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These are the judge database attempts:
The first step to cleaning up the data and trying to look at it
library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondirtyJudge <- read.csv("historydata/jb.csv", stringsAsFactors = FALSE)
dirtyJudge <- tbl_df(dirtyJudge)
#dirtyJudge
#whew, file is too big to preview. Let's filter out some data we're interested in.
managableJudge <- dirtyJudge %>%
select(lastname = Judge.Last.Name,
firstname = Judge.First.Name,
middlename = Judge.Middle.Name,
birth = Birth.year,
death = Death.year,
gender = Gender,
raceth = Race.or.Ethnicity,
court = Court.Name)
managableJudge## Source: local data frame [3,532 x 8]
##
## lastname firstname middlename birth death gender raceth
## 1 Abrams Ronnie 1968 NA F White
## 2 Abruzzo Matthew T. 1889 1971 M White
## 3 Acheson Marcus Wilson 1828 1906 M White
## 4 Acker William Marsh 1927 NA M White
## 5 Ackerman Harold Arnold 1928 2009 M White
## 6 Ackerman James Waldo 1926 1984 M White
## 7 Acosta Raymond L. 1925 NA M Hispanic
## 8 Adair J[ackson] Leroy 1887 1956 M White
## 9 Adams Arlin Marvin 1921 NA M White
## 10 Adams Elmer Bragg 1842 1916 M White
## .. ... ... ... ... ... ... ...
## Variables not shown: court (chr)#okay, more managable, but still ugly. # Let's look at the judges in chronological order by birth year
chronologyJudge <- managableJudge %>%
arrange(birth)
chronologyJudge## Source: local data frame [3,532 x 8]
##
## lastname firstname middlename birth death gender raceth
## 1 Blair John 1732 1800 M White
## 2 Cushing William 1732 1810 M White
## 3 Drayton William 1732 1790 M White
## 4 Johnson Thomas 1732 1819 M White
## 5 Duane James 1733 1797 M White
## 6 Law Richard 1733 1806 M White
## 7 Sewall David 1735 1825 M White
## 8 Hopkinson Francis 1737 1791 M White
## 9 Pickering John 1737 1805 M White
## 10 Hobart John Sloss 1738 1805 M White
## .. ... ... ... ... ... ... ...
## Variables not shown: court (chr)median(chronologyJudge$birth, na.rm = TRUE)## [1] 1923# median year of birth for judges = 1923
# lets look at median age
ageJudge <- managableJudge %>%
mutate(ageJudge = death - birth)
median(ageJudge$ageJudge, na.rm = TRUE)## [1] 76mean(ageJudge$ageJudge, na.rm = TRUE)## [1] 75.24# median age for the judges is 76, mean age is 75.23 (this is more like lifespan than age... )
# that was fun. I guess.#Let's look at how many female judges there are!
# First, we'll make sure there are no empty gender fields - organize by gender so I can look through.
genderJudge <- managableJudge %>%
arrange(gender)
genderJudge## Source: local data frame [3,532 x 8]
##
## lastname firstname middlename birth death gender raceth
## 1 Abrams Ronnie 1968 NA F White
## 2 Aiken Ann L. 1951 NA F White
## 3 Aldrich Ann 1927 2010 F White
## 4 Allen Arenda Lauretta Wright 1960 NA F African American
## 5 Allen Florence Ellinwood 1884 1966 F White
## 6 Altonaga Cecilia M. 1962 NA F Hispanic
## 7 Alvarez Micaela 1958 NA F Hispanic
## 8 Ambrose Donetta W. 1945 NA F White
## 9 Amon Carol Bagley 1946 NA F White
## 10 Arguello Christine M. 1955 NA F Hispanic
## .. ... ... ... ... ... ... ...
## Variables not shown: court (chr)# great. All M or F, add a filter and see ONLY female Judges
femaleJudge <- managableJudge %>%
filter(gender == "F")
femaleJudge## Source: local data frame [389 x 8]
##
## lastname firstname middlename birth death gender raceth
## 1 Abrams Ronnie 1968 NA F White
## 2 Aiken Ann L. 1951 NA F White
## 3 Aldrich Ann 1927 2010 F White
## 4 Allen Arenda Lauretta Wright 1960 NA F African American
## 5 Allen Florence Ellinwood 1884 1966 F White
## 6 Altonaga Cecilia M. 1962 NA F Hispanic
## 7 Alvarez Micaela 1958 NA F Hispanic
## 8 Ambrose Donetta W. 1945 NA F White
## 9 Amon Carol Bagley 1946 NA F White
## 10 Arguello Christine M. 1955 NA F Hispanic
## .. ... ... ... ... ... ... ...
## Variables not shown: court (chr)# great - but that I still have to look to see how many there are or how many male judges there are: give me a number and do the work for me!
gendersJudge <- managableJudge %>%
group_by(gender) %>%
summarize(amountGender = n())
gendersJudge## Source: local data frame [2 x 2]
##
## gender amountGender
## 1 F 389
## 2 M 3143#what can I learn about the distribution of race/ethnicity?
reJudge <- managableJudge %>%
group_by(raceth) %>%
summarize(distribRE = n()) %>%
arrange(desc(distribRE))
reJudge## Source: local data frame [12 x 2]
##
## raceth distribRE
## 1 White 3164
## 2 African American 201
## 3 Hispanic 116
## 4 Asian American 31
## 5 9
## 6 American Indian 3
## 7 African Am./Hispanic 2
## 8 Pac. Isl./Asian Am. 2
## 9 Hispanic/Asian Am. 1
## 10 Hispanic/White 1
## 11 Pac. Isl./White 1
## 12 White/Asian Am. 1#fascinating.#next I'd like to play with race and gender - this will require me to mutate/spread the column to set a new variable. Ran out of time.