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I have a time series object suppose :

library(xts)

exposure <- xts(Google = c(100, 200,300,400,500,600,700,800),
                Apple = c(10, 20,30,40,50,60,70,80),
                Audi = c(1,2,3,4,5,6,7,8),
                BMW = c(1000, 2000,3000,4000,5000,6000,7000,8000),
                AENA = c(50,51,52,53,54,55,56,57,58),
                order.by = Sys.Date() - 1:8)

I have a dataframe :

map <- data.frame(Company = c("Google", "Apple", " Audi", "BMW", " AENA"),
                  Country = c("US", "US", " GERMANY", "GERMANY", " SPAIN"))

I want to aggregate in exposure object based on the country to which the companies are mapped. Basically my output will be a xts object with same index as exposure but column names would be that of US, GERMANY, SPAIN. For example for a particular date under US column I would want sum of exposures for Google and Apple for that date.

Any help is welcome.

ss003
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1 Answers1

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I think there was an error with your original data specification. This is a way to do it by first moving it out of the xts format and then back into it again.

data

I made a few changes to how the xts object is created. I also cleaned some mistaken spaces up.

library(xts)

df <- data.frame(Google = c(100, 200,300,400,500,600,700,800),
                 Apple = c(10, 20,30,40,50,60,70,80),
                 Audi = c(1,2,3,4,5,6,7,8),
                 BMW = c(1000, 2000,3000,4000,5000,6000,7000,8000),
                 AENA = c(50,51,52,53,54,55,56,57)) 

exposure <- xts(df, order.by = Sys.Date() - 1:8)

map <- data.frame(Company = c("Google", "Apple", "Audi", "BMW", "AENA"),
                  Country = c("US", "US", "GERMANY", "GERMANY", "SPAIN"),
                  stringsAsFactors = F)

aggregation

I use tbl2xts to convert the format. Then, we use dplyr and tidyr to pivot the data to a long format, join in the Country to each Company, and summarize over the Country. We then convert back to xts, spreading the data wide by Country.

library(tbl2xts)
library(dplyr)
library(tidyr)

xts_tbl(exposure) %>%
  pivot_longer(-date, names_to = "Company") %>%
  left_join(map, by = "Company") %>%
  group_by(date, Country) %>%
  summarize(value = sum(value)) %>%
  ungroup() %>%
  tbl_xts(spread_by = "Country")

result

           GERMANY SPAIN  US
2019-10-28    8008    57 880
2019-10-29    7007    56 770
2019-10-30    6006    55 660
2019-10-31    5005    54 550
2019-11-01    4004    53 440
2019-11-02    3003    52 330
2019-11-03    2002    51 220
2019-11-04    1001    50 110