Purr and Basic Functional Programming Tasks

Does this answer the first question?
rel <- map(zz, function(x){
+   group_by(x, relation) %>% summarise(tot = mean(tot_i))
+ })
rel
[[1]]
# A tibble: 3 x 2
  relation                 tot
  <chr>                  <dbl>
1 EU28-Algeria       22186767.
2 Extra EU28-Algeria 12884156.
3 World-Algeria      35070922.

[[2]]
# A tibble: 3 x 2
  relation               tot
  <chr>                <dbl>
1 EU28-Egypt        7692530.
2 Extra EU28-Egypt 11494855.
3 World-Egypt      19187385.

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.isella at 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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Purr and Basic Functional Programming Tasks

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