This post will take you from a variable that shows numeric values, such as the number of days to purchase a product:

To a state where you can visualize the probability of an event at a certain time interval, such as the proportion of people who have yet to purchase at each point in time. Survival curves are also known as Kaplan-Meier curves:

## Requirements

You will need a *numeric* variable in your **Data Sets** tree. Numeric variables are represented by a "*2*" next to their name:

## Method

- From the
**toolbar**, go to**Calculation > Custom Code.** - In the
**object inspector**, go to**Properties > R CODE**and paste in the following code and click**Calculate.**

library(survival)

surv.days = Surv(days)

surv.fit = survfit(surv.days~1)

plot(surv.fit, main = "Kaplan-Meier estimate with 95% confidence bounds (86% of data)",

xlab = "Days since trial started",

xlim = c(0, 180),

ylab = "Survival function")

grid(20, 10, lwd = 2)

In the code above, "surv.days = Surv(*days*)", *days *is the name of your numeric variable being used to create the density plot. This must be updated with the variable's name in your data set.

## Next

How to Create a Density Plot Using R