R Dataset / Package vcd / SpaceShuttle

On this R-data statistics page, you will find information about the SpaceShuttle data set which pertains to Space Shuttle O-ring Failures. The SpaceShuttle data set is found in the vcd R package. You can load the SpaceShuttle data set in R by issuing the following command at the console data("SpaceShuttle"). This will load the data into a variable called SpaceShuttle. If R says the SpaceShuttle data set is not found, you can try installing the package by issuing this command install.packages("vcd") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the SpaceShuttle R data set. The size of this file is about 578 bytes.

Space Shuttle O-ring Failures

Description

Data from Dalal et al. (1989) about O-ring failures in the NASA space shuttle program. The damage index comes from a discussion of the data by Tufte (1997).

Usage

data("SpaceShuttle")

Format

A data frame with 24 observations and 6 variables.

FlightNumber: Number of space shuttle flight.
Temperature: temperature during start (in degrees F).
Pressure: pressure.
Fail: did any O-ring failures occur? (no, yes).
nFailures: how many (of six) 0-rings failed?.
Damage: damage index.

Source

Michael Friendly (2000), Visualizing Categorical Data: http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/orings.sas

References

S. Dalal, E. B. Fowlkes, B. Hoadly (1989), Risk analysis of the space shuttle: Pre-Challenger prediction of failure, Journal of the American Statistical Association, 84, 945–957.

E. R. Tufte (1997), Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press, Cheshire, CT.

M. Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.

Examples

data("SpaceShuttle")
plot(nFailures/6 ~ Temperature, data = SpaceShuttle,
 xlim = c(30, 81), ylim = c(0,1),
 main = "NASA Space Shuttle O-Ring Failures",
 ylab = "Estimated failure probability",
 pch = 19, col = 4)
fm <- glm(cbind(nFailures, 6 - nFailures) ~ Temperature,
data = SpaceShuttle,
family = binomial)
lines(30 : 81,
predict(fm, data.frame(Temperature = 30 : 81), type = "re"),
lwd = 2)
abline(v = 31, lty = 3)

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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