Adding new columns to r dataframe based on multiple columns within the dataframe

We could use data.table. Convert the 'data.frame' to 'data.table' (setDT(df1)), grouped by "Id", we match the 'Date' with unique values of 'Date' to create the 'Day' column. Then, we group by 'Id', 'Date' and assign (:=) the sequence of rows to "Encounter".

library(data.table)
setDT(df1)[, Day:= match(Date, unique(Date)), by = Id
         ][, Encounter := seq_len(.N), by = .(Id, Date)]
df1
#    Id     Date  Time Day Encounter
#1:  A 20110527 10:42   1         1
#2:  A 20110527 11:24   1         2
#3:  A 20110527 11:52   1         3
#4:  A 20110603 10:29   2         1
#5:  A 20110603 10:59   2         2
#6:  B 20110503 11:23   1         1
#7:  B 20110503 11:45   1         2
#8:  B 20110503 12:05   1         3
#9:  B 20110503 12:17   1         4

data

df1 <- structure(list(Id = c("A", "A", "A", "A", "A", 
 "B", "B", "B", 
"B"), Date = c(20110527L, 20110527L, 20110527L, 
 20110603L, 20110603L, 
 20110503L, 20110503L, 20110503L, 20110503L), 
 Time = c("10:42", 
 "11:24", "11:52", "10:29", "10:59", "11:23", "11:45", "12:05", 
 "12:17")), .Names = c("Id", "Date", "Time"),
  class = "data.frame", row.names = c(NA, -9L))

here is a reproducible example:

df <- structure(list(
  Id = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
                 .Label = c("A", "B"), class = "factor"),
  Date = c(20110527L, 20110527L, 20110527L, 20110603L,
           20110603L, 20110503L, 20110503L, 
           20110503L, 20110503L),
  Time = structure(c(2L, 5L, 7L, 1L, 3L, 4L, 6L, 8L, 9L),
                   .Label = c("10:29", "10:42", "10:59", "11:23", "11:24", "11:45", "11:52", "12:05", "12:17"), class = "factor")),
  .Names = c("Id",  "Date", "Time"), class = "data.frame", row.names = c(NA, -9L))

then one can use dplyr and

library(dplyr)
group_by(df, Id, Date) %>% mutate(Encounter=1:n()) %>% ungroup()

Source: local data frame [9 x 4]

Id     Date   Time Encounter
(fctr)    (int) (fctr)     (int)
1      A 20110527  10:42         1
2      A 20110527  11:24         2
3      A 20110527  11:52         3
4      A 20110603  10:29         1
5      A 20110603  10:59         2
6      B 20110503  11:23         1
7      B 20110503  11:45         2
8      B 20110503  12:05         3
9      B 20110503  12:17         4

Or Base R using ave and by:

I used the data posted by Vincent Bonhomme (Data should be sorted by Date and Id):

# Function to count the days per individual using factor levels 
foo <- function(x){as.numeric(as.character(factor(x,labels = 1:nlevels(factor(x)))))}

# Add the columns Day & Encounter
df$Day <-unlist(by(df$Date,list(df$Id),FUN=foo))
df$Encounter <- ave(1:nrow(df),list(df$Id,df$Date),FUN=seq_along)