trackfieldrecords: trackfieldrecords: National Records for Track and Field...
In thoree/stat340: Data and functions for BIAS at NMBU

trackfieldrecords

R Documentation

trackfieldrecords: National Records for Track and Field Events

Description

This data set contains data of national records for male and female athletics for several track and field events like 100m, 200m and so on up to marathon. All records are given in minutes.

Usage

trackfieldrecords

Format

A list with 2 elements of type data frame.

The columns of the 2 data frames, runMen and runWomen, in the list:

runMen, a data frame with 52 observations (rows) and 9 variables (columns):

	Column name	Data type	Description	Values
`[,1]`	`M100`	numeric	Records for 100 m	(0.1655 - 0.2030)
`[,2]`	`M200`	numeric	Records for 200 m	(0.3287 - 0.3867)
`[,3]`	`M400`	numeric	Records for 400 m	(0.7310 - 0.8823)
`[,4]`	`M800`	numeric	Records for 800 m	(1.70 - 2.02)
`[,5]`	`M1500`	numeric	Records for 1500 m	(3.51 - 4.24)
`[,6]`	`M5000`	numeric	Records for 5000 m	(13.01 - 16.70)
`[,7]`	`M10K`	numeric	Records for 10 000 m	(27.38 - 35.38)
`[,8]`	`M42K`	numeric	Records for marathon	(128.2 - 164.7)
`[,9]`	`Nation`	factor	The nationality of the athletics	(argentin...wsamoa)

runWomen, a data frame with 52 observations (rows) and 8 variables (columns):

	Column name	Data type	Description	Values
`[,1]`	`M100`	numeric	Records for 100 m	(0.1798 - 0.2150)
`[,2]`	`M200`	numeric	Records for 200 m	(0.3638 - 0.4517)
`[,3]`	`M400`	numeric	Records for 400 m	(0.7998 - 1.0067)
`[,4]`	`M800`	numeric	Records for 800 m	(1.89 - 2.33)
`[,5]`	`M1500`	numeric	Records for 1500 m	(3.95 - 5.81)
`[,6]`	`M3000`	numeric	Records for 3000 m	(8.50 - 13.04)
`[,7]`	`M42K`	numeric	Records for marathon	(142.7 - 306.0)
`[,8]`	`Nation`	factor	The nationality of the athletics	(argentin...wsamoa)

Details

In the first 5 columns of the two data frames, the variables are the same (M100 - M1500), and they both have the columns Nation and M42K. While runMen has the columns M5000 and M10K, runWomen has M3000.

Examples


# Get the data frames into "Environment"
runMen <- trackfieldrecords$runMen
runWomen <- trackfieldrecords$runWomen

# Hierarchical Clustering
clust <-hclust(dist(runWomen[,-8]), method = "average")
plot(clust, hang = -1, xlab = "", sub ="", cex = 0.6,
     labels = runWomen[,8])

thoree/stat340 documentation built on June 30, 2024, 4:04 p.m.