apply dplyr function to time series failed

Question

There is time series ttrc_total , when applying the dplyr function group_by mutate ..., there are failures. Is there way to solve it? Thanks!

library(TTR)
library(tidyverse)
data(ttrc)
ttrc_total <- rbind(ttrc %>% mutate(category='A'),
      ttrc %>% mutate(category='B'),
      ttrc %>% mutate(category='C'))

Below code show error message : no applicable method for 'group_by' applied to an object of class "c('xts', 'zoo')"

ttrc_total %>% xts::xts(., order.by=.$Date) %>% 
  group_by(category) %>% 
  mutate(ATR_10 = ATR(.[,c("High","Low","Close")], n=10))

Answer 1

I might try to split the data by category and shape it back to long format.

library(TTR)
library(tidyverse)
data(ttrc)
ttrc_total <- rbind(ttrc %>% mutate(category='A'),
                    ttrc %>% mutate(category='B'),
                    ttrc %>% mutate(category='C'))

nested_data <- ttrc_total %>%
  as_tibble() %>%
  group_nest(category)

nested_data <- nested_data %>%
  mutate(atr = map(data, \(x) as.data.frame(ATR(select(x, High, Low, Close), n = 10))))

nested_data
#> # A tibble: 3 × 3
#>   category               data atr             
#>   <chr>    <list<tibble[,6]>> <list>          
#> 1 A               [5,550 × 6] <df [5,550 × 4]>
#> 2 B               [5,550 × 6] <df [5,550 × 4]>
#> 3 C               [5,550 × 6] <df [5,550 × 4]>

atr_df <- bind_rows(nested_data$atr) %>%
  mutate(category = rep(nested_data$category, map_int(nested_data$atr, nrow)))

head(atr_df)
#>     tr atr trueHigh trueLow category
#> 1   NA  NA       NA      NA        A
#> 2 0.07  NA     3.15    3.08        A
#> 3 0.04  NA     3.12    3.08        A
#> 4 0.05  NA     3.12    3.07        A
#> 5 0.04  NA     3.12    3.08        A
#> 6 0.07  NA     3.17    3.10        A

^{Created on 2023-09-01 with reprex v2.0.2}