R Dataset / Package datasets / faithful

How To Create a Barplot

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Category
Visual
Instructional

How To Create a Stacked Barplot

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Visual
Instructional

How To Create a Pie Chart

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Visual

How To Compute the Mean

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Category
Numerical
Instructional

How To Create a Plot

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Visual
Instructional

How to Compute the Median

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Numerical

Boxplot

Submitted by pmagunia on April 22, 2018 - 3:07 PM
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Correlation Coefficient

Submitted by pmagunia on April 22, 2018 - 3:08 PM
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Cumulative Frequency Histogram

Submitted by pmagunia on April 22, 2018 - 3:09 PM
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Dotplot

Submitted by pmagunia on April 22, 2018 - 3:10 PM
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Hollow Histogram

Submitted by pmagunia on April 22, 2018 - 3:10 PM
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Numerical Summaries

Submitted by pmagunia on April 22, 2018 - 2:51 PM
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Pie Chart

Submitted by pmagunia on April 22, 2018 - 3:11 PM
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Plot

Submitted by pmagunia on April 22, 2018 - 3:07 PM
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Regression

Submitted by pmagunia on April 22, 2018 - 3:12 PM

Stem and Leaf Plots

Submitted by pmagunia on April 22, 2018 - 3:12 PM
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Visual Summaries

Submitted by pmagunia on April 22, 2018 - 3:13 PM
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Submitted by pmagunia on March 9, 2018 - 1:06 PM
<iframe src="https://embed.picostat.com/r-dataset-package-datasets-faithful.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-32741.csv 2.22 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the faithful data set which pertains to Old Faithful Geyser Data. The faithful data set is found in the datasets R package. You can load the faithful data set in R by issuing the following command at the console data("faithful"). This will load the data into a variable called faithful. If R says the faithful data set is not found, you can try installing the package by issuing this command install.packages("datasets") and then attempt to reload the data. 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 faithful R data set. The size of this file is about 2,275 bytes.


Old Faithful Geyser Data

Description

Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

Usage

faithful

Format

A data frame with 272 observations on 2 variables.

[,1] eruptions numeric Eruption time in mins
[,2] waiting numeric Waiting time to next eruption (in mins)

Details

A closer look at faithful$eruptions reveals that these are heavily rounded times originally in seconds, where multiples of 5 are more frequent than expected under non-human measurement. For a better version of the eruption times, see the example below.

There are many versions of this dataset around: Azzalini and Bowman (1990) use a more complete version.

Source

W. Härdle.

References

Härdle, W. (1991) Smoothing Techniques with Implementation in S. New York: Springer.

Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics 39, 357–365.

See Also

geyser in package MASS for the Azzalini–Bowman version.

Examples

require(stats); require(graphics)
f.tit <-  "faithful data: Eruptions of Old Faithful"ne60 <- round(e60 <- 60 * faithful$eruptions)
all.equal(e60, ne60)             # relative diff. ~ 1/10000
table(zapsmall(abs(e60 - ne60))) # 0, 0.02 or 0.04
faithful$better.eruptions <- ne60 / 60
te <- table(ne60)
te[te >= 4]                      # (too) many multiples of 5 !
plot(names(te), te, type = "h", main = f.tit, xlab = "Eruption time (sec)")plot(faithful[, -3], main = f.tit,
     xlab = "Eruption time (min)",
     ylab = "Waiting time to next eruption (min)")
lines(lowess(faithful$eruptions, faithful$waiting, f = 2/3, iter = 3),
      col = "red")

--

Dataset imported from https://www.r-project.org.
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R Output Date
Picostat Output - Simple Linear Regression Apr 30, 2020
Picostat Output - Numerical Summaries Apr 30, 2020
Picostat Output - How to Create a Plot Apr 30, 2020
Picostat Output - Bivariate Plot Apr 30, 2020
Picostat Output - Correlation Coefficients Jul 7, 2019
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