Extracting elements from a list to create a matrix - r

Question

I have a list of countries with lists inside each one of them.
Just to give you an example of a list object for one country with lists for two countries (df_DOTS):
df_DOTS <- list(BR = structure(list(`#FREQ` = "M", `#REF_AREA` = "AU", `#INDICATOR` = "TXG_FOB_USD",
`#COUNTERPART_AREA` = "BR", `#UNIT_MULT` = "6", `#TIME_FORMAT` = "P1M",
Obs = list(structure(list(`#TIME_PERIOD` = c("2019-07", "2019-08",
"2019-09"), `#OBS_VALUE` = c("55.687747", "36.076581", "57.764474"
)), class = "data.frame", row.names = c(NA, 3L)))), row.names = 2L, class = "data.frame"),
US = structure(list(`#FREQ` = "M", `#REF_AREA` = "AU", `#INDICATOR` = "TXG_FOB_USD",
`#COUNTERPART_AREA` = "US", `#UNIT_MULT` = "6", `#TIME_FORMAT` = "P1M",
Obs = list(structure(list(`#TIME_PERIOD` = c("2019-07",
"2019-08", "2019-09"), `#OBS_VALUE` = c("876.025841",
"872.02118", "787.272851")), class = "data.frame", row.names = c(NA,
3L)))), row.names = 1L, class = "data.frame"))
I can reach the matrix (matrix_DOTS) I am looking for using these lines of code:
library(dplyr)
library(rlist)
library(magrittr)
BR <- df_DOTS[["BR"]][["Obs"]] %>%
list.select(.$`#OBS_VALUE`) %>%
unlist() %>%
sapply(function(x) as.numeric(as.character(x))) %>%
mean()
US <- df_DOTS[["US"]][["Obs"]] %>%
list.select(.$`#OBS_VALUE`) %>%
unlist() %>%
sapply(function(x) as.numeric(as.character(x))) %>%
mean()
matrix_DOTS <- matrix(c(BR, US), nrow = 1, dimnames = list(c("AU"), c("BR", "US")))
Since I have a list of several countries with lists of other several countries inside them, I am looking for a more practical way of achieving matrix_DOTS. Any help is highly appreciated!
PS: This is the dput for the final matrix in this example:
matrix_DOTS <- structure(c(49.842934, 845.106624), .Dim = 1:2, .Dimnames = list(
"AU", c("BR", "US")))
EDIT
This is the procedure to obtain df_DOTS:
library(IMFData)
databaseID <- "DOT"
startdate = "2019-07-01"
enddate = "2019-09-01"
checkquery = FALSE
queryfilter <- list(CL_FREQ = "M", CL_AREA_DOT = "AU",
CL_INDICATOR_DOT = "TXG_FOB_USD",
CL_COUNTERPART_AREA_DOT = c("BR", "US"))
df_DOTS <- CompactDataMethod(databaseID, queryfilter, startdate, enddate, checkquery) %>%
split(.$`#COUNTERPART_AREA`)

Answer 1

Just add tidy = TRUE to the CompactDataMethod call:
library(IMFData)
databaseID <- "DOT"
startdate = "2019-07-01"
enddate = "2019-09-01"
checkquery = FALSE
queryfilter <- list(CL_FREQ = "M", CL_AREA_DOT = "AU",
CL_INDICATOR_DOT = "TXG_FOB_USD",
CL_COUNTERPART_AREA_DOT = c("BR", "US"))
df_DOTS <- CompactDataMethod(databaseID,
queryfilter,
startdate,
enddate,
checkquery,
tidy = TRUE)
df_DOTS
#TIME_PERIOD #OBS_VALUE #FREQ #REF_AREA #INDICATOR #COUNTERPART_AREA #UNIT_MULT #TIME_FORMAT
1 2019-07 876.025841 M AU TXG_FOB_USD US 6 P1M
2 2019-08 872.02118 M AU TXG_FOB_USD US 6 P1M
3 2019-09 787.272851 M AU TXG_FOB_USD US 6 P1M
4 2019-07 55.687747 M AU TXG_FOB_USD BR 6 P1M
5 2019-08 36.076581 M AU TXG_FOB_USD BR 6 P1M
6 2019-09 57.764474 M AU TXG_FOB_USD BR 6 P1M
you just need one group_by(#COUNTERPART_AREA) %>% summarise(mean = mean(#OBS_VALUE)):
library(tidyverse)
df_DOTS %>%
group_by(`#COUNTERPART_AREA`, `#REF_AREA`) %>%
summarise(mean = mean(as.numeric(`#OBS_VALUE`))) %>%
spread( `#COUNTERPART_AREA`, mean)
#output
`#REF_AREA` BR US
<chr> <dbl> <dbl>
1 AU 49.8 845.
Or if you insist on a matrix
df_DOTS %>%
group_by(`#COUNTERPART_AREA`, `#REF_AREA`) %>%
summarise(mean = mean(as.numeric(`#OBS_VALUE`))) %>%
spread( `#COUNTERPART_AREA`, mean) %>%
column_to_rownames("#REF_AREA") %>%
as.matrix
#output
BR US
AU 49.84293 845.1066

Answer 2

From the input data, we could loop over with map, pluck the elements that is needed, convert to numeric, get the mean, and convert to a two column tibble with enframe
library(purrr)
library(tidyr)
map(df_DOTS, ~ .x %>%
pluck("Obs", 1, "#OBS_VALUE") %>%
as.numeric %>%
mean) %>%
enframe %>%
unnest(c(value))
# A tibble: 2 x 2
# name value
# <chr> <dbl>
#1 BR 49.8
#2 US 845.

Answer 3

Another option would be like this:
tmp <- df_DOTS %>%
as_tibble() %>%
summarise(across(everything(), ~mean(as.numeric(.x$Obs[[1]]$`#OBS_VALUE`))))
tmp
# # A tibble: 1 x 2
# BR US
# <dbl> <dbl>
# 1 49.8 845.