##Final R assignment in Intro to Statistics course, fall semster.
#+Written by Matan Horovitz (207130253) and Guy Amzaleg ()
#+We have chosen a dataset of CPU and GPU performance trends since 2000 - as published on Kaggle:
#+https://www.kaggle.com/datasets/michaelbryantds/cpu-and-gpu-product-data

chip <- read.csv("/home/shmick/Downloads/chip_dataset.csv")
#chip <- na.omit(chip)
##BONUS: convert from EPOCH: as.Date(as.POSIXct(1100171890,origin = "1970-01-01"))
#View(chip)
##For question 1, we have chosen to examine which type of chip has examined the greater improvement over the years - GPU chips or CPU chips.
#+As chip performance is most directly correlated with the number of transistors, we have measured the pace of development based on pace of 
#+increasing transistor count.
CPU <- chip[chip$Type == 'CPU',]
CPU <- subset(CPU, select= c(Product,Type,Release.Date,Process.Size..nm.,TDP..W.,Die.Size..mm.2.,Transistors..million.,Freq..MHz.))
GPU <- chip[chip$Type == 'GPU',]
GPU <- subset(GPU, select= c(Product,Type,Release.Date,Process.Size..nm.,TDP..W.,Die.Size..mm.2.,Transistors..million.,Freq..MHz.))
#Calculate a crude 'performance factor' - the number of transistors multiplied by their frequency.
#CPU["Performance Factor"])
#Range of total transistor advancement
max(CPU$Transistors..million.,na.rm=TRUE) - min(CPU$Transistors..million.,na.rm=TRUE)
max(GPU$Transistors..million.,na.rm=TRUE) - min(GPU$Transistors..million.,na.rm=TRUE)
#Omit chips with missing data
#CPU <- na.omit(CPU)
#GPU <- na.omit(GPU)
##Iterate over date entries
#for (i in 1:length(CPU$Release.Date)){print(i)}
##Get date
##Install the 'lubridate' package to deal with conversion to EPOCH time
#install.packages('lubridate')
#library(lubridate)
#dates <- strptime(CPU$Release.Date,format="%Y-%m-%d")
#as.integer(as.POSIXct(CPU$Release.Date))
#posix_format_date <- c()
#or (date in 1:length(CPU$Release.Date)){
#  cat("Date is", date)
#  human_format_date <- CPU$Release.Date[date]
#  print(human_format_date)
#  posix_format_date[date] <- strptime(human_format_date,format="%Y-%m-%d")
#}
#for (i in CPU$Release.Date){
#  print(i)
#}


##QUESTION 2: measure number of columns in our dataset and calculate a permutation and combination of 
#+that number, minus two, and 3.

#Calculate total number of columns in our dataset
#n <- ncol(kernel_commits)
#View(n)

##QUESTION 3: pick two categorcial variables (Chip type, foundry) and see whether they're dependent
#+1. Probablity of chip type
#+2. Probability of foundry
#+3. Multiplty

#Sample 1 variable from 'Type' column
chip_type_sample <- sample(chip$Type,1)
#Count how many times it appears in it's column
p_chip_type_sample <- (length(which(chip$Type==chip_type_sample)))/length(chip$Type)
chip_foundry_sample <- sample (chip$Foundry,1)
p_chip_foundry_sample <- (length(which(chip$Foundry==chip_foundry_sample)))/length(chip$Foundry)
chip_type_sample_matrix <- chip[chip$Type == chip_type_sample,]
p_chip_type_foundry_sample <- (length(which(chip_type_sample_matrix$Foundry==chip_foundry_sample)))/length(chip_type_sample_matrix$Foundry)
#p_victim_bastard <- p_neo_bastard * nrow(CPU )
p_chip_type_foundry_sample * p_chip_type_sample