getwd()
setwd("~/Desktop/CMPT318")

df <- read.table("Data_Assignment_1.txt",header = TRUE, sep = ",",dec = ".")
library(EnvStats)
library(modeest)
class(df)


# A: Global_active_power
# B : Global_reactive_power
# C: Voltage
# D: Global_intensity


vec_of_A <- c(df$Global_active_power)

mean_of_A <- mean(vec_of_A)
median_of_A <- median(vec_of_A)
mode_of_A <- mfv(vec_of_A)
sd_of_A <- sd(vec_of_A)
geomean_of_A <- geoMean( vec_of_A [vec_of_A >0])




vec_of_B <- c(df$Global_reactive_power)

geomean_of_B <- geoMean( vec_of_B[vec_of_B >0], na.rm= TRUE)
mean_of_B <- mean(vec_of_B, na.rm= TRUE )
median_of_B <- median(vec_of_B, na.rm= TRUE)
mode_of_B <- mfv(vec_of_B, na_rm= TRUE)
sd_of_B <- sd(vec_of_B, na.rm= TRUE)

vec_of_C <-c(df$Voltage)
vec_of_D <-c(df$Global_intensity)


corelation_of_AB<- (cor(vec_of_A, vec_of_B, use = "complete.obs", method = "pearson"))


corelation_of_AC<- (cor(vec_of_A, vec_of_C, use = "complete.obs", method = "pearson"))


corelation_of_AD<- (cor(vec_of_A, vec_of_D, use = "complete.obs", method = "pearson"))
corelation_of_BC<- (cor(vec_of_B, vec_of_C, use = "complete.obs", method = "pearson"))
corelation_of_BD<- (cor(vec_of_B, vec_of_D, use = "complete.obs", method = "pearson"))
corelation_of_CD<- (cor(vec_of_C, vec_of_D, use = "complete.obs", method = "pearson"))

print(geomean_of_A)
print(geomean_of_B)
print(mean_of_A)
print(mean_of_B)
print(median_of_A)
print(median_of_B)
print(mode_of_A)
print(mode_of_B)
print(sd_of_A)
print(sd_of_B)
print(corelation_of_AB)
print(corelation_of_AC)
print(corelation_of_AD)
print(corelation_of_BC)
print(corelation_of_BD)
print(corelation_of_CD)

df$Date <- as.POSIXlt(df$Date ,format = "%d/%m/%Y")
df$Time <- strptime(df$Time,format = "%H:%M:%S")


weekdays_df <- df[df$Date == "2007-04-02" | df$Date =="2007-04-03" |df$Date == "2007-04-04"| df$Date == "2007-04-05"|df$Date == "2007-12-06",]
weekends_df <- df[df$Date == "2007-04-07" | df$Date == "2007-04-08",]


library(lubridate)
day_weekdays_df <-weekdays_df[ hour(weekdays_df$Time) >= 6 & hour(weekdays_df$Time) <18 ,]
night_weekdays_df <- weekdays_df[ hour(weekdays_df$Time) >= 18 | hour(weekdays_df$Time) <6 ,]

day_weekends_df <-weekends_df[ hour(weekends_df$Time) >= 6 & hour(weekends_df$Time) <18 ,]
night_weekends_df <- weekends_df[ hour(weekends_df$Time) >= 18 | hour(weekends_df$Time) <6 ,]

#A Global_active_power
print(max(day_weekdays_df$Global_active_power))
print(min(day_weekdays_df$Global_active_power))
print(max(night_weekdays_df$Global_active_power))
print(min(night_weekdays_df$Global_active_power))
print(max(day_weekends_df$Global_active_power))
print(min(day_weekends_df$Global_active_power))
print(max(night_weekends_df$Global_active_power))
print(min(night_weekends_df$Global_active_power))



#B global_reactive_power

print(max(day_weekdays_df$Global_reactive_power))
print(min(day_weekdays_df$Global_reactive_power))
print(max(night_weekdays_df$Global_reactive_power))
print(min(night_weekdays_df$Global_reactive_power))
print(max(day_weekends_df$Global_reactive_power))
print(min(day_weekends_df$Global_reactive_power))
print(max(night_weekends_df$Global_reactive_power))
print(min(night_weekends_df$Global_reactive_power))