# Import data
data <- read.csv("C:/Users/sclee1/OneDrive/Documents/R/legalAnalytics/data/cv88on.csv", header=TRUE)
library(tidyverse)
# Separte date into year, month, day
data <- separate(data, FILEDATE, c("FILEMONTH", "FILEDAY", "FILEYEAR"), sep = "/", remove = TRUE)
data$FILEYEAR <- as.integer(data$FILEYEAR)
data$FILEMONTH <- NULL
data$FILEDAY <- NULL# Filter for cases filed after 2010
data <-
data %>%
filter(FILEYEAR > 2010) %>%
droplevels()
# Label state names
data <-
data %>%
mutate(DISTRICT = str_replace(DISTRICT, "10", "VT"),
DISTRICT = str_replace(DISTRICT, "0", "ME"),
DISTRICT = str_replace(DISTRICT, "1", "MA"),
DISTRICT = str_replace(DISTRICT, "2", "NH"),
DISTRICT = str_replace(DISTRICT, "3", "RI"),
DISTRICT = str_replace(DISTRICT, "5", "CT"))
# Convert to factors
data$DISTRICT <- factor(data$DISTRICT)
data$NOS <- factor(data$NOS)
# Get a sense of the data
str(data)
## 'data.frame': 36643 obs. of 6 variables:
## $ DISTRICT : Factor w/ 6 levels "CT","MA","ME",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ PLT : Factor w/ 19900 levels "'DAVIS","'DAVIS, ET AL",..: 6393 3300 5130 19442 7175 3482 6269 4384 12436 13162 ...
## $ DEF : Factor w/ 19496 levels "'47 BRAND, LLC",..: 8018 11968 5576 10445 5251 14988 7759 1510 8210 13180 ...
## $ FILEYEAR : int 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
## $ NOS : Factor w/ 44 levels "110","140","160",..: 28 22 26 25 25 4 25 26 4 1 ...
## $ nature_of_suit: Factor w/ 44 levels "ADMINISTRATIVE PROCEDURE ACT/REVIEW OR APPEAL OF AGENCY\nDECISION",..: 7 37 9 28 28 29 28 9 29 19 ...
summary(data)
## DISTRICT PLT DEF
## CT: 9718 SMITH : 185 FRESENIUS MEDICAL CARE , ET AL: 3590
## MA:18705 SEALED : 173 ATRIUM MEDICAL CORPORAT, ET AL: 617
## ME: 1988 BROWN : 165 GLAXOSMITHKLINE LLC : 439
## NH: 2556 JOHNSON : 154 FRESENIUS USA, INC., ET AL : 379
## RI: 2628 WILLIAMS: 149 DAVOL, INC., ET AL : 189
## VT: 1048 MARRADI : 146 BOSTON SCIENTIFIC CORP. : 179
## (Other) :35671 (Other) :31250
## FILEYEAR NOS nature_of_suit
## Min. :2011 367 : 5145 HEALTH CARE / PHARM : 5145
## 1st Qu.:2013 440 : 4101 OTHER CIVIL RIGHTS : 4101
## Median :2014 190 : 3945 OTHER CONTRACT ACTIONS : 3945
## Mean :2014 442 : 3026 CIVIL RIGHTS JOBS : 3026
## 3rd Qu.:2016 365 : 2988 PERSONAL INJURY -PRODUCT LIABILITY: 2988
## Max. :2018 360 : 2035 OTHER PERSONAL INJURY : 2035
## (Other):15403 (Other) :15403data %>%
group_by(DISTRICT, DEF) %>%
summarise(n = n()) %>%
arrange(DISTRICT, desc(n)) %>%
top_n(10, n) %>%
data.frame()
## DISTRICT DEF n
## 1 CT DOE 163
## 2 CT USA 122
## 3 CT PERLITZ, ET AL 76
## 4 CT CONNECTICUT, ET AL 51
## 5 CT SEALED 44
## 6 CT HARTFORD, ET AL 40
## 7 CT METRO-NORTH COMMUTER RAILROAD 40
## 8 CT BRIDGEPORT, ET AL 31
## 9 CT NEW HAVEN, ET AL 30
## 10 CT DEPUY ORTHOPAEDICS, INC, ET AL 29
## 11 MA FRESENIUS MEDICAL CARE , ET AL 3589
## 12 MA GLAXOSMITHKLINE LLC 439
## 13 MA FRESENIUS USA, INC., ET AL 379
## 14 MA BOSTON SCIENTIFIC CORP. 177
## 15 MA NEW ENGLAND COMPOUNDING, ET AL 171
## 16 MA HOWMEDICA OSTEONICS CORP. 149
## 17 MA FRESENIUS U.S.A., INC., ET AL 141
## 18 MA AMERIDOSE LLC, ET AL 135
## 19 MA UNIFIRST CORPORATION 105
## 20 MA SEALED 104
## 21 ME JOHNSON & JOHNSON, ET AL 30
## 22 ME CANADIAN PACIFIC RAILWA, ET AL 25
## 23 ME WESTERN PETROLEUM CO, ET AL 18
## 24 ME ETHICON INC, ET AL 17
## 25 ME UNITED STATES OF AMERICA 15
## 26 ME STATE OF MAINE, ET AL 11
## 27 ME DEPUY ORTHOPAEDICS INC, ET AL 10
## 28 ME RAIL WORLD INC, ET AL 10
## 29 ME USA 10
## 30 ME ZIMMER INC, ET AL 10
## 31 NH ATRIUM MEDICAL CORPORAT, ET AL 617
## 32 NH -8 26
## 33 NH USA 22
## 34 NH ATRIUM MEDICAL CORP, ET AL 16
## 35 NH ATRIUM MEDICAL CORPORATION 14
## 36 NH GUTIERREZ, ET AL 14
## 37 NH THE DIAL CORPORATION 13
## 38 NH COLGATE-PALMOLIVE COMPANY 9
## 39 NH PORTFOLIO RECOVERY ASSOCIATES, 9
## 40 NH SALEM, NH, TOWN OF, ET AL 8
## 41 RI DAVOL, INC., ET AL 189
## 42 RI DAVOL INC, ET AL 43
## 43 RI C.R. BARD, INC., ET AL 36
## 44 RI JOHNSON & JOHNSON, ET AL 33
## 45 RI DAVOL INC., ET AL 29
## 46 RI CITY OF PROVIDENCE, ET AL 23
## 47 RI MORTGAGE ELECTRONIC REG, ET AL 22
## 48 RI STATE OF RHODE ISLAND, ET AL 17
## 49 RI CVS PHARMACY, INC. 14
## 50 RI UNITED STATES OF AMERICA 14
## 51 VT UNITED STATES OF AMERICA 14
## 52 VT STATE OF VERMONT, ET AL 9
## 53 VT BUILDING PRODUCTS OF CANADA CO 5
## 54 VT FLETCHER ALLEN HEALTH C, ET AL 5
## 55 VT SHUMLIN, ET AL 5
## 56 VT DARTMOUTH-HITCHCOCK MED, ET AL 4
## 57 VT DARTMOUTH HITCHCOCK MED, ET AL 4
## 58 VT GENERAL ELECTRIC COMPANY 4
## 59 VT NORWICH UNIVERSITY 4
## 60 VT SEALED 4
## 61 VT STILLER, ET AL 4
## 62 VT UNITED STATES POSTAL SERVICE 4
## 63 VT UNIVERSITY OF VERMONT MEDICAL 4data %>%
group_by(DISTRICT, PLT) %>%
summarise(n = n()) %>%
arrange(DISTRICT, desc(n)) %>%
top_n(10, n) %>%
data.frame()
## DISTRICT PLT n
## 1 CT MALIBU MEDIA, LLC 109
## 2 CT STRIKE 3 HOLDINGS, LLC 47
## 3 CT BROWN 44
## 4 CT SEALED 44
## 5 CT WILLIAMS 44
## 6 CT SUBWAY INTERNATIONAL B.V. 40
## 7 CT DOE 39
## 8 CT USA 38
## 9 CT JOHNSON 32
## 10 CT SMITH 31
## 11 MA MARRADI 146
## 12 MA SMITH 105
## 13 MA SEALED 104
## 14 MA BROWN 96
## 15 MA JOHNSON 94
## 16 MA WILLIAMS 88
## 17 MA UNITED STATES OF AMERICA 85
## 18 MA JONES 81
## 19 MA UNITED STATES OF AMERIC, ET AL 73
## 20 MA FORWARD FINANCING LLC 70
## 21 ME SHUPER 27
## 22 ME HURT 16
## 23 ME SMITH 15
## 24 ME USA 11
## 25 ME BROWN 9
## 26 ME WILLIAMS 9
## 27 ME ADAMS 8
## 28 ME MURPHY 8
## 29 ME MARTIN 7
## 30 ME ROY 7
## 31 ME SEALED 7
## 32 NH USA 34
## 33 NH AMATUCCI 21
## 34 NH SMITH 14
## 35 NH JOHNSON 13
## 36 NH THEODORE, ET AL 10
## 37 NH TAYLOR 9
## 38 NH TOMPSON 9
## 39 NH BERSAW 8
## 40 NH COLEMAN 8
## 41 NH BROWN 7
## 42 RI LACCINOLE 27
## 43 RI SMITH 16
## 44 RI UNITED STATES OF AMERICA 11
## 45 RI SEALED 9
## 46 RI DOE 8
## 47 RI MARSHALL 8
## 48 RI SILVIA 8
## 49 RI ALLSTATE INSURANCE COMPANY 7
## 50 RI AMICA MUTUAL INSURANCE COMPANY 7
## 51 RI BROWN 7
## 52 RI GARLICK 7
## 53 RI HORSCH 7
## 54 RI JACOBOWITZ, ET AL 7
## 55 VT UNITED STATES OF AMERICA 8
## 56 VT PORTER 7
## 57 VT CHANDLER 6
## 58 VT DAVIS 6
## 59 VT DOE 4
## 60 VT JOHNSON 4
## 61 VT MARINO 4
## 62 VT MCCAIN 4
## 63 VT MOORE 4
## 64 VT PAPAZONI 4
## 65 VT REYNOLDS 4
## 66 VT RYAN 4
## 67 VT SEALED 4
## 68 VT SMITH 4
## 69 VT STATE OF VERMONT 4
## 70 VT UNITED STATES OF AMERIC, ET AL 4
## 71 VT VERMONT MUTUAL INSURANCE COMPA 4# Import data
data <- read.csv("C:/Users/sclee1/OneDrive/Documents/R/legalAnalytics/data/data_merged.csv", header=TRUE)
library(tidyverse)
# Separte date into year, month, day
data <- separate(data, FILEDATE, c("FILEMONTH", "FILEDAY", "FILEYEAR"), sep = "/", remove = TRUE)
data$FILEYEAR <- as.integer(data$FILEYEAR)
data$FILEMONTH <- NULL
data$FILEDAY <- NULL# Filter for cases filed after 2010
data <-
data %>%
filter(FILEYEAR > 2010) %>%
droplevels()
# Convert to factors
data$NOS <- factor(data$NOS)
# Get a sense of the data
str(data)
## 'data.frame': 1510 obs. of 50 variables:
## $ CIRCUIT : int 1 1 1 1 1 1 1 1 1 1 ...
## $ DISTRICT : int 2 2 2 2 2 2 2 2 2 2 ...
## $ OFFICE : int 1 1 1 1 1 1 1 1 1 1 ...
## $ DOCKET : int 900365 1000489 1000529 1000607 1100014 1100025 1100033 1100039 1100049 1100054 ...
## $ ORIGIN : int 4 4 4 1 2 1 1 2 1 1 ...
## $ FILEYEAR : int 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
## $ FDATEUSE : Factor w/ 84 levels "1/1/2011","1/1/2012",..: 64 64 78 1 1 1 1 1 29 29 ...
## $ JURIS : int 3 4 3 3 4 1 3 4 4 4 ...
## $ NOS : Factor w/ 42 levels "110","140","160",..: 40 12 40 31 12 32 38 4 18 1 ...
## $ TITLE : Factor w/ 18 levels "12","15","17",..: 13 7 13 2 7 8 7 7 10 7 ...
## $ SECTION : Factor w/ 87 levels "1","10","1001",..: 50 24 60 35 24 76 15 24 63 12 ...
## $ SUBSECT : Factor w/ 53 levels "-8","1","AT",..: 1 49 1 1 37 1 51 34 1 16 ...
## $ RESIDENC : int -8 12 -8 -8 12 -8 -8 15 24 55 ...
## $ CLASSACT : int -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 ...
## $ DEMANDED : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FILEJUDG : logi NA NA NA NA NA NA ...
## $ FILEMAG : logi NA NA NA NA NA NA ...
## $ COUNTY : int 88888 33001 88888 33011 33009 33005 88888 33009 33007 88888 ...
## $ ARBIT : Factor w/ 4 levels "-8","E","M","V": 1 1 1 1 1 1 1 1 1 1 ...
## $ MDLDOCK : int -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 ...
## $ PLT : Factor w/ 1252 levels "-8","17 OUTLETS, LLC",..: 566 442 828 785 577 1176 1061 990 686 256 ...
## $ DEF : Factor w/ 1234 levels "-8","10 IRON HORSE DRIVE, LL, ET AL",..: 123 663 969 824 663 322 10 1199 26 872 ...
## $ TRANSDAT : logi NA NA NA NA NA NA ...
## $ TRANSOFF : int -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 ...
## $ TRANSDOC : int -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 ...
## $ TRANSORG : int -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 ...
## $ TERMDATE : Factor w/ 965 levels "1/10/2012","1/10/2013",..: 751 783 933 804 962 883 420 374 576 841 ...
## $ TDATEUSE : Factor w/ 83 levels "1/1/2012","1/1/2013",..: 63 63 77 70 77 70 35 28 49 70 ...
## $ TRCLACT : int -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 ...
## $ TERMJUDG : logi NA NA NA NA NA NA ...
## $ TERMMAG : logi NA NA NA NA NA NA ...
## $ PROCPROG : int 5 5 2 4 5 1 2 3 3 12 ...
## $ DISP : int 14 18 14 6 13 4 12 1 13 6 ...
## $ NOJ : int 0 0 0 0 0 1 0 0 0 0 ...
## $ AMTREC : int 0 0 0 0 0 16 0 0 0 0 ...
## $ JUDGMENT : int 0 0 0 2 0 1 0 0 0 2 ...
## $ DJOINED : Factor w/ 663 levels "","1/11/2012",..: 507 537 644 277 34 1 1 238 309 1 ...
## $ PRETRIAL : Factor w/ 211 levels "","1/10/2012",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ TRIBEGAN : Factor w/ 20 levels "","1/20/2016",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ TRIALEND : Factor w/ 28 levels "","1/20/2016",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ TRMARB : int -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 ...
## $ PROSE : int 0 0 0 3 0 0 0 0 2 0 ...
## $ IFP : Factor w/ 2 levels "-8","FP": 1 1 1 2 1 1 1 1 1 1 ...
## $ STATUSCD : Factor w/ 1 level "L": 1 1 1 1 1 1 1 1 1 1 ...
## $ TAPEYEAR : int 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
## $ nature_of_suit: Factor w/ 42 levels "ADMINISTRATIVE PROCEDURE ACT/REVIEW OR APPEAL OF AGENCY DECISION",..: 33 22 33 10 22 25 41 27 28 18 ...
## $ busType_def : Factor w/ 5 levels "CORP","LLC","PARTNERSHIP",..: 2 NA NA NA NA 1 NA 1 NA NA ...
## $ busType_plt : Factor w/ 4 levels "CORP","LLC","PARTNERSHIP",..: NA NA 2 NA NA NA 1 NA NA NA ...
## $ busType : Factor w/ 3 levels "Both","Neither",..: 3 2 3 2 2 3 3 3 2 2 ...
## $ cause : Factor w/ 124 levels "","05:551 Administrative Procedure Act",..: 123 NA NA 12 NA 110 59 NA NA 39 ...
summary(data)
## CIRCUIT DISTRICT OFFICE DOCKET ORIGIN
## Min. :1 Min. :2 Min. :1 Min. : 500145 Min. : 1.000
## 1st Qu.:1 1st Qu.:2 1st Qu.:1 1st Qu.:1200153 1st Qu.: 1.000
## Median :1 Median :2 Median :1 Median :1300403 Median : 1.000
## Mean :1 Mean :2 Mean :1 Mean :1337053 Mean : 1.423
## 3rd Qu.:1 3rd Qu.:2 3rd Qu.:1 3rd Qu.:1500192 3rd Qu.: 2.000
## Max. :1 Max. :2 Max. :1 Max. :1700670 Max. :13.000
##
## FILEYEAR FDATEUSE JURIS NOS
## Min. :2011 4/1/2013 : 36 Min. :1.000 440 :200
## 1st Qu.:2012 10/1/2014: 32 1st Qu.:3.000 190 :193
## Median :2013 8/1/2011 : 32 Median :3.000 442 :160
## Mean :2013 5/1/2011 : 31 Mean :3.328 360 :110
## 3rd Qu.:2015 3/1/2012 : 29 3rd Qu.:4.000 890 :109
## Max. :2017 3/1/2013 : 28 Max. :4.000 480 :100
## (Other) :1322 (Other):638
## TITLE SECTION SUBSECT RESIDENC
## 28 :977 1332 :386 -8 :336 Min. :-8.000
## 42 :230 1441 :373 CV :196 1st Qu.:-8.000
## 15 :182 1983 :130 ED :117 Median :-8.000
## 29 : 45 1692 :123 PI :115 Mean : 5.677
## CO : 19 1331 :111 BC :110 3rd Qu.:15.000
## 47 : 15 1210 : 47 PL : 63 Max. :64.000
## (Other): 42 (Other):340 (Other):573
## CLASSACT DEMANDED FILEJUDG FILEMAG
## Min. :-8.000 Min. : 0.00000 Mode:logical Mode:logical
## 1st Qu.:-8.000 1st Qu.: 0.00000 NA's:1510 NA's:1510
## Median :-8.000 Median : 0.00000
## Mean :-7.928 Mean : 0.03311
## 3rd Qu.:-8.000 3rd Qu.: 0.00000
## Max. : 1.000 Max. :25.00000
##
## COUNTY ARBIT MDLDOCK PLT
## Min. :33001 -8:1490 Min. : -8.00 USA : 32
## 1st Qu.:33011 E : 7 1st Qu.: -8.00 AMATUCCI : 17
## Median :33013 M : 5 Median : -8.00 BERSAW : 8
## Mean :42950 V : 8 Mean : 11.85 JOHNSON : 8
## 3rd Qu.:33017 3rd Qu.: -8.00 THEODORE, ET AL: 8
## Max. :99999 Max. :2320.00 COLEMAN : 7
## (Other) :1430
## DEF TRANSDAT TRANSOFF
## -8 : 25 Mode:logical Min. :-8
## USA : 16 NA's:1510 1st Qu.:-8
## GUTIERREZ, ET AL : 12 Median :-8
## COLGATE-PALMOLIVE COMPANY : 9 Mean :-8
## PORTFOLIO RECOVERY ASSOCIATES,: 9 3rd Qu.:-8
## DEPUY ORTHOPAEDICS, INC, ET AL: 7 Max. :-8
## (Other) :1432
## TRANSDOC TRANSORG TERMDATE TDATEUSE
## Min. :-8 Min. :-8 2/4/2015 : 13 5/1/2013 : 33
## 1st Qu.:-8 1st Qu.:-8 2/1/2016 : 7 2/1/2015 : 31
## Median :-8 Median :-8 12/16/2013: 6 1/1/2013 : 29
## Mean :-8 Mean :-8 2/28/2014 : 6 10/1/2014: 27
## 3rd Qu.:-8 3rd Qu.:-8 11/18/2014: 5 11/1/2017: 27
## Max. :-8 Max. :-8 5/13/2013 : 5 12/1/2015: 27
## (Other) :1468 (Other) :1336
## TRCLACT TERMJUDG TERMMAG PROCPROG
## Min. :-8.000 Mode:logical Mode:logical Min. : 1.000
## 1st Qu.:-8.000 NA's:1510 NA's:1510 1st Qu.: 2.000
## Median :-8.000 Median : 5.000
## Mean :-7.779 Mean : 4.103
## 3rd Qu.:-8.000 3rd Qu.: 5.000
## Max. : 3.000 Max. :12.000
##
## DISP NOJ AMTREC JUDGMENT
## Min. : 0.00 Min. :0.00000 Min. : 0.00 Min. :0.000
## 1st Qu.: 6.00 1st Qu.:0.00000 1st Qu.: 0.00 1st Qu.:0.000
## Median :12.00 Median :0.00000 Median : 0.00 Median :0.000
## Mean :10.65 Mean :0.03377 Mean : 41.45 Mean :0.398
## 3rd Qu.:13.00 3rd Qu.:0.00000 3rd Qu.: 0.00 3rd Qu.:0.000
## Max. :20.00 Max. :2.00000 Max. :9999.00 Max. :4.000
##
## DJOINED PRETRIAL TRIBEGAN TRIALEND
## :631 :1261 :1490 :1483
## 7/1/2013 : 8 10/12/2011: 7 2/17/2016 : 2 1/20/2016 : 1
## 11/14/2011: 5 3/5/2012 : 4 1/20/2016 : 1 1/27/2016 : 1
## 12/20/2012: 4 10/24/2011: 3 1/6/2015 : 1 1/7/2015 : 1
## 8/7/2012 : 4 12/16/2011: 3 1/6/2016 : 1 1/7/2016 : 1
## 1/13/2014 : 3 1/19/2012 : 2 10/20/2017: 1 10/24/2014: 1
## (Other) :855 (Other) : 230 (Other) : 14 (Other) : 22
## TRMARB PROSE IFP STATUSCD TAPEYEAR
## Min. :-8 Min. :0.0000 -8:1383 L:1510 Min. :2011
## 1st Qu.:-8 1st Qu.:0.0000 FP: 127 1st Qu.:2013
## Median :-8 Median :0.0000 Median :2014
## Mean :-8 Mean :0.2225 Mean :2015
## 3rd Qu.:-8 3rd Qu.:0.0000 3rd Qu.:2016
## Max. :-8 Max. :3.0000 Max. :2018
##
## nature_of_suit busType_def busType_plt
## OTHER CIVIL RIGHTS :200 CORP :338 CORP : 88
## OTHER CONTRACT ACTIONS :193 LLC :167 LLC : 95
## CIVIL RIGHTS JOBS :160 PARTNERSHIP: 21 PARTNERSHIP: 2
## OTHER PERSONAL INJURY :110 PC : 1 PC : 5
## OTHER STATUTORY ACTIONS:109 PLLC : 1 NA's :1320
## CONSUMER CREDIT :100 NA's :982
## (Other) :638
## busType cause
## Both : 77 42:1983 Civil Rights Act : 84
## Neither :869 15:1692 Fair Debt Collection Act : 40
## Only one:564 28:1441 Petition for Removal - Employment Discrim: 33
## 28:1332 Diversity-Breach of Contract : 32
## 28:1331 Federal Question: Other Civil Rights : 31
## (Other) :534
## NA's :756data %>%
group_by(DISTRICT, DEF) %>%
summarise(n = n()) %>%
arrange(DISTRICT, desc(n)) %>%
top_n(10, n) %>%
data.frame()
## DISTRICT DEF n
## 1 2 -8 25
## 2 2 USA 16
## 3 2 GUTIERREZ, ET AL 12
## 4 2 COLGATE-PALMOLIVE COMPANY 9
## 5 2 PORTFOLIO RECOVERY ASSOCIATES, 9
## 6 2 DEPUY ORTHOPAEDICS, INC, ET AL 7
## 7 2 SALEM, NH, TOWN OF, ET AL 6
## 8 2 ZIMMER, INC., ET AL 6
## 9 2 BANK OF AMERICA, N.A. 5
## 10 2 MIDLAND CREDIT MANAGEME, ET AL 5
## 11 2 USA, ET AL 5data %>%
group_by(DISTRICT, PLT) %>%
summarise(n = n()) %>%
arrange(DISTRICT, desc(n)) %>%
top_n(10, n) %>%
data.frame()
## DISTRICT PLT n
## 1 2 USA 32
## 2 2 AMATUCCI 17
## 3 2 BERSAW 8
## 4 2 JOHNSON 8
## 5 2 THEODORE, ET AL 8
## 6 2 COLEMAN 7
## 7 2 FISCHER 6
## 8 2 TAYLOR 6
## 9 2 BROADCAST MUSIC, INC., ET AL 5
## 10 2 TOMPSON 5The table below shows break-outs of defendants by type of suits. The type of suits is represented by the nature of suit and the causes of action. For example, GUTIERREZ, ET AL had been sued 12 times since 2010. All 12 were SECURITIES, COMMODITIES, EXCHANGE in the nature of suit. In terms of the causes of action, 2 of 12 were 15:78m(a) Securities Exchange Act, while the rest is unknown.
The table is sorted so that top defendants show at the top of the table.
# Calculate total number of cases per defendant
data_Total <-
data %>%
count(DEF, sort = TRUE) %>%
rename(Total = n)
library(DT)
table <- data %>%
count(DEF, nature_of_suit, cause, sort = TRUE) %>%
left_join(data_Total) %>%
mutate(percent = format((n / Total * 100), digits = 2)) %>%
arrange(desc(Total), DEF) %>%
filter(DEF != "-8")
datatable(table, options = list(pageLength = 10))# Calculate total number of cases per defendant
data_Total <-
data %>%
count(PLT, sort = TRUE) %>%
rename(Total = n)
library(DT)
table <- data %>%
count(PLT, nature_of_suit, cause, sort = TRUE) %>%
left_join(data_Total) %>%
mutate(percent = format((n / Total * 100), digits = 2)) %>%
arrange(desc(Total), PLT) %>%
filter(PLT != "-8")
datatable(table, options = list(pageLength = 10))