1. 현재 날짜, 시간
Sys.Date()
Sys.time()
date()
2. as.Date :문자 날짜를 날짜형으로 변환하는 함수
as.Date('2018-07-25'); as.Date('2018/07/25') as.#as.Date('20180725') :에러
as.Date('20180725', format='%Y%m%d')
3. format
%Y :세기를 포함한 년도(4자리)
%y :세기를 생략한 년도(2자리)
%m :숫자 달 / %B :문자달 / %b :숫자달의 약어
%d :일 / %A :요일 / %a :요일의 약어
%u :숫자 요일(1~7:월~일) / %w :숫자 요일(0~6:일~토)
%H :시 / %M :분 / %S :초 / %z :Timezone의 시간 / %Z :Timezone의 이름as.Date('2018년 1월 2일', format='%Y년%m월%d일')## [1] "2018-01-02"format(Sys.time(),'%y%m%d %z%Z %A%u')## [1] "180726 +0900KST 목요일4"format(Sys.time(),'%A %a %u %w')## [1] "목요일 목 4 4"weekdays(Sys.Date())## [1] "목요일"weekdays(as.Date('1994/04/18')) #format(as.Date('1994-04-18'), '%A')## [1] "월요일"Sys.Date()+100## [1] "2018-11-03"Sys.Date()-206## [1] "2018-01-01"as.Date('2018-07-26', format='%Y-%m-%d')+120 #날짜+날짜=에러## [1] "2018-11-23"as.Date('2018-05-24', format='%Y-%m-%d')-as.Date('2018-11-23', format='%Y-%m-%d')## Time difference of -183 daysas.numeric(as.Date('2018-05-24', format='%Y-%m-%d')-as.Date('2018-11-23', format='%Y-%m-%d'))## [1] -183difftime('2018-11-23',Sys.Date()) #difftime(as.Date('2018-11-23'),Sys.Date())## Time difference of 119.625 daysas.numeric(difftime('2018-11-23',Sys.Date()))## [1] 119.625as.difftime('09:30:00')-as.difftime('18:20:00')## Time difference of -8.833333 hoursas.numeric(as.difftime('09:30:00')-as.difftime('18:20:00'))## [1] -8.833333#install.packages("lubridate")
library(lubridate) #패키지 설치시 R 삭제 전까진 유지, 다만 사용할 때마다 library로 불러온다.##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## dateemp<-read.csv(choose.files(), header=T, stringsAsFactors = FALSE)
now() #now( ) :현재 시간 정보 →now는 lubridate패키지 내장 함수## [1] "2018-07-26 18:50:52 KST"year(now()) #현재의 년도 추출## [1] 2018month(now()) #현재의 달 추출## [1] 7date<-now()
month(date)## [1] 7month(date, label=T); month(date, label=F) #factor형이기 때문에 level이 있어 label로 확인한다## [1] 7
## Levels: 1 < 2 < 3 < 4 < 5 < 6 < 7 < 8 < 9 < 10 < 11 < 12## [1] 7format(Sys.time(), '%m')## [1] "07"day(now()) #현재의 일 추출## [1] 26day(date); format(Sys.time(), '%d')## [1] 26## [1] "26"format(Sys.time(), '%A') #현재의 요일 추출## [1] "목요일"format(Sys.time(), '%u'); format(Sys.time(), '%w');## [1] "4"## [1] "4"wday(now())## [1] 5wday(now(), week_start=1) #월요일 기준## [1] 4wday(now(), week_start=7) #일요일 기준## [1] 5wday(now(), week_start=7, label=T)## [1] 목
## Levels: 일 < 월 < 화 < 수 < 목 < 금 < 토years(10); months(100)## [1] "10y 0m 0d 0H 0M 0S"## [1] "100m 0d 0H 0M 0S"now()+years(10) #select sysdate+to_yminterval('10-00') from dual;## [1] "2028-07-26 18:50:52 KST"now()+months(100)## [1] "2026-11-26 18:50:52 KST"now()+days(100) #select sysdate+to_dsinterval('100 00:00:00') from dual;## [1] "2018-11-03 18:50:52 KST"now()+hours(3); now()+minutes(100); now()+seconds(100)## [1] "2018-07-26 21:50:52 KST"## [1] "2018-07-26 20:30:52 KST"## [1] "2018-07-26 18:52:32 KST"now()+years(1)+months(1)+days(1)+hours(10)+minutes(20)+seconds(60)## [1] "2019-08-28 05:11:52 KST"hm('8:00') #시간/분/초 출력## [1] "8H 0M 0S"now()+hm('8:00'); now()+hm('02:30:59')## [1] "2018-07-27 02:50:52 KST"## Warning in .parse_hms(..., order = "HM", quiet = quiet): Some strings
## failed to parse, or all strings are NAs## [1] NAdate<-now(); date; year(date)<-2017; date #연도 수정## [1] "2018-07-26 18:50:52 KST"## [1] "2017-07-26 18:50:52 KST"month(date)<-1; date #달 수정## [1] "2017-01-26 18:50:52 KST"day(date)<-1; date #일 수정## [1] "2017-01-01 18:50:52 KST"hour(date)<-00; date #시간 수정## [1] "2017-01-01 00:50:52 KST"minute(date)<-00; date #분 수정## [1] "2017-01-01 00:00:52 KST"second(date)<-00; date #초 수정## [1] "2017-01-01 KST"quarters(Sys.Date())## [1] "Q3"-UNIX간 소통 가능한 프로그램 인터페이스 규약
-POSIXct(continuous) POSIXt(POSIXlt)(list time)
-R은 날짜, 시간 데이터를 처리 할 수 있도록 POSIXct, POSIXt(POSIXlt)클래스를 이용한다.
Sys.time()## [1] "2018-07-26 18:50:52 KST"as.numeric(Sys.time())## [1] 1532598653time<-as.POSIXlt(Sys.time()); unlist(time)## sec min hour
## "52.7847170829773" "50" "18"
## mday mon year
## "26" "6" "118"
## wday yday isdst
## "4" "206" "0"
## zone gmtoff
## "KST" "32400"#sec :초
#min :분
#hour :시
#mday :그 달의 일
#mon :1월을 0으로 두고 계산된 값. 즉, (n월-1)이 된다
#year :1900년을 0으로 두고 계산 된 값. 즉, 2018=1900+118
#wday :일요일을 0으로 두고 센다.
#yday :1월 1일을 0으로 두고 계산.
#isdst :서머타임
#zone :timezone name
#gmtoff :timezone의 시 단위를 초(sec)로 나타낸 것 #+9(지역)*60*60date<-'2018-07-26'
class(as.Date(date, format='%Y-%m-%d'))## [1] "Date"strptime(date, format='%Y-%m-%d') #strptime :POSIX로 바꾸는 형전환 함수## [1] "2018-07-26 KST"class(strptime(date, format='%Y-%m-%d'))## [1] "POSIXlt" "POSIXt"unique(emp$JOB_ID)## [1] "SH_CLERK" "AD_ASST" "MK_MAN" "MK_REP" "HR_REP"
## [6] "PR_REP" "AC_MGR" "AC_ACCOUNT" "AD_PRES" "AD_VP"
## [11] "IT_PROG" "FI_MGR" "FI_ACCOUNT" "PU_MAN" "PU_CLERK"
## [16] "ST_MAN" "ST_CLERK" "SA_MAN" "SA_REP"x<-c(3,2,4,8,6,5,10,NA,1,11,NA,15)
sort(x)## [1] 1 2 3 4 5 6 8 10 11 15sort(x, decreasing=FALSE) #오름차순 정렬(기본값)## [1] 1 2 3 4 5 6 8 10 11 15sort(x, decreasing=TRUE) #내림차순 정렬## [1] 15 11 10 8 6 5 4 3 2 1sort(x, decreasing=FALSE, na.last=NA) #NA출력되지 않음(기본값)## [1] 1 2 3 4 5 6 8 10 11 15sort(x, decreasing=FALSE, na.last=TRUE) #결과값 맨 뒤에 NA출력## [1] 1 2 3 4 5 6 8 10 11 15 NA NAsort(x, decreasing=FALSE, na.last=FALSE) #결과값 맨 앞에 NA출력## [1] NA NA 1 2 3 4 5 6 8 10 11 15rev(sort(x)) #리버스. 내림차순 정렬과 동일## [1] 15 11 10 8 6 5 4 3 2 1x<-c(30,50,10,40,20); sort(x)## [1] 10 20 30 40 50order(x) #10이 3번 자리, 20이 5번 자리, 30이 1번 자리...## [1] 3 5 1 4 2x[order(x)] #인덱스 번호 sort## [1] 10 20 30 40 50x[order(x, decreasing=TRUE, na.last=NA)]## [1] 50 40 30 20 10x[order(x, decreasing=TRUE, na.last=TRUE)]## [1] 50 40 30 20 10x[order(x, decreasing=TRUE, na.last=FALSE)]## [1] 50 40 30 20 10#install.packages("doBy")
library(doBy)
orderBy(~SALARY, emp[,c("LAST_NAME", "SALARY")]) #오름차순 정렬## LAST_NAME SALARY
## 42 Olson 2100
## 38 Markle 2200
## 46 Philtanker 2200
## 37 Landry 2400
## 45 Gee 2400
## 29 Colmenares 2500
## 41 Marlow 2500
## 50 Patel 2500
## 54 Vargas 2500
## 92 Sullivan 2500
## 101 Perkins 2500
## 1 OConnell 2600
## 2 Grant 2600
## 28 Himuro 2600
## 53 Matos 2600
## 36 Mikkilineni 2700
## 49 Seo 2700
## 27 Tobias 2800
## 40 Atkinson 2800
## 93 Geoni 2800
## 105 Jones 2800
## 26 Baida 2900
## 44 Rogers 2900
## 100 Gates 2900
## 97 Cabrio 3000
## 107 Feeney 3000
## 25 Khoo 3100
## 52 Davies 3100
## 91 Fleaur 3100
## 106 Walsh 3100
## 35 Nayer 3200
## 48 Stiles 3200
## 90 Taylor 3200
## 104 McCain 3200
## 39 Bissot 3300
## 43 Mallin 3300
## 96 Dellinger 3400
## 51 Rajs 3500
## 47 Ladwig 3600
## 99 Dilly 3600
## 98 Chung 3800
## 103 Everett 3900
## 102 Bell 4000
## 95 Bull 4100
## 17 Lorentz 4200
## 94 Sarchand 4200
## 3 Whalen 4400
## 15 Austin 4800
## 16 Pataballa 4800
## 34 Mourgos 5800
## 5 Fay 6000
## 14 Ernst 6000
## 83 Kumar 6100
## 77 Banda 6200
## 89 Johnson 6200
## 76 Ande 6400
## 6 Mavris 6500
## 33 Vollman 6500
## 75 Lee 6800
## 23 Popp 6900
## 65 Tuvault 7000
## 71 Sewall 7000
## 88 Grant 7000
## 74 Marvins 7200
## 82 Bates 7300
## 81 Smith 7400
## 64 Cambrault 7500
## 70 Doran 7500
## 21 Sciarra 7700
## 22 Urman 7800
## 32 Kaufling 7900
## 30 Weiss 8000
## 63 Olsen 8000
## 69 Smith 8000
## 20 Chen 8200
## 31 Fripp 8200
## 9 Gietz 8300
## 87 Livingston 8400
## 86 Taylor 8600
## 85 Hutton 8800
## 13 Hunold 9000
## 19 Faviet 9000
## 62 Hall 9000
## 68 McEwen 9000
## 61 Bernstein 9500
## 67 Sully 9500
## 73 Greene 9500
## 80 Fox 9600
## 7 Baer 10000
## 60 Tucker 10000
## 66 King 10000
## 79 Bloom 10000
## 59 Zlotkey 10500
## 72 Vishney 10500
## 24 Raphaely 11000
## 58 Cambrault 11000
## 84 Abel 11000
## 78 Ozer 11500
## 57 Errazuriz 12000
## 8 Higgins 12008
## 18 Greenberg 12008
## 4 Hartstein 13000
## 56 Partners 13500
## 55 Russell 14000
## 11 Kochhar 17000
## 12 De Haan 17000
## 10 King 29040orderBy(~-SALARY, emp[,c("LAST_NAME", "SALARY")]) #내림차순 정렬## LAST_NAME SALARY
## 10 King 29040
## 11 Kochhar 17000
## 12 De Haan 17000
## 55 Russell 14000
## 56 Partners 13500
## 4 Hartstein 13000
## 8 Higgins 12008
## 18 Greenberg 12008
## 57 Errazuriz 12000
## 78 Ozer 11500
## 24 Raphaely 11000
## 58 Cambrault 11000
## 84 Abel 11000
## 59 Zlotkey 10500
## 72 Vishney 10500
## 7 Baer 10000
## 60 Tucker 10000
## 66 King 10000
## 79 Bloom 10000
## 80 Fox 9600
## 61 Bernstein 9500
## 67 Sully 9500
## 73 Greene 9500
## 13 Hunold 9000
## 19 Faviet 9000
## 62 Hall 9000
## 68 McEwen 9000
## 85 Hutton 8800
## 86 Taylor 8600
## 87 Livingston 8400
## 9 Gietz 8300
## 20 Chen 8200
## 31 Fripp 8200
## 30 Weiss 8000
## 63 Olsen 8000
## 69 Smith 8000
## 32 Kaufling 7900
## 22 Urman 7800
## 21 Sciarra 7700
## 64 Cambrault 7500
## 70 Doran 7500
## 81 Smith 7400
## 82 Bates 7300
## 74 Marvins 7200
## 65 Tuvault 7000
## 71 Sewall 7000
## 88 Grant 7000
## 23 Popp 6900
## 75 Lee 6800
## 6 Mavris 6500
## 33 Vollman 6500
## 76 Ande 6400
## 77 Banda 6200
## 89 Johnson 6200
## 83 Kumar 6100
## 5 Fay 6000
## 14 Ernst 6000
## 34 Mourgos 5800
## 15 Austin 4800
## 16 Pataballa 4800
## 3 Whalen 4400
## 17 Lorentz 4200
## 94 Sarchand 4200
## 95 Bull 4100
## 102 Bell 4000
## 103 Everett 3900
## 98 Chung 3800
## 47 Ladwig 3600
## 99 Dilly 3600
## 51 Rajs 3500
## 96 Dellinger 3400
## 39 Bissot 3300
## 43 Mallin 3300
## 35 Nayer 3200
## 48 Stiles 3200
## 90 Taylor 3200
## 104 McCain 3200
## 25 Khoo 3100
## 52 Davies 3100
## 91 Fleaur 3100
## 106 Walsh 3100
## 97 Cabrio 3000
## 107 Feeney 3000
## 26 Baida 2900
## 44 Rogers 2900
## 100 Gates 2900
## 27 Tobias 2800
## 40 Atkinson 2800
## 93 Geoni 2800
## 105 Jones 2800
## 36 Mikkilineni 2700
## 49 Seo 2700
## 1 OConnell 2600
## 2 Grant 2600
## 28 Himuro 2600
## 53 Matos 2600
## 29 Colmenares 2500
## 41 Marlow 2500
## 50 Patel 2500
## 54 Vargas 2500
## 92 Sullivan 2500
## 101 Perkins 2500
## 37 Landry 2400
## 45 Gee 2400
## 38 Markle 2200
## 46 Philtanker 2200
## 42 Olson 2100orderBy(~DEPARTMENT_ID+SALARY, emp[,c("LAST_NAME", "SALARY", "DEPARTMENT_ID")]) #=select last_name, salary, department_id from employees order by department_id, salary## LAST_NAME SALARY DEPARTMENT_ID
## 3 Whalen 4400 10
## 5 Fay 6000 20
## 4 Hartstein 13000 20
## 29 Colmenares 2500 30
## 28 Himuro 2600 30
## 27 Tobias 2800 30
## 26 Baida 2900 30
## 25 Khoo 3100 30
## 24 Raphaely 11000 30
## 6 Mavris 6500 40
## 42 Olson 2100 50
## 38 Markle 2200 50
## 46 Philtanker 2200 50
## 37 Landry 2400 50
## 45 Gee 2400 50
## 41 Marlow 2500 50
## 50 Patel 2500 50
## 54 Vargas 2500 50
## 92 Sullivan 2500 50
## 101 Perkins 2500 50
## 1 OConnell 2600 50
## 2 Grant 2600 50
## 53 Matos 2600 50
## 36 Mikkilineni 2700 50
## 49 Seo 2700 50
## 40 Atkinson 2800 50
## 93 Geoni 2800 50
## 105 Jones 2800 50
## 44 Rogers 2900 50
## 100 Gates 2900 50
## 97 Cabrio 3000 50
## 107 Feeney 3000 50
## 52 Davies 3100 50
## 91 Fleaur 3100 50
## 106 Walsh 3100 50
## 35 Nayer 3200 50
## 48 Stiles 3200 50
## 90 Taylor 3200 50
## 104 McCain 3200 50
## 39 Bissot 3300 50
## 43 Mallin 3300 50
## 96 Dellinger 3400 50
## 51 Rajs 3500 50
## 47 Ladwig 3600 50
## 99 Dilly 3600 50
## 98 Chung 3800 50
## 103 Everett 3900 50
## 102 Bell 4000 50
## 95 Bull 4100 50
## 94 Sarchand 4200 50
## 34 Mourgos 5800 50
## 33 Vollman 6500 50
## 32 Kaufling 7900 50
## 30 Weiss 8000 50
## 31 Fripp 8200 50
## 17 Lorentz 4200 60
## 15 Austin 4800 60
## 16 Pataballa 4800 60
## 14 Ernst 6000 60
## 13 Hunold 9000 60
## 7 Baer 10000 70
## 83 Kumar 6100 80
## 77 Banda 6200 80
## 89 Johnson 6200 80
## 76 Ande 6400 80
## 75 Lee 6800 80
## 65 Tuvault 7000 80
## 71 Sewall 7000 80
## 74 Marvins 7200 80
## 82 Bates 7300 80
## 81 Smith 7400 80
## 64 Cambrault 7500 80
## 70 Doran 7500 80
## 63 Olsen 8000 80
## 69 Smith 8000 80
## 87 Livingston 8400 80
## 86 Taylor 8600 80
## 85 Hutton 8800 80
## 62 Hall 9000 80
## 68 McEwen 9000 80
## 61 Bernstein 9500 80
## 67 Sully 9500 80
## 73 Greene 9500 80
## 80 Fox 9600 80
## 60 Tucker 10000 80
## 66 King 10000 80
## 79 Bloom 10000 80
## 59 Zlotkey 10500 80
## 72 Vishney 10500 80
## 58 Cambrault 11000 80
## 84 Abel 11000 80
## 78 Ozer 11500 80
## 57 Errazuriz 12000 80
## 56 Partners 13500 80
## 55 Russell 14000 80
## 11 Kochhar 17000 90
## 12 De Haan 17000 90
## 10 King 29040 90
## 23 Popp 6900 100
## 21 Sciarra 7700 100
## 22 Urman 7800 100
## 20 Chen 8200 100
## 19 Faviet 9000 100
## 18 Greenberg 12008 100
## 9 Gietz 8300 110
## 8 Higgins 12008 110
## 88 Grant 7000 NAorderBy(~-DEPARTMENT_ID-SALARY, emp[,c("LAST_NAME", "SALARY", "DEPARTMENT_ID")]) #=select last_name, salary, department_id from employees order by department_id desc, salary desc## LAST_NAME SALARY DEPARTMENT_ID
## 8 Higgins 12008 110
## 9 Gietz 8300 110
## 18 Greenberg 12008 100
## 19 Faviet 9000 100
## 20 Chen 8200 100
## 22 Urman 7800 100
## 21 Sciarra 7700 100
## 23 Popp 6900 100
## 10 King 29040 90
## 11 Kochhar 17000 90
## 12 De Haan 17000 90
## 55 Russell 14000 80
## 56 Partners 13500 80
## 57 Errazuriz 12000 80
## 78 Ozer 11500 80
## 58 Cambrault 11000 80
## 84 Abel 11000 80
## 59 Zlotkey 10500 80
## 72 Vishney 10500 80
## 60 Tucker 10000 80
## 66 King 10000 80
## 79 Bloom 10000 80
## 80 Fox 9600 80
## 61 Bernstein 9500 80
## 67 Sully 9500 80
## 73 Greene 9500 80
## 62 Hall 9000 80
## 68 McEwen 9000 80
## 85 Hutton 8800 80
## 86 Taylor 8600 80
## 87 Livingston 8400 80
## 63 Olsen 8000 80
## 69 Smith 8000 80
## 64 Cambrault 7500 80
## 70 Doran 7500 80
## 81 Smith 7400 80
## 82 Bates 7300 80
## 74 Marvins 7200 80
## 65 Tuvault 7000 80
## 71 Sewall 7000 80
## 75 Lee 6800 80
## 76 Ande 6400 80
## 77 Banda 6200 80
## 89 Johnson 6200 80
## 83 Kumar 6100 80
## 7 Baer 10000 70
## 13 Hunold 9000 60
## 14 Ernst 6000 60
## 15 Austin 4800 60
## 16 Pataballa 4800 60
## 17 Lorentz 4200 60
## 31 Fripp 8200 50
## 30 Weiss 8000 50
## 32 Kaufling 7900 50
## 33 Vollman 6500 50
## 34 Mourgos 5800 50
## 94 Sarchand 4200 50
## 95 Bull 4100 50
## 102 Bell 4000 50
## 103 Everett 3900 50
## 98 Chung 3800 50
## 47 Ladwig 3600 50
## 99 Dilly 3600 50
## 51 Rajs 3500 50
## 96 Dellinger 3400 50
## 39 Bissot 3300 50
## 43 Mallin 3300 50
## 35 Nayer 3200 50
## 48 Stiles 3200 50
## 90 Taylor 3200 50
## 104 McCain 3200 50
## 52 Davies 3100 50
## 91 Fleaur 3100 50
## 106 Walsh 3100 50
## 97 Cabrio 3000 50
## 107 Feeney 3000 50
## 44 Rogers 2900 50
## 100 Gates 2900 50
## 40 Atkinson 2800 50
## 93 Geoni 2800 50
## 105 Jones 2800 50
## 36 Mikkilineni 2700 50
## 49 Seo 2700 50
## 1 OConnell 2600 50
## 2 Grant 2600 50
## 53 Matos 2600 50
## 41 Marlow 2500 50
## 50 Patel 2500 50
## 54 Vargas 2500 50
## 92 Sullivan 2500 50
## 101 Perkins 2500 50
## 37 Landry 2400 50
## 45 Gee 2400 50
## 38 Markle 2200 50
## 46 Philtanker 2200 50
## 42 Olson 2100 50
## 6 Mavris 6500 40
## 24 Raphaely 11000 30
## 25 Khoo 3100 30
## 26 Baida 2900 30
## 27 Tobias 2800 30
## 28 Himuro 2600 30
## 29 Colmenares 2500 30
## 4 Hartstein 13000 20
## 5 Fay 6000 20
## 3 Whalen 4400 10
## 88 Grant 7000 NAorderBy(~DEPARTMENT_ID-SALARY, emp[,c("LAST_NAME", "SALARY", "DEPARTMENT_ID")]) #=select last_name, salary, department_id from employees order by department_id, salary desc## LAST_NAME SALARY DEPARTMENT_ID
## 3 Whalen 4400 10
## 4 Hartstein 13000 20
## 5 Fay 6000 20
## 24 Raphaely 11000 30
## 25 Khoo 3100 30
## 26 Baida 2900 30
## 27 Tobias 2800 30
## 28 Himuro 2600 30
## 29 Colmenares 2500 30
## 6 Mavris 6500 40
## 31 Fripp 8200 50
## 30 Weiss 8000 50
## 32 Kaufling 7900 50
## 33 Vollman 6500 50
## 34 Mourgos 5800 50
## 94 Sarchand 4200 50
## 95 Bull 4100 50
## 102 Bell 4000 50
## 103 Everett 3900 50
## 98 Chung 3800 50
## 47 Ladwig 3600 50
## 99 Dilly 3600 50
## 51 Rajs 3500 50
## 96 Dellinger 3400 50
## 39 Bissot 3300 50
## 43 Mallin 3300 50
## 35 Nayer 3200 50
## 48 Stiles 3200 50
## 90 Taylor 3200 50
## 104 McCain 3200 50
## 52 Davies 3100 50
## 91 Fleaur 3100 50
## 106 Walsh 3100 50
## 97 Cabrio 3000 50
## 107 Feeney 3000 50
## 44 Rogers 2900 50
## 100 Gates 2900 50
## 40 Atkinson 2800 50
## 93 Geoni 2800 50
## 105 Jones 2800 50
## 36 Mikkilineni 2700 50
## 49 Seo 2700 50
## 1 OConnell 2600 50
## 2 Grant 2600 50
## 53 Matos 2600 50
## 41 Marlow 2500 50
## 50 Patel 2500 50
## 54 Vargas 2500 50
## 92 Sullivan 2500 50
## 101 Perkins 2500 50
## 37 Landry 2400 50
## 45 Gee 2400 50
## 38 Markle 2200 50
## 46 Philtanker 2200 50
## 42 Olson 2100 50
## 13 Hunold 9000 60
## 14 Ernst 6000 60
## 15 Austin 4800 60
## 16 Pataballa 4800 60
## 17 Lorentz 4200 60
## 7 Baer 10000 70
## 55 Russell 14000 80
## 56 Partners 13500 80
## 57 Errazuriz 12000 80
## 78 Ozer 11500 80
## 58 Cambrault 11000 80
## 84 Abel 11000 80
## 59 Zlotkey 10500 80
## 72 Vishney 10500 80
## 60 Tucker 10000 80
## 66 King 10000 80
## 79 Bloom 10000 80
## 80 Fox 9600 80
## 61 Bernstein 9500 80
## 67 Sully 9500 80
## 73 Greene 9500 80
## 62 Hall 9000 80
## 68 McEwen 9000 80
## 85 Hutton 8800 80
## 86 Taylor 8600 80
## 87 Livingston 8400 80
## 63 Olsen 8000 80
## 69 Smith 8000 80
## 64 Cambrault 7500 80
## 70 Doran 7500 80
## 81 Smith 7400 80
## 82 Bates 7300 80
## 74 Marvins 7200 80
## 65 Tuvault 7000 80
## 71 Sewall 7000 80
## 75 Lee 6800 80
## 76 Ande 6400 80
## 77 Banda 6200 80
## 89 Johnson 6200 80
## 83 Kumar 6100 80
## 10 King 29040 90
## 11 Kochhar 17000 90
## 12 De Haan 17000 90
## 18 Greenberg 12008 100
## 19 Faviet 9000 100
## 20 Chen 8200 100
## 22 Urman 7800 100
## 21 Sciarra 7700 100
## 23 Popp 6900 100
## 8 Higgins 12008 110
## 9 Gietz 8300 110
## 88 Grant 7000 NA