I have a data set with Likert scale answers. I want to create a new data frame that will calculate the percentages of each category of each level. Doing so i have made the following code:

likert_levels <- c(
  "Very Dissatisfied",
  "Dissatisfied",
  "Average",
  "Satisfied",
  "Very Satisfied"
)

df <-
  tibble(
    "q1" = sample(likert_levels, 10, replace = TRUE),
    "q2" = sample(likert_levels, 10, replace = TRUE, prob = 5:1),
    "q3" = sample(likert_levels, 10, replace = TRUE, prob = 1:5),
    "q4" = sample(likert_levels, 10, replace = TRUE, prob = 1:5),
    "q5" = sample(c(likert_levels, NA), 10, replace = TRUE)
  ) %>%
  mutate(across(everything(), ~ factor(.x, levels = likert_levels)))%>%
  mutate(Country = c("USA","BRAZIL","BRAZIL","BRAZIL","USA","GERMANY","ITALY","GERMANY","BRAZIL","USA"))%>%
  relocate(Country,.before=q1)


df$O_1 <- apply(df, 1, function(x) sum(x=="Very Dissatisfied", na.rm=TRUE)) #How many ONEstars in                                    
df$O_2 <- apply(df, 1, function(x) sum(x=="Dissatisfied", na.rm=TRUE)) #each row
df$O_3 <- apply(df, 1, function(x) sum(x=="Average", na.rm=TRUE))
df$O_4 <- apply(df, 1, function(x) sum(x=="Satisfied", na.rm=TRUE))
df$O_5 <- apply(df, 1, function(x) sum(x=="Very Satisfied", na.rm=TRUE))

df$O_sum <-  df$O_1 + df$O_2 + df$O_3 + df$O_4 + df$O_5
df <- df[,-c(2: (ncol(df)-6))]



Likert_df =  as.data.frame (df %>% group_by(Country,O_sum) %>% summarise( 
  OO_1 = sum(O_1) / (n() * (O_sum[1])) * 100,
  OO_2 = sum(O_2) / (n() * (O_sum[1])) * 100,
  OO_3 = sum(O_3) / (n() * (O_sum[1])) * 100,
  OO_4 = sum(O_4) / (n() * (O_sum[1])) * 100,
  OO_5 = sum(O_5) / (n() * (O_sum[1])) * 100 ) ) 

Likert_df$ O_sum <- NULL

Likert_df <- as.data.frame(Likert_df %>% group_by(Country) %>% summarise(
  
  OO_1 = mean(OO_1),
  OO_2 = mean(OO_2),
  OO_3 = mean(OO_3),
  OO_4 = mean(OO_4),
  OO_5 = mean(OO_5) ))


colnames(Likert_df) <- c("Item",  "Strongly Disagree",  "Disagree",  "So So",  "Agree",  "Strongly Agree")
DF = Likert_df

resulting to :

DF
     Item Strongly Disagree Disagree So So Agree Strongly Agree
1  BRAZIL                10       25  25.0  30.0             10
2 GERMANY                30       20  20.0  20.0             10
3   ITALY                20       40  20.0   0.0             20
4     USA                 5       10  42.5  17.5             25

My question is: Is there another way more simpler and faster using dplyr functions with pipes to create the same result ?