Introduction:
For our week 5 discussion, we were to come forward with datasets exemplifying “untidy” data. Project 2 requires us to take 3 of the peer posted examples from Week 5, tidy the data, and then perform the analysis requested.
This portion will be focusing on NBA Rankings posted by Jacob.
nbaRankings <- read_csv("https://raw.githubusercontent.com/d-ev-craig/DATA607/main/Projects/Project2%20-%20Untidy%20Data/Ringer%20Top%20100%20NBA%20Players%20UNTIDY%20DATA%20-%20Sheet1.csv")## Rows: 400 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): 1, 2, 3, 4, 5
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.nbaRankings## # A tibble: 400 × 5
## `1` `2` `3` `4` `5`
## <chr> <chr> <chr> <chr> <chr>
## 1 Nikola Jokic Team: DEN Position: BIG Age:… Poin…
## 2 <NA> Field goals made: 9.3 Field goal perce… 3-po… 3-po…
## 3 <NA> Free throw attempts: 6.1 Free throw perce… Offe… Defe…
## 4 <NA> Assists: 10.0 Turnovers: 3.6 Stea… Bloc…
## 5 Giannis Antetokounmpo Team: MIL Position: BIG Age:… Poin…
## 6 <NA> Field goals made: 11.1 Field goal perce… 3-po… 3-po…
## 7 <NA> Free throw attempts: 12.7 Free throw perce… Offe… Defe…
## 8 <NA> Assists: 5.4 Turnovers: 3.9 Stea… Bloc…
## 9 Luka Doncic Team: DAL Position: GUARD Age:… Poin…
## 10 <NA> Field goals made: 11.2 Field goal perce… 3-po… 3-po…
## # … with 390 more rowsBelow we are going to create the columns that we need with the value the column is interested in within the rows. My idea is that as long as each column has at least one record within it that corresponds to its correct variable value, we can filter rows using string recognition. Once I filter those rows, I can create a new column and start cbinding all my columns together.
nbaRankings <- nbaRankings %>% mutate(team=nbaRankings$`2`)%>% mutate(fieldGoalsMade=nbaRankings$`2`)%>% mutate(freeThrowAttempts=nbaRankings$`2`) %>% mutate(assists=nbaRankings$`2`)
nbaRankings <- nbaRankings %>% mutate(position=nbaRankings$`3`)%>% mutate(fieldGoalPercentage=nbaRankings$`3`)%>% mutate(freeThrowPercentage=nbaRankings$`3`) %>% mutate(turnovers=nbaRankings$`3`)
nbaRankings <- nbaRankings %>% mutate(age=nbaRankings$`4`)%>% mutate(threePtrsMade=nbaRankings$`4`)%>% mutate(offRebounds=nbaRankings$`4`) %>% mutate(steals=nbaRankings$`4`)
nbaRankings <- nbaRankings %>% mutate(points=nbaRankings$`5`)%>% mutate(threePtrPercentage=nbaRankings$`5`)%>% mutate(defRebounds=nbaRankings$`5`) %>% mutate(blocks=nbaRankings$`5`)
nbaRankings <- nbaRankings[,-(2:5)]
colnames(nbaRankings)[1] <- 'playerName'nbaRankings## # A tibble: 400 × 17
## playerN…¹ team field…² freeT…³ assists posit…⁴ field…⁵ freeT…⁶ turno…⁷ age
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Nikola J… Team… Team: … Team: … Team: … Positi… Positi… Positi… Positi… Age:…
## 2 <NA> Fiel… Field … Field … Field … Field … Field … Field … Field … 3-po…
## 3 <NA> Free… Free t… Free t… Free t… Free t… Free t… Free t… Free t… Offe…
## 4 <NA> Assi… Assist… Assist… Assist… Turnov… Turnov… Turnov… Turnov… Stea…
## 5 Giannis … Team… Team: … Team: … Team: … Positi… Positi… Positi… Positi… Age:…
## 6 <NA> Fiel… Field … Field … Field … Field … Field … Field … Field … 3-po…
## 7 <NA> Free… Free t… Free t… Free t… Free t… Free t… Free t… Free t… Offe…
## 8 <NA> Assi… Assist… Assist… Assist… Turnov… Turnov… Turnov… Turnov… Stea…
## 9 Luka Don… Team… Team: … Team: … Team: … Positi… Positi… Positi… Positi… Age:…
## 10 <NA> Fiel… Field … Field … Field … Field … Field … Field … Field … 3-po…
## # … with 390 more rows, 7 more variables: threePtrsMade <chr>,
## # offRebounds <chr>, steals <chr>, points <chr>, threePtrPercentage <chr>,
## # defRebounds <chr>, blocks <chr>, and abbreviated variable names
## # ¹playerName, ²fieldGoalsMade, ³freeThrowAttempts, ⁴position,
## # ⁵fieldGoalPercentage, ⁶freeThrowPercentage, ⁷turnovers#Player Name and Team
playerTeam <- nbaRankings %>% filter(str_detect(nbaRankings$team, '^Team:')) %>% select(playerName,team) #filter rows by detecting strings that contain our data
playerTeam$team <- gsub('Team: ','',playerTeam$team) #clean up the string
#Field Goals Made
fGM <- nbaRankings %>% filter(str_detect(nbaRankings$fieldGoalsMade, '^Field goals made: ')) %>% select(fieldGoalsMade) #filter rows by detecting strings that contain our data
fGM$fieldGoalsMade <- gsub('Field goals made: ','',fGM$fieldGoalsMade) #clean up the string
playerTeam <- cbind(playerTeam,fGM$fieldGoalsMade) #add our new column
colnames(playerTeam)[3] <- 'fieldGoalsMade' #ensure the name is what we want
# Repeat
#freeThrowAttempts
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$freeThrowAttempts, '^Free throw attempts: ')) %>% select(freeThrowAttempts)
fTA$freeThrowAttempts <- gsub('^Free throw attempts: ','',fTA$freeThrowAttempts)
playerTeam <- cbind(playerTeam,fTA$freeThrowAttempts)
colnames(playerTeam)[4] <- 'freeThrowAttempts'
#assists
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$assists, '^Assists: ')) %>% select(assists)
fTA$assists <- gsub('^Assists: ','',fTA$assists)
playerTeam <- cbind(playerTeam,fTA$assists)
colnames(playerTeam)[5] <- 'assists'
#position
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$position, '^Position: ')) %>% select(position)
fTA$position <- gsub('^Position: ','',fTA$position)
playerTeam <- cbind(playerTeam,fTA$position)
colnames(playerTeam)[6] <- 'position'
#fieldGoalPercentage
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$fieldGoalPercentage, '^Field goal percentage: ')) %>% select(fieldGoalPercentage)
fTA$fieldGoalPercentage <- gsub('^Field goal percentage: ','',fTA$fieldGoalPercentage)
playerTeam <- cbind(playerTeam,fTA$fieldGoalPercentage)
colnames(playerTeam)[7] <- 'fieldGoalPercentage'
#freeThrowPercentage
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$freeThrowPercentage, '^Free throw percentage: ')) %>% select(freeThrowPercentage)
fTA$freeThrowPercentage <- gsub('^Free throw percentage: ','',fTA$freeThrowPercentage)
playerTeam <- cbind(playerTeam,fTA$freeThrowPercentage)
colnames(playerTeam)[8] <- 'freeThrowPercentage'
#turnovers
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$turnovers, '^Turnovers: ')) %>% select(turnovers)
fTA$turnovers <- gsub('^Turnovers: ','',fTA$turnovers)
playerTeam <- cbind(playerTeam,fTA$turnovers)
colnames(playerTeam)[9] <- 'turnovers'
#age
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$age, '^Age: ')) %>% select(age)
fTA$age <- gsub('^Age: ','',fTA$age)
playerTeam <- cbind(playerTeam,fTA$age)
colnames(playerTeam)[10] <- 'age'
#threePtrsMade
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$threePtrsMade, '^3-pointers made: ')) %>% select(threePtrsMade)
fTA$threePtrsMade <- gsub('^3-pointers made: ','',fTA$threePtrsMade)
playerTeam <- cbind(playerTeam,fTA$threePtrsMade)
colnames(playerTeam)[11] <- 'threePtrsMade'
#offRebounds
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$offRebounds, '^Offensive rebounds: ')) %>% select(offRebounds)
fTA$offRebounds <- gsub('^Offensive rebounds: ','',fTA$offRebounds)
playerTeam <- cbind(playerTeam,fTA$offRebounds)
colnames(playerTeam)[12] <- 'offRebounds'
#steals
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$steals, '^Steals: ')) %>% select(steals)
fTA$steals <- gsub('^Steals: ','',fTA$steals)
playerTeam <- cbind(playerTeam,fTA$steals)
colnames(playerTeam)[13] <- 'steals'
#points
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$points, '^Points: ')) %>% select(points)
fTA$points <- gsub('^Points: ','',fTA$points)
playerTeam <- cbind(playerTeam,fTA$points)
colnames(playerTeam)[14] <- 'points'
#threePtrPercentage
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$threePtrPercentage, '^3-pointer percentage: ')) %>% select(threePtrPercentage)
fTA$threePtrPercentage <- gsub('^3-pointer percentage: ','',fTA$threePtrPercentage)
playerTeam <- cbind(playerTeam,fTA$threePtrPercentage)
colnames(playerTeam)[15] <- 'threePtrPercentage'
#defRebounds
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$defRebounds, '^Defensive rebounds: ')) %>% select(defRebounds)
fTA$defRebounds <- gsub('^Defensive rebounds: ','',fTA$defRebounds)
playerTeam <- cbind(playerTeam,fTA$defRebounds)
colnames(playerTeam)[16] <- 'defRebounds'
#blocks
fTA <- nbaRankings %>% filter(str_detect(nbaRankings$blocks, '^Blocks: ')) %>% select(blocks)
fTA$blocks <- gsub('^Blocks: ','',fTA$blocks)
playerTeam <- cbind(playerTeam,fTA$blocks)
colnames(playerTeam)[17] <- 'blocks'
#Final
playerTeam## playerName team fieldGoalsMade freeThrowAttempts assists
## 1 Nikola Jokic DEN 9.3 6.1 10.0
## 2 Giannis Antetokounmpo MIL 11.1 12.7 5.4
## 3 Luka Doncic DAL 11.2 11.1 8.2
## 4 Stephen Curry GSW 9.8 5.4 6.4
## 5 Kevin Durant PHX 10.5 7.3 5.3
## 6 Joel Embiid PHI 11.0 11.8 4.1
## 7 Jayson Tatum BOS 9.9 8.5 4.6
## 8 LeBron James LAL 11.3 6.3 7.0
## 9 Kawhi Leonard LAC 8.3 5.0 4.0
## 10 Shai Gilgeous-Alexander OKC 10.2 10.6 5.7
## 11 Devin Booker PHX 9.5 6.2 5.6
## 12 Damian Lillard POR 9.5 9.1 7.3
## 13 Donovan Mitchell CLE 9.4 5.3 4.8
## 14 Ja Morant MEM 9.6 8.4 8.1
## 15 Zion Williamson NOP 9.8 8.6 4.6
## 16 Anthony Davis LAL 9.6 7.3 2.5
## 17 Jimmy Butler MIA 7.2 8.2 5.0
## 18 Paul George LAC 8.1 4.9 5.2
## 19 Tyrese Haliburton IND 7.1 3.1 10.2
## 20 James Harden PHI 6.6 6.5 10.7
## 21 Pascal Siakam TOR 9.1 7.6 6.1
## 22 Jaylen Brown BOS 9.9 5.5 3.2
## 23 Domantas Sabonis SAC 7.1 5.5 6.9
## 24 Kyrie Irving DAL 9.9 4.5 5.4
## 25 Bam Adebayo MIA 8.5 5.5 3.3
## 26 Trae Young ATL 8.4 8.8 10.2
## 27 De'Aaron Fox SAC 9.4 6.2 6.2
## 28 Anthony Edwards MIN 9.0 5.5 4.5
## 29 Darius Garland CLE 7.6 4.9 7.8
## 30 Lauri Markkanen UTA 8.5 5.8 1.8
## 31 DeMar DeRozan CHI 9.1 7.4 5.1
## 32 Jrue Holiday MIL 7.4 2.7 7.1
## 33 Julius Randle NYK 8.5 6.9 4.1
## 34 Brandon Ingram NOP 8.2 5.4 4.7
## 35 Jaren Jackson Jr. MEM 5.9 4.2 0.9
## 36 Desmond Bane MEM 7.5 3.6 4.1
## 37 Karl-Anthony Towns MIN 7.3 5.0 5.3
## 38 Jarrett Allen CLE 6.1 3.4 1.7
## 39 Evan Mobley CLE 6.5 3.7 2.6
## 40 Jalen Brunson NYK 8.5 5.8 6.2
## 41 Khris Middleton MIL 4.8 2.6 4.0
## 42 Draymond Green GSW 3.2 1.5 6.8
## 43 Jamal Murray DEN 7.2 3.6 5.8
## 44 Chris Paul PHX 4.8 3.2 9.0
## 45 LaMelo Ball CHA 8.3 3.4 8.5
## 46 Andrew Wiggins GSW 6.8 1.9 2.3
## 47 Klay Thompson GSW 7.8 2.1 2.4
## 48 Rudy Gobert MIN 5.1 4.6 0.9
## 49 Mikal Bridges BKN 6.4 3.2 3.5
## 50 O.G. Anunoby TOR 6.1 2.9 2.0
## 51 Dejounte Murray ATL 8.4 2.5 6.1
## 52 Aaron Gordon DEN 6.6 5.0 2.9
## 53 Jerami Grant POR 7.1 5.3 2.3
## 54 Bradley Beal WAS 8.7 4.4 5.2
## 55 Robert Williams III BOS 4.0 0.9 1.6
## 56 Brook Lopez MIL 5.6 2.1 1.2
## 57 Zach LaVine CHI 8.4 5.5 4.0
## 58 Kristaps Porzingis WAS 7.4 7.0 2.5
## 59 Nic Claxton BKN 5.5 3.3 1.7
## 60 Myles Turner IND 6.5 4.4 1.3
## 61 Anfernee Simons POR 7.6 2.9 4.2
## 62 Marcus Smart BOS 4.0 1.8 7.0
## 63 Michael Porter Jr. DEN 6.1 1.9 1.0
## 64 Franz Wagner ORL 6.9 4.2 3.5
## 65 CJ McCollum NOP 7.9 3.1 5.9
## 66 Tyler Herro MIA 7.5 2.9 4.4
## 67 Fred VanVleet TOR 6.4 4.3 6.6
## 68 Bojan Bogdanovic DET 7.2 5.2 2.6
## 69 Deandre Ayton PHX 8.1 3.1 2.0
## 70 Paolo Banchero ORL 6.4 7.5 3.6
## 71 Kevin Huerter SAC 5.4 1.8 2.9
## 72 Al Horford BOS 3.5 0.4 2.7
## 73 Scottie Barnes TOR 6.0 3.4 4.8
## 74 Malcolm Brogdon BOS 5.1 2.7 3.7
## 75 Kyle Kuzma WAS 8.1 3.6 3.9
## 76 Devin Vassell SAS 7.1 3.0 3.6
## 77 Mike Conley MIN 3.5 2.1 7.5
## 78 Wendell Carter Jr. ORL 5.6 3.7 2.6
## 79 Keldon Johnson SAS 7.8 5.3 2.8
## 80 Tyrese Maxey PHI 7.0 3.9 3.6
## 81 Josh Giddey OKC 7.0 1.6 5.8
## 82 Cade Cunningham DET 7.8 3.6 6.0
## 83 Spencer Dinwiddie BKN 5.8 4.0 5.3
## 84 Christian Wood DAL 6.4 4.2 1.7
## 85 Clint Capela ATL 5.5 1.8 0.8
## 86 Ivica Zubac LAC 4.0 3.1 1.1
## 87 Alex Caruso CHI 1.9 1.2 3.2
## 88 Kevon Looney GSW 2.9 1.8 2.5
## 89 Derrick White BOS 4.1 2.2 3.8
## 90 Norman Powell LAC 5.7 4.6 1.8
## 91 Jordan Clarkson UTA 7.5 4.0 4.3
## 92 Alperen Sengun HOU 5.9 4.0 3.7
## 93 Tobias Harris PHI 6.1 1.6 2.5
## 94 Cameron Johnson BKN 4.8 2.0 1.6
## 95 RJ Barrett NYK 6.9 5.3 2.8
## 96 Nikola Vucevic CHI 7.3 1.9 3.3
## 97 Jakob Poeltl TOR 5.3 2.9 3.0
## 98 Jaden McDaniels MIN 4.5 1.7 2.0
## 99 Dillon Brooks MEM 5.5 2.3 2.5
## 100 Nicolas Batum LAC 2.1 0.7 1.5
## position fieldGoalPercentage freeThrowPercentage turnovers age
## 1 BIG 63.2 82.6 3.6 28
## 2 BIG 53.8 64.6 3.9 28
## 3 GUARD 50.5 73.4 3.6 23
## 4 GUARD 49.5 92.2 3.2 34
## 5 FORWARD 55.9 93.4 3.5 34
## 6 BIG 53.1 85.8 3.5 28
## 7 FORWARD 46.1 86.4 2.9 24
## 8 FORWARD 50.3 76.0 3.1 38
## 9 FORWARD 50.6 87.4 1.5 31
## 10 GUARD 50.7 90.8 2.9 24
## 11 GUARD 47.4 85.3 2.5 26
## 12 GUARD 46.7 91.6 3.2 32
## 13 GUARD 47.6 87.6 2.7 26
## 14 GUARD 46.3 74.1 3.5 23
## 15 BIG 60.8 71.4 3.4 22
## 16 BIG 56.2 81.7 2.1 29
## 17 FORWARD 51.5 84.7 1.7 33
## 18 FORWARD 46.1 86.9 3.3 32
## 19 GUARD 48.2 86.5 2.6 22
## 20 GUARD 45.2 86.4 3.4 33
## 21 FORWARD 48.0 76.7 2.5 28
## 22 GUARD 48.6 77.7 3.1 26
## 23 BIG 61.0 77.1 2.9 26
## 24 GUARD 49.2 89.6 2.3 30
## 25 BIG 54.0 79.9 2.5 25
## 26 GUARD 42.8 88.7 4.1 24
## 27 GUARD 51.4 79.0 2.7 25
## 28 FORWARD 46.3 77.9 3.4 21
## 29 GUARD 46.7 86.6 2.9 23
## 30 BIG 51.4 88.3 1.9 25
## 31 FORWARD 50.6 88.2 2.2 33
## 32 GUARD 46.5 86.3 3.0 32
## 33 FORWARD 46.4 76.0 2.8 28
## 34 FORWARD 46.0 87.9 3.3 25
## 35 BIG 49.3 78.7 1.5 23
## 36 GUARD 46.3 90.4 2.2 24
## 37 BIG 50.5 88.5 3.1 27
## 38 BIG 65.1 72.9 1.5 24
## 39 BIG 55.3 69.9 2.0 21
## 40 GUARD 48.0 83.7 2.0 26
## 41 FORWARD 42.4 93.5 2.0 31
## 42 BIG 51.7 70.0 2.6 32
## 43 GUARD 45.3 83.0 2.1 26
## 44 GUARD 43.0 82.0 2.0 37
## 45 GUARD 40.7 85.0 3.5 21
## 46 FORWARD 47.3 61.1 1.3 28
## 47 GUARD 42.6 89.0 1.7 33
## 48 BIG 67.9 67.1 1.7 30
## 49 FORWARD 46.7 89.6 1.5 26
## 50 FORWARD 45.4 82.5 2.2 25
## 51 GUARD 46.5 83.7 2.3 26
## 52 FORWARD 58.7 62.3 1.6 27
## 53 FORWARD 48.4 80.6 1.8 28
## 54 GUARD 51.8 85.7 2.8 29
## 55 BIG 75.0 65.2 0.9 25
## 56 BIG 50.9 77.4 1.4 34
## 57 GUARD 46.9 83.8 2.6 27
## 58 BIG 48.7 84.6 2.2 27
## 59 BIG 71.7 50.6 1.3 23
## 60 BIG 54.5 78.7 1.7 26
## 61 GUARD 45.0 91.3 2.2 23
## 62 GUARD 42.4 76.9 2.4 28
## 63 FORWARD 47.4 79.8 1.1 24
## 64 FORWARD 48.6 83.3 2.1 21
## 65 GUARD 43.4 79.2 2.6 31
## 66 GUARD 43.5 92.0 2.4 23
## 67 GUARD 39.4 89.3 1.9 29
## 68 FORWARD 48.7 88.3 2.3 33
## 69 BIG 59.3 73.9 1.9 24
## 70 FORWARD 41.8 75.2 2.7 20
## 71 GUARD 47.9 69.9 1.4 24
## 72 BIG 47.0 72.2 0.6 36
## 73 FORWARD 45.4 77.0 2.1 21
## 74 GUARD 48.6 88.7 1.6 30
## 75 FORWARD 45.1 71.9 3.0 27
## 76 GUARD 44.5 79.3 1.6 22
## 77 GUARD 40.6 81.2 1.7 35
## 78 BIG 51.7 73.9 2.2 23
## 79 FORWARD 44.6 77.4 2.1 23
## 80 GUARD 45.1 81.3 1.4 22
## 81 GUARD 48.0 77.5 3.0 20
## 82 GUARD 41.5 83.7 3.3 21
## 83 GUARD 44.8 82.5 1.7 29
## 84 BIG 52.6 76.3 1.9 27
## 85 BIG 62.9 61.3 0.8 28
## 86 BIG 61.7 69.9 1.7 25
## 87 GUARD 44.5 79.3 1.3 28
## 88 BIG 65.9 61.0 0.5 27
## 89 GUARD 45.7 85.9 1.1 28
## 90 GUARD 48.5 80.8 1.7 29
## 91 GUARD 44.5 81.9 3.1 30
## 92 BIG 55.5 75.3 2.5 20
## 93 FORWARD 49.9 85.6 1.3 30
## 94 FORWARD 45.3 79.1 0.5 26
## 95 GUARD 43.1 75.2 2.3 22
## 96 BIG 51.6 81.9 1.7 32
## 97 BIG 62.9 57.7 2.0 27
## 98 FORWARD 51.6 73.3 1.4 22
## 99 FORWARD 39.2 79.5 1.5 27
## 100 FORWARD 41.5 65.9 0.7 34
## threePtrsMade offRebounds steals points threePtrPercentage defRebounds
## 1 0.8 2.2 1.3 24.5 40.2 9.4
## 2 0.8 2.3 0.8 31.3 27.0 9.7
## 3 2.7 0.9 1.5 33.2 34.7 7.9
## 4 4.9 0.6 1.0 29.4 42.7 5.8
## 5 1.8 0.4 0.8 29.7 37.6 6.4
## 6 1.1 1.8 1.1 33.1 34.0 8.6
## 7 3.3 1.1 1.0 30.4 35.5 7.6
## 8 2.2 1.2 0.9 29.6 31.0 7.3
## 9 1.8 1.2 1.4 22.7 39.6 4.9
## 10 0.9 0.9 1.6 31.0 33.8 3.9
## 11 2.2 0.8 0.9 26.5 35.1 3.8
## 12 4.2 0.6 0.8 31.4 37.2 3.7
## 13 3.6 0.9 1.5 27.0 38.2 3.1
## 14 1.6 1.1 1.1 27.0 31.7 4.8
## 15 0.2 2.0 1.1 26.0 36.8 5.0
## 16 0.4 3.3 1.1 25.6 25.0 9.1
## 17 0.5 2.2 1.9 21.9 31.2 3.7
## 18 3.0 0.7 1.4 23.5 38.8 5.5
## 19 2.9 0.6 1.7 19.8 39.9 3.0
## 20 2.9 0.5 1.2 21.6 39.3 5.7
## 21 1.4 1.8 0.8 25.3 34.1 5.9
## 22 2.5 1.3 1.1 26.5 33.6 5.8
## 23 0.4 3.0 0.9 18.8 33.8 9.3
## 24 3.3 1.0 1.0 27.1 38.2 4.0
## 25 0.0 2.6 1.2 21.4 8.3 7.2
## 26 2.2 0.7 1.0 26.9 32.9 2.2
## 27 1.6 0.5 1.1 25.3 33.2 3.8
## 28 2.7 0.6 1.6 25.0 36.5 5.3
## 29 2.5 0.4 1.3 22.0 41.7 2.2
## 30 3.0 1.8 0.6 25.2 40.9 6.7
## 31 0.5 0.4 1.0 25.2 30.7 4.2
## 32 2.4 1.3 1.3 19.5 38.2 4.0
## 33 2.8 2.1 0.7 25.1 34.5 8.5
## 34 1.7 0.4 0.7 22.8 41.3 4.6
## 35 1.5 1.7 1.1 16.7 34.8 5.0
## 36 3.0 0.7 1.0 21.3 41.6 4.2
## 37 1.8 1.7 0.8 20.8 32.5 6.5
## 38 0.0 3.2 0.8 14.7 12.5 6.7
## 39 0.3 2.3 0.8 15.9 21.1 6.5
## 40 1.9 0.6 0.9 23.7 40.7 3.0
## 41 1.4 0.6 0.7 13.5 29.9 3.2
## 42 0.6 0.9 1.0 8.1 33.0 6.5
## 43 2.4 0.8 1.0 19.9 38.8 3.3
## 44 1.6 0.4 1.6 13.7 37.6 3.8
## 45 3.9 1.2 1.3 23.4 36.7 5.2
## 46 2.4 1.6 1.2 17.1 39.6 3.4
## 47 4.4 0.7 0.7 21.9 40.8 3.2
## 48 0.0 3.2 0.8 13.4 0.0 8.4
## 49 1.9 1.1 1.2 17.6 39.6 3.4
## 50 1.9 1.5 2.0 16.6 35.5 4.0
## 51 2.0 0.7 1.5 20.8 36.2 4.9
## 52 1.0 2.4 0.8 17.3 39.7 4.4
## 53 2.3 0.9 0.8 20.8 40.6 3.5
## 54 1.7 0.8 0.9 22.9 37.2 2.7
## 55 0.0 3.2 0.5 8.5 0.0 5.5
## 56 1.8 2.0 0.5 14.6 37.5 4.6
## 57 2.8 0.5 1.0 24.1 37.3 4.4
## 58 2.1 1.9 0.9 22.8 37.6 6.7
## 59 0.0 2.5 0.8 12.7 0.0 6.5
## 60 1.7 1.6 0.6 18.1 40.2 6.4
## 61 3.5 0.3 0.7 21.4 38.1 2.4
## 62 1.8 0.7 1.4 11.2 33.0 2.8
## 63 3.0 1.0 0.7 16.8 40.6 4.4
## 64 1.7 0.9 0.9 19.0 36.8 3.1
## 65 2.8 0.8 0.9 21.0 38.2 3.5
## 66 3.1 0.5 0.9 20.7 37.4 5.3
## 67 3.0 0.5 1.6 19.7 34.4 3.8
## 68 2.4 0.6 0.6 21.4 40.2 3.2
## 69 0.1 2.8 0.6 18.7 30.4 7.3
## 70 1.1 1.2 0.9 19.7 27.7 5.4
## 71 2.6 0.6 1.1 14.8 39.2 2.8
## 72 2.1 1.2 0.5 9.3 43.0 5.0
## 73 0.9 2.3 1.0 15.6 30.2 4.8
## 74 2.1 0.7 0.6 14.7 46.5 3.6
## 75 2.6 0.9 0.5 21.4 33.5 6.6
## 76 2.8 0.2 1.2 19.4 40.4 3.8
## 77 1.8 0.4 1.0 10.6 35.6 2.0
## 78 1.2 2.1 0.4 15.1 34.9 6.5
## 79 2.1 0.9 0.7 21.8 32.4 4.0
## 80 2.2 0.4 0.8 19.4 38.0 2.4
## 81 0.9 2.0 0.8 16.1 30.4 5.8
## 82 1.4 1.0 0.8 19.9 27.9 5.2
## 83 2.5 0.3 0.8 17.5 39.6 2.8
## 84 1.7 1.3 0.4 17.7 37.5 6.7
## 85 0.0 4.2 0.7 12.1 0.0 7.0
## 86 0.0 3.3 0.4 10.2 0.0 6.8
## 87 0.8 0.6 1.6 5.5 37.7 2.5
## 88 0.0 3.1 0.6 6.8 0.0 5.9
## 89 1.8 0.6 0.7 12.0 39.1 2.7
## 90 2.1 0.4 0.8 17.2 42.4 2.6
## 91 2.6 1.2 0.5 20.9 34.1 2.9
## 92 0.2 3.1 0.8 14.9 33.3 5.6
## 93 1.8 0.9 1.0 15.5 38.3 4.9
## 94 2.6 0.7 1.0 13.8 41.2 3.0
## 95 1.8 0.9 0.5 19.7 33.0 4.3
## 96 1.6 2.1 0.7 17.8 35.6 9.4
## 97 0.0 3.4 0.9 12.3 0.0 5.7
## 98 1.2 1.3 0.9 11.4 38.2 2.6
## 99 1.8 0.6 0.8 14.6 31.3 2.6
## 100 1.6 0.9 0.6 6.2 38.3 3.4
## blocks
## 1 0.7
## 2 0.8
## 3 0.5
## 4 0.4
## 5 1.5
## 6 1.6
## 7 0.8
## 8 0.5
## 9 0.6
## 10 1.1
## 11 0.4
## 12 0.3
## 13 0.4
## 14 0.3
## 15 0.6
## 16 2.0
## 17 0.3
## 18 0.4
## 19 0.4
## 20 0.6
## 21 0.6
## 22 0.4
## 23 0.5
## 24 0.8
## 25 0.8
## 26 0.2
## 27 0.3
## 28 0.6
## 29 0.1
## 30 0.6
## 31 0.5
## 32 0.4
## 33 0.3
## 34 0.5
## 35 3.2
## 36 0.4
## 37 0.7
## 38 1.2
## 39 1.3
## 40 0.3
## 41 0.2
## 42 0.8
## 43 0.2
## 44 0.5
## 45 0.3
## 46 0.8
## 47 0.3
## 48 1.3
## 49 0.8
## 50 0.8
## 51 0.3
## 52 0.8
## 53 0.8
## 54 0.7
## 55 1.1
## 56 2.4
## 57 0.2
## 58 1.5
## 59 2.5
## 60 2.3
## 61 0.2
## 62 0.4
## 63 0.5
## 64 0.2
## 65 0.6
## 66 0.3
## 67 0.6
## 68 0.1
## 69 0.8
## 70 0.5
## 71 0.3
## 72 0.9
## 73 0.8
## 74 0.3
## 75 0.5
## 76 0.4
## 77 0.2
## 78 0.6
## 79 0.2
## 80 0.1
## 81 0.5
## 82 0.6
## 83 0.3
## 84 1.2
## 85 1.2
## 86 1.3
## 87 0.7
## 88 0.6
## 89 0.9
## 90 0.3
## 91 0.2
## 92 0.9
## 93 0.5
## 94 0.3
## 95 0.2
## 96 0.8
## 97 1.2
## 98 1.0
## 99 0.2
## 100 0.7There were a few questions posed for analysis in this post. The following are the ones I chose. “Are the best teams those with the most players in the top 100 regardless of placement? Or is ‘one guy’ at the top of the mountain enough?”
I’m not sure which teams are the best but what I can do is show the number of times a team appears in a graph. With some contextual knowledge of which teams are the best, a reader could determine rough tendencies.
top100Members <- playerTeam %>% arrange(team) %>% count(team) %>% arrange(desc(n))
top100Members## team n
## 1 BOS 7
## 2 GSW 5
## 3 LAC 5
## 4 MIN 5
## 5 TOR 5
## 6 BKN 4
## 7 CHI 4
## 8 CLE 4
## 9 DEN 4
## 10 MEM 4
## 11 MIL 4
## 12 PHI 4
## 13 PHX 4
## 14 ATL 3
## 15 DAL 3
## 16 MIA 3
## 17 NOP 3
## 18 NYK 3
## 19 ORL 3
## 20 POR 3
## 21 SAC 3
## 22 WAS 3
## 23 DET 2
## 24 IND 2
## 25 LAL 2
## 26 OKC 2
## 27 SAS 2
## 28 UTA 2
## 29 CHA 1
## 30 HOU 1So from the chart above we can see BOS, TOR, MIN, LAC, and GSW contain the most players in the top 100. If you go to https://www.teamrankings.com/nba/ and check their ratings we see the following:
Boston : Rank 1 : 7 members
Golden State: Rank 10 : 5 members
LA Clippers : Rank 17 : 5 members
Toronto : Rank 14 : 5 members
Minnesota : Rank 18 : 5 members
Some others:
Memphis : Rank 5 : 4 members Portland : Rank 23 : 3 members Houston : Rank 29 : 1 member