Try this for the second question:
> years <- map2(zz,
+ list(c(2000, 2001), c(2001, 2003)),
+ ~ filter(.x, year %in% .y)
+ )
> years
[[1]]
# A tibble: 6 x 4
year tot_i relation g_rate
<dbl> <dbl> <chr> <dbl>
1 2000 22393349. EU28-Algeria 0.736
2 2001 23000574. EU28-Algeria 0.0271
3 2000 34361300. World-Algeria 0.615
4 2001 35297815. World-Algeria 0.0273
5 2000 11967951. Extra EU28-Algeria 0.428
6 2001 12297241. Extra EU28-Algeria 0.0275
[[2]]
# A tibble: 6 x 4
year tot_i relation g_rate
<dbl> <dbl> <chr> <dbl>
1 2001 7869288. EU28-Egypt -0.148
2 2003 6395999. EU28-Egypt -0.120
3 2001 19851236. World-Egypt -0.0721
4 2003 16055014. World-Egypt -0.175
5 2001 11981948. Extra EU28-Egypt -0.0147
6 2003 9659015. Extra EU28-Egypt -0.207
>
Jim Holtman
*Data Munger Guru*
*What is the problem that you are trying to solve?Tell me what you want to
do, not how you want to do it.*
On Fri, Jan 25, 2019 at 5:45 AM Lorenzo Isella <lorenzo.ise...@gmail.com>
wrote:
> Dear All,
> I am making my baby steps with the tidyverse purr package and I am
> stuck with some probably trivial tasks.
> Consider the following data set
>
>
> zz<-list(structure(list(year = c(2000, 2001, 2002, 2003, 2000, 2001,
> 2002, 2003, 2000, 2001, 2002, 2003), tot_i = c(22393349.081,
> 23000574.372, 21682040.898, 21671102.853, 34361300.338, 35297814.942,
> 34745691.204, 35878883.117, 11967951.257, 12297240.57, 13063650.306,
> 14207780.264), relation = c("EU28-Algeria", "EU28-Algeria",
> "EU28-Algeria",
> "EU28-Algeria", "World-Algeria", "World-Algeria", "World-Algeria",
> "World-Algeria", "Extra EU28-Algeria", "Extra EU28-Algeria",
> "Extra EU28-Algeria", "Extra EU28-Algeria"), g_rate = c(0.736046372770467,
> 0.0271163231905857, -0.0573261107603093, -0.000504474880914325,
> 0.614846575418334, 0.0272549232650638, -0.0156418673197543,
> 0.0326138831530727,
> 0.428272657063707, 0.0275142592018328, 0.0623237165799383,
> 0.0875811837579971
> )), row.names = c(NA, -12L), class = c("tbl_df", "tbl", "data.frame"
> )), structure(list(year = c(2000, 2001, 2002, 2003, 2000, 2001,
> 2002, 2003, 2000, 2001, 2002, 2003), tot_i = c(9233346.648, 7869288.171,
> 7271485.687, 6395999.102, 21393949.287, 19851236.26, 19449339.887,
> 16055014.309, 12160602.639, 11981948.089, 12177854.2, 9659015.207
> ), relation = c("EU28-Egypt", "EU28-Egypt", "EU28-Egypt", "EU28-Egypt",
> "World-Egypt", "World-Egypt", "World-Egypt", "World-Egypt", "Extra
> EU28-Egypt",
> "Extra EU28-Egypt", "Extra EU28-Egypt", "Extra EU28-Egypt"),
> g_rate = c(0.0970653722744164, -0.147731751985664, -0.0759665259436081,
> -0.120399959882366, 0.124744629514854, -0.0721097823643728,
> -0.0202454077789513, -0.174521376957825, 0.146712116047648,
> -0.0146912579338002, 0.0163501051368976, -0.206837670383671
> )), row.names = c(NA, -12L), class = c("tbl_df", "tbl", "data.frame"
> )))
>
> I am capable of doing very simple stuff with maps for instance taking the
> iteratively the mean of a certain column
>
> map(zz, function(x) mean(x$tot_i))
>
> or filtering the values of the years
>
> map(zz, function(x) filter(x, year==2000))
>
> however, I bang my head against the wall as soon as I want to add a bit of
> complexity. For instance
>
> 1) I want to iteratively group the data in zz by relation and summarise
> them by taking the average of tot_i and
>
> 2) Given a list of years
>
> ll<-list(c(2000, 2001), c(2001, 2003))
>
> I would like to filter the two elements of the zz list according to the
> years listed in ll.
>
> I would then have plenty of other operations to carry out on the data, but
> already understanding 1 and 2 would take me a long way from where I am
> stuck now.
>
> Any suggestion is welcome.
> Cheers
>
> Lorenzo
>
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>
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