Reputation: 1773
I wanted to create two columns that take into account prior history[t-1] to create new columns that specify how many new activities and old activities are repeated or carried out in the current period [see below for the data structure].For example row 5, the algorithm should compare the new event of 'think' to the prior period [read, write] and as there was no prior 'think' in t-1 it is listed as 1 [for new] and no old events have been used in the period 3 [ 5th row] and hence it is 0.
event<- c('read', 'write', 'read', 'write', 'think', 'read', 'think', 'read')
person<- c('arun', 'arun','arun','arun','arun','john','john', 'john')
time <- c(1, 1,2,2,3,1,2,3)
df<- data.frame(event, person,time)
event person time new old
read arun 1 . .
write arun 1 . .
read arun 2 0 2
write arun 2 0 2
think arun 3 1 0
read john 1 . .
think john 2 1 0
read john 3 1 0
Any suggestions on how this can be in achieved?
Upvotes: 0
Views: 200
Reputation: 26218
event<- c('read', 'write', 'read', 'write', 'think', 'read', 'think', 'read')
person<- c('arun', 'arun','arun','arun','arun','john','john', 'john')
time <- c(1, 1,2,2,3,1,2,3)
df<- data.frame(event, person,time)
library(tidyverse, warn.conflicts = FALSE)
df %>%
group_by(person, time) %>%
summarise(new = list(event), .groups = 'drop') %>%
group_by(person) %>%
mutate(old = map2_int(new, lag(new), ~ sum(.x %in% .y)),
new = map_int(new, length) - old) %>%
mutate(across(new:old, ~ifelse(time == 1, NA, .))) %>%
left_join( df, ., by = c('person', 'time'))
#> event person time new old
#> 1 read arun 1 NA NA
#> 2 write arun 1 NA NA
#> 3 read arun 2 0 2
#> 4 write arun 2 0 2
#> 5 think arun 3 1 0
#> 6 read john 1 NA NA
#> 7 think john 2 1 0
#> 8 read john 3 1 0
Created on 2021-07-21 by the reprex package (v2.0.0)
Upvotes: 1