, -72.35960029, -77.34157443)), row.names = c(NA,
6L), class = "data.frame")
Then the actual codes to get the subset.
# first get info of years from the "lsp" dataset
# following your example in your comments
year_min <- list()
year_max <- list()
all_years <- list()
for(i in 1:nrow(lsp)){
year_min[[i]] <- round(lsp$Age.min[[i]])
year_max[[i]] <- round(lsp$Age.max[[i]])
all_years[[i]] <- c(year_min[[i]]:year_max[[i]])
all_years[[i]] <- as.data.frame(all_years[[i]])
colnames(all_years[[i]]) <- "Year"
}
# now join the info on "Year" from "lsp" data with "dynamics" data to get the subset
library(dplyr)
subset_output <- list()
for (i in 1:length(all_years)){
subset_output[[i]] <- left_join(dynamics,all_years[[i]])
}