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## renamelibrary(ggthemes)Lets keep exponenets out of our graphs.
options("scipen"=1000000)This paper will look at the same thing from three different levels: all politcial parties lumped in together , Republicans v Democrats, and the candidates against each other regardless of party.
Primarily we ill be working withthe Federal Election Data for NY stat as provided by Udacity. We will suppliment this dataset with dataset (ZipPop2) which provides the population per zip code as per 2010 and lastly with a dataset that provides each candidates political party and that party’s color.
The Links to the two data sets that I provided. https://drive.google.com/open?id=0B-ygjCols7cANTR3WGFjbUM4UU0
https://drive.google.com/open?id=0B-ygjCols7cAaUZ2ME1RN0tZM1k
The NYS election data is very lasrge so we will be working with a sample of it.
NY2016<-read.csv(“NY2016.csv”)
NYSample<-NY2016[sample(nrow(NY_1), 100000),]
NYSample<-read.csv("NYSample.csv")ZipPop2<- read.csv("Zip_Pop2.csv")CandidateParty<-read.csv("CandidateParty.csv")Let us drop unneceassary columns
NY_1<- select(NYSample,cmte_id,contbr_nm,contbr_city,Zip,
contbr_employer,contbr_occupation,contb_receipt_amt,
contb_receipt_dt,cand_nm)The Zip codes of NY2016 are 9 digits long so we will shorten them to 5 digits.
NY_1$Zip<- substr(NY_1$Zip , 0,5)Some of the zips are only 3 numbers but these three numbers tell us it is a NYS zip.
Lets shorten the candidate’s name to just their last name.
View(NY_1)NY_1<-cSplit(NY_1 , "cand_nm", sep=",")View(NY_1)Lets drop extra the now unneeded cand_nm_2
NY_1$cand_nm_2<-NULLView(NY_1)Lets rename cand_nm to Last_Name
setnames(NY_1,"cand_nm_1", "Last_Name")Now we will join our 3 datasets together. Joining Zip_Pop2 adds population per zip to our dataset. But first lets make sure all of the data types are the matching.
View(ZipPop2)ZipPop2$Zip<-as.factor(ZipPop2$Zip)NY_1<- NY_1%>% left_join(ZipPop2, by= c("Zip"="Zip"))## Warning: Column `Zip` joining character vector and factor, coercing into
## character vectorNow lets join Candidate Party this will bring in the candidates respective political parties and colors.
NY_1<- NY_1%>% left_join(CandidateParty, by= c("Last_Name"="Last_Name"))## Warning: Column `Last_Name` joining factors with different levels, coercing
## to character vectorstr(NY_1)## 'data.frame': 100000 obs. of 12 variables:
## $ cmte_id : Factor w/ 25 levels "C00458844","C00500587",..: 6 6 15 7 7 6 1 6 6 7 ...
## $ contbr_nm : Factor w/ 47556 levels " CALVEY, DONNA MORRIS",..: 39537 45251 12870 22387 11418 34406 14763 40339 8413 743 ...
## $ contbr_city : Factor w/ 1596 levels "11590 SALLELES D'AUDE",..: 971 1186 304 1186 976 971 619 174 174 174 ...
## $ Zip : chr "10023" "11385" "11725" "11385" ...
## $ contbr_employer : Factor w/ 17350 levels "","'SELF'","'TRANSPARENT'",..: 10102 13267 7217 5552 10158 904 7218 10824 13650 12002 ...
## $ contbr_occupation: Factor w/ 8689 levels "","''RETIRED''",..: 6685 4765 3917 720 852 3428 5638 63 734 2185 ...
## $ contb_receipt_amt: num 10 50 85.7 10 200 25 -2700 25 19 5 ...
## $ contb_receipt_dt : Factor w/ 652 levels "1-Apr-15","1-Apr-16",..: 396 63 648 414 285 652 502 306 26 456 ...
## $ Last_Name : chr "Clinton" "Clinton" "Trump" "Sanders" ...
## $ Population : int 60998 98592 29150 98592 54447 50984 7823 80018 101572 81677 ...
## $ Party : Factor w/ 5 levels "Conservative",..: 2 2 5 2 2 2 5 2 2 2 ...
## $ Party_Color : Factor w/ 5 levels "Blue","Green",..: 1 1 4 1 1 1 4 1 1 1 ...Let us now convert our dataframe to a tbl_df.
dplyr::tbl_df(NY_1)## # A tibble: 100,000 x 12
## cmte_id contbr_nm contbr_city Zip
## <fctr> <fctr> <fctr> <chr>
## 1 C00575795 SHIMANSKY, REBA NEW YORK 10023
## 2 C00575795 WEBB, LOYANA RIDGEWOOD 11385
## 3 C00580100 FELDMAN, ROB COMMACK 11725
## 4 C00577130 KING, MICHAEL RIDGEWOOD 11385
## 5 C00577130 DUNLOP, DAVID S. NEWBURGH 12550
## 6 C00575795 PRIORE, PATRICK NEW YORK 10011
## 7 C00458844 GANZ, ZEV HEWLETT 11557
## 8 C00575795 SMAYLOVSKY, BELLA BROOKLYN 11229
## 9 C00575795 COOPER, ANDREW BROOKLYN 11226
## 10 C00577130 ALLISON, MATT BROOKLYN 11206
## # ... with 99,990 more rows, and 8 more variables: contbr_employer <fctr>,
## # contbr_occupation <fctr>, contb_receipt_amt <dbl>,
## # contb_receipt_dt <fctr>, Last_Name <chr>, Population <int>,
## # Party <fctr>, Party_Color <fctr>str(NY_1)## 'data.frame': 100000 obs. of 12 variables:
## $ cmte_id : Factor w/ 25 levels "C00458844","C00500587",..: 6 6 15 7 7 6 1 6 6 7 ...
## $ contbr_nm : Factor w/ 47556 levels " CALVEY, DONNA MORRIS",..: 39537 45251 12870 22387 11418 34406 14763 40339 8413 743 ...
## $ contbr_city : Factor w/ 1596 levels "11590 SALLELES D'AUDE",..: 971 1186 304 1186 976 971 619 174 174 174 ...
## $ Zip : chr "10023" "11385" "11725" "11385" ...
## $ contbr_employer : Factor w/ 17350 levels "","'SELF'","'TRANSPARENT'",..: 10102 13267 7217 5552 10158 904 7218 10824 13650 12002 ...
## $ contbr_occupation: Factor w/ 8689 levels "","''RETIRED''",..: 6685 4765 3917 720 852 3428 5638 63 734 2185 ...
## $ contb_receipt_amt: num 10 50 85.7 10 200 25 -2700 25 19 5 ...
## $ contb_receipt_dt : Factor w/ 652 levels "1-Apr-15","1-Apr-16",..: 396 63 648 414 285 652 502 306 26 456 ...
## $ Last_Name : chr "Clinton" "Clinton" "Trump" "Sanders" ...
## $ Population : int 60998 98592 29150 98592 54447 50984 7823 80018 101572 81677 ...
## $ Party : Factor w/ 5 levels "Conservative",..: 2 2 5 2 2 2 5 2 2 2 ...
## $ Party_Color : Factor w/ 5 levels "Blue","Green",..: 1 1 4 1 1 1 4 1 1 1 ...Hmm. I would have thought str would have indicated NY_1’s new structure.
Contrb_name came in as a factor. Needless, but I suppose but will change to character. Zip came back as chr and we specifically do not want that. Will change it to factor. Employer too needlessly came back as factor. Guess I will change that. Occupation cmae back as factor , that is good. Date came back as a factor. . Last_Name is chr. Specifically wanted this as a factor. Party and Party_Color came in as desired. Well that is something, I suppose.
NY_1$Last_Name <- as.factor(NY_1$Last_Name)
NY_1$Zip <- as.factor(NY_1$Zip)
NY_1$contbr_nm <- as.character(NY_1$contbr_nm)
NY_1$contbr_employer<-as.character(NY_1$contbr_employer)
NY_1$contbr_city<-as.character(NY_1$contbr_city)Ok, now let us create Enthusiasm as a variable. Enthusiasm is the percentage of a population that donated to a candidate.
Freq are the number of people who contributed per zip.
Freq<-data.frame(table(NY_1$Zip))View(Freq)Let us revise Var1 to Zip as a column heading.
setnames(Freq,"Var1","Zip")There are some blank observations which is odd since it is only a record of donors and all donors have to give acomplete address. In any event lets drop any rows that contain a blank.
Freq%>%
filter(Zip !="")## Zip Freq
## 1 `1136 1
## 2 0 2
## 3 10 4
## 4 10000 1
## 5 10001 883
## 6 10002 628
## 7 10003 1495
## 8 10004 132
## 9 10005 162
## 10 10006 98
## 11 10007 272
## 12 10008 7
## 13 10009 994
## 14 10010 780
## 15 10011 2350
## 16 10012 687
## 17 10013 708
## 18 10014 1277
## 19 10015 1
## 20 10016 895
## 21 10017 334
## 22 10018 272
## 23 10019 1149
## 24 10020 19
## 25 10021 1081
## 26 10022 1046
## 27 10023 2280
## 28 10024 2341
## 29 10025 2582
## 30 10026 420
## 31 10027 636
## 32 10028 1214
## 33 10029 319
## 34 10030 123
## 35 10031 331
## 36 10032 287
## 37 10033 466
## 38 10034 290
## 39 10035 147
## 40 10036 742
## 41 10037 81
## 42 10038 307
## 43 10039 93
## 44 10040 277
## 45 10041 1
## 46 10044 153
## 47 10055 2
## 48 10065 685
## 49 10069 135
## 50 10075 591
## 51 10090 2
## 52 10101 5
## 53 10103 7
## 54 10104 3
## 55 10105 1
## 56 10106 8
## 57 10107 23
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## 1631 99999 31Candidate_List$Last_Name<- NY_1[!duplicated(NY_1[,c(‘Last_Name’)]) ,
NY_1<- NY_1 %>% left_join(Freq,
by = c("Zip"="Zip"))View(NY_1)