R Dataset / Package DAAG / moths

How To Create a Barplot

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Visual
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How To Create a Stacked Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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Numerical
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How To Create a Plot

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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

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Dotplot

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

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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

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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-daag-moths.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-76376.csv 746 bytes
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 moths data set which pertains to Moths Data. The moths data set is found in the DAAG R package. You can load the moths data set in R by issuing the following command at the console data("moths"). This will load the data into a variable called moths. If R says the moths data set is not found, you can try installing the package by issuing this command install.packages("DAAG") 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 moths R data set. The size of this file is about 746 bytes.


Moths Data

Description

The moths data frame has 41 rows and 4 columns. These data are from a study of the effect of habitat on the densities of two species of moth (A and P). Transects were set across the search area. Within transects, sections were identified according to habitat type.

Usage

moths

Format

This data frame contains the following columns:

meters

length of transect

A

number of type A moths found

P

number of type P moths found

habitat

a factor with levels Bank, Disturbed, Lowerside, NEsoak, NWsoak, SEsoak, SWsoak, Upperside

Source

Sharyn Wragg, formerly of Australian National University

Examples

print("Quasi Poisson Regression - Example 8.3")
rbind(table(moths[,4]), sapply(split(moths[,-4], moths$habitat), apply,2,
sum))
A.glm <- glm(formula = A ~ log(meters) + factor(habitat), family =
quasipoisson, data = moths)
summary(A.glm)
  # Note the huge standard errors
moths$habitat <- relevel(moths$habitat, ref="Lowerside")
A.glm <- glm(A ~ habitat + log(meters), family=quasipoisson, data=moths)
summary(A.glm)$coef
## Consider as another possibility
A2.glm <- glm(formula = A ~ sqrt(meters) + factor(habitat), family =
                  quasipoisson(link=sqrt), data = moths)
summary(A2.glm)

--

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