Summing Multiple Groups of Columns

Always it is easier to work on data in long format. Hence, change class_df to long format using tidyr:gather and join with class_fg. Perform analysis in long format on your data. Finally, spread in wide-format to match expected result.

library(tidyverse)

class_df %>% gather(key, value, -id) %>% 
  inner_join(class_fg, by=c("key" = "class")) %>%
  group_by(id, fg) %>%
  summarise(value = sum(value)) %>%
  spread(fg, value) %>%
  inner_join(class_df, by="id") %>% as.data.frame()

#   id   X   Y   Z    A   B    C    D    E   F
# 1  1 0.0 0.4 0.6 0.20 0.3 0.10 0.15 0.25 0.0
# 2  2 0.4 0.4 0.2 0.05 0.1 0.05 0.30 0.10 0.4
# 3  3 0.3 0.4 0.3 0.10 0.1 0.10 0.20 0.20 0.3

Data:

class_fg <- read.table(text = 
"class         fg
                 A          Z
                 B          Z
                 C          Z
                 D          Y
                 E          Y
                 F          X",
header = TRUE, stringsAsFactors = FALSE)

class_df  <- read.table(text = 
"id    A    B    C    D    E    F
1 0.20 0.30 0.10 0.15 0.25 0.00 
2 0.05 0.10 0.05 0.30 0.10 0.40
3 0.10 0.10 0.10 0.20 0.20 0.30",
header = TRUE, stringsAsFactors = FALSE)

We could split the 'class_df' by 'class', loop through the list elements with map, select the columns of 'class_df' and get the rowSums

library(tidyverse)
class_fg %>%
    split(.$fg) %>% 
    map_df(~ class_df %>%
                select(one_of(.x$class)) %>% 
                rowSums) %>%
    bind_cols(class_df, .)
#  id    A   B    C    D    E   F   X   Y   Z
#1  1 0.20 0.3 0.10 0.15 0.25 0.0 0.0 0.4 0.6
#2  2 0.05 0.1 0.05 0.30 0.10 0.4 0.4 0.4 0.2
#3  3 0.10 0.1 0.10 0.20 0.20 0.3 0.3 0.4 0.3

---

Or do a group by nesting, and then do the rowSums by mapping over the list elements

class_fg %>% 
   group_by(fg) %>%
   nest %>%
   mutate(out = map(data, ~  class_df %>%
                               select(one_of(.x$class)) %>% 
                               rowSums)) %>% 
   select(-data)  %>%
   unnest %>% 
   unstack(., out ~ fg) %>% 
   bind_cols(class_df, .)