CODEWITHSUNDEEP
rdplyrpercentage
Juanito Tomas
Juanito Tomas

Reputation: 151

Percentage of 2 responses in a variable using Dplyr

For an assignment, I would like to see the number of subjects who have 0 for the variable CIU vs. 1 for CIU.

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", "Black", "Caucasian", 
"Caucasian", "Caucasian", "Caucasian", "Black", "Black", "Black", 
"Caucasian", "Black", "Caucasian", "Caucasian", "Caucasian", 
"Black", "Caucasian", "Caucasian", "Black", "Caucasian", "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, 0, 0, 0, 0, 0, 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"))

Using the dplyr package, I accomplished this much:

CUI <- jail %>%
  group_by(CIU)  %>% 
  summarize(count = n())

Now I would like to create a table showing the percentage of each group within the "State_Claim_Made" category, but I am unsure what to do from here. In the end I would like to see the percent of CUI=0 that have State_Claim_Made=0 vs. State_Claim_Made=1 and same for CUI=1; a 2-2 table of sorts. I also prefer to continue to use the dplyr package but not necessary.

Upvotes: 0

Views: 41

Answers (2)

Julius Vainora
Julius Vainora

Reputation: 48211

Your example doesn't really let to see the full picture, so let

df <- data.frame(CIU = rep(0:1, times = c(20, 30)),
                 State_Claim_Made = rep(1:0, times = c(15, 35)))

Then

table(CIU = df$CIU, State_Claim_Made = df$State_Claim_Made)
#    State_Claim_Made
# CIU  0  1
#   0  5 15
#   1 30  0
table(CIU = df$CIU, State_Claim_Made = df$State_Claim_Made) / c(table(df$CIU))
#    State_Claim_Made
# CIU    0    1
#   0 0.25 0.75
#   1 1.00 0.00

Upvotes: 1

morgan121
morgan121

Reputation: 2253

Using base R you can just use the table command:

 table(data$CIU, data$State_Claim_Made)

Output:

     0  1
  0 15  5

If you have data including CUI =1 then the output would be a 2x2 table like you need

Upvotes: 0

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