Venire Dataset
The venire dataset contains all of the potential jurors who showed up at the courthouse on the day of their jury summons.
All Individuals
venire |>
count() |>
rename(Individuals = "n")
#> # A tibble: 1 × 1
#> Individuals
#> <int>
#> 1 3300#> # A tibble: 6 × 2
#> Race Jurors
#> <fct> <int>
#> 1 Black 605
#> 2 Hispanic 639
#> 3 White 1648
#> 4 Asian 200
#> 5 Other 195
#> 6 Native American 13#> # A tibble: 3 × 2
#> Gender Jurors
#> <fct> <int>
#> 1 Female 1739
#> 2 Male 1510
#> 3 Other 51#> # A tibble: 4 × 2
#> `Age Group` Jurors
#> <fct> <int>
#> 1 18 - 30 580
#> 2 31 - 44 911
#> 3 45 - 54 742
#> 4 55 + 1067Disqualified
Before proceeding to juror questioning, the Court determines whether any potential jurors should be disqualified.
#> # A tibble: 5 × 2
#> Race Jurors
#> <fct> <int>
#> 1 Black 8
#> 2 Hispanic 96
#> 3 White 14
#> 4 Asian 43
#> 5 Other 11#> # A tibble: 3 × 2
#> Gender Jurors
#> <fct> <int>
#> 1 Female 88
#> 2 Male 74
#> 3 Other 10#> # A tibble: 4 × 2
#> `Age Group` Jurors
#> <fct> <int>
#> 1 18 - 30 8
#> 2 31 - 44 40
#> 3 45 - 54 55
#> 4 55 + 69
bullpen Dataset
Before beginning the questioning of potential jurors, the Court ensures that everyone is eligible to serve and should not be disqualified. Once those individuals have been identified and dismissed, the remaining potential jurors can be found in the bullpen dataset.
For race, due to the small number who identified as “Native American”, those were combined with “Other” for the remaining analyses.
A Note About Manually Tallying Numbers
It is possible that numbers may not add up to their parent totals. For example, a potential juror might be peremptory challenged by both the State and the Defense leading the individual counts to be greater than the single total of all challenges.
Caused
#> # A tibble: 5 × 2
#> Race Jurors
#> <fct> <int>
#> 1 Black 316
#> 2 Hispanic 302
#> 3 White 675
#> 4 Asian 82
#> 5 Other 93#> # A tibble: 3 × 2
#> Gender Jurors
#> <fct> <int>
#> 1 Female 754
#> 2 Male 688
#> 3 Other 26#> # A tibble: 4 × 2
#> Age Jurors
#> <fct> <int>
#> 1 18 - 30 295
#> 2 31 - 44 445
#> 3 45 - 54 277
#> 4 55 + 451#> # A tibble: 3 × 2
#> `I-30` Jurors
#> <fct> <int>
#> 1 South 488
#> 2 North 928
#> 3 Split 52Jurors Dismissed Through Peremptory Challenges by State
#> # A tibble: 5 × 2
#> Race Jurors
#> <fct> <int>
#> 1 Black 107
#> 2 Hispanic 76
#> 3 White 221
#> 4 Asian 24
#> 5 Other 33#> # A tibble: 3 × 2
#> Gender Jurors
#> <fct> <int>
#> 1 Female 248
#> 2 Male 209
#> 3 Other 4#> # A tibble: 4 × 2
#> Age Jurors
#> <fct> <int>
#> 1 18 - 30 98
#> 2 31 - 44 128
#> 3 45 - 54 92
#> 4 55 + 143#> # A tibble: 3 × 2
#> `I-30` Jurors
#> <fct> <int>
#> 1 South 147
#> 2 North 304
#> 3 Split 10Jurors Dismissed Through Peremptory Challenges by Defense
#> # A tibble: 5 × 2
#> Race Jurors
#> <fct> <int>
#> 1 Black 46
#> 2 Hispanic 46
#> 3 White 356
#> 4 Asian 16
#> 5 Other 29#> # A tibble: 3 × 2
#> Gender Jurors
#> <fct> <int>
#> 1 Female 280
#> 2 Male 210
#> 3 Other 3#> # A tibble: 4 × 2
#> Age Jurors
#> <fct> <int>
#> 1 18 - 30 60
#> 2 31 - 44 110
#> 3 45 - 54 145
#> 4 55 + 178#> # A tibble: 3 × 2
#> `I-30` Jurors
#> <fct> <int>
#> 1 South 123
#> 2 North 353
#> 3 Split 17Averages per Trial
#> # A tibble: 5 × 2
#> Race `Avg. # Jurors`
#> <fct> <dbl>
#> 1 Black 2.91
#> 2 Hispanic 2.95
#> 3 White 8.48
#> 4 Asian 1.38
#> 5 Other 1.91#> # A tibble: 3 × 2
#> Gender `Avg. # Jurors`
#> <fct> <dbl>
#> 1 Female 8.08
#> 2 Male 7.02
#> 3 Other 1.14#> # A tibble: 4 × 2
#> `Age Group` `Avg. # Jurors`
#> <fct> <dbl>
#> 1 18 - 30 2.55
#> 2 31 - 44 4.22
#> 3 45 - 54 4.06
#> 4 55 + 4.83#> # A tibble: 4 × 2
#> `I-30` `Avg. # Jurors`
#> <fct> <dbl>
#> 1 South 4.48
#> 2 North 10.3
#> 3 Split 1.28
#> 4 NA 1The missing data point comes from a data entry error with the zip code for one juror.
The specific reason for the highest occurring causes for each column.