Dates & times, Maps

Onevoice

2020-05-25

Dates & Times

In-class exercises:

1.Find out the number of days you have spent at NCKU as a registered student or staff person. DONE

library(lubridate)
#> 
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#> 
#>     date, intersect, setdiff, union

stdate <- dmy("1-September-2019")
endate <- dmy("18-May-2020")

endate - stdate
#> Time difference of 260 days

2.Reproduce the plot of calls for police assistances around 24 hours in New York City using the data set here. DONE

library(ggplot2)
library(tidyverse)
#> -- Attaching packages ------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
#> √ tibble  3.0.1     √ dplyr   0.8.5
#> √ tidyr   1.0.3     √ stringr 1.4.0
#> √ readr   1.3.1     √ forcats 0.5.0
#> √ purrr   0.3.4
#> -- Conflicts ---------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
#> x lubridate::as.difftime() masks base::as.difftime()
#> x lubridate::date()        masks base::date()
#> x dplyr::filter()          masks stats::filter()
#> x lubridate::intersect()   masks base::intersect()
#> x dplyr::lag()             masks stats::lag()
#> x lubridate::setdiff()     masks base::setdiff()
#> x lubridate::union()       masks base::union()
library(hrbrthemes)
#> NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
#>       Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
#>       if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

dta <- read.csv("C:/Users/boss/Desktop/data_management/calls_nyc.csv", h=T)

dta
#>    Hour Calls
#> 1   0.5  1080
#> 2   1.5   910
#> 3   2.5   770
#> 4   3.5   780
#> 5   4.5   380
#> 6   5.5   390
#> 7   6.5   200
#> 8   7.5   300
#> 9   8.5   275
#> 10  9.5   395
#> 11 10.5   305
#> 12 11.5   560
#> 13 12.5   450
#> 14 13.5   600
#> 15 14.5   600
#> 16 15.5   700
#> 17 16.5   690
#> 18 17.5   680
#> 19 18.5   860
#> 20 19.5   750
#> 21 20.5   900
#> 22 21.5  1150
#> 23 22.5  1100
#> 24 23.5   830

class(dta)
#> [1] "data.frame"

str(dta)
#> '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 ...

dta1 <- dta %>% mutate(dtah = c(0:23))

rt <- data.frame(Hour = factor(dta1[, 3]),  
                 Calls=(dta1[, 2]))

ggplot(rt, aes(Hour, 
               Calls, 
               group=1)) +
 
  geom_bar(width=1, 
          stat="identity", 
          fill="cyan", 
          col="gray", 
          alpha=0.2) +
 geom_abline(intercept=mean(dta1$Calls), 
             slope=0, 
             col="pink") +
 coord_polar(theta="x", 
             start=-pi/12) +
 theme_ipsum()
#> Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
#> found in Windows font database

#> Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
#> found in Windows font database
#> Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
#> family not found in Windows font database

#> Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
#> family not found in Windows font database

#> Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
#> family not found in Windows font database

#> Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
#> family not found in Windows font database
#> Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
#> font family not found in Windows font database
#> Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
#> family not found in Windows font database
#> Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
#> font family not found in Windows font database

3.Assume that a friend of yours will live to be 100 years old. Find out how often his or her birthday falls on each day of the week. Plot it. DONE

db_ov <- as.Date("1993/10/18")
dd_ov <- as.Date("2093/10/18")


bDays <- seq(from=db_ov, to=dd_ov, by="years")

sum(weekdays(bDays) %in% "星期一")
#> [1] 15
sum(weekdays(bDays) %in% "星期二")
#> [1] 14
sum(weekdays(bDays) %in% "星期三")
#> [1] 15
sum(weekdays(bDays) %in% "星期四")
#> [1] 14
sum(weekdays(bDays) %in% "星期五")
#> [1] 14
sum(weekdays(bDays) %in% "星期六")
#> [1] 15
sum(weekdays(bDays) %in% "星期日")
#> [1] 14

dta <-data.frame(table(weekdays(bDays)))

library(tidyverse)

names(dta) <- c("weekdays", "Freq")

dta
#>   weekdays Freq
#> 1   星期一   15
#> 2   星期二   14
#> 3   星期三   15
#> 4   星期五   14
#> 5   星期六   15
#> 6   星期日   14
#> 7   星期四   14

ggplot(dta, aes(x=weekdays, y=Freq)) +
  geom_segment( aes(x=weekdays, xend=weekdays, y=0, yend=Freq)) +
  geom_point( size=5, color="gray", fill=alpha("black", 0.3), alpha=0.7, shape=21, stroke=2)

4.Reproduce the plot of fertility rate and college acceptance rate in Taiwan from 1981 to 2009 using the data set here. Done, but not very well.

dta <- read.table("C:/Users/boss/Desktop/data_management/birth_college.txt", h=T)

str(dta)
#> 'data.frame':    29 obs. of  2 variables:
#>  $ Birth   : num  23 22.1 20.6 19.6 18 15.9 16 17.2 15.7 16.6 ...
#>  $ Entrance: int  NA NA NA NA NA NA NA NA NA NA ...

head(dta)
#>   Birth Entrance
#> 1  23.0       NA
#> 2  22.1       NA
#> 3  20.6       NA
#> 4  19.6       NA
#> 5  18.0       NA
#> 6  15.9       NA

dta$year <- 1981:2009

dta$College <- dta$Entrance-40

tail(dta)
#>    Birth Entrance year College
#> 24   9.6       87 2004      47
#> 25   9.1       89 2005      49
#> 26   9.0       91 2006      51
#> 27   8.9       96 2007      56
#> 28   8.6       97 2008      57
#> 29   8.3       97 2009      57

dta
#>    Birth Entrance year College
#> 1   23.0       NA 1981      NA
#> 2   22.1       NA 1982      NA
#> 3   20.6       NA 1983      NA
#> 4   19.6       NA 1984      NA
#> 5   18.0       NA 1985      NA
#> 6   15.9       NA 1986      NA
#> 7   16.0       NA 1987      NA
#> 8   17.2       NA 1988      NA
#> 9   15.7       NA 1989      NA
#> 10  16.6       NA 1990      NA
#> 11  15.7       NA 1991      NA
#> 12  15.5       NA 1992      NA
#> 13  15.6       NA 1993      NA
#> 14  15.3       44 1994       4
#> 15  15.5       44 1995       4
#> 16  15.2       49 1996       9
#> 17  15.1       60 1997      20
#> 18  12.4       60 1998      20
#> 19  12.9       60 1999      20
#> 20  13.8       58 2000      18
#> 21  11.7       61 2001      21
#> 22  11.0       80 2002      40
#> 23  10.1       83 2003      43
#> 24   9.6       87 2004      47
#> 25   9.1       89 2005      49
#> 26   9.0       91 2006      51
#> 27   8.9       96 2007      56
#> 28   8.6       97 2008      57
#> 29   8.3       97 2009      57

library(ggplot2)

ggplot(data = dta, aes(x = year, y = Birth )) + 
  geom_point(data = dta, aes(year, Birth), pch = 1) +
  geom_point(data = dta, aes(year, College)) +
  labs(x = "Year", y = "Birth rate(0.1%)") +
    theme_minimal()  
#> Warning: Removed 13 rows containing missing values (geom_point).

Exercises:

1.Use the dataset containing the average number of visitors (monthly) in New Zealand by country of residence to explore the seasonal patterns between the eight countries. Is there a hemisphere effect? unfinish

2.Use the sample data set to estimate the mean life expectancy of Nobel prize winners. Done, but I can’t transform days to years.


dta <- read.table("C:/Users/boss/Desktop/data_management/nobel_lspan.txt", h=T)

str(dta)
#> 'data.frame':    21 obs. of  3 variables:
#>  $ ID  : chr  "David H. Hubel" "Herbert A. Simon" "Ronald H. Coase" "Bernard Katz" ...
#>  $ Born: chr  "February 27, 1926" "June 15, 1916" "December 29, 1910" "March 26, 1911" ...
#>  $ Died: chr  "September 22, 2013" "February 9, 2001" "September 2, 2013" "April 20, 2003" ...

dta
#>                        ID               Born               Died
#> 1          David H. Hubel  February 27, 1926 September 22, 2013
#> 2        Herbert A. Simon      June 15, 1916   February 9, 2001
#> 3         Ronald H. Coase  December 29, 1910  September 2, 2013
#> 4            Bernard Katz     March 26, 1911     April 20, 2003
#> 5            P.A.M. Dirac     August 8, 1902   October 20, 1984
#> 6         Richard Feynman       May 11, 1918  February 15, 1988
#> 7       George J. Stigler   January 17, 1911   December 1, 1991
#> 8           Pearl S. Buck       June 26 1892      March 6, 1973
#> 9  Bertrand A. W. Russell       May 18, 1872   February 2, 1970
#> 10         Roger Y. Tsien   February 1, 1952    August 24, 2016
#> 11       William Shockley  February 13, 1910    August 12, 1989
#> 12            T. S. Eliot September 26, 1888    January 4, 1965
#> 13         John C. Eccles   January 27, 1903        May 2, 1997
#> 14      W. Lawrence Bragg     March 31, 1890       July 1, 1971
#> 15    Francis H. C. Crick       June 8, 1916      July 28, 2004
#> 16            Marie Curie   November 7, 1867       July 4, 1934
#> 17     Irene Joliot-Curie September 12, 1897     March 17, 1956
#> 18  Dorothy M. C. Hodgkin       May 12, 1910      July 29, 1994
#> 19        Nadine Gordimer  November 20, 1923      July 13, 2014
#> 20          Doris Lessing   October 22, 1919  November 17, 2013
#> 21      Yasunari Kawabata      June 11, 1899     April 16, 1972

library("lubridate")

b1 <- mdy("February 27, 1926")
b2 <- mdy(" June 15, 1916")
b3 <- mdy("December 29, 1910")
b4 <- mdy("March 26, 1911")
b5 <- mdy("August 8, 1902")
b6 <- mdy("May 11, 1918")
b7 <- mdy(" January 17, 1911")
b8 <- mdy("June 26 1892")
b9 <- mdy("May 18, 1872")
b10 <- mdy("February 1, 1952")
b11 <- mdy("February 13, 1910")
b12 <- mdy("September 26, 1888")
b13 <- mdy("January 27, 1903")
b14 <- mdy("March 31, 1890")
b15 <- mdy("June 8, 1916")
b16 <- mdy("November 7, 1867")
b17 <- mdy("September 12, 1897")
b18 <- mdy("May 12, 1910")
b19 <- mdy("November 20, 1923")
b20 <- mdy("October 22, 1919")
b21 <- mdy("June 11, 1899")

d1 <- mdy("September 22, 2013")
d2 <- mdy("February 9, 2001")
d3 <- mdy("September 2, 2013")
d4 <- mdy("April 20, 2003")
d5 <- mdy("October 20, 1984")
d6 <- mdy(" February 15, 1988")
d7 <- mdy("December 1, 1991")
d8 <- mdy("March 6, 1973")
d9 <- mdy("February 2, 1970")
d10 <- mdy("August 24, 2016")
d11 <- mdy("August 12, 1989")
d12 <- mdy("January 4, 1965")
d13 <- mdy("May 2, 1997")
d14 <- mdy("July 1, 1971")
d15 <- mdy("    July 28, 2004")
d16 <- mdy("July 4, 1934")
d17 <- mdy("March 17, 1956")
d18 <- mdy("    July 29, 1994")
d19 <- mdy("July 13, 2014")
d20 <- mdy("November 17, 2013")
d21 <- mdy("April 16, 1972")

dta1 <- mean((d1-b1)+(d2-b2)+(d3-b3)+(d4-b4)+(d5-b5)+(d6-b6)+(d7-b7)+(d8-b8)+(d9-b9)+(d10-b10)+(d11-b11)+(d12-b12)+(d13-b13)+(d14-b14)+(d15-b15)+(d16-b16)+(d17-b17)+(d18-b18)+(d19-b19)+(d20-b20)+(d21-b21))/21

dta1/365.25
#> Time difference of 82.33878 days

3.Use the following sample of records for profit made, arrival date, and departure date of group travel booked at a travel agency in Taiwan to estimate the mean profit per day of service. DONE~


dta <- read.csv("C:/Users/boss/Desktop/data_management/Visit_TW.csv", h=T)

dta
#>    Expense    Arrival   Depature
#> 1    15393  2015/2/16  2015/2/17
#> 2    27616   2015/3/6  2015/3/11
#> 3     8876  2015/2/14  2015/2/17
#> 4    57378  2015/1/30   2015/2/9
#> 5    32613  2015/1/31   2015/2/6
#> 6    46998 2014/12/27 2014/12/31
#> 7    10744 2014/12/27   2015/1/4
#> 8     3269 2014/12/19 2014/12/24
#> 9    16195 2014/12/23 2014/12/26
#> 10   55842  2015/2/11  2015/2/17
#> 11    6593 2014/12/22 2014/12/29
#> 12   12616 2014/12/30   2015/1/5
#> 13   37059  2014/12/6 2014/12/13
#> 14   45619  2014/12/1 2014/12/13
#> 15   62725  2015/1/20  2015/1/23
#> 16   45262   2015/1/1   2015/1/8
#> 17   39408  2015/2/28  2015/3/13
#> 18   41179  2015/1/23  2015/1/27
#> 19   37363  2015/2/18  2015/2/25
#> 20   11969 2014/11/11 2014/11/14
#> 21   14379  2014/9/14  2014/9/17
#> 22   28579  2014/11/6 2014/11/10
#> 23   28738 2014/11/22 2014/11/25
#> 24   77510 2014/12/18 2014/12/23
#> 25    2402 2014/11/10 2014/11/14
#> 26   10614  2014/11/5  2014/11/6
#> 27    1864  2014/7/24  2014/7/28
#> 28  111940 2014/11/18 2014/11/23
#> 29    -489  2014/10/6 2014/10/13
#> 30   67857 2014/11/17 2014/11/21
#> 31     -85 2014/10/24 2014/10/28
#> 32  122418 2014/11/16 2014/11/21
#> 33    1014 2014/10/13 2014/10/20
#> 34   37406 2014/11/21 2014/11/28
#> 35   87197 2014/12/17 2014/12/25
#> 36    1525  2014/11/6  2014/11/6
#> 37    2883  2014/9/20  2014/9/20
#> 38   42105 2014/12/29   2015/1/6
#> 39   89304 2014/11/23 2014/11/30
#> 40   48294 2014/12/12 2014/12/21
#> 41    7000  2014/9/28  2014/10/3
#> 42   22743   2015/1/7  2015/1/11
#> 43   66549  2014/9/26 2014/10/30
#> 44   56523 2014/10/10 2014/10/20
#> 45    2655 2014/11/25 2014/11/25
#> 46   54985   2014/9/7  2014/9/15
#> 47    8962  2014/4/22  2014/4/22
#> 48  121132 2014/12/11 2014/12/19
#> 49   33193  2014/12/9 2014/12/14
#> 50   47590  2014/12/6 2014/12/11
#> 51   30885  2014/12/9 2014/12/22
#> 52  131482 2014/12/26   2015/1/5
#> 53   30964   2014/8/5   2014/8/9
#> 54    4202  2014/7/30   2014/8/1
#> 55   20334  2014/8/14  2014/8/20
#> 56   56328   2014/6/5  2014/6/15
#> 57   11168  2014/7/12  2014/7/13
#> 58   31266   2014/8/2   2014/8/8
#> 59  124388 2014/12/12 2014/12/20
#> 60   73627 2014/11/15 2014/11/26
#> 61   37522  2014/7/26   2014/8/1
#> 62   36011  2014/7/25  2014/7/30
#> 63   74698  2014/7/25   2014/8/3
#> 64   73825  2014/9/24  2014/10/3
#> 65   31362  2014/9/29  2014/10/6
#> 66   46062  2014/7/24  2014/7/31
#> 67   57321 2014/12/20 2014/12/30
#> 68    1979  2014/6/25  2014/6/29
#> 69   14433   2014/6/3   2014/6/5
#> 70   43137  2014/8/31   2014/9/8
#> 71   69468  2014/7/26   2014/8/3
#> 72    3843  2014/5/22  2014/5/26
#> 73   19587   2014/7/9  2014/7/13
#> 74   34523 2014/10/14 2014/10/20
#> 75   25162  2014/9/22  2014/10/1
#> 76   15961  2014/11/8 2014/11/10
#> 77   42979 2014/10/21 2014/10/29
#> 78   31589   2014/6/6  2014/6/13
#> 79  183522 2014/11/25  2014/12/3
#> 80   53969  2014/5/11  2014/5/18
#> 81   30082   2014/6/7  2014/6/11
#> 82   72609   2014/6/2  2014/6/16
#> 83   36197  2014/5/31   2014/6/7
#> 84   58113   2014/5/6  2014/5/13
#> 85   26067 2014/10/20 2014/10/20
#> 86  106673  2014/8/31  2014/9/10
#> 87     613   2015/3/4   2015/3/4
#> 88    4598 2014/10/20 2014/10/20
#> 89    5198  2014/7/12  2014/7/18
#> 90    4349  2014/9/27  2014/9/29
#> 91   -5797  2014/3/18  2014/3/23
#> 92   21777  2014/3/13  2014/3/20
#> 93   30466  2014/3/26   2014/4/5
#> 94   81221   2014/6/1   2014/6/9
#> 95    8622  2014/4/15  2014/4/18
#> 96   18244  2014/4/15  2014/4/15

library("lubridate")

A <-ymd(dta$Arrival)

D <- ymd(dta$Depature)

E <- mean(dta$Expense)

R <- mean(D-A)

E
#> [1] 38772.22

R
#> Time difference of 6.020833 days

38772.22/6.020833
#> [1] 6439.677

4.The following rather awful plot is shown on a web page hosted by the Taiwanese Ministry of Education

Revise it so that it is a proper time series plot. For your convenience, the data points have been extracted and saved here .

What had happened in the early 1990’s and how do we know if the trend reversal is real?

You may want to augment the data set with further data points from 2012 to 2018 available in the foreign students in the U.S. data file.

unfinish



# dta2 <- read.table("C:/Users/boss/Desktop/data_management/foreign_students_us.txt", h=T)

5.How different groups spend their day is an article published in The New York Times using the data collected from The American Time Use Survey.

Discuss what we need to have in order to replicate this piece of graphical journalism in Taiwan.

unfinish

Maps

In-class exercises:

1.Indicate countries you have visited so far on a world map in the style of the ebola outbreaks example. unfinish

2.Plot places in administrative areas of Taiwan you have visited so far. unfinish

3.Map an area of Tainan city to include three of your favorite places to eat as landmarks.

Done

Exercises:

1.Build a thematic plot of the results of Taiwan 2020 presidential election between the DDP and the KMT. The geographical data (maps) for Taiwan can be obtained from DIVA-GIS: Geographic Information System for Biodiversity Research. unfinish

2.Traffic accidents on roads in Taiwan in 2011 is available on-line from the Department of Transportation. Plot the number of deaths per 10,000 vehicles over the administrative units. unfinish

3.Download the data for age fisrt have sex across several countries to make the following plot:

unfinish

4.Download all the files from github (click the downward triangle in the clone or download button in green) for flood in schools in Taipei to replicate the analysis with the markdown file included.

unfinish