Data Science in Context - Lobbying in the U.S.
Mohamed Hassan-El Serafi
2023-04-16
Using datasets from OpenSecrets.org, I explored various areas of money in politics concerning lobbying.
Top Recipients
recipient_df <- read.csv("~/Downloads/Top Recipients of Contributions from Lobbyists, 2022 Cycle.csv", check.names = FALSE)
head(recipient_df)## Recipient From Lobbyists From Lobbyists + Family
## 1 Charles E Schumer (D-NY) $723967 $787867
## 2 Kevin McCarthy (R-Calif) $557843 $561843
## 3 Maggie Hassan (D-NH) $494712 $512362
## 4 Tim Ryan (D-Ohio) $464898 $490695
## 5 Catherine Cortez Masto (D-Nev) $429569 $451204
## 6 Raphael Warnock (D-Ga) $407876 $430191recipient_df <- recipient_df %>%
clean_names()
head(recipient_df)## recipient from_lobbyists from_lobbyists_family
## 1 Charles E Schumer (D-NY) $723967 $787867
## 2 Kevin McCarthy (R-Calif) $557843 $561843
## 3 Maggie Hassan (D-NH) $494712 $512362
## 4 Tim Ryan (D-Ohio) $464898 $490695
## 5 Catherine Cortez Masto (D-Nev) $429569 $451204
## 6 Raphael Warnock (D-Ga) $407876 $430191recipient_df$from_lobbyists <- str_replace_all(recipient_df$from_lobbyists, "\\$", "")
recipient_df$from_lobbyists_family <- str_replace_all(recipient_df$from_lobbyists_family, "\\$", "")recipient_df <- recipient_df %>%
mutate(Party=str_extract(recipient, "\\([A-Z]"))
recipient_df## recipient from_lobbyists from_lobbyists_family Party
## 1 Charles E Schumer (D-NY) 723967 787867 (D
## 2 Kevin McCarthy (R-Calif) 557843 561843 (R
## 3 Maggie Hassan (D-NH) 494712 512362 (D
## 4 Tim Ryan (D-Ohio) 464898 490695 (D
## 5 Catherine Cortez Masto (D-Nev) 429569 451204 (D
## 6 Raphael Warnock (D-Ga) 407876 430191 (D
## 7 Patty Murray (D-Wash) 399950 414050 (D
## 8 John Thune (R-SD) 347478 351978 (R
## 9 Tim Scott (R-SC) 325554 347204 (R
## 10 Ron Wyden (D-Ore) 316153 330753 (D
## 11 Cathy McMorris Rodgers (R-Wash) 308621 312621 (R
## 12 Katie Britt (R-Ala) 298038 313378 (R
## 13 Robert Menendez (D-NJ) 287460 297160 (D
## 14 Liz Cheney (R-Wyo) 281840 295140 (R
## 15 Mark Kelly (D-Ariz) 275470 290452 (D
## 16 Todd Young (R-Ind) 258160 263060 (R
## 17 Alex Padilla (D-Calif) 248170 263370 (D
## 18 Richard Blumenthal (D-Conn) 246658 253158 (D
## 19 Chris Van Hollen (D-Md) 240907 245407 (D
## 20 Hakeem Jeffries (D-NY) 230305 234705 (D
## 21 Mike Crapo (R-Idaho) 229308 231308 (R
## 22 Richard E Neal (D-Mass) 225657 226657 (D
## 23 Mark Warner (D-Va) 221361 227161 (D
## 24 Darin LaHood (R-Ill) 219899 224399 (R
## 25 Lisa Murkowski (R-Alaska) 217985 225935 (R
## 26 Ted Budd (R-NC) 217420 223910 (R
## 27 Marco Rubio (R-Fla) 216594 219594 (R
## 28 Markwayne Mullin (R-Okla) 214650 226800 (R
## 29 John Boozman (R-Ark) 212665 222465 (R
## 30 Ken Calvert (R-Calif) 209563 215963 (R
## 31 Mike Lee (R-Utah) 207326 213226 (R
## 32 Kyrsten Sinema (D-Ariz) 207200 219800 (D
## 33 Chuck Grassley (R-Iowa) 200242 209542 (R
## 34 Vernon Buchanan (R-Fla) 193600 203800 (R
## 35 Frank Pallone Jr. (D-NJ) 192900 192900 (D
## 36 Herschel Walker (R-Ga) 187446 199511 (R
## 37 Steve Scalise (R-La) 184805 184805 (R
## 38 J D Vance (R-Ohio) 182459 190259 (R
## 39 Adam Laxalt (R-Nev) 174000 197150 (R
## 40 Jerry Moran (R-Kan) 167449 168449 (R
## 41 Gary Peters (D-Mich) 165910 168110 (D
## 42 Cheri Beasley (D-NC) 163375 175525 (D
## 43 Drew Ferguson (R-Ga) 157740 157960 (R
## 44 Peter Welch (D-Vt) 156100 160000 (D
## 45 Jon Tester (D-Mont) 150729 150729 (D
## 46 Bob Casey (D-Pa) 148624 151524 (D
## 47 Nancy Pelosi (D-Calif) 147437 159117 (D
## 48 Joe Manchin (D-WVa) 145327 148227 (D
## 49 Patrick McHenry (R-NC) 145000 145000 (R
## 50 Dave McCormick (R) 144350 165650 (R
## 51 Lori Trahan (D-Mass) 143344 144344 (D
## 52 Henry Cuellar (D-Texas) 140402 147502 (D
## 53 James Lankford (R-Okla) 136456 143506 (R
## 54 Martin Heinrich (D-NM) 136350 136350 (D
## 55 Val Demings (D-Fla) 135442 149598 (D
## 56 Ben Ray Lujan (D-NM) 132800 132800 (D
## 57 Brett Guthrie (R-Ky) 127700 128200 (R
## 58 Blaine Luetkemeyer (R-Mo) 125892 135592 (R
## 59 Steven Horsford (D-Nev) 124239 127139 (D
## 60 Chris Stewart (R-Utah) 123200 124200 (R
## 61 Mandela Barnes (D-Wis) 122863 134263 (D
## 62 Abigail Spanberger (D-Va) 121988 125025 (D
## 63 Tammy Duckworth (D-Ill) 119335 123335 (D
## 64 Luke Holland (R) 117550 121675 (R
## 65 Rob Menendez (D-NJ) 115256 116506 (D
## 66 Jason Smith (R-Mo) 115050 119450 (R
## 67 Richard Hudson (R-NC) 112570 116570 (R
## 68 Katherine Clark (D-Mass) 112135 112635 (D
## 69 John Curtis (R-Utah) 109800 109800 (R
## 70 Elaine Luria (D-Va) 109493 111493 (D
## 71 Patrick Leahy (D-Vt) 109448 111948 (D
## 72 Pete Aguilar (D-Calif) 109050 114850 (D
## 73 Dan Newhouse (R-Wash) 108900 109150 (R
## 74 Bill Hagerty (R-Tenn) 108650 108900 (R
## 75 Rodney Davis (R) 108545 110545 (R
## 76 Steny H Hoyer (D-Md) 108020 123020 (D
## 77 Tim Kaine (D-Va) 107878 111778 (D
## 78 Mikie Sherrill (D-NJ) 106940 108440 (D
## 79 Ashley Hinson (R-Iowa) 106550 113200 (R
## 80 French Hill (R-Ark) 106270 106270 (R
## 81 James E Clyburn (D-SC) 105050 105050 (D
## 82 Adam Kinzinger (R-Ill) 104850 107750 (R
## 83 John Kennedy (R-La) 104500 112800 (R
## 84 Rosa DeLauro (D-Conn) 104062 104562 (D
## 85 Mike Simpson (R-Idaho) 103669 105169 (R
## 86 Lucy McBath (D-Ga) 102751 104251 (D
## 87 Haley Stevens (D-Mich) 102612 106012 (D
## 88 Brian Schatz (D-Hawaii) 100790 101040 (D
## 89 Tom Cole (R-Okla) 99100 102100 (R
## 90 Ann Kuster (D-NH) 98600 98600 (D
## 91 Dan Kildee (D-Mich) 98070 103070 (D
## 92 Angie Craig (D-Minn) 96231 96231 (D
## 93 Heather Mizeur (D-Md) 93475 97600 (D
## 94 John Fetterman (D-Pa) 92729 112605 (D
## 95 Scott Peters (D-Calif) 92600 93100 (D
## 96 Josh Gottheimer (D-NJ) 90581 96631 (D
## 97 Joyce Beatty (D-Ohio) 90500 90500 (D
## 98 Chrissy Houlahan (D-Pa) 89644 90644 (D
## 99 Adrian Smith (R-Neb) 89600 90100 (R
## 100 Don Beyer (D-Va) 86977 97877 (Drecipient_df$Party <- str_replace_all(recipient_df$Party, "\\(", "")recipient_df <- recipient_df %>%
mutate(Party = case_when(endsWith(Party, "R") ~ "Republican",
endsWith(Party, "D") ~ "Democrat"))
recipient_df## recipient from_lobbyists from_lobbyists_family
## 1 Charles E Schumer (D-NY) 723967 787867
## 2 Kevin McCarthy (R-Calif) 557843 561843
## 3 Maggie Hassan (D-NH) 494712 512362
## 4 Tim Ryan (D-Ohio) 464898 490695
## 5 Catherine Cortez Masto (D-Nev) 429569 451204
## 6 Raphael Warnock (D-Ga) 407876 430191
## 7 Patty Murray (D-Wash) 399950 414050
## 8 John Thune (R-SD) 347478 351978
## 9 Tim Scott (R-SC) 325554 347204
## 10 Ron Wyden (D-Ore) 316153 330753
## 11 Cathy McMorris Rodgers (R-Wash) 308621 312621
## 12 Katie Britt (R-Ala) 298038 313378
## 13 Robert Menendez (D-NJ) 287460 297160
## 14 Liz Cheney (R-Wyo) 281840 295140
## 15 Mark Kelly (D-Ariz) 275470 290452
## 16 Todd Young (R-Ind) 258160 263060
## 17 Alex Padilla (D-Calif) 248170 263370
## 18 Richard Blumenthal (D-Conn) 246658 253158
## 19 Chris Van Hollen (D-Md) 240907 245407
## 20 Hakeem Jeffries (D-NY) 230305 234705
## 21 Mike Crapo (R-Idaho) 229308 231308
## 22 Richard E Neal (D-Mass) 225657 226657
## 23 Mark Warner (D-Va) 221361 227161
## 24 Darin LaHood (R-Ill) 219899 224399
## 25 Lisa Murkowski (R-Alaska) 217985 225935
## 26 Ted Budd (R-NC) 217420 223910
## 27 Marco Rubio (R-Fla) 216594 219594
## 28 Markwayne Mullin (R-Okla) 214650 226800
## 29 John Boozman (R-Ark) 212665 222465
## 30 Ken Calvert (R-Calif) 209563 215963
## 31 Mike Lee (R-Utah) 207326 213226
## 32 Kyrsten Sinema (D-Ariz) 207200 219800
## 33 Chuck Grassley (R-Iowa) 200242 209542
## 34 Vernon Buchanan (R-Fla) 193600 203800
## 35 Frank Pallone Jr. (D-NJ) 192900 192900
## 36 Herschel Walker (R-Ga) 187446 199511
## 37 Steve Scalise (R-La) 184805 184805
## 38 J D Vance (R-Ohio) 182459 190259
## 39 Adam Laxalt (R-Nev) 174000 197150
## 40 Jerry Moran (R-Kan) 167449 168449
## 41 Gary Peters (D-Mich) 165910 168110
## 42 Cheri Beasley (D-NC) 163375 175525
## 43 Drew Ferguson (R-Ga) 157740 157960
## 44 Peter Welch (D-Vt) 156100 160000
## 45 Jon Tester (D-Mont) 150729 150729
## 46 Bob Casey (D-Pa) 148624 151524
## 47 Nancy Pelosi (D-Calif) 147437 159117
## 48 Joe Manchin (D-WVa) 145327 148227
## 49 Patrick McHenry (R-NC) 145000 145000
## 50 Dave McCormick (R) 144350 165650
## 51 Lori Trahan (D-Mass) 143344 144344
## 52 Henry Cuellar (D-Texas) 140402 147502
## 53 James Lankford (R-Okla) 136456 143506
## 54 Martin Heinrich (D-NM) 136350 136350
## 55 Val Demings (D-Fla) 135442 149598
## 56 Ben Ray Lujan (D-NM) 132800 132800
## 57 Brett Guthrie (R-Ky) 127700 128200
## 58 Blaine Luetkemeyer (R-Mo) 125892 135592
## 59 Steven Horsford (D-Nev) 124239 127139
## 60 Chris Stewart (R-Utah) 123200 124200
## 61 Mandela Barnes (D-Wis) 122863 134263
## 62 Abigail Spanberger (D-Va) 121988 125025
## 63 Tammy Duckworth (D-Ill) 119335 123335
## 64 Luke Holland (R) 117550 121675
## 65 Rob Menendez (D-NJ) 115256 116506
## 66 Jason Smith (R-Mo) 115050 119450
## 67 Richard Hudson (R-NC) 112570 116570
## 68 Katherine Clark (D-Mass) 112135 112635
## 69 John Curtis (R-Utah) 109800 109800
## 70 Elaine Luria (D-Va) 109493 111493
## 71 Patrick Leahy (D-Vt) 109448 111948
## 72 Pete Aguilar (D-Calif) 109050 114850
## 73 Dan Newhouse (R-Wash) 108900 109150
## 74 Bill Hagerty (R-Tenn) 108650 108900
## 75 Rodney Davis (R) 108545 110545
## 76 Steny H Hoyer (D-Md) 108020 123020
## 77 Tim Kaine (D-Va) 107878 111778
## 78 Mikie Sherrill (D-NJ) 106940 108440
## 79 Ashley Hinson (R-Iowa) 106550 113200
## 80 French Hill (R-Ark) 106270 106270
## 81 James E Clyburn (D-SC) 105050 105050
## 82 Adam Kinzinger (R-Ill) 104850 107750
## 83 John Kennedy (R-La) 104500 112800
## 84 Rosa DeLauro (D-Conn) 104062 104562
## 85 Mike Simpson (R-Idaho) 103669 105169
## 86 Lucy McBath (D-Ga) 102751 104251
## 87 Haley Stevens (D-Mich) 102612 106012
## 88 Brian Schatz (D-Hawaii) 100790 101040
## 89 Tom Cole (R-Okla) 99100 102100
## 90 Ann Kuster (D-NH) 98600 98600
## 91 Dan Kildee (D-Mich) 98070 103070
## 92 Angie Craig (D-Minn) 96231 96231
## 93 Heather Mizeur (D-Md) 93475 97600
## 94 John Fetterman (D-Pa) 92729 112605
## 95 Scott Peters (D-Calif) 92600 93100
## 96 Josh Gottheimer (D-NJ) 90581 96631
## 97 Joyce Beatty (D-Ohio) 90500 90500
## 98 Chrissy Houlahan (D-Pa) 89644 90644
## 99 Adrian Smith (R-Neb) 89600 90100
## 100 Don Beyer (D-Va) 86977 97877
## Party
## 1 Democrat
## 2 Republican
## 3 Democrat
## 4 Democrat
## 5 Democrat
## 6 Democrat
## 7 Democrat
## 8 Republican
## 9 Republican
## 10 Democrat
## 11 Republican
## 12 Republican
## 13 Democrat
## 14 Republican
## 15 Democrat
## 16 Republican
## 17 Democrat
## 18 Democrat
## 19 Democrat
## 20 Democrat
## 21 Republican
## 22 Democrat
## 23 Democrat
## 24 Republican
## 25 Republican
## 26 Republican
## 27 Republican
## 28 Republican
## 29 Republican
## 30 Republican
## 31 Republican
## 32 Democrat
## 33 Republican
## 34 Republican
## 35 Democrat
## 36 Republican
## 37 Republican
## 38 Republican
## 39 Republican
## 40 Republican
## 41 Democrat
## 42 Democrat
## 43 Republican
## 44 Democrat
## 45 Democrat
## 46 Democrat
## 47 Democrat
## 48 Democrat
## 49 Republican
## 50 Republican
## 51 Democrat
## 52 Democrat
## 53 Republican
## 54 Democrat
## 55 Democrat
## 56 Democrat
## 57 Republican
## 58 Republican
## 59 Democrat
## 60 Republican
## 61 Democrat
## 62 Democrat
## 63 Democrat
## 64 Republican
## 65 Democrat
## 66 Republican
## 67 Republican
## 68 Democrat
## 69 Republican
## 70 Democrat
## 71 Democrat
## 72 Democrat
## 73 Republican
## 74 Republican
## 75 Republican
## 76 Democrat
## 77 Democrat
## 78 Democrat
## 79 Republican
## 80 Republican
## 81 Democrat
## 82 Republican
## 83 Republican
## 84 Democrat
## 85 Republican
## 86 Democrat
## 87 Democrat
## 88 Democrat
## 89 Republican
## 90 Democrat
## 91 Democrat
## 92 Democrat
## 93 Democrat
## 94 Democrat
## 95 Democrat
## 96 Democrat
## 97 Democrat
## 98 Democrat
## 99 Republican
## 100 Democratrecipient_df$recipient <- str_replace_all(recipient_df$recipient, "\\([A-Z].*", "")
recipient_df## recipient from_lobbyists from_lobbyists_family Party
## 1 Charles E Schumer 723967 787867 Democrat
## 2 Kevin McCarthy 557843 561843 Republican
## 3 Maggie Hassan 494712 512362 Democrat
## 4 Tim Ryan 464898 490695 Democrat
## 5 Catherine Cortez Masto 429569 451204 Democrat
## 6 Raphael Warnock 407876 430191 Democrat
## 7 Patty Murray 399950 414050 Democrat
## 8 John Thune 347478 351978 Republican
## 9 Tim Scott 325554 347204 Republican
## 10 Ron Wyden 316153 330753 Democrat
## 11 Cathy McMorris Rodgers 308621 312621 Republican
## 12 Katie Britt 298038 313378 Republican
## 13 Robert Menendez 287460 297160 Democrat
## 14 Liz Cheney 281840 295140 Republican
## 15 Mark Kelly 275470 290452 Democrat
## 16 Todd Young 258160 263060 Republican
## 17 Alex Padilla 248170 263370 Democrat
## 18 Richard Blumenthal 246658 253158 Democrat
## 19 Chris Van Hollen 240907 245407 Democrat
## 20 Hakeem Jeffries 230305 234705 Democrat
## 21 Mike Crapo 229308 231308 Republican
## 22 Richard E Neal 225657 226657 Democrat
## 23 Mark Warner 221361 227161 Democrat
## 24 Darin LaHood 219899 224399 Republican
## 25 Lisa Murkowski 217985 225935 Republican
## 26 Ted Budd 217420 223910 Republican
## 27 Marco Rubio 216594 219594 Republican
## 28 Markwayne Mullin 214650 226800 Republican
## 29 John Boozman 212665 222465 Republican
## 30 Ken Calvert 209563 215963 Republican
## 31 Mike Lee 207326 213226 Republican
## 32 Kyrsten Sinema 207200 219800 Democrat
## 33 Chuck Grassley 200242 209542 Republican
## 34 Vernon Buchanan 193600 203800 Republican
## 35 Frank Pallone Jr. 192900 192900 Democrat
## 36 Herschel Walker 187446 199511 Republican
## 37 Steve Scalise 184805 184805 Republican
## 38 J D Vance 182459 190259 Republican
## 39 Adam Laxalt 174000 197150 Republican
## 40 Jerry Moran 167449 168449 Republican
## 41 Gary Peters 165910 168110 Democrat
## 42 Cheri Beasley 163375 175525 Democrat
## 43 Drew Ferguson 157740 157960 Republican
## 44 Peter Welch 156100 160000 Democrat
## 45 Jon Tester 150729 150729 Democrat
## 46 Bob Casey 148624 151524 Democrat
## 47 Nancy Pelosi 147437 159117 Democrat
## 48 Joe Manchin 145327 148227 Democrat
## 49 Patrick McHenry 145000 145000 Republican
## 50 Dave McCormick 144350 165650 Republican
## 51 Lori Trahan 143344 144344 Democrat
## 52 Henry Cuellar 140402 147502 Democrat
## 53 James Lankford 136456 143506 Republican
## 54 Martin Heinrich 136350 136350 Democrat
## 55 Val Demings 135442 149598 Democrat
## 56 Ben Ray Lujan 132800 132800 Democrat
## 57 Brett Guthrie 127700 128200 Republican
## 58 Blaine Luetkemeyer 125892 135592 Republican
## 59 Steven Horsford 124239 127139 Democrat
## 60 Chris Stewart 123200 124200 Republican
## 61 Mandela Barnes 122863 134263 Democrat
## 62 Abigail Spanberger 121988 125025 Democrat
## 63 Tammy Duckworth 119335 123335 Democrat
## 64 Luke Holland 117550 121675 Republican
## 65 Rob Menendez 115256 116506 Democrat
## 66 Jason Smith 115050 119450 Republican
## 67 Richard Hudson 112570 116570 Republican
## 68 Katherine Clark 112135 112635 Democrat
## 69 John Curtis 109800 109800 Republican
## 70 Elaine Luria 109493 111493 Democrat
## 71 Patrick Leahy 109448 111948 Democrat
## 72 Pete Aguilar 109050 114850 Democrat
## 73 Dan Newhouse 108900 109150 Republican
## 74 Bill Hagerty 108650 108900 Republican
## 75 Rodney Davis 108545 110545 Republican
## 76 Steny H Hoyer 108020 123020 Democrat
## 77 Tim Kaine 107878 111778 Democrat
## 78 Mikie Sherrill 106940 108440 Democrat
## 79 Ashley Hinson 106550 113200 Republican
## 80 French Hill 106270 106270 Republican
## 81 James E Clyburn 105050 105050 Democrat
## 82 Adam Kinzinger 104850 107750 Republican
## 83 John Kennedy 104500 112800 Republican
## 84 Rosa DeLauro 104062 104562 Democrat
## 85 Mike Simpson 103669 105169 Republican
## 86 Lucy McBath 102751 104251 Democrat
## 87 Haley Stevens 102612 106012 Democrat
## 88 Brian Schatz 100790 101040 Democrat
## 89 Tom Cole 99100 102100 Republican
## 90 Ann Kuster 98600 98600 Democrat
## 91 Dan Kildee 98070 103070 Democrat
## 92 Angie Craig 96231 96231 Democrat
## 93 Heather Mizeur 93475 97600 Democrat
## 94 John Fetterman 92729 112605 Democrat
## 95 Scott Peters 92600 93100 Democrat
## 96 Josh Gottheimer 90581 96631 Democrat
## 97 Joyce Beatty 90500 90500 Democrat
## 98 Chrissy Houlahan 89644 90644 Democrat
## 99 Adrian Smith 89600 90100 Republican
## 100 Don Beyer 86977 97877 Democratrecipient_df$from_lobbyists <- as.integer(recipient_df$from_lobbyists)
recipient_df$from_lobbyists_family <- as.integer(recipient_df$from_lobbyists_family)ggplot(recipient_df[1:20, ], aes(x=reorder(recipient, desc(from_lobbyists)), y=`from_lobbyists`, fill=`Party`)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
scale_fill_manual(values=c("blue", "red")) +
ylim(0,1000000) +
labs(title="Top 10 Recipients From Lobbying in 2022",
x="Recipient",
y="Amount (in 100000 $)") +
coord_flip()
ggplot(recipient_df[1:10, ], aes(x=reorder(recipient, desc(from_lobbyists_family)), y=`from_lobbyists_family`, fill=`Party`)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
scale_fill_manual(values=c("blue", "red")) +
ylim(0,1000000) +
labs(title="Top 10 Recipients From Lobbyists' Family in 2022",
x="Recipient",
y="Amount (in 100000 $)") +
coord_flip()
total_df <- recipient_df %>%
group_by(Party) %>%
summarise(Total = sum(from_lobbyists))
total_df## # A tibble: 2 × 2
## Party Total
## <chr> <int>
## 1 Democrat 10190370
## 2 Republican 8068887ggplot(total_df, aes(x=Party, y=Total)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
labs(title="Total Amount of Lobbying Money Received by Party in 2022",
x="Party",
y="Amount (in millions $)") +
coord_flip()
Top Spenders
spenders98_df <- read.csv("~/Downloads/Top Spenders (1).csv", check.names = FALSE)
spenders98_df## Lobbying Client Total Spent
## 1 Philip Morris $30508000
## 2 British American Tobacco $25180000
## 3 Bell Atlantic $21260000
## 4 US Chamber of Commerce $17000000
## 5 American Medical Assn $16920000
## 6 Ford Motor Co $13807000
## 7 Business Roundtable $11640000
## 8 Edison Electric Institute $11540000
## 9 American Hospital Assn $10660000
## 10 Blue Cross/Blue Shield $9291571
## 11 General Motors $8454900
## 12 Boeing Co $8440000
## 13 AT&T $8110000
## 14 Susquehanna Pfaltzgraff $8000000
## 15 Ameritech Corp $7560000
## 16 Lockheed Martin $7492480
## 17 Sprint Corp $7398665
## 18 Citigroup Inc $7380000
## 19 General Electric $7280000
## 20 American Council of Life Insurance $7050000spenders98_df <- spenders98_df %>%
clean_names()
spenders98_df## lobbying_client total_spent
## 1 Philip Morris $30508000
## 2 British American Tobacco $25180000
## 3 Bell Atlantic $21260000
## 4 US Chamber of Commerce $17000000
## 5 American Medical Assn $16920000
## 6 Ford Motor Co $13807000
## 7 Business Roundtable $11640000
## 8 Edison Electric Institute $11540000
## 9 American Hospital Assn $10660000
## 10 Blue Cross/Blue Shield $9291571
## 11 General Motors $8454900
## 12 Boeing Co $8440000
## 13 AT&T $8110000
## 14 Susquehanna Pfaltzgraff $8000000
## 15 Ameritech Corp $7560000
## 16 Lockheed Martin $7492480
## 17 Sprint Corp $7398665
## 18 Citigroup Inc $7380000
## 19 General Electric $7280000
## 20 American Council of Life Insurance $7050000spenders98_df$total_spent <- str_replace_all(spenders98_df$total_spent, "\\$", "")spenders98_df$total_spent <- as.integer(spenders98_df$total_spent)
spenders98_df## lobbying_client total_spent
## 1 Philip Morris 30508000
## 2 British American Tobacco 25180000
## 3 Bell Atlantic 21260000
## 4 US Chamber of Commerce 17000000
## 5 American Medical Assn 16920000
## 6 Ford Motor Co 13807000
## 7 Business Roundtable 11640000
## 8 Edison Electric Institute 11540000
## 9 American Hospital Assn 10660000
## 10 Blue Cross/Blue Shield 9291571
## 11 General Motors 8454900
## 12 Boeing Co 8440000
## 13 AT&T 8110000
## 14 Susquehanna Pfaltzgraff 8000000
## 15 Ameritech Corp 7560000
## 16 Lockheed Martin 7492480
## 17 Sprint Corp 7398665
## 18 Citigroup Inc 7380000
## 19 General Electric 7280000
## 20 American Council of Life Insurance 7050000ggplot(spenders98_df, aes(x=reorder(lobbying_client, desc(total_spent)), y=`total_spent`)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
ylim(0, 40000000) +
labs(title="Top Spenders in 1998",
x="Lobbying Clients",
y="Total Spent (in 10 million $)") +
coord_flip()
spenders22_df <- read.csv("~/Downloads/Top Spenders.csv", check.names = FALSE)
spenders22_df## Lobbying Client Total Spent
## 1 National Assn of Realtors $81738132
## 2 US Chamber of Commerce $81010000
## 3 Pharmaceutical Research & Manufacturers of America $29226000
## 4 American Hospital Assn $27086084
## 5 Blue Cross/Blue Shield $26937752
## 6 Amazon.com $21380000
## 7 American Medical Assn $21060000
## 8 Business Roundtable $20410000
## 9 American Chemistry Council $19820000
## 10 Meta $19150000
## 11 AARP $15900000
## 12 Pfizer Inc $14820000
## 13 Comcast Corp $14420000
## 14 CTIA $13890000
## 15 NCTA The Internet & Television Assn $13720000
## 16 Lockheed Martin $13603465
## 17 America's Health Insurance Plans $13270000
## 18 Biotechnology Innovation Organization $13250000
## 19 Alphabet Inc $13184000
## 20 Boeing Co $13170000spenders22_df <- spenders22_df %>%
clean_names()
spenders22_df## lobbying_client total_spent
## 1 National Assn of Realtors $81738132
## 2 US Chamber of Commerce $81010000
## 3 Pharmaceutical Research & Manufacturers of America $29226000
## 4 American Hospital Assn $27086084
## 5 Blue Cross/Blue Shield $26937752
## 6 Amazon.com $21380000
## 7 American Medical Assn $21060000
## 8 Business Roundtable $20410000
## 9 American Chemistry Council $19820000
## 10 Meta $19150000
## 11 AARP $15900000
## 12 Pfizer Inc $14820000
## 13 Comcast Corp $14420000
## 14 CTIA $13890000
## 15 NCTA The Internet & Television Assn $13720000
## 16 Lockheed Martin $13603465
## 17 America's Health Insurance Plans $13270000
## 18 Biotechnology Innovation Organization $13250000
## 19 Alphabet Inc $13184000
## 20 Boeing Co $13170000spenders22_df$total_spent <- str_replace_all(spenders22_df$total_spent, "\\$", "")spenders22_df$total_spent <- as.integer(spenders22_df$total_spent)ggplot(spenders22_df, aes(x=reorder(lobbying_client, desc(total_spent)), y=`total_spent`)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
ylim(0, 100000000) +
labs(title="Top Spenders in 2022",
x="Lobbying Clients",
y="Total Spent (in 10 million $)") +
coord_flip() 
Top Issues
issues22 <- read.csv("~/Downloads/Top Issues.csv", check.names = FALSE)
head(issues22)## Issue No. of Clients* No. of Lobbyists*
## 1 Fed Budget & Appropriations 4253 5120
## 2 Health Issues 2526 4331
## 3 Taxes 2026 4563
## 4 Defense 1765 2543
## 5 Transportation 1733 2833
## 6 Energy & Nuclear Power 1266 2491issues22 <- issues22 %>%
clean_names()
issues22## issue no_of_clients no_of_lobbyists
## 1 Fed Budget & Appropriations 4253 5120
## 2 Health Issues 2526 4331
## 3 Taxes 2026 4563
## 4 Defense 1765 2543
## 5 Transportation 1733 2833
## 6 Energy & Nuclear Power 1266 2491
## 7 Trade 1175 2780
## 8 Environment & Superfund 1097 2197
## 9 Medicare & Medicaid 1075 2112
## 10 Education 1035 1876
## 11 Labor Antitrust & Workplace 920 2357
## 12 Agriculture 884 1727
## 13 Government Issues 848 1713
## 14 Natural Resources 781 1156
## 15 Finance 745 1843
## 16 Science & Technology 711 1549
## 17 Homeland Security 620 1444
## 18 Immigration 550 1355
## 19 Telecommunications 533 1533
## 20 Law Enforcement & Crime 476 1054
## 21 Banking 473 1283
## 22 Manufacturing 471 1070
## 23 Economics & Econ Development 470 895
## 24 Foreign Relations 453 1030
## 25 Clean Air & Water 433 846
## 26 Consumer Product Safety 417 1309
## 27 Housing 414 888
## 28 Veterans Affairs 388 938
## 29 Small Business 371 1004
## 30 Aviation Airlines & Airports 357 904
## 31 Disaster & Emergency Planning 344 713
## 32 Marine Boats & Fisheries 340 568
## 33 Medical Research & Clin Labs 332 661
## 34 Indian/Native American Affairs 308 326
## 35 Computers & Information Tech 288 884
## 36 Copyright Patent & Trademark 285 1020
## 37 Civil Rights & Civil Liberties 271 747
## 38 Urban Development 265 259
## 39 Food Industry 252 656
## 40 Insurance 233 811
## 41 Pharmacy 209 657
## 42 Retirement 209 692
## 43 Radio & TV Broadcasting 189 684
## 44 Real Estate & Land Use 178 381
## 45 Fuel Gas & Oil 172 487
## 46 Utilities 163 396
## 47 Aerospace 155 495
## 48 Chemical Industry 143 374
## 49 Animals 142 360
## 50 Automotive Industry 140 648
## 51 Roads & Highways 140 269
## 52 Tariffs 135 288
## 53 Railroads 133 351
## 54 Postal 113 365
## 55 Family Abortion & Adoption 107 238
## 56 Intelligence 102 309
## 57 Trucking & Shipping 100 243
## 58 Travel & Tourism 97 330
## 59 Sports & Athletics 80 198
## 60 Arts & Entertainment 78 183
## 61 Alcohol & Drug Abuse 76 223
## 62 Accounting 72 222
## 63 Firearms Guns & Ammunition 69 195
## 64 Constitution 64 126
## 65 Gaming Gambling & Casinos 64 181
## 66 Welfare 60 115
## 67 Tobacco 59 296
## 68 Hazardous & Solid Waste 56 133
## 69 Torts 48 169
## 70 Media Information & Publishing 43 151
## 71 Commodities 41 114
## 72 Unemployment 38 96
## 73 Apparel Clothing & Textiles 35 79
## 74 Advertising 32 91
## 75 Beverage Industry 31 138
## 76 Bankruptcy 30 90
## 77 Religion 19 37
## 78 District of Columbia 16 43
## 79 Minting Money & Gold Standard 13 63ggplot(issues22[1:20, ], aes(x=reorder(issue, desc(no_of_lobbyists)), y=`no_of_lobbyists`)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
ylim(0, 5000) +
labs(title="Top 20 Issues Based on Number of Lobbyists in 2022",
x="Top Issues",
y="Total Number of Lobbyists") +
coord_flip() 
issues11 <- read.csv("~/Downloads/Top Issues (3).csv", check.names = FALSE)issues11 <- issues11 %>%
clean_names()
head(issues11)## issue no_of_clients no_of_lobbyists
## 1 Fed Budget & Appropriations 4208 4884
## 2 Health Issues 1981 4007
## 3 Taxes 1817 4155
## 4 Transportation 1622 2428
## 5 Defense 1581 2058
## 6 Energy & Nuclear Power 1475 2914ggplot(issues11[1:20, ], aes(x=reorder(issue, desc(no_of_lobbyists)), y=`no_of_lobbyists`)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
ylim(0, 5000) +
labs(title="Top 20 Issues Based on Number of Lobbyists in 2011",
x="Top Issues",
y="Total Number of Lobbyists") +
coord_flip() 
Industries
2022
industries22 <- read.csv("~/Downloads/Industries.csv")
industries22## Industry Total
## 1 Pharmaceuticals/Health Products $373743282
## 2 Electronics Mfg & Equip $221523270
## 3 Insurance $158454609
## 4 Securities & Investment $137779163
## 5 Real Estate $135572239
## 6 Business Associations $131458961
## 7 Hospitals/Nursing Homes $124661121
## 8 Oil & Gas $124378864
## 9 Electric Utilities $124028671
## 10 Health Services/HMOs $122008218
## 11 Air Transport $118131273
## 12 Telecom Services $117540160
## 13 Misc Manufacturing & Distributing $116135527
## 14 Civil Servants/Public Officials $108453083
## 15 Health Professionals $95715934
## 16 Internet $94603591
## 17 Education $90832939
## 18 Automotive $81307605
## 19 Chemical & Related Manufacturing $65907144
## 20 Commercial Banks $64822888industries22$Total <- str_replace_all(industries22$Total, "\\$", "")industries22$Total <- as.integer(industries22$Total)ggplot(industries22, aes(x=reorder(Industry, desc(Total)), y=`Total`)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
ylim(0, 400000000) +
labs(title="Lobbying Spending by Industry in 2022",
x="Industry",
y="Total Spent (in 100 million $)") +
coord_flip()
1998
industries98 <- read.csv("~/Downloads/Industries (1).csv")
industries98## Industry Total
## 1 Tobacco $73347172
## 2 Electric Utilities $71762414
## 3 Insurance $70494689
## 4 Pharmaceuticals/Health Products $69618254
## 5 Telephone Utilities $67294593
## 6 Oil & Gas $62236290
## 7 Electronics Mfg & Equip $53495348
## 8 Business Associations $45545243
## 9 Health Professionals $45434502
## 10 Misc Manufacturing & Distributing $42461400
## 11 Air Transport $40393536
## 12 Misc Issues $37429198
## 13 Automotive $36396291
## 14 Civil Servants/Public Officials $36094561
## 15 Commercial Banks $33183164
## 16 Defense Aerospace $32726157
## 17 Securities & Investment $32014883
## 18 Education $30780785
## 19 TV/Movies/Music $30195288
## 20 Hospitals/Nursing Homes $28575461industries98$Total <- str_replace_all(industries98$Total, "\\$", "")industries98$Total <- as.integer(industries98$Total)ggplot(industries98, aes(x=reorder(Industry, desc(Total)), y=`Total`)) +
geom_bar(stat="identity", position="dodge", width=0.5) +
ylim(0, 80000000) +
labs(title="Lobbying Spending by Industry in 1998",
x="Industry",
y="Total Spent (in 10 million $)") +
coord_flip()