The data came from two NH county superior courts: Belknap and Concord.
library(tidyverse)
data <- read.csv("data/NH.csv", skip = 1) # 1st row is the description of variables
# Select 2 variables: 1) case type and 2) defendant names
data <-
data %>%
select(CaseType, DNames, PNames)
colnames(data) <- c("CaseType", "D", "P")
str(data)
## 'data.frame': 409 obs. of 3 variables:
## $ CaseType: Factor w/ 41 levels "1","10","10; 26",..: 9 13 28 28 22 11 11 11 11 11 ...
## $ D : Factor w/ 398 levels "223 D.W. Highway, LLC",..: 347 374 208 239 123 358 149 285 1 66 ...
## $ P : Factor w/ 344 levels "A.O. Phaneuf & Son Funeral Home and Crematorium, Inc.; Cremation Society of New Hampshire, Inc.; Arthur Phaneuf",..: 21 236 259 259 15 71 72 157 207 12 ...
head(data)
## CaseType D
## 1 17 Stephanie Michaud
## 2 21 Town of Gilford
## 3 4 Jennifer Colwell
## 4 4 Kevin D. Rooney
## 5 34 Deborah Foote
## 6 2 Terrence J. Coyman
## P
## 1 Anthony Signorine
## 2 Monique A. Twomey Revocable Trust; Monique Twomey
## 3 Paugus Bay Plaza Condominium Association
## 4 Paugus Bay Plaza Condominium Association
## 5 American Modern Home Insurance Co.
## 6 Concord HospitalTransform so that the row represents businesses instead of court cases.
dataT_Def <-
data %>%
select(-P) %>%
separate(D, c("D1","D2","D3","D4","D5","D6","D7","D8","D9","D10"),
sep = ";", extra = "merge") %>%
gather("D", "Dname", 2:11) %>%
select(-D) %>%
filter(!is.na(Dname))
## Warning: Expected 10 pieces. Missing pieces filled with `NA` in 407
## rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
## 20, ...].
str(dataT_Def)
## 'data.frame': 730 obs. of 2 variables:
## $ CaseType: Factor w/ 41 levels "1","10","10; 26",..: 9 13 28 28 22 11 11 11 11 11 ...
## $ Dname : chr "Stephanie Michaud" "Town of Gilford" "Jennifer Colwell" "Kevin D. Rooney" ...
head(dataT_Def)
## CaseType Dname
## 1 17 Stephanie Michaud
## 2 21 Town of Gilford
## 3 4 Jennifer Colwell
## 4 4 Kevin D. Rooney
## 5 34 Deborah Foote
## 6 2 Terrence J. Coyman
summary(dataT_Def)
## CaseType Dname
## 2 :160 Length:730
## 47 : 67 Class :character
## 4 : 65 Mode :character
## 38 : 53
## 37 : 42
## 40 : 37
## (Other):306dataT_Def %>%
mutate_if(is.character, factor) %>%
group_by(Dname) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
head(20)
## # A tibble: 20 x 2
## Dname n
## <fctr> <int>
## 1 Town of Gilford 6
## 2 " Aaron Olson" 3
## 3 " Huggins Hospital" 3
## 4 " Barry Cynewski" 2
## 5 " Brenda M. Stowe" 2
## 6 " Brian Littizzio" 2
## 7 " Clough Development, LLC" 2
## 8 " Clough Work Force Housing Limited Partnership" 2
## 9 " Glenridge, LLC" 2
## 10 " KMO Associates, LLC" 2
## 11 " KMO Associates, LP" 2
## 12 AEO Associates, LLC 2
## 13 Amanda Cynewski, Attorney in Fact Under Durable Power of Attorne~ 2
## 14 Bank of New England 2
## 15 Barry K. Meyers 2
## 16 Barry Myers 2
## 17 Brazilian Resources, Inc. 2
## 18 Brian James Carpentry, LLC 2
## 19 City of Laconia 2
## 20 LASC, Inc. 2Transform so that the row represents businesses instead of court cases.
dataT_Pl <-
data %>%
select(-D) %>%
separate(P, c("P1","P2","P3","P4","P5","P6","P7","P8","P9","P10"),
sep = ";", extra = "merge") %>%
gather("P", "Pname", 2:11) %>%
select(-P) %>%
filter(!is.na(Pname))
## Warning: Expected 10 pieces. Missing pieces filled with `NA` in 408
## rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
## 20, ...].
str(dataT_Pl)
## 'data.frame': 494 obs. of 2 variables:
## $ CaseType: Factor w/ 41 levels "1","10","10; 26",..: 9 13 28 28 22 11 11 11 11 11 ...
## $ Pname : chr "Anthony Signorine" "Monique A. Twomey Revocable Trust" "Paugus Bay Plaza Condominium Association" "Paugus Bay Plaza Condominium Association" ...
head(dataT_Pl)
## CaseType Pname
## 1 17 Anthony Signorine
## 2 21 Monique A. Twomey Revocable Trust
## 3 4 Paugus Bay Plaza Condominium Association
## 4 4 Paugus Bay Plaza Condominium Association
## 5 34 American Modern Home Insurance Co.
## 6 2 Concord Hospital
summary(dataT_Pl)
## CaseType Pname
## 2 :121 Length:494
## 37 : 47 Class :character
## 38 : 46 Mode :character
## 4 : 37
## 47 : 37
## 35 : 29
## (Other):177It’s interesting that the plaintiff’s list is dominated by financial institutions.
dataT_Pl %>%
mutate_if(is.character, factor) %>%
group_by(Pname) %>%
summarise(n = n()) %>%
arrange(desc(n)) %>%
head(20)
## # A tibble: 20 x 2
## Pname n
## <fctr> <int>
## 1 Discover Bank 14
## 2 Paugus Bay Plaza Condominium Association 8
## 3 Barcklay Bank Delaware 7
## 4 American Express Centurion Bank 6
## 5 Concord Hospital 6
## 6 American Express 5
## 7 Bank of New Hampshire 5
## 8 " Monique Twomey" 4
## 9 American Express Bank FSB 4
## 10 Monique A. Twomey Revocable Trust 4
## 11 Office of the Attorney General 4
## 12 " Mark Norby" 2
## 13 " Park Construction Corporation" 2
## 14 " Steven Bauher" 2
## 15 " Steven Norby" 2
## 16 Alan Hawley 2
## 17 Concord Hospital, Inc. 2
## 18 David Norby 2
## 19 General Linen Service Co, Inc. 2
## 20 Huggins Hospital 2They seems to be all individual citizens.
data %>%
filter(P %in% c("Discover Bank",
"Barcklay Bank Delaware",
"American Express Centurion Bank",
"American Express")) %>%
group_by(D) %>%
summarise(n = n())
## # A tibble: 31 x 2
## D n
## <fctr> <int>
## 1 Alton Transmission and Auto Repair Service, LLC; Wayne Gordon 1
## 2 Anne Cook 1
## 3 Anne Soucy 1
## 4 Bradley Preston 1
## 5 Charles Trites 1
## 6 David Pabst 1
## 7 Don Chin 1
## 8 Douglas White 1
## 9 Eileen Cusick 1
## 10 Elaine Wakefield 1
## # ... with 21 more rowsThey are all contract - collection. I think you would have figured that already.
data %>%
filter(P %in% c("Discover Bank",
"Barcklay Bank Delaware",
"American Express Centurion Bank",
"American Express")) %>%
group_by(CaseType) %>%
summarise(n = n())
## # A tibble: 1 x 2
## CaseType n
## <fctr> <int>
## 1 2 31The data we are using here are obtained from the different sources: the federal judiciary center and the free law project.
# Import data
data <- read.csv("data/data_merged.csv")
# Sturecture of data
str(data)
## 'data.frame': 4200 obs. of 51 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 265 409 100015 100016 100018 100019 100020 100028 100030 100031 ...
## $ ORIGIN : int 4 4 1 1 5 1 1 5 1 1 ...
## $ FILEDATE : Factor w/ 2566 levels "1/1/2012","1/10/2001",..: 1107 1237 2 2 18 18 18 85 85 85 ...
## $ FILEYEAR : int 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 ...
## $ FDATEUSE : Factor w/ 204 levels "1/1/2001","1/1/2002",..: 86 103 1 1 1 1 1 1 1 1 ...
## $ JURIS : int 3 4 1 1 4 3 4 4 4 4 ...
## $ NOS : int 840 190 190 190 190 440 110 365 365 365 ...
## $ TITLE : Factor w/ 33 levels "-8","0","10",..: 5 13 13 13 13 20 13 20 13 13 ...
## $ SECTION : Factor w/ 144 levels "-8","1","10",..: 10 20 27 27 20 73 22 138 20 20 ...
## $ SUBSECT : Factor w/ 73 levels "-8","1","12",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ RESIDENC : int -8 43 -8 -8 25 -8 52 55 15 15 ...
## $ 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 99999 33015 33013 33013 88888 88888 88888 88888 33007 33015 ...
## $ 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/ 3128 levels "-8","108 DEGREES. LLC",..: 2968 1362 2944 2944 1546 268 85 1813 2166 1767 ...
## $ DEF : Factor w/ 3369 levels "-8",", ET AL",..: 1024 1637 993 2405 3274 649 2845 76 118 119 ...
## $ 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/ 2560 levels "1/10/2002","1/10/2003",..: 1184 2184 1584 1191 1082 2161 2042 1013 1100 1043 ...
## $ TDATEUSE : Factor w/ 204 levels "1/1/2001","1/1/2002",..: 86 171 120 86 86 171 154 69 86 69 ...
## $ 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 4 2 2 2 2 5 2 1 2 ...
## $ DISP : int 5 13 18 18 14 3 6 14 10 12 ...
## $ NOJ : int 2 -8 0 0 -8 -8 2 -8 -8 -8 ...
## $ AMTREC : int 5 54 0 0 0 0 33 0 0 0 ...
## $ JUDGMENT : int 1 -8 -8 -8 -8 -8 2 -8 -8 -8 ...
## $ DJOINED : Factor w/ 1512 levels "","1/10/2005",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ PRETRIAL : Factor w/ 418 levels "","1/10/2012",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ TRIBEGAN : Factor w/ 40 levels "","1/20/2016",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ TRIALEND : Factor w/ 83 levels "","1/13/2004",..: 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 0 0 1 2 0 0 0 ...
## $ IFP : Factor w/ 2 levels "-8","FP": 1 1 1 1 1 1 1 1 1 1 ...
## $ STATUSCD : Factor w/ 1 level "L": 1 1 1 1 1 1 1 1 1 1 ...
## $ TAPEYEAR : int 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 ...
## $ nature_of_suit: Factor w/ 44 levels "ADMINISTRATIVE PROCEDURE ACT/REVIEW OR APPEAL OF AGENCY DECISION",..: 43 29 29 29 29 28 19 36 36 36 ...
## $ busType_def : Factor w/ 5 levels "CORP","LLC","PARTNERSHIP",..: NA NA NA NA NA NA NA NA NA NA ...
## $ busType_plt : Factor w/ 4 levels "CORP","LLC","PARTNERSHIP",..: NA NA NA NA NA NA NA NA NA NA ...
## $ busType : Factor w/ 3 levels "Both","Neither",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ cause : Factor w/ 173 levels "","0.415277778",..: NA NA NA NA NA NA NA NA NA NA ...
summary(data)
## CIRCUIT DISTRICT OFFICE DOCKET ORIGIN
## Min. :1 Min. :2 Min. :1 Min. : 73 Min. : 1.000
## 1st Qu.:1 1st Qu.:2 1st Qu.:1 1st Qu.: 400138 1st Qu.: 1.000
## Median :1 Median :2 Median :1 Median : 800364 Median : 1.000
## Mean :1 Mean :2 Mean :1 Mean : 855885 Mean : 1.467
## 3rd Qu.:1 3rd Qu.:2 3rd Qu.:1 3rd Qu.:1200343 3rd Qu.: 2.000
## Max. :1 Max. :2 Max. :1 Max. :9900606 Max. :13.000
##
## FILEDATE FILEYEAR FDATEUSE JURIS
## 9/16/2002 : 10 Min. :2001 10/1/2002: 50 Min. :1.000
## 11/25/2003: 9 1st Qu.:2004 5/1/2002 : 40 1st Qu.:3.000
## 9/24/2002 : 8 Median :2008 9/1/2002 : 40 Median :3.000
## 11/23/2010: 7 Mean :2008 10/1/2003: 36 Mean :3.315
## 5/7/2004 : 7 3rd Qu.:2012 4/1/2013 : 36 3rd Qu.:4.000
## 11/23/2004: 6 Max. :2017 6/1/2005 : 34 Max. :4.000
## (Other) :4153 (Other) :3964
## NOS TITLE SECTION SUBSECT
## Min. :110.0 28 :2717 1332 :1147 -8 :1562
## 1st Qu.:350.0 42 : 656 1441 : 912 CV : 430
## Median :440.0 15 : 396 1983 : 355 PI : 269
## Mean :443.9 29 : 112 1331 : 322 BC : 249
## 3rd Qu.:446.0 17 : 63 1692 : 163 ED : 229
## Max. :899.0 47 : 55 2000 : 146 OC : 177
## (Other): 201 (Other):1155 (Other):1284
## RESIDENC CLASSACT DEMANDED FILEJUDG
## Min. :-8.000 Min. :-8.000 Min. : 0.00 Mode:logical
## 1st Qu.:-8.000 1st Qu.:-8.000 1st Qu.: 0.00 NA's:4200
## Median :-8.000 Median :-8.000 Median : 0.00
## Mean : 6.339 Mean :-7.916 Mean : 2.64
## 3rd Qu.:15.000 3rd Qu.:-8.000 3rd Qu.: 0.00
## Max. :64.000 Max. : 1.000 Max. :5000.00
##
## FILEMAG COUNTY ARBIT MDLDOCK
## Mode:logical Min. :33001 -8:4180 Min. : -8.000
## NA's:4200 1st Qu.:33011 E : 7 1st Qu.: -8.000
## Median :33013 M : 5 Median : -8.000
## Mean :46637 V : 8 Mean : 9.216
## 3rd Qu.:33019 3rd Qu.: -8.000
## Max. :99999 Max. :2320.000
##
## PLT DEF TRANSDAT
## USA : 82 -8 : 61 Mode:logical
## -8 : 20 TYCO INTERNATIONAL, ET AL : 34 NA's:4200
## DIRECTV, INC.: 20 USA : 34
## AMATUCCI : 17 GUTIERREZ, ET AL : 12
## WILSON : 15 NH DEPARTMENT OF HEALTH AND HU: 12
## JOHNSON : 13 SEALED : 11
## (Other) :4033 (Other) :4036
## TRANSOFF TRANSDOC TRANSORG TERMDATE TDATEUSE
## Min. :-8 Min. :-8 Min. :-8 2/4/2015 : 13 3/1/2006 : 43
## 1st Qu.:-8 1st Qu.:-8 1st Qu.:-8 3/3/2006 : 13 12/1/2011: 42
## Median :-8 Median :-8 Median :-8 10/14/2004: 9 4/1/2003 : 35
## Mean :-8 Mean :-8 Mean :-8 3/6/2006 : 9 10/1/2003: 34
## 3rd Qu.:-8 3rd Qu.:-8 3rd Qu.:-8 12/19/2011: 8 10/1/2006: 33
## Max. :-8 Max. :-8 Max. :-8 2/1/2016 : 7 3/1/2005 : 33
## (Other) :4141 (Other) :3980
## TRCLACT TERMJUDG TERMMAG PROCPROG
## Min. :-8.000 Mode:logical Mode:logical Min. : 1.000
## 1st Qu.:-8.000 NA's:4200 NA's:4200 1st Qu.: 2.000
## Median :-8.000 Median : 5.000
## Mean :-7.854 Mean : 4.229
## 3rd Qu.:-8.000 3rd Qu.: 5.000
## Max. : 3.000 Max. :13.000
##
## DISP NOJ AMTREC JUDGMENT
## Min. : 0.0 Min. :-8.000 Min. : 0.00 Min. :-8.00
## 1st Qu.: 6.0 1st Qu.:-8.000 1st Qu.: 0.00 1st Qu.:-8.00
## Median :13.0 Median : 0.000 Median : 0.00 Median : 0.00
## Mean :10.5 Mean :-3.177 Mean : 35.09 Mean :-3.25
## 3rd Qu.:13.0 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :20.0 Max. : 6.000 Max. :9999.00 Max. : 4.00
##
## DJOINED PRETRIAL TRIBEGAN TRIALEND
## :2163 :3717 :4160 :4118
## 7/1/2013 : 8 10/12/2011: 7 2/17/2016: 2 1/13/2004: 1
## 11/14/2011: 6 10/24/2011: 4 1/20/2016: 1 1/20/2016: 1
## 10/12/2006: 5 3/5/2012 : 4 1/4/2011 : 1 1/23/2007: 1
## 10/22/2004: 5 11/24/2009: 3 1/6/2015 : 1 1/27/2009: 1
## 5/14/2007 : 5 12/16/2011: 3 1/6/2016 : 1 1/27/2016: 1
## (Other) :2008 (Other) : 462 (Other) : 34 (Other) : 77
## TRMARB PROSE IFP STATUSCD TAPEYEAR
## Min. :-8 Min. :0.000 -8:3943 L:4200 Min. :2001
## 1st Qu.:-8 1st Qu.:0.000 FP: 257 1st Qu.:2005
## Median :-8 Median :0.000 Median :2009
## Mean :-8 Mean :0.221 Mean :2009
## 3rd Qu.:-8 3rd Qu.:0.000 3rd Qu.:2013
## Max. :-8 Max. :3.000 Max. :2018
##
## nature_of_suit busType_def busType_plt
## OTHER CONTRACT ACTIONS : 610 CORP : 877 CORP : 409
## OTHER CIVIL RIGHTS : 574 LLC : 390 LLC : 264
## CIVIL RIGHTS JOBS : 437 PARTNERSHIP: 30 PARTNERSHIP: 4
## OTHER PERSONAL INJURY : 356 PC : 6 PC : 9
## OTHER STATUTORY ACTIONS: 344 PLLC : 2 NA's :3514
## INSURANCE : 245 NA's :2895
## (Other) :1634
## busType cause
## Both : 275 42:1983 Civil Rights Act : 183
## Neither :2484 28:1332 Diversity-Personal Injury : 79
## Only one:1441 28:1332 Diversity-Breach of Contract : 78
## 28:1441 Petition for Removal - Employment Discrim: 70
## 28:1331 Federal Question: Other Civil Rights : 68
## (Other) :1250
## NA's :2472The summary statistics below shows the list of top 20 defendants and top 20 plaintiffs.
data <-
data %>%
select(DEF, PLT, nature_of_suit, cause)
str(data)
## 'data.frame': 4200 obs. of 4 variables:
## $ DEF : Factor w/ 3369 levels "-8",", ET AL",..: 1024 1637 993 2405 3274 649 2845 76 118 119 ...
## $ PLT : Factor w/ 3128 levels "-8","108 DEGREES. LLC",..: 2968 1362 2944 2944 1546 268 85 1813 2166 1767 ...
## $ nature_of_suit: Factor w/ 44 levels "ADMINISTRATIVE PROCEDURE ACT/REVIEW OR APPEAL OF AGENCY DECISION",..: 43 29 29 29 29 28 19 36 36 36 ...
## $ cause : Factor w/ 173 levels "","0.415277778",..: NA NA NA NA NA NA NA NA NA NA ...
summary(data, maxsum = 20)
## DEF
## -8 : 61
## TYCO INTERNATIONAL, ET AL : 34
## USA : 34
## GUTIERREZ, ET AL : 12
## NH DEPARTMENT OF HEALTH AND HU: 12
## SEALED : 11
## WAL-MART STORES, INC. : 11
## PORTFOLIO RECOVERY ASSOCIATES,: 10
## STATE OF NEW HAMPSHIRE : 10
## COLGATE-PALMOLIVE COMPANY : 9
## USA, ET AL : 9
## US POSTAL SERVICE, POSTMASTER : 8
## DEPUY ORTHOPAEDICS, INC, ET AL: 7
## MIDLAND CREDIT MANAGEME, ET AL: 7
## TYCO INTERNATIONAL, LTD. : 7
## US ATTORNEY GENERAL : 7
## BANK OF AMERICA, N.A. : 6
## BAYER CORPORATION : 6
## ENTERASYS NETWORKS, ET AL : 6
## (Other) :3933
## PLT
## USA : 82
## -8 : 20
## DIRECTV, INC. : 20
## AMATUCCI : 17
## WILSON : 15
## JOHNSON : 13
## PFIP, LLC : 12
## SEALED : 11
## COLEMAN : 10
## DAVIS : 10
## THEODORE, ET AL : 10
## US DEPARTMENT OF LABOR, SECRET: 10
## BROWN : 9
## FISCHER : 9
## T.R. WORLD GYM-IP, LLC : 9
## VELCRO INDUSTRIES B.V. : 9
## BERSAW : 8
## HOLDER : 8
## LEWIS : 8
## (Other) :3910
## nature_of_suit
## OTHER CONTRACT ACTIONS :610
## OTHER CIVIL RIGHTS :574
## CIVIL RIGHTS JOBS :437
## OTHER PERSONAL INJURY :356
## OTHER STATUTORY ACTIONS :344
## INSURANCE :245
## PERSONAL INJURY -PRODUCT LIABILITY:181
## TRADEMARK :143
## CONSUMER CREDIT :137
## MOTOR VEHICLE PERSONAL INJURY :136
## COPYRIGHT :116
## SECURITIES, COMMODITIES, EXCHANGE :111
## CIVIL RIGHTS ADA EMPLOYMENT : 93
## OTHER FRAUD : 73
## OTHER REAL PROPERTY ACTIONS : 72
## MEDICAL MALPRACTICE : 70
## FAIR LABOR STANDARDS ACT : 64
## CIVIL RIGHTS ADA OTHER : 54
## OTHER PERSONAL PROPERTY DAMAGE : 54
## (Other) :330
## cause
## 42:1983 Civil Rights Act : 183
## 28:1332 Diversity-Personal Injury : 79
## 28:1332 Diversity-Breach of Contract : 78
## 28:1441 Petition for Removal - Employment Discrim: 70
## 28:1331 Federal Question: Other Civil Rights : 68
## 28:1441 Petition for Removal- Civil Rights Act : 60
## 28:1332 Diversity-Other Contract : 54
## 15:1692 Fair Debt Collection Act : 51
## 28:1441 Petition for Removal- Insurance Contract : 44
## 15:78m(a) Securities Exchange Act : 41
## 28:1441 Petition For Removal--Other Contract : 41
## 42:12101 Americans With Disabilities Act : 38
## 28:1441 Petition for Removal- Breach of Contract : 30
## 28:1332 Diversity-Fraud : 29
## 28:1441 Petition for Removal- Personal Injury : 28
## 28:1332 Diversity-Product Liability : 26
## 42:2000e Job Discrimination (Employment) : 26
## 28:1331 Fed. Question: Personal Injury : 25
## (Other) : 757
## NA's :2472data %>%
filter(str_detect(PLT, "VELCRO INDUSTRIES")) %>%
group_by(nature_of_suit) %>%
summarise(n = n())
## # A tibble: 2 x 2
## nature_of_suit n
## <fctr> <int>
## 1 COPYRIGHT 1
## 2 TRADEMARK 12
data %>%
filter(str_detect(PLT, "VELCRO INDUSTRIES")) %>%
group_by(cause) %>%
summarise(n = n())
## # A tibble: 2 x 2
## cause n
## <fctr> <int>
## 1 28:1338 Trademark Infringement 2
## 2 <NA> 11data %>%
filter(str_detect(DEF, "TYCO INTERNATIONAL")) %>%
group_by(nature_of_suit) %>%
summarise(n = n())
## # A tibble: 1 x 2
## nature_of_suit n
## <fctr> <int>
## 1 SECURITIES, COMMODITIES, EXCHANGE 47
data %>%
filter(str_detect(DEF, "TYCO INTERNATIONAL")) %>%
group_by(cause) %>%
summarise(n = n())
## # A tibble: 4 x 2
## cause n
## <fctr> <int>
## 1 15:78m(a) Securities Exchange Act 18
## 2 28:1331 Fed. Question: Fair Labor Standards 1
## 3 No cause code entered 1
## 4 <NA> 27