Comparing multiple categorical variables in R





.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
}







0















So I would like to stack the two bars from each of these graphs into one big graph. That is, I would like Black State Claim (from plot a) to be right next to Black Civil Rights Claim (from plot b) and consequently for all races into one graph.



Since some of the data, like asian, is so low, is there a more ideal way to compare State Claim/Civil Rights Claim Status with Race???



#a) State Claim?        
race_claim <- data.frame(table(jail$Race,jail$State_Claim_Made))
names(race_claim) <- c("Race","Claim","Count")


ggplot(data=race_claim, aes(x=Race, y=Count, fill=Claim)) + geom_bar(stat = "identity")

#b) civil rights claim?

race_claim_civ <- data.frame(table(jail$Race,jail$Non_Statutory))
names(race_claim_civ) <- c("Race","Claim","Count")

ggplot(data=race_claim_civ, aes(x=Race, y=Count, fill=Claim)) + geom_bar(stat = "identity")


DATA SAMPLE:



structure(list(Last_Name = c("Banks", "Beamon", "Dandridge", 
"Deakle, Jr.", "Doyle", "Drinkard", "Ellis", "Embry", "Gaines",
"Gurley", "Hinton", "Holemon", "Holsomback", "Hunt", "Jones",
"Mahan", "Mahan", "McMillian", "Moore", "Padgett"), First_Name = c("Medell",
"Melvin Todd", "Beniah Alton", "Evan Lee", "Robert E.", "Gary",
"Andre", "Anthony", "Freddie Lee", "Timothy", "Anthony", "Jeffrey",
"John", "H. Guy", "Lydia Diane", "Dale", "Ronnie", "Walter",
"Daniel Wade", "Larry Randal"), Age = c("27", "24", "29", "59",
"44", "37", "35", "23", "22", "22", "29", "23", "33", "54", "40",
"22", "26", "45", "24", "40"), Race = c("Black", "Asian", "Caucasian",
"Caucasian", "Other", "Asian", "Black", "Black", "Black",
"Caucasian", "Black", "Caucasian", "Caucasian", "Other",
"Black", "Caucasian", "Asian", "Black", "Native American", "Caucasian"
), Sex = c("Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Male", "Male", "Male", "Male", "Male"), State = c("Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama"), CIU = c(0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 0), Guilty_Plea = c(1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), IO = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Worst_Crime = c(6, 1,
1, 4, 4, 1, 2, 1, 1, 6, 1, 2, 4, 6, 3, 2, 2, 1, 1, 1), Occurred = c(1999,
1988, 1994, 2014, 1991, 1993, 2012, 1992, 1972, 1999, 1985, 1987,
1987, 1987, 1997, 1983, 1983, 1986, 1999, 1990), Convicted = c(2001,
1989, 1996, 2015, 1992, 1995, 2013, 1993, 1974, 2000, 1986, 1988,
1988, 1993, 2000, 1986, 1986, 1988, 2002, 1992), Exonerated = c(2003,
1990, 2015, 2015, 2001, 2001, 2014, 1997, 1991, 2002, 2015, 1999,
2000, 1998, 2006, 1998, 1998, 1993, 2009, 1997), Sentence = c("15",
"25", "Life", "Not sentenced", "20", "Death", "85", "20", "30",
"35", "Death", "Life", "25", "Probation", "Life without parole",
"35", "Life without parole", "Death", "Death", "Death"), Death_Penalty = c(0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1), DNA_Only = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0), FC = c(1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), MWID = c(0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0), F_MFE = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1), P_FA = c(1,
1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0), OM = c(1,
1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1), ILD = c(0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0), State_Statute = c("Y",
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",
"Y", "Y", "Y", "Y", "Y", "Y"), State_Claim_Made = c(0, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1 0), Zero_time = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), Prem = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Pending = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), Denied = c(0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), State_Award = c("0",
"0", "2", "0", "1", "0", "0", "0", "1", "0", "2", "0", "0", "0",
"0", "0", "0", "0", "0", "0"), Amount = c("0", "0", NA, "0",
"129041.88", "0", "0", "0", "1000000", "0", NA, "0", "0", "0",
"0", "0", "0", "0", "0", "0"), `Non-Statutory_Case_Filed` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0), No_Time = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), Unfiled = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1), Dismissed = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0), Pending__1 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Award = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0), Premature = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Amount__1 = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "$ undisclosed", "0", "0"), Years_Lost = c(1.7,
0.1, 19.5, 0, 2.6, 5.7, 1.8, 4, 10.7, 1.5, 28.5, 10.6, 10.1,
0, 5.8, 11.4, 11.4, 4.5, 5.4, 5.5), State_Award2 = c("0", "0",
"0", "0", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0")), row.names = c(NA, -20L), class = c("tbl_df",
"tbl", "data.frame"))









share|improve this question























  • Something seems to be off with the structure you posted. Could you check again? Also it would be helpful if you could provide copy your output plot into the question.

    – Roman
    Nov 24 '18 at 10:58


















0















So I would like to stack the two bars from each of these graphs into one big graph. That is, I would like Black State Claim (from plot a) to be right next to Black Civil Rights Claim (from plot b) and consequently for all races into one graph.



Since some of the data, like asian, is so low, is there a more ideal way to compare State Claim/Civil Rights Claim Status with Race???



#a) State Claim?        
race_claim <- data.frame(table(jail$Race,jail$State_Claim_Made))
names(race_claim) <- c("Race","Claim","Count")


ggplot(data=race_claim, aes(x=Race, y=Count, fill=Claim)) + geom_bar(stat = "identity")

#b) civil rights claim?

race_claim_civ <- data.frame(table(jail$Race,jail$Non_Statutory))
names(race_claim_civ) <- c("Race","Claim","Count")

ggplot(data=race_claim_civ, aes(x=Race, y=Count, fill=Claim)) + geom_bar(stat = "identity")


DATA SAMPLE:



structure(list(Last_Name = c("Banks", "Beamon", "Dandridge", 
"Deakle, Jr.", "Doyle", "Drinkard", "Ellis", "Embry", "Gaines",
"Gurley", "Hinton", "Holemon", "Holsomback", "Hunt", "Jones",
"Mahan", "Mahan", "McMillian", "Moore", "Padgett"), First_Name = c("Medell",
"Melvin Todd", "Beniah Alton", "Evan Lee", "Robert E.", "Gary",
"Andre", "Anthony", "Freddie Lee", "Timothy", "Anthony", "Jeffrey",
"John", "H. Guy", "Lydia Diane", "Dale", "Ronnie", "Walter",
"Daniel Wade", "Larry Randal"), Age = c("27", "24", "29", "59",
"44", "37", "35", "23", "22", "22", "29", "23", "33", "54", "40",
"22", "26", "45", "24", "40"), Race = c("Black", "Asian", "Caucasian",
"Caucasian", "Other", "Asian", "Black", "Black", "Black",
"Caucasian", "Black", "Caucasian", "Caucasian", "Other",
"Black", "Caucasian", "Asian", "Black", "Native American", "Caucasian"
), Sex = c("Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Male", "Male", "Male", "Male", "Male"), State = c("Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama"), CIU = c(0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 0), Guilty_Plea = c(1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), IO = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Worst_Crime = c(6, 1,
1, 4, 4, 1, 2, 1, 1, 6, 1, 2, 4, 6, 3, 2, 2, 1, 1, 1), Occurred = c(1999,
1988, 1994, 2014, 1991, 1993, 2012, 1992, 1972, 1999, 1985, 1987,
1987, 1987, 1997, 1983, 1983, 1986, 1999, 1990), Convicted = c(2001,
1989, 1996, 2015, 1992, 1995, 2013, 1993, 1974, 2000, 1986, 1988,
1988, 1993, 2000, 1986, 1986, 1988, 2002, 1992), Exonerated = c(2003,
1990, 2015, 2015, 2001, 2001, 2014, 1997, 1991, 2002, 2015, 1999,
2000, 1998, 2006, 1998, 1998, 1993, 2009, 1997), Sentence = c("15",
"25", "Life", "Not sentenced", "20", "Death", "85", "20", "30",
"35", "Death", "Life", "25", "Probation", "Life without parole",
"35", "Life without parole", "Death", "Death", "Death"), Death_Penalty = c(0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1), DNA_Only = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0), FC = c(1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), MWID = c(0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0), F_MFE = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1), P_FA = c(1,
1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0), OM = c(1,
1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1), ILD = c(0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0), State_Statute = c("Y",
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",
"Y", "Y", "Y", "Y", "Y", "Y"), State_Claim_Made = c(0, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1 0), Zero_time = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), Prem = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Pending = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), Denied = c(0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), State_Award = c("0",
"0", "2", "0", "1", "0", "0", "0", "1", "0", "2", "0", "0", "0",
"0", "0", "0", "0", "0", "0"), Amount = c("0", "0", NA, "0",
"129041.88", "0", "0", "0", "1000000", "0", NA, "0", "0", "0",
"0", "0", "0", "0", "0", "0"), `Non-Statutory_Case_Filed` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0), No_Time = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), Unfiled = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1), Dismissed = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0), Pending__1 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Award = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0), Premature = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Amount__1 = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "$ undisclosed", "0", "0"), Years_Lost = c(1.7,
0.1, 19.5, 0, 2.6, 5.7, 1.8, 4, 10.7, 1.5, 28.5, 10.6, 10.1,
0, 5.8, 11.4, 11.4, 4.5, 5.4, 5.5), State_Award2 = c("0", "0",
"0", "0", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0")), row.names = c(NA, -20L), class = c("tbl_df",
"tbl", "data.frame"))









share|improve this question























  • Something seems to be off with the structure you posted. Could you check again? Also it would be helpful if you could provide copy your output plot into the question.

    – Roman
    Nov 24 '18 at 10:58














0












0








0








So I would like to stack the two bars from each of these graphs into one big graph. That is, I would like Black State Claim (from plot a) to be right next to Black Civil Rights Claim (from plot b) and consequently for all races into one graph.



Since some of the data, like asian, is so low, is there a more ideal way to compare State Claim/Civil Rights Claim Status with Race???



#a) State Claim?        
race_claim <- data.frame(table(jail$Race,jail$State_Claim_Made))
names(race_claim) <- c("Race","Claim","Count")


ggplot(data=race_claim, aes(x=Race, y=Count, fill=Claim)) + geom_bar(stat = "identity")

#b) civil rights claim?

race_claim_civ <- data.frame(table(jail$Race,jail$Non_Statutory))
names(race_claim_civ) <- c("Race","Claim","Count")

ggplot(data=race_claim_civ, aes(x=Race, y=Count, fill=Claim)) + geom_bar(stat = "identity")


DATA SAMPLE:



structure(list(Last_Name = c("Banks", "Beamon", "Dandridge", 
"Deakle, Jr.", "Doyle", "Drinkard", "Ellis", "Embry", "Gaines",
"Gurley", "Hinton", "Holemon", "Holsomback", "Hunt", "Jones",
"Mahan", "Mahan", "McMillian", "Moore", "Padgett"), First_Name = c("Medell",
"Melvin Todd", "Beniah Alton", "Evan Lee", "Robert E.", "Gary",
"Andre", "Anthony", "Freddie Lee", "Timothy", "Anthony", "Jeffrey",
"John", "H. Guy", "Lydia Diane", "Dale", "Ronnie", "Walter",
"Daniel Wade", "Larry Randal"), Age = c("27", "24", "29", "59",
"44", "37", "35", "23", "22", "22", "29", "23", "33", "54", "40",
"22", "26", "45", "24", "40"), Race = c("Black", "Asian", "Caucasian",
"Caucasian", "Other", "Asian", "Black", "Black", "Black",
"Caucasian", "Black", "Caucasian", "Caucasian", "Other",
"Black", "Caucasian", "Asian", "Black", "Native American", "Caucasian"
), Sex = c("Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Male", "Male", "Male", "Male", "Male"), State = c("Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama"), CIU = c(0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 0), Guilty_Plea = c(1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), IO = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Worst_Crime = c(6, 1,
1, 4, 4, 1, 2, 1, 1, 6, 1, 2, 4, 6, 3, 2, 2, 1, 1, 1), Occurred = c(1999,
1988, 1994, 2014, 1991, 1993, 2012, 1992, 1972, 1999, 1985, 1987,
1987, 1987, 1997, 1983, 1983, 1986, 1999, 1990), Convicted = c(2001,
1989, 1996, 2015, 1992, 1995, 2013, 1993, 1974, 2000, 1986, 1988,
1988, 1993, 2000, 1986, 1986, 1988, 2002, 1992), Exonerated = c(2003,
1990, 2015, 2015, 2001, 2001, 2014, 1997, 1991, 2002, 2015, 1999,
2000, 1998, 2006, 1998, 1998, 1993, 2009, 1997), Sentence = c("15",
"25", "Life", "Not sentenced", "20", "Death", "85", "20", "30",
"35", "Death", "Life", "25", "Probation", "Life without parole",
"35", "Life without parole", "Death", "Death", "Death"), Death_Penalty = c(0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1), DNA_Only = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0), FC = c(1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), MWID = c(0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0), F_MFE = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1), P_FA = c(1,
1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0), OM = c(1,
1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1), ILD = c(0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0), State_Statute = c("Y",
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",
"Y", "Y", "Y", "Y", "Y", "Y"), State_Claim_Made = c(0, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1 0), Zero_time = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), Prem = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Pending = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), Denied = c(0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), State_Award = c("0",
"0", "2", "0", "1", "0", "0", "0", "1", "0", "2", "0", "0", "0",
"0", "0", "0", "0", "0", "0"), Amount = c("0", "0", NA, "0",
"129041.88", "0", "0", "0", "1000000", "0", NA, "0", "0", "0",
"0", "0", "0", "0", "0", "0"), `Non-Statutory_Case_Filed` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0), No_Time = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), Unfiled = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1), Dismissed = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0), Pending__1 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Award = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0), Premature = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Amount__1 = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "$ undisclosed", "0", "0"), Years_Lost = c(1.7,
0.1, 19.5, 0, 2.6, 5.7, 1.8, 4, 10.7, 1.5, 28.5, 10.6, 10.1,
0, 5.8, 11.4, 11.4, 4.5, 5.4, 5.5), State_Award2 = c("0", "0",
"0", "0", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0")), row.names = c(NA, -20L), class = c("tbl_df",
"tbl", "data.frame"))









share|improve this question














So I would like to stack the two bars from each of these graphs into one big graph. That is, I would like Black State Claim (from plot a) to be right next to Black Civil Rights Claim (from plot b) and consequently for all races into one graph.



Since some of the data, like asian, is so low, is there a more ideal way to compare State Claim/Civil Rights Claim Status with Race???



#a) State Claim?        
race_claim <- data.frame(table(jail$Race,jail$State_Claim_Made))
names(race_claim) <- c("Race","Claim","Count")


ggplot(data=race_claim, aes(x=Race, y=Count, fill=Claim)) + geom_bar(stat = "identity")

#b) civil rights claim?

race_claim_civ <- data.frame(table(jail$Race,jail$Non_Statutory))
names(race_claim_civ) <- c("Race","Claim","Count")

ggplot(data=race_claim_civ, aes(x=Race, y=Count, fill=Claim)) + geom_bar(stat = "identity")


DATA SAMPLE:



structure(list(Last_Name = c("Banks", "Beamon", "Dandridge", 
"Deakle, Jr.", "Doyle", "Drinkard", "Ellis", "Embry", "Gaines",
"Gurley", "Hinton", "Holemon", "Holsomback", "Hunt", "Jones",
"Mahan", "Mahan", "McMillian", "Moore", "Padgett"), First_Name = c("Medell",
"Melvin Todd", "Beniah Alton", "Evan Lee", "Robert E.", "Gary",
"Andre", "Anthony", "Freddie Lee", "Timothy", "Anthony", "Jeffrey",
"John", "H. Guy", "Lydia Diane", "Dale", "Ronnie", "Walter",
"Daniel Wade", "Larry Randal"), Age = c("27", "24", "29", "59",
"44", "37", "35", "23", "22", "22", "29", "23", "33", "54", "40",
"22", "26", "45", "24", "40"), Race = c("Black", "Asian", "Caucasian",
"Caucasian", "Other", "Asian", "Black", "Black", "Black",
"Caucasian", "Black", "Caucasian", "Caucasian", "Other",
"Black", "Caucasian", "Asian", "Black", "Native American", "Caucasian"
), Sex = c("Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Male", "Male", "Male", "Male", "Male"), State = c("Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama",
"Alabama"), CIU = c(0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 0), Guilty_Plea = c(1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), IO = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Worst_Crime = c(6, 1,
1, 4, 4, 1, 2, 1, 1, 6, 1, 2, 4, 6, 3, 2, 2, 1, 1, 1), Occurred = c(1999,
1988, 1994, 2014, 1991, 1993, 2012, 1992, 1972, 1999, 1985, 1987,
1987, 1987, 1997, 1983, 1983, 1986, 1999, 1990), Convicted = c(2001,
1989, 1996, 2015, 1992, 1995, 2013, 1993, 1974, 2000, 1986, 1988,
1988, 1993, 2000, 1986, 1986, 1988, 2002, 1992), Exonerated = c(2003,
1990, 2015, 2015, 2001, 2001, 2014, 1997, 1991, 2002, 2015, 1999,
2000, 1998, 2006, 1998, 1998, 1993, 2009, 1997), Sentence = c("15",
"25", "Life", "Not sentenced", "20", "Death", "85", "20", "30",
"35", "Death", "Life", "25", "Probation", "Life without parole",
"35", "Life without parole", "Death", "Death", "Death"), Death_Penalty = c(0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1), DNA_Only = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0), FC = c(1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), MWID = c(0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0), F_MFE = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1), P_FA = c(1,
1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0), OM = c(1,
1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1), ILD = c(0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0), State_Statute = c("Y",
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y",
"Y", "Y", "Y", "Y", "Y", "Y"), State_Claim_Made = c(0, 0, 1,
0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1 0), Zero_time = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), Prem = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Pending = c(0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), Denied = c(0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), State_Award = c("0",
"0", "2", "0", "1", "0", "0", "0", "1", "0", "2", "0", "0", "0",
"0", "0", "0", "0", "0", "0"), Amount = c("0", "0", NA, "0",
"129041.88", "0", "0", "0", "1000000", "0", NA, "0", "0", "0",
"0", "0", "0", "0", "0", "0"), `Non-Statutory_Case_Filed` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0), No_Time = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), Unfiled = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1), Dismissed = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0), Pending__1 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Award = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0), Premature = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Amount__1 = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "$ undisclosed", "0", "0"), Years_Lost = c(1.7,
0.1, 19.5, 0, 2.6, 5.7, 1.8, 4, 10.7, 1.5, 28.5, 10.6, 10.1,
0, 5.8, 11.4, 11.4, 4.5, 5.4, 5.5), State_Award2 = c("0", "0",
"0", "0", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0")), row.names = c(NA, -20L), class = c("tbl_df",
"tbl", "data.frame"))






r ggplot2 categorical-data






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 24 '18 at 2:56









Juanito TomasJuanito Tomas

669




669













  • Something seems to be off with the structure you posted. Could you check again? Also it would be helpful if you could provide copy your output plot into the question.

    – Roman
    Nov 24 '18 at 10:58



















  • Something seems to be off with the structure you posted. Could you check again? Also it would be helpful if you could provide copy your output plot into the question.

    – Roman
    Nov 24 '18 at 10:58

















Something seems to be off with the structure you posted. Could you check again? Also it would be helpful if you could provide copy your output plot into the question.

– Roman
Nov 24 '18 at 10:58





Something seems to be off with the structure you posted. Could you check again? Also it would be helpful if you could provide copy your output plot into the question.

– Roman
Nov 24 '18 at 10:58












1 Answer
1






active

oldest

votes


















1














I think there is a clash between two requirements: to make the barplot stack-ed and at the same time - dodge-d. Probably my solution isn't the best, and someone would do better. But that's what I've got right now:



Preprocessing



library(tidyverse)

dat <- jail %>%
rename_all(tolower) %>%
select(race, state_claim_made, non_statutory_case_filed) %>%
gather(key = action, value = claim, 2, 3) %>%
count(race, action, claim) %>%
mutate(action = ifelse(action == "state_claim_made", "state", "civil")) %>%
mutate(x = as.numeric(reorder(interaction(race, action), 1:n())))


Output:

# # A tibble: 15 x 5
# race action claim n x
# <chr> <chr> <dbl> <int> <dbl>
# 1 Asian civil 0 3 1
# 2 Asian state 0 2 2
# 3 Asian state 1 1 2
# 4 Black civil 0 6 3
# 5 Black civil 1 1 3
# 6 Black state 0 3 4
# 7 Black state 1 4 4
# 8 Caucasian civil 0 7 5
# 9 Caucasian state 0 6 6
# 10 Caucasian state 1 1 6
# 11 Native American civil 1 1 7
# 12 Native American state 1 1 8
# 13 Other civil 0 2 9
# 14 Other state 0 1 10
# 15 Other state 1 1 10


Some necessary tweaks for x-axis labels:



Adapted from this answer:



breaks = sort(c(unique(dat$x), seq(min(dat$x) + .5, 
max(dat$x) + .5,
length(unique(dat$action))
)
)
)

labels = unlist(
lapply(unique(dat$race), function(i) c("civil", paste0("n", i), "state"))
)


Plot data



ggplot(dat, aes(x = x, y = n, fill = factor(claim))) +
geom_col(show.legend = T) +
ggthemes::theme_few() +
scale_fill_manual(name = NULL,
values = c("gray75", "gray25"),
breaks= c("0", "1"),
labels = c("false", "true")
) +
scale_x_continuous(breaks = breaks, labels = labels) +
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank()) +
labs(title = "Jail Plot", y = "Count")


jailplot



Data



The data you attached are corrupted - missing comma or $ somewhere in the table (I don't remember what that was). There are the same data, but without variables we don't to solve the problem.



structure(
list(Race = c("Black", "Asian", "Caucasian", "Caucasian", "Other", "Asian",
"Black", "Black", "Black", "Caucasian", "Black", "Caucasian",
"Caucasian", "Other", "Black", "Caucasian", "Asian", "Black",
"Native American", "Caucasian"),
State_Claim_Made = c(0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
0, 1, 0),
Non_Statutory_Case_Filed = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0)
),
row.names = c(NA, -20L),
class = c("tbl_df", "tbl", "data.frame")
)





share|improve this answer


























  • Wow good stuff, wish I could code like this haha. Now I would like to further compare these groups. To see if the proportion of True/False (Filed a claim vs. Not filed a claim) is the same for each race, how would I do an ANOVA test with this setup?

    – Juanito Tomas
    Nov 26 '18 at 23:14












Your Answer






StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});














draft saved

draft discarded


















StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53454787%2fcomparing-multiple-categorical-variables-in-r%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown

























1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









1














I think there is a clash between two requirements: to make the barplot stack-ed and at the same time - dodge-d. Probably my solution isn't the best, and someone would do better. But that's what I've got right now:



Preprocessing



library(tidyverse)

dat <- jail %>%
rename_all(tolower) %>%
select(race, state_claim_made, non_statutory_case_filed) %>%
gather(key = action, value = claim, 2, 3) %>%
count(race, action, claim) %>%
mutate(action = ifelse(action == "state_claim_made", "state", "civil")) %>%
mutate(x = as.numeric(reorder(interaction(race, action), 1:n())))


Output:

# # A tibble: 15 x 5
# race action claim n x
# <chr> <chr> <dbl> <int> <dbl>
# 1 Asian civil 0 3 1
# 2 Asian state 0 2 2
# 3 Asian state 1 1 2
# 4 Black civil 0 6 3
# 5 Black civil 1 1 3
# 6 Black state 0 3 4
# 7 Black state 1 4 4
# 8 Caucasian civil 0 7 5
# 9 Caucasian state 0 6 6
# 10 Caucasian state 1 1 6
# 11 Native American civil 1 1 7
# 12 Native American state 1 1 8
# 13 Other civil 0 2 9
# 14 Other state 0 1 10
# 15 Other state 1 1 10


Some necessary tweaks for x-axis labels:



Adapted from this answer:



breaks = sort(c(unique(dat$x), seq(min(dat$x) + .5, 
max(dat$x) + .5,
length(unique(dat$action))
)
)
)

labels = unlist(
lapply(unique(dat$race), function(i) c("civil", paste0("n", i), "state"))
)


Plot data



ggplot(dat, aes(x = x, y = n, fill = factor(claim))) +
geom_col(show.legend = T) +
ggthemes::theme_few() +
scale_fill_manual(name = NULL,
values = c("gray75", "gray25"),
breaks= c("0", "1"),
labels = c("false", "true")
) +
scale_x_continuous(breaks = breaks, labels = labels) +
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank()) +
labs(title = "Jail Plot", y = "Count")


jailplot



Data



The data you attached are corrupted - missing comma or $ somewhere in the table (I don't remember what that was). There are the same data, but without variables we don't to solve the problem.



structure(
list(Race = c("Black", "Asian", "Caucasian", "Caucasian", "Other", "Asian",
"Black", "Black", "Black", "Caucasian", "Black", "Caucasian",
"Caucasian", "Other", "Black", "Caucasian", "Asian", "Black",
"Native American", "Caucasian"),
State_Claim_Made = c(0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
0, 1, 0),
Non_Statutory_Case_Filed = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0)
),
row.names = c(NA, -20L),
class = c("tbl_df", "tbl", "data.frame")
)





share|improve this answer


























  • Wow good stuff, wish I could code like this haha. Now I would like to further compare these groups. To see if the proportion of True/False (Filed a claim vs. Not filed a claim) is the same for each race, how would I do an ANOVA test with this setup?

    – Juanito Tomas
    Nov 26 '18 at 23:14
















1














I think there is a clash between two requirements: to make the barplot stack-ed and at the same time - dodge-d. Probably my solution isn't the best, and someone would do better. But that's what I've got right now:



Preprocessing



library(tidyverse)

dat <- jail %>%
rename_all(tolower) %>%
select(race, state_claim_made, non_statutory_case_filed) %>%
gather(key = action, value = claim, 2, 3) %>%
count(race, action, claim) %>%
mutate(action = ifelse(action == "state_claim_made", "state", "civil")) %>%
mutate(x = as.numeric(reorder(interaction(race, action), 1:n())))


Output:

# # A tibble: 15 x 5
# race action claim n x
# <chr> <chr> <dbl> <int> <dbl>
# 1 Asian civil 0 3 1
# 2 Asian state 0 2 2
# 3 Asian state 1 1 2
# 4 Black civil 0 6 3
# 5 Black civil 1 1 3
# 6 Black state 0 3 4
# 7 Black state 1 4 4
# 8 Caucasian civil 0 7 5
# 9 Caucasian state 0 6 6
# 10 Caucasian state 1 1 6
# 11 Native American civil 1 1 7
# 12 Native American state 1 1 8
# 13 Other civil 0 2 9
# 14 Other state 0 1 10
# 15 Other state 1 1 10


Some necessary tweaks for x-axis labels:



Adapted from this answer:



breaks = sort(c(unique(dat$x), seq(min(dat$x) + .5, 
max(dat$x) + .5,
length(unique(dat$action))
)
)
)

labels = unlist(
lapply(unique(dat$race), function(i) c("civil", paste0("n", i), "state"))
)


Plot data



ggplot(dat, aes(x = x, y = n, fill = factor(claim))) +
geom_col(show.legend = T) +
ggthemes::theme_few() +
scale_fill_manual(name = NULL,
values = c("gray75", "gray25"),
breaks= c("0", "1"),
labels = c("false", "true")
) +
scale_x_continuous(breaks = breaks, labels = labels) +
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank()) +
labs(title = "Jail Plot", y = "Count")


jailplot



Data



The data you attached are corrupted - missing comma or $ somewhere in the table (I don't remember what that was). There are the same data, but without variables we don't to solve the problem.



structure(
list(Race = c("Black", "Asian", "Caucasian", "Caucasian", "Other", "Asian",
"Black", "Black", "Black", "Caucasian", "Black", "Caucasian",
"Caucasian", "Other", "Black", "Caucasian", "Asian", "Black",
"Native American", "Caucasian"),
State_Claim_Made = c(0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
0, 1, 0),
Non_Statutory_Case_Filed = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0)
),
row.names = c(NA, -20L),
class = c("tbl_df", "tbl", "data.frame")
)





share|improve this answer


























  • Wow good stuff, wish I could code like this haha. Now I would like to further compare these groups. To see if the proportion of True/False (Filed a claim vs. Not filed a claim) is the same for each race, how would I do an ANOVA test with this setup?

    – Juanito Tomas
    Nov 26 '18 at 23:14














1












1








1







I think there is a clash between two requirements: to make the barplot stack-ed and at the same time - dodge-d. Probably my solution isn't the best, and someone would do better. But that's what I've got right now:



Preprocessing



library(tidyverse)

dat <- jail %>%
rename_all(tolower) %>%
select(race, state_claim_made, non_statutory_case_filed) %>%
gather(key = action, value = claim, 2, 3) %>%
count(race, action, claim) %>%
mutate(action = ifelse(action == "state_claim_made", "state", "civil")) %>%
mutate(x = as.numeric(reorder(interaction(race, action), 1:n())))


Output:

# # A tibble: 15 x 5
# race action claim n x
# <chr> <chr> <dbl> <int> <dbl>
# 1 Asian civil 0 3 1
# 2 Asian state 0 2 2
# 3 Asian state 1 1 2
# 4 Black civil 0 6 3
# 5 Black civil 1 1 3
# 6 Black state 0 3 4
# 7 Black state 1 4 4
# 8 Caucasian civil 0 7 5
# 9 Caucasian state 0 6 6
# 10 Caucasian state 1 1 6
# 11 Native American civil 1 1 7
# 12 Native American state 1 1 8
# 13 Other civil 0 2 9
# 14 Other state 0 1 10
# 15 Other state 1 1 10


Some necessary tweaks for x-axis labels:



Adapted from this answer:



breaks = sort(c(unique(dat$x), seq(min(dat$x) + .5, 
max(dat$x) + .5,
length(unique(dat$action))
)
)
)

labels = unlist(
lapply(unique(dat$race), function(i) c("civil", paste0("n", i), "state"))
)


Plot data



ggplot(dat, aes(x = x, y = n, fill = factor(claim))) +
geom_col(show.legend = T) +
ggthemes::theme_few() +
scale_fill_manual(name = NULL,
values = c("gray75", "gray25"),
breaks= c("0", "1"),
labels = c("false", "true")
) +
scale_x_continuous(breaks = breaks, labels = labels) +
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank()) +
labs(title = "Jail Plot", y = "Count")


jailplot



Data



The data you attached are corrupted - missing comma or $ somewhere in the table (I don't remember what that was). There are the same data, but without variables we don't to solve the problem.



structure(
list(Race = c("Black", "Asian", "Caucasian", "Caucasian", "Other", "Asian",
"Black", "Black", "Black", "Caucasian", "Black", "Caucasian",
"Caucasian", "Other", "Black", "Caucasian", "Asian", "Black",
"Native American", "Caucasian"),
State_Claim_Made = c(0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
0, 1, 0),
Non_Statutory_Case_Filed = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0)
),
row.names = c(NA, -20L),
class = c("tbl_df", "tbl", "data.frame")
)





share|improve this answer















I think there is a clash between two requirements: to make the barplot stack-ed and at the same time - dodge-d. Probably my solution isn't the best, and someone would do better. But that's what I've got right now:



Preprocessing



library(tidyverse)

dat <- jail %>%
rename_all(tolower) %>%
select(race, state_claim_made, non_statutory_case_filed) %>%
gather(key = action, value = claim, 2, 3) %>%
count(race, action, claim) %>%
mutate(action = ifelse(action == "state_claim_made", "state", "civil")) %>%
mutate(x = as.numeric(reorder(interaction(race, action), 1:n())))


Output:

# # A tibble: 15 x 5
# race action claim n x
# <chr> <chr> <dbl> <int> <dbl>
# 1 Asian civil 0 3 1
# 2 Asian state 0 2 2
# 3 Asian state 1 1 2
# 4 Black civil 0 6 3
# 5 Black civil 1 1 3
# 6 Black state 0 3 4
# 7 Black state 1 4 4
# 8 Caucasian civil 0 7 5
# 9 Caucasian state 0 6 6
# 10 Caucasian state 1 1 6
# 11 Native American civil 1 1 7
# 12 Native American state 1 1 8
# 13 Other civil 0 2 9
# 14 Other state 0 1 10
# 15 Other state 1 1 10


Some necessary tweaks for x-axis labels:



Adapted from this answer:



breaks = sort(c(unique(dat$x), seq(min(dat$x) + .5, 
max(dat$x) + .5,
length(unique(dat$action))
)
)
)

labels = unlist(
lapply(unique(dat$race), function(i) c("civil", paste0("n", i), "state"))
)


Plot data



ggplot(dat, aes(x = x, y = n, fill = factor(claim))) +
geom_col(show.legend = T) +
ggthemes::theme_few() +
scale_fill_manual(name = NULL,
values = c("gray75", "gray25"),
breaks= c("0", "1"),
labels = c("false", "true")
) +
scale_x_continuous(breaks = breaks, labels = labels) +
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank()) +
labs(title = "Jail Plot", y = "Count")


jailplot



Data



The data you attached are corrupted - missing comma or $ somewhere in the table (I don't remember what that was). There are the same data, but without variables we don't to solve the problem.



structure(
list(Race = c("Black", "Asian", "Caucasian", "Caucasian", "Other", "Asian",
"Black", "Black", "Black", "Caucasian", "Black", "Caucasian",
"Caucasian", "Other", "Black", "Caucasian", "Asian", "Black",
"Native American", "Caucasian"),
State_Claim_Made = c(0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
0, 1, 0),
Non_Statutory_Case_Filed = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0)
),
row.names = c(NA, -20L),
class = c("tbl_df", "tbl", "data.frame")
)






share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 24 '18 at 16:15

























answered Nov 24 '18 at 15:58









utubunutubun

1,8501914




1,8501914













  • Wow good stuff, wish I could code like this haha. Now I would like to further compare these groups. To see if the proportion of True/False (Filed a claim vs. Not filed a claim) is the same for each race, how would I do an ANOVA test with this setup?

    – Juanito Tomas
    Nov 26 '18 at 23:14



















  • Wow good stuff, wish I could code like this haha. Now I would like to further compare these groups. To see if the proportion of True/False (Filed a claim vs. Not filed a claim) is the same for each race, how would I do an ANOVA test with this setup?

    – Juanito Tomas
    Nov 26 '18 at 23:14

















Wow good stuff, wish I could code like this haha. Now I would like to further compare these groups. To see if the proportion of True/False (Filed a claim vs. Not filed a claim) is the same for each race, how would I do an ANOVA test with this setup?

– Juanito Tomas
Nov 26 '18 at 23:14





Wow good stuff, wish I could code like this haha. Now I would like to further compare these groups. To see if the proportion of True/False (Filed a claim vs. Not filed a claim) is the same for each race, how would I do an ANOVA test with this setup?

– Juanito Tomas
Nov 26 '18 at 23:14




















draft saved

draft discarded




















































Thanks for contributing an answer to Stack Overflow!


  • Please be sure to answer the question. Provide details and share your research!

But avoid



  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53454787%2fcomparing-multiple-categorical-variables-in-r%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







這個網誌中的熱門文章

Xamarin.form Move up view when keyboard appear

Post-Redirect-Get with Spring WebFlux and Thymeleaf

Anylogic : not able to use stopDelay()