NBA Players salaries
Maneesh
2023-10-17
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data=read.csv("C:/Coding/DV/nba_salaries.csv")head(data)## X Player.Name Salary Position Age Team GP GS MP FG FGA FG.
## 1 0 Stephen Curry 48070014 PG 34 GSW 56 56 34.7 10.0 20.2 0.493
## 2 1 John Wall 47345760 PG 32 LAC 34 3 22.2 4.1 9.9 0.408
## 3 2 Russell Westbrook 47080179 PG 34 LAL/LAC 73 24 29.1 5.9 13.6 0.436
## 4 3 LeBron James 44474988 PF 38 LAL 55 54 35.5 11.1 22.2 0.500
## 5 4 Kevin Durant 44119845 PF 34 BRK/PHO 47 47 35.6 10.3 18.3 0.560
## 6 5 Bradley Beal 43279250 SG 29 WAS 50 50 33.5 8.9 17.6 0.506
## X3P X3PA X3P. X2P X2PA X2P. eFG. FT FTA FT. ORB DRB TRB AST STL BLK TOV
## 1 4.9 11.4 0.427 5.1 8.8 0.579 0.614 4.6 5.0 0.915 0.7 5.4 6.1 6.3 0.9 0.4 3.2
## 2 1.0 3.2 0.303 3.1 6.7 0.459 0.457 2.3 3.3 0.681 0.4 2.3 2.7 5.2 0.8 0.4 2.4
## 3 1.2 3.9 0.311 4.7 9.7 0.487 0.481 2.8 4.3 0.656 1.2 4.6 5.8 7.5 1.0 0.5 3.5
## 4 2.2 6.9 0.321 8.9 15.3 0.580 0.549 4.6 5.9 0.768 1.2 7.1 8.3 6.8 0.9 0.6 3.2
## 5 2.0 4.9 0.404 8.3 13.4 0.617 0.614 6.5 7.1 0.919 0.4 6.3 6.7 5.0 0.7 1.4 3.3
## 6 1.6 4.4 0.365 7.3 13.2 0.552 0.551 3.8 4.6 0.842 0.8 3.1 3.9 5.4 0.9 0.7 2.9
## PF PTS Player.additional
## 1 2.1 29.4 curryst01
## 2 1.7 11.4 walljo01
## 3 2.2 15.9 westbru01
## 4 1.6 28.9 jamesle01
## 5 2.1 29.1 duranke01
## 6 2.1 23.2 bealbr01summary(data)## X Player.Name Salary Position
## Min. : 0.00 Length:100 Min. :12939848 Length:100
## 1st Qu.:24.75 Class :character 1st Qu.:17064704 Class :character
## Median :49.50 Mode :character Median :23090000 Mode :character
## Mean :49.50 Mean :26047328
## 3rd Qu.:74.25 3rd Qu.:33833400
## Max. :99.00 Max. :48070014
##
## Age Team GP GS
## Min. :22.00 Length:100 Min. : 9.00 Min. : 0.00
## 1st Qu.:26.00 Class :character 1st Qu.:56.00 1st Qu.:40.75
## Median :28.00 Mode :character Median :66.00 Median :62.50
## Mean :28.81 Mean :62.16 Mean :52.60
## 3rd Qu.:32.00 3rd Qu.:73.00 3rd Qu.:69.25
## Max. :38.00 Max. :83.00 Max. :83.00
##
## MP FG FGA FG.
## Min. :11.30 Min. : 2.000 Min. : 4.70 Min. :0.3370
## 1st Qu.:27.85 1st Qu.: 4.100 1st Qu.: 9.05 1st Qu.:0.4377
## Median :32.15 Median : 6.000 Median :12.10 Median :0.4750
## Mean :30.44 Mean : 6.245 Mean :12.93 Mean :0.4802
## 3rd Qu.:34.62 3rd Qu.: 8.225 3rd Qu.:17.60 3rd Qu.:0.5085
## Max. :37.40 Max. :11.200 Max. :22.20 Max. :0.6710
##
## X3P X3PA X3P. X2P
## Min. :0.000 Min. : 0.000 Min. :0.0000 Min. : 0.500
## 1st Qu.:1.000 1st Qu.: 3.200 1st Qu.:0.3290 1st Qu.: 2.575
## Median :1.800 Median : 4.700 Median :0.3680 Median : 4.350
## Mean :1.752 Mean : 4.758 Mean :0.3436 Mean : 4.492
## 3rd Qu.:2.325 3rd Qu.: 6.225 3rd Qu.:0.3895 3rd Qu.: 6.100
## Max. :4.900 Max. :11.400 Max. :0.4940 Max. :10.500
## NA's :1
## X2PA X2P. eFG. FT
## Min. : 0.800 Min. :0.3910 Min. :0.4430 Min. : 0.200
## 1st Qu.: 4.700 1st Qu.:0.5060 1st Qu.:0.5188 1st Qu.: 1.375
## Median : 7.750 Median :0.5390 Median :0.5550 Median : 2.600
## Mean : 8.176 Mean :0.5409 Mean :0.5515 Mean : 3.108
## 3rd Qu.:10.850 3rd Qu.:0.5767 3rd Qu.:0.5743 3rd Qu.: 4.450
## Max. :17.800 Max. :0.6710 Max. :0.6710 Max. :10.000
##
## FTA FT. ORB DRB
## Min. : 0.300 Min. :0.3640 Min. :0.100 Min. :1.200
## 1st Qu.: 1.900 1st Qu.:0.7570 1st Qu.:0.500 1st Qu.:2.900
## Median : 3.250 Median :0.8125 Median :0.800 Median :3.650
## Mean : 3.849 Mean :0.7936 Mean :1.149 Mean :4.234
## 3rd Qu.: 5.400 3rd Qu.:0.8678 3rd Qu.:1.400 3rd Qu.:5.400
## Max. :12.300 Max. :0.9490 Max. :5.100 Max. :9.600
##
## TRB AST STL BLK
## Min. : 1.500 Min. : 0.500 Min. :0.200 Min. :0.000
## 1st Qu.: 3.675 1st Qu.: 1.975 1st Qu.:0.700 1st Qu.:0.300
## Median : 4.550 Median : 3.900 Median :0.900 Median :0.400
## Mean : 5.376 Mean : 3.969 Mean :0.918 Mean :0.556
## 3rd Qu.: 6.700 3rd Qu.: 5.550 3rd Qu.:1.100 3rd Qu.:0.700
## Max. :12.500 Max. :10.700 Max. :1.900 Max. :2.500
##
## TOV PF PTS Player.additional
## Min. :0.400 Min. :0.40 Min. : 5.00 Length:100
## 1st Qu.:1.200 1st Qu.:1.90 1st Qu.:11.47 Class :character
## Median :1.900 Median :2.15 Median :16.80 Mode :character
## Mean :1.944 Mean :2.20 Mean :17.36
## 3rd Qu.:2.600 3rd Qu.:2.60 3rd Qu.:23.35
## Max. :4.100 Max. :3.80 Max. :33.10
## str(data)## 'data.frame': 100 obs. of 32 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ Player.Name : chr "Stephen Curry" "John Wall" "Russell Westbrook" "LeBron James" ...
## $ Salary : int 48070014 47345760 47080179 44474988 44119845 43279250 42492492 42492492 42492492 42492492 ...
## $ Position : chr "PG" "PG" "PG" "PF" ...
## $ Age : int 34 32 34 38 34 29 31 32 28 32 ...
## $ Team : chr "GSW" "LAC" "LAL/LAC" "LAL" ...
## $ GP : int 56 34 73 55 47 50 52 56 63 58 ...
## $ GS : int 56 3 24 54 47 50 50 56 63 58 ...
## $ MP : num 34.7 22.2 29.1 35.5 35.6 33.5 33.6 34.6 32.1 36.3 ...
## $ FG : num 10 4.1 5.9 11.1 10.3 8.9 8.6 8.2 11.2 9.6 ...
## $ FGA : num 20.2 9.9 13.6 22.2 18.3 17.6 16.8 17.9 20.3 20.7 ...
## $ FG. : num 0.493 0.408 0.436 0.5 0.56 0.506 0.512 0.457 0.553 0.463 ...
## $ X3P : num 4.9 1 1.2 2.2 2 1.6 2 2.8 0.7 4.2 ...
## $ X3PA : num 11.4 3.2 3.9 6.9 4.9 4.4 4.8 7.6 2.7 11.3 ...
## $ X3P. : num 0.427 0.303 0.311 0.321 0.404 0.365 0.416 0.371 0.275 0.371 ...
## $ X2P : num 5.1 3.1 4.7 8.9 8.3 7.3 6.6 5.4 10.5 5.4 ...
## $ X2PA : num 8.8 6.7 9.7 15.3 13.4 13.2 11.9 10.3 17.6 9.4 ...
## $ X2P. : num 0.579 0.459 0.487 0.58 0.617 0.552 0.551 0.521 0.596 0.574 ...
## $ eFG. : num 0.614 0.457 0.481 0.549 0.614 0.551 0.572 0.536 0.572 0.564 ...
## $ FT : num 4.6 2.3 2.8 4.6 6.5 3.8 4.7 4.6 7.9 8.8 ...
## $ FTA : num 5 3.3 4.3 5.9 7.1 4.6 5.4 5.3 12.3 9.6 ...
## $ FT. : num 0.915 0.681 0.656 0.768 0.919 0.842 0.871 0.871 0.645 0.914 ...
## $ ORB : num 0.7 0.4 1.2 1.2 0.4 0.8 1.1 0.8 2.2 0.8 ...
## $ DRB : num 5.4 2.3 4.6 7.1 6.3 3.1 5.4 5.3 9.6 4 ...
## $ TRB : num 6.1 2.7 5.8 8.3 6.7 3.9 6.5 6.1 11.8 4.8 ...
## $ AST : num 6.3 5.2 7.5 6.8 5 5.4 3.9 5.1 5.7 7.3 ...
## $ STL : num 0.9 0.8 1 0.9 0.7 0.9 1.4 1.5 0.8 0.9 ...
## $ BLK : num 0.4 0.4 0.5 0.6 1.4 0.7 0.5 0.4 0.8 0.3 ...
## $ TOV : num 3.2 2.4 3.5 3.2 3.3 2.9 1.7 3.1 3.9 3.3 ...
## $ PF : num 2.1 1.7 2.2 1.6 2.1 2.1 1.6 2.8 3.1 1.9 ...
## $ PTS : num 29.4 11.4 15.9 28.9 29.1 23.2 23.8 23.8 31.1 32.2 ...
## $ Player.additional: chr "curryst01" "walljo01" "westbru01" "jamesle01" ...library(ggplot2)ggplot(data)+labs(title="NBA players salaries")
ggplot(data,aes(x=Salary,y=Age,col=GP))+labs(title="NBA players Salaries")
ggplot(data,aes(x=Salary,y=Age,col=GP))+geom_point()+labs(title = "NBA players salaries")
ggplot(data = data, aes(x =Salary , y = Age, col = GP, shape = factor(Team))) +geom_point() +
labs(title = "Salary vs Age", x = "Salary", y = "Age")## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 40. Consider
## specifying shapes manually if you must have them.## Warning: Removed 81 rows containing missing values (`geom_point()`).
data$valence_.<-factor(data$Salary)
ggplot(data, aes(x = Salary, y =Age )) +
geom_point()
ggplot(data=data, aes(x = GP )) +
geom_histogram(binwidth = 5,color="black", fill="lightblue") +
labs(title = "Histogram of GP", x = "Grade Pay", y = "Count")
ggplot(data = data, aes(x=GS, fill=GS)) +
geom_bar(stat="count")## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
Position = table(data$Position)
data.labels = names(Position)
Position= round( Position /sum(Position)*100)
data.labels = paste(data.labels,Position)
data.labels = paste(data.labels,"%",sep="")
pie(Position,labels = data.labels,clockwise=TRUE, col=heat.colors(length(data.labels)), main="Gross salary")
bx <- ggplot(data = data, aes(x = factor(Position), y = Salary)) +
geom_boxplot(fill = "blue") +
ggtitle("Salary by position") +
ylab(" Salary") +
xlab(" Position")
bx
ggplot(data, aes(x = as.factor(Position), y =Salary, col =Salary)) +
geom_jitter() +
facet_grid(. ~ Position)