Grouping data starting with specific number in R - r

Question

I am sorry if the title is incomprehensible. I have a data as shown below; 1, 2, 3.. are months of various years. And I want to gather months separately for a and l.
a l
1-2006 3.498939 0.8523857
1-2007 14.801777 0.2457656
1-2008 6.893728 0.5381691
2-2006 2.090962 0.6764694
2-2007 9.192913 0.8740950
2-2016 5.059505 1.1761113
Structure of data is;
data<-structure(list(a = c(3.49893890760882, 14.8017770056402, 6.89372828391484,
2.0909624091048, 9.19291324208917, 5.05950526612261, 13.1570625271881,
14.9570662205959, 7.72453112976811, 12.9331892673657
), l = c(0.852385662732809,
0.245765570168399, 0.538169092055646, 0.676469362818052, 0.874095005203713,
1.17611132212132, 0.76857056091243, 0.622533767341579, 0.9562200838363,
1.10064589903771, 0.85863722854391
)), class = "data.frame", row.names = c("1-2006",
"1-2007", "1-2008",
"2-2006", "2-2007",
"2-2016",
"3-2015", "3-2016", "3-2017", "3-2018"
))
For example; I want to gather all january (1-2005, 1-2006..) and march data(3-2012, 3-2015..) data for a and also for l. Like this one:
january_a
1-2006 3.498939
1-2007 14.801777
1-2008 6.893728
january_l
1-2006 0.8523857
1-2007 0.2457656
1-2008 0.5381691
march_a
3-2012 9.192913
3-2015 5.059505
march_l
3-2012 0.8740950
3-2015 1.1761113

Answer 1

You could add a column which contains only the numerical prefix, and then split on that:
data$prefix <- sub("^(\\d+).*$", "\\1", row.names(data))
data_a <- split(data[,"a"], data$prefix)
data_a
$`1`
[1] 3.498939 14.801777 6.893728
$`2`
[1] 2.090962 9.192913 5.059505
Data:
data <- data.frame(a=c(3.498939, 14.801777, 6.893728, 2.090962, 9.192913, 5.059505),
l=c(0.8523857, 0.2457656, 0.5381691, 0.6764694, 0.8740950, 1.1761113))
row.names(data) <- c("1-2006", "1-2007", "1-2008", "2-2006", "2-2007", "2-2016")

Answer 2

This is another variation that you can try using tidyverse which returns a list of dataframes, where every element has a combination of month and "a" or "l".
library(tidyverse)
data %>%
rownames_to_column('date') %>%
pivot_longer(cols = -date) %>%
separate(date, c('month', 'year'), sep = "-", remove = FALSE) %>%
group_split(month, name)
#[[1]]
# A tibble: 3 x 5
# date month year name value
# <chr> <chr> <chr> <chr> <dbl>
#1 1-2006 1 2006 a 3.50
#2 1-2007 1 2007 a 14.8
#3 1-2008 1 2008 a 6.89
#[[2]]
# A tibble: 3 x 5
# date month year name value
# <chr> <chr> <chr> <chr> <dbl>
#1 1-2006 1 2006 l 0.852
#2 1-2007 1 2007 l 0.246
#3 1-2008 1 2008 l 0.538
#...
#...
This has some additional columns to uniquely identify values which you can remove if not needed.

Answer 3

Another option is group_split
library(purrr)
library(dplyr)
library(stringr)
data %>%
rownames_to_column('rn') %>%
select(rn, a) %>%
group_split(rn = str_remove(rn, '-.*'), keep = FALSE) %>%
map(flatten_dbl)
#[[1]]
#[1] 3.498939 14.801777 6.893728
#[[2]]
#[1] 2.090962 9.192913 5.059505
data
data <- data.frame(a=c(3.498939, 14.801777, 6.893728, 2.090962, 9.192913, 5.059505),
l=c(0.8523857, 0.2457656, 0.5381691, 0.6764694, 0.8740950, 1.1761113))
row.names(data) <- c("1-2006", "1-2007", "1-2008", "2-2006", "2-2007", "2-2016")