Making censored variables for surival analysis from dates - r

Question

I am a beginner in R, and I would like to do a survival analysis on the dataset about light bulbs I have. I would like to calculate the lifetime of a light bulb, so I need to calculate the time period between date_broken in row 2 and date_solved in row 1 for example.
I know I can use difftime(time, time2, units = "days") to calculate the time between date_fixed and date_broken in the same row, but then I would calculate the time the light bulb was broken and that is not what I am interested in.
I provided a small sample of my data below. For each light bulb on a particular location I have information about the date it broke and the day it was fixed.
(Besides the columns given in the example below, I have other features that should have predictive value.)
# date_broken date_fixed lightbulb location
# 1 26-2-2015 17-3-2015 1 A
# 2 19-3-2015 26-3-2015 1 A
# 3 26-3-2015 26-3-2015 1 A
# 4 17-4-2015 29-4-2015 2 B
# 5 19-6-2015 25-6-2015 2 B
# 6 9-7-2015 30-7-2015 2 B
ds <- data.frame( date_broken = c("26-2-2015", "19-3-2015",
"26-3-2015", "17-4-2015",
"19-6-2015", "9-7-2015"),
date_fixed = c("17-3-2015", "26-3-2015", "26-3-2015", "29-4-2015", "25-6-2015", "30-7-2015"),
lightbulb = c("1`", "1", "1", "2", "2", "2"), location = c("A", "A", "A", "B", "B", "B"))

Answer 1

First you'll need to fix your dates, as #Gaurav suggested. Then, you'll need to summarize by lightbulb, or the difference will be meaningless.
I present here an alternative using packages lubridate and data.table:
library(lubridate)
library(data.table)
ds$date_broken <- dmy(ds$date_broken)
ds$date_fixed <- dmy(ds$date_fixed)
setDT(ds)
setDT(ds)[, dt := difftime(date_fixed, shift(date_broken, 1L, type="lag"), "days"), by = lightbulb]
ds
Which produces:
## date_broken date_fixed lightbulb location dt
## 1: 2015-02-26 2015-03-17 1 A NA days
## 2: 2015-03-19 2015-03-26 1 A 28 days
## 3: 2015-03-26 2015-03-26 1 A 7 days
## 4: 2015-04-17 2015-04-29 2 B NA days
## 5: 2015-06-19 2015-06-25 2 B 69 days
## 6: 2015-07-09 2015-07-30 2 B 41 days
For a future opportunity, it's a lot of help when you produce some expected results, along with your question.

Answer 2

This should help
library(dplyr)
ds2 <- ds %>%
group_by(lightbulb) %>%
mutate(tp = as.Date(date_broken, "%d-%m-%Y") -
as.Date(lag(date_fixed,1), "%d-%m-%Y"))

Answer 3

I really love those super-impressive pipe operators in R. They're so elegant, and great if someone's got a ready-to-go solution.
I mostly do loops, probably 'cos I like something I see what's going on, and I can debug as I go. (I was also brought up on BASIC some decades ago - but don't tell anyone.)
Anyway this was my approach for something very similar that I was doing, with hopefully a bit of added value using a sequence counter. This might be useful as a regression variable (covariate or stratification) or something by which you can subset, assuming you might for example want to look at later failures separately from earlier failures. Survival curves by sequence counter were quite informative in my work.
First convert the dates...
## convert dates. once done it's done
ds$date_broken <- as.Date(ds$date_broken, "%d-%m-%Y")
ds$date_fixed <- as.Date(ds$date_fixed, "%d-%m-%Y")
Add a sequence counter column (called seq) to keep track of number of failures
ds$seq <- 1
Populate that counter column
for (rdx in 2:nrow(ds)) {
## if same item, increment count. If new item, start new count at seq = 1
ifelse(ds$lightbulb[rdx] == ds$lightbulb[rdx-1], ds$seq[rdx] <- ds$seq[rdx-1]+1, 1)
}
Now add a difference column
ds$diff <- NA
Populate that difference column
for (rdx in 2:nrow(ds)) {
## if same item, difference is current failure date minus previous in-service date
ifelse(ds$seq[rdx] != 1, ds$diff[rdx] <- ds$date_broken[rdx] - ds$date_fixed[rdx-1], NA)
}
Well that worked for me, both to conceptualise and to implement. Please note that some folk do get a bit hung up with loops (http://paleocave.sciencesortof.com/2013/03/writing-a-for-loop-in-r/), but around my neck of the woods life is for living, not coding, and even I sometimes stir paint with a screwdriver (an old one though).