How to check previous row value with present row value in dataframe

Question

How to check previous row value with present row value dynamically for all column of data frame by grouping specific ID.

my data frame:

ID  ITEM1     ITEM2    ITEM3           
  1    A         A        A     
  2    C         B        C       
  1    A         B        C
  1    B         A        C    
  2    NA        B        F      
  3    A         A        D     
  4    R         G        J 
  4    H         T        J

For Ex:

ID  ITEM1     ITEM2    ITEM3    ITEM1change  ITEM2change   ITEM3change                   
  1    A         A        A       0            0             0          
  1    A         B        C       0            1             1
  1    B         A        C       1            1             0 
  2    C         B        C       0            0             0
  2    NA        B        F       1            0             1
  3    A         A        D       0            0             0
  4    R         G        J       0            0             0
  4    H         T        J       1            1             0

My final output will be:

  Fiels         modifiedcout   unmodifiedcount Total      
  ITEM1change     3               5              8                
  ITEM2change     3               5              8    
  ITEM3change     2               6              8

my data:

structure(list(ID = c(1, 2, 1, 1, 2, 3, 4, 4), ITEM1 = structure(c(1L, 
3L, 1L, 2L, NA, 1L, 5L, 4L), .Label = c("A", "B", "C", "H", "R"
), class = "factor"), ITEM2 = structure(c(1L, 2L, 2L, 1L, 2L, 
1L, 3L, 4L), .Label = c("A", "B", "G", "T"), class = "factor"), 
    ITEM3 = structure(c(1L, 2L, 2L, 2L, 4L, 3L, 5L, 5L), .Label = c("A", 
    "C", "D", "F", "J"), class = "factor")), .Names = c("ID", 
"ITEM1", "ITEM2", "ITEM3"), row.names = c(NA, -8L), class = "data.frame")

Answer 1

A possible solution:

library(dplyr)
library(tidyr)

df %>% 
  gather(item, value, -1) %>% 
  group_by(ID, item) %>% 
  mutate(change = lag(value, default = first(value)) != value,
         change = replace(change, is.na(change), TRUE)) %>% 
  group_by(item) %>% 
  summarise(modified = sum(change, na.rm = TRUE),
            unmodified = sum(!change, na.rm = TRUE)) %>% 
  mutate(total = modified + unmodified)

which gives:

# A tibble: 3 x 4
   item modified unmodified total
  <chr>    <int>      <int> <int>
1 ITEM1        3          5     8
2 ITEM2        3          5     8
3 ITEM3        2          6     8