Detect a pattern in a column with R - r

Question

I am trying to calculate how many times a person moved from one job to another. This can be calculated every time the Job column has this pattern 1 -> 0 -> 1.
In this example, it happened one rotation:
Person Job
A 1
A 0
A 1
A 1
In this another example, person B had one rotation as well.
Person Job
A 1
A 0
A 1
A 1
B 1
B 0
B 0
B 1
Whats would be a good approach to measure this pattern in a new column 'rotation', by person ?
Person Job Rotation
A 1 0
A 0 0
A 1 1
A 1 1
B 1 0
B 0 0
B 0 0
B 1 1

Answer 1

You can use regular expressions to capture a group with 101 and count it as a 1. so you use a pattern="(?<=1)0+(?=1)" where for all zeros, check whether they are preceeded by 1 and also succeeded by a 1
library(tidyverse)
df%>%
group_by(Person)%>%
mutate(Rotation=str_count(accumulate(Job,str_c,collapse=""),"(?<=1)0+(?=1)"))
# A tibble: 12 x 3
# Groups: Person [3]
Person Job Rotation
<fct> <int> <int>
1 A 1 0
2 A 0 0
3 A 1 1
4 A 1 1
5 B 1 0
6 B 0 0
7 B 0 0
8 B 1 1
9 C 0 0
10 C 1 0
11 C 0 0
12 C 1 1

Answer 2

One solution is to use lag with default = 0 and count cumulative sum of condition when value changes from 0 to 1. Just subtract 1 from the cumsum to get the rotation.
The solution using dplyr can be as:
library(dplyr)
df %>% group_by(Person) %>%
mutate(Rotation = cumsum(lag(Job, default = 0) == 0 & Job ==1) - 1) %>%
as.data.frame()
# Person Job Rotation
# 1 A 1 0
# 2 A 0 0
# 3 A 1 1
# 4 A 1 1
# 5 B 1 0
# 6 B 0 0
# 7 B 0 0
# 8 B 1 1
Data:
df <- read.table(text ="
Person Job
A 1
A 0
A 1
A 1
B 1
B 0
B 0
B 1",
header = TRUE, stringsAsFactors = FALSE)

Answer 3

Here is an option with data.table
library(data.table)
setDT(df)[, Rotation := +(grepl("101", do.call(paste0,
shift(Job, 0:.N, fill = 0)))), Person]
df
# Person Job Rotation
# 1: A 1 0
# 2: A 0 0
# 3: A 1 1
# 4: A 1 1
# 5: B 1 0
# 6: B 0 0
# 7: B 0 0
# 8: B 1 0
# 9: C 0 0
#10: C 1 0
#11: C 0 0
#12: C 1 1
A base R option would be
f1 <- function(x) Reduce(paste0, x, accumulate = TRUE)
df$Rotation <- with(df, +grepl("101", ave(Job, Person, FUN = f1)))
data
df <- data.frame(Person = rep(c("A", "B", "C"), each = 4L),
Job = as.integer(c(1,0,1,1,
1,0,0,1,
0,1,0,1)))

Answer 4

I'm assuming that if a person starts unemployed,
the first job they get doesn't count as rotation.
In that case:
library(dplyr)
rotation <- function(x) {
# this will have 1 when a person got a new job
dif <- c(0L, diff(x))
dif[dif < 0L] <- 0L
if (x[1L] == 0L) {
# unemployed at the beginning,
# first job doesn't count as change from one to another
dif[which.max(dif)] <- 0L
}
# return
cumsum(dif)
}
df <- data.frame(Person = rep(c("A", "B", "C"), each = 4L),
Job = as.integer(c(1,0,1,1,
1,0,0,1,
0,1,0,1)))
df %>%
group_by(Person) %>%
mutate(Rotation = rotation(Job))
# A tibble: 12 x 3
# Groups: Person [3]
Person Job Rotation
<fct> <int> <int>
1 A 1 0
2 A 0 0
3 A 1 1
4 A 1 1
5 B 1 0
6 B 0 0
7 B 0 0
8 B 1 1
9 C 0 0
10 C 1 0
11 C 0 0
12 C 1 1