R Dataset / Package Zelig / homerun

How To Create a Barplot

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Description

Describes how to create a bar plot based on count data. For an example of count data, see the email50 curated data set which was taken from the Open Intro AHSS textbook (not affiliated). An example of count data in this dataset would be the spam column.

Usage

Select one (1) column to create its barplot and then click 'Submit'. If you do not choose count data, you may get unexpected results.

See Also

Students may also be interested in creating barplots for contingency tables.

For a stacked side-by-side barplot, see the other barplot app.

How To Create a Stacked Barplot

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Usage

Select 1 (one) column from a contingency table like the Gender and Politics or VADeaths curated datasets.

If you do not choose a contingency table, you may get unexpected results. You can import a dataset if you are logged-in.

Details

Shows the student how to create a single stacked bar plot based on a column in a contingency table.

See Also

For a basic barplot (single column) based on count data see the count data barplot app.

For a stacked side-by-side barplot see the other stacked barplot app for categorical data.

How To Create a Pie Chart

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Usage

Select 1 (one) column from a contingency table. If you don't have your own dataset, you can choose the Gender and Politics or VADeaths curated datasets. If a contingency table is not chosen, you may get unexpected results.

A contingency table has columns like a regular dataset, but the first row contains row names that categorize and "split-up" the dataset. An example of a contingency table would be something like this:

LIBERAL CONSERVATIVE
F 762 468
M 484 477

This contingency table is take from the Gender and Politics dataset. You can get a preview by selecting the dataset from the Curated Data dropdown above.

Details

This app shows the student how to create a pie chart from a contingency table by hand using a Quadstat dataset.

A pie chart shows proportions of a sample or population. Each piece of a pie chart corresponds to some subset of the sample or population. In this case, we will use the contingency table rows to subset the sample.

See Also

Students may also want to view the app for creating a pie chart from count data.

How To Compute the Mean

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Usage

Click "Submit" after selecting one column to see how to compute the arithmetic mean (average) of data (vectors).

Description

If all the values of a sample were plotted on a number line, the average would be the point in the middle that would balance the two sides.

The average is greatly influenced by outliers, meaning extreme points can pull the average to the left or right.

If we are referring to the average of population (all observations), the symbol for the average (arithmetic mean) is $\mu$.

If we are referring to the average of a sample (a subset of the population), the symbol for the average (arithmetic mean) is $\bar{x}$.

Computing the average

Suppose we have a sample consisting of $x_1, x_2, x_3,...,x_n$. This means we have $n$ observations. Then,

$$\bar{x}=\frac{x_1, x_2, x_3,...,x_n}{n}.$$

The formula tells us that we need to add all the observations and then divide by the number of observations to compute the mean.

Example 1

Compute the mean of $A = \{1,2,3\}$.

$$\bar{x} = \frac{1+2+3}{3} = 2.$$

How To Create a Plot

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Usage

Select two columns which are to be used in the scatterplot. The first column clicked will be the independent variable (X-axis).

Description

This web application describes how to create a scatterplot of two dataset variables plotted on the xy-axes.

How to Compute the Median

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Median Value

Description

Compute the sample median.

Usage

median(x, na.rm = FALSE, ...)

Arguments

x

an object for which a method has been defined, or a numeric vector containing the values whose median is to be computed.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

...

potentially further arguments for methods; not used in the default method.

Value

The default method returns a length-one object of the same type as x, except when x is logical or integer of even length, when the result will be double.

If there are no values or if na.rm = FALSE and there are NA values the result is NA of the same type as x (or more generally the result of x[FALSE][NA]).

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Mean

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Pie Chart

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Plot

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Regression

Stem and Leaf Plots

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Summary

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Visual Summaries

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R Data Set Description

On this Quadstat.net statistics page, you will find information about the homerun data set which pertains to Sample Data on Home Runs Hit By Mark McGwire and Sammy Sosa in 1998.. The homerun data set is found in the Zelig R package. You can load the homerun data set in R by issuing the following command at the console data("homerun"). This will load the data into a variable called homerun. If R says the homerun data set is not found, you can try installing the package by issuing this command install.packages("Zelig") 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 homerun R data set. The size of this file is about 7,582 bytes. You can share this page with this URL: http://tinyurl.com/t5xawa8.

Feel free to try out any of the free statistical operations found on this page. They are broken up into instructional and analysis applications. Two neat features include PDF export and auto-generation of an R command file which you can copy and paste into R or R Studio. You can try as many operations as you like. Quadstat is great for elementary statistics classes. If you are an instructor, feel free create a data set for your students.

Attachment Size
dataset-31749.csv 7.4 KB
Dataset License
GNU General Public License v2.0
Documentation

Sample Data on Home Runs Hit By Mark McGwire and Sammy Sosa in 1998.

Description

Game-by-game information for the 1998 season for Mark McGwire and Sammy Sosa. Data are a subset of the dataset provided in Simonoff (1998).

Usage

data(homerun)

Format

A data frame containing 5 variables ("gameno", "month", "homeruns", "playerstatus", "player") and 326 observations.

gameno

an integer variable denoting the game number

month

a factor variable taking with levels "March" through "September" denoting the month of the game

homeruns

an integer vector denoting the number of homeruns hit in that game for that player

playerstatus

an integer vector equal to "0" if the player played in the game, and "1" if they did not.

player

an integer vector equal to "0" (McGwire) or "1" (Sosa)

Source

https://ww2.amstat.org/publications/jse/v6n3/datasets.simonoff.html

References

Simonoff, Jeffrey S. 1998. “Move Over, Roger Maris: Breaking Baseball's Most Famous Record.” Journal of Statistics Education 6(3). Data used are a subset of the data in the article.

--

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

Documentation License
GNU General Public License v2.0

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