Part 1.

  1. The data set did have headers
  2. The opperation is seterated by blanks
  3. If Data is missing they will leave it blank
?read.csv
?read.table
data_types <- c(
  player = "character", team = "factor",position = "factor",height = "integer", weight = "integer", age = "integer", experience = "integer",college = "character",  salary = "double",  games = "integer", minutes = "integer", points = "integer",points3 = "integer",  points2 = "integer", points1 = "integer"
)
nba <- read.csv("~/Downloads/nba2018-players.csv", colClasses = data_types, header = TRUE, stringsAsFactors = FALSE)

nba2 <- read.table("~/Downloads/nba2018-players.csv", colClasses = data_types, header = TRUE, sep = ",", stringsAsFactors = FALSE)

str(nba, ven.len =1 )
## 'data.frame':    477 obs. of  15 variables:
##  $ player    : chr  "Al Horford" "Amir Johnson" "Avery Bradley" "Demetrius Jackson" ...
##  $ team      : Factor w/ 30 levels "ATL","BOS","BRK",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ position  : Factor w/ 5 levels "C","PF","PG",..: 1 2 5 3 4 3 4 5 4 2 ...
##  $ height    : int  82 81 74 73 79 69 78 78 79 82 ...
##  $ weight    : int  245 240 180 201 205 185 235 215 225 231 ...
##  $ age       : int  30 29 26 22 31 27 26 21 20 29 ...
##  $ experience: int  9 11 6 0 9 5 4 2 0 6 ...
##  $ college   : chr  "University of Florida" "" "University of Texas at Austin" "University of Notre Dame" ...
##  $ salary    : num  26540100 12000000 8269663 1450000 1410598 ...
##  $ games     : int  68 80 55 5 47 76 72 29 78 78 ...
##  $ minutes   : int  2193 1608 1835 17 538 2569 2335 220 1341 1232 ...
##  $ points    : int  952 520 894 10 262 2199 999 68 515 299 ...
##  $ points3   : int  86 27 108 1 39 245 157 12 46 45 ...
##  $ points2   : int  293 186 251 2 56 437 176 13 146 69 ...
##  $ points1   : int  108 67 68 3 33 590 176 6 85 26 ...
identical(nba, nba2) 
## [1] TRUE

Part 2.

  1. What happens to the column names of the imported data when you invoke read.csv(datafile.csv, header = FALSE, nrows = 10)?
  1. What happens to the data types of the columns when you invoke read.csv(datafile.csv, header = FALSE, nrows = 10)? -There will be no column names along with the fact that the types of the data can be wrong if not carfuly structured
  2. Why does the command read.table(datafile.csv, nrows = 10) fail to import the data?
  1. Say you import nba2018-players.csv in two different ways. In the first option you import the data without specifying the data type of each column. In the second option you do specify the data types as in data_types. You may wonder whether both options return a data frame of the same memory size. You can actually use the function object.size() that provides an estimate of the memory that is being used to store an R object. What data importing function returns the smallest data frame (in terms of memory size)?
  1. Say the object nba is the data frame produced when importing nba2018-players.csv. If you invoke as.matrix(nba), what happens to the data values of this matrix?

Part 3.

hist(nba$salary )

# Create the new column 'salary2' and ensure it's numeric
nba$salary2 <- as.numeric(nba$salary) / 1e6

# Plot histogram for 'salary2' in millions
hist(nba$salary2, main = "Histogram of Salary (in millions)", xlab = "Salary in Millions USD")

nba$salary2_log <- log(nba$salary2 + 1) 
hist(nba$salary2_log, main = "Histogram of Log-Transformed Salary (in millions)", 
     xlab = "Log of Salary in Millions USD")

#this graph is slightly skewed left but seems closely symmetrical when you preform the log function 
low <- nba$salary2_log < -3
sum(low)
## [1] 0
low_salary_players <- nba[low, c("player", "weight", "height", "team", "position")]

low_salary_players
## [1] player   weight   height   team     position
## <0 rows> (or 0-length row.names)

Part 4.

summary(nba$points)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0   124.0   403.0   510.3   756.0  2558.0
hist(nba$points, main = "Histogram of Points Scored", xlab = "Points")

nba$points[nba$points == 0] <- 0.1
nba$log_points <- log(nba$points)
hist(nba$log_points, main = "Histogram of Log-Transformed Points", xlab = "Log of Points")

#this graph is skewed right wtch siggest that it is more frequent that more plaers score at a higher rate than the opposite

Part 5.

boxplot(salary2 ~ age, data = nba, xlab = "Age", ylab = "Salary (in millions)", main = "Boxplot of Points by Salary")

boxplot(points ~ age, data = nba, xlab = "Age", ylab = "Points", main = "Boxplot of Points by Age")

age2 <- nba$age
age2[age2 < 19] <- 20
age2 <- cut(age2, 
            breaks = c(19, 24, 29, Inf),  
            labels = c("20-24", "25-29", "30+"), 
            right = TRUE)  

summary(age2)
## 20-24 25-29   30+  NA's 
##   177   176   115     9

party ## Part 6.

palette1 <- c("#D4D62A", "#4F9D66", "#9575AB")
age_colors <- palette1[as.numeric(age2)]

plot(salary ~ points, data = nba, log = "xy", main = "", xlab = "Points (log scale)"
, ylab ="Salary (log $)", col = age_colors, pch = 19, cex = 0.6)

legend("bottomright", fill = palette1, legend = levels(age2), title = "Age Groups", cex = 0.8)

# Part 7.

boxplot(salary2 ~ position, data = nba, 
        xlab = "Position", ylab = "Salary (in millions)", 
        main = "Salary by Position")

##for the positions. there aren't as many outliers in centers and their median price is higher than the rest of the positions. A salary per positions is visible when it comes to different positions like PG and C but there are outliers that even the payment out a little more.

palette_pos <- c("C" = "#66c2a5",   "PF" = "#fc8d62","PG" = "#8da0cb",  "SG" = "#e78ac3",  "SF" = "#a6d854")  

pos_colors <- palette_pos[match(nba$position, names(palette_pos))]

# Make the boxplot
boxplot(salary2 ~ position, data = nba, main = "Salary by Position", xlab = "Position", ylab = "Salary (in millions)", col = palette_pos[nba$position], pch = 19,  cex = 0.6) 


legend("topright", 
       fill = c("#66c2a5", "#fc8d62", "#8da0cb", "#e78ac3", "#a6d854"), legend = c("Center", "Power Forward", "Point Guard", "Shooting Guard", "Small Forward"),title = "Position", cex = 0.8)