diff --git a/R_Final_Tasks_Statistics.R b/R_Final_Tasks_Statistics.R
index 23e09ca..d3db9d9 100755
--- a/R_Final_Tasks_Statistics.R
+++ b/R_Final_Tasks_Statistics.R
@@ -3,27 +3,38 @@
 #+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
 
-raw_perf_data <- read.csv("/home/shmick/Downloads/chip_dataset.csv")
+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(raw_perf_data)
+#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 perfomance 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',]
-
-CPU_Transistor_Count <- order(CPU$Transistors..million.)
-GPU_Transistor_Count <- order(GPU$Transistors..million.)
+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"] <- CPU$Transistors..million.*CPU$Freq..MHz.
+GPU["Performance Factor"] <- GPU$Transistors..million.*GPU$Freq..MHz.
+View(CPU)
+View(GPU)
+#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$Release.Date)){print(i)}
+#for (i in 1:length(CPU$Release.Date)){print(i)}
 ##Get date
-for (i in 1:length(CPU$Release.Date)){print(CPU$Release.Date[i])}
+#for (i in 1:length(CPU$Release.Date)){print(CPU$Release.Date[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)
+#n <- ncol(kernel_commits)
+#View(n)
 
-##QUESTION 3: pick two categorial variables - month (?), is documentation
\ No newline at end of file
+##QUESTION 3: pick two categorcial variables - month (?), is documentation
\ No newline at end of file