Wooldridge Source: Compiled by J. Monroe Gamble for a Summer Research Opportunities Program (SROP) at Michigan State University, Summer 2014. Monroe obtained data from the U.S. Census Bureau, the FBI Uniform Crime Reports, and the Death Penalty Information Center. Data loads lazily.

data('countymurders')

Format

A data.frame with 37349 observations on 20 variables:

  • arrests: # of murder arrests

  • countyid: county identifier: 1000*statefips + countyfips

  • density: population density; per square mile

  • popul: county population

  • perc1019: percent pop. age 10-19

  • perc2029: percent pop. age 20-29

  • percblack: percent population black

  • percmale: percent population male

  • rpcincmaint: real per capita income maintenance

  • rpcpersinc: real per capita personal income

  • rpcunemins: real per capita unem insurance payments

  • year: 1980-1996

  • murders: # of murders

  • murdrate: murders per 10,000 people

  • arrestrate: murder arrests per 10,000

  • statefips: state FIPS code

  • countyfips: county FIPS code

  • execs: # of executions

  • lpopul: log(popul)

  • execrate: executions per 10,000

Used in Text

pages 16, 58, 431, 457

Examples

 str(countymurders)
#> 'data.frame':	37349 obs. of  20 variables:
#>  $ arrests    : int  2 3 2 7 3 1 1 2 0 5 ...
#>  $ countyid   : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
#>  $ density    : num  54 53.7 53.8 53.8 53.9 ...
#>  $ popul      : int  32216 31984 32036 32056 32128 32248 32888 33264 33632 33992 ...
#>  $ perc1019   : num  20.6 20.2 19.7 19.1 18.5 ...
#>  $ perc2029   : num  15.3 15.6 15.7 15.9 15.9 ...
#>  $ percblack  : num  22.3 22.1 21.8 21.5 21.3 ...
#>  $ percmale   : num  40.2 40.4 40.4 40.5 40.5 ...
#>  $ rpcincmaint: num  168 168 167 177 166 ...
#>  $ rpcpersinc : num  8781 8233 8328 8546 8965 ...
#>  $ rpcunemins : num  29.2 43.9 71.4 72.2 40.4 ...
#>  $ year       : int  1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ...
#>  $ murders    : int  2 1 3 7 2 2 4 1 0 3 ...
#>  $ murdrate   : num  0.621 0.313 0.936 2.184 0.623 ...
#>  $ arrestrate : num  0.621 0.938 0.624 2.184 0.934 ...
#>  $ statefips  : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ countyfips : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ execs      : int  0 0 0 0 0 0 0 0 0 0 ...
#>  $ lpopul     : num  10.4 10.4 10.4 10.4 10.4 ...
#>  $ execrate   : num  0 0 0 0 0 0 0 0 0 0 ...