dataM_time

Q1

##Q1

Sys.setlocale("LC_TIME", "English")

## [1] "English_United States.1252"

dta1 <- weekdays(seq(as.Date("1993-12-24"), by = "1 years", length = 100))
dta1 <- as.factor(dta1)%>%as.data.frame()
colnames(dta1) <- c("weekday")

##

ggplot(data = dta1,aes(x = weekday))+
  geom_bar(width = 0.5)+
  labs(x = "")+
  theme_bw()

Q2

as.Date("2017-06-30")-as.Date("2012-09-01")

## Time difference of 1763 days

Q3

dta3 <- read.csv("http://titan.ccunix.ccu.edu.tw/~psycfs/dataM/Data/calls_nyc.csv")
str(dta3)

## 'data.frame':    24 obs. of  2 variables:
##  $ Hour : num  0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 ...
##  $ Calls: int  1080 910 770 780 380 390 200 300 275 395 ...

##
ggplot(dta3, aes(Hour, Calls, group = 1)) +
  geom_bar(width = 1, stat = "identity", fill = "cyan",
           color = "gray", alpha = 0.2) +
  geom_abline(intercept = mean(dta3$Calls), slope = 0,
              size = 1, color = "lightpink") +
  coord_polar(theta = "x", start = -pi/12) +
  theme_bw()

Q4

dta4 <- readLines("http://titan.ccunix.ccu.edu.tw/~psycfs/dataM/Data/nobel_winners.txt")
dta4 <- dta4[2:19]

dta4 <- unlist(strsplit(dta4, "\""))
dta4 <- unique(dta4[dta4!=""])
dta4 <- unique(dta4[dta4!=" "])
dta4 <- unique(dta4[dta4!="  "])

dta4 <- dta4%>%data.frame(ID = dta4[seq(1,54,by = 3)], Born = dta4[seq(2,54,by = 3)], Died = dta4[seq(3,54,by = 3)])
dta4 <- dta4[1:18,2:4]
dta4$Born <- as.Date(dta4$Born,"%B %d, %Y")
dta4$Died <- as.Date(dta4$Died,"%B %d, %Y")

dta4$Born[8] <- as.Date("1892-06-26")

#mean life expectancy

dta4 <- dta4%>%mutate(life = (Died-Born)/365)

Q5

dta5 <- read.table("http://titan.ccunix.ccu.edu.tw/~psycfs/dataM/Data/tw_to_us.txt",header = F)
str(dta5)

## 'data.frame':    23 obs. of  1 variable:
##  $ V1: int  3637 2553 4564 6780 12029 12250 17560 22590 30960 33530 ...

dta5$year <- c(seq(1950,1990,by = 5),seq(1991,2004,by = 1))

ggplot(data = dta5,aes(x = year,y = V1))+
  geom_point()+
  geom_line()+
  labs(title = "ROC(Taiwan) Students in the U.S.A (1950-2004)")+
  theme_bw()

Q6

dta6 <- read.csv("http://titan.ccunix.ccu.edu.tw/~psycfs/dataM/Data/Visit_TW.csv")
dta6 <- dta6%>%mutate(Arrival = as.Date(Arrival,"%Y/%m/%d"),
                      Depature = as.Date(Depature,"%Y/%m/%d"))
dta6 <- dta6%>%mutate(stay = Depature-Arrival)
dta6$stay <- as.numeric(dta6$stay)
dta6$stay[dta6$stay == 0] <- 1
dta6$perday <- dta6$Expense/dta6$stay

mean(dta6$perday)

## [1] 6861.279

boxplot(dta6$perday,horizontal = T)

Q7

dta7 <- read.table("http://titan.ccunix.ccu.edu.tw/~psycfs/dataM/Data/chiayi_rainfall.txt",header = T,na.strings = ".")

dta7 <- dta7%>%gather(key = month,value = rainfall,2:13)
dta7$month <- as.factor(dta7$month)

ggplot(data = dta7,aes(x = month,y = rainfall))+
  geom_boxplot()+
  labs(x = "",y = "Rainfall(mm)",title = "Average monthly rainfall at Chia-Yi county from 1969 to 2010")+
  theme_bw()

Q8

dta8 <- as.data.frame(cpi)
str(dta8)

## 'data.frame':    103 obs. of  1 variable:
##  $ cpi: num  9.9 10 10.1 10.9 12.8 15.1 17.3 20 17.9 16.8 ...

dta8$year <- seq(1913,2015,by = 1)
dta8$cpiChange <- c(NA,diff(dta8$cpi))


##
plot(cpiChange~year,data = dta8,type = "b",xlab = "Year",ylab = "Annual Change in CPI")
abline(h = mean(dta8$cpiChange,na.rm = T),col = "red",lty = 2)
grid()