Lab0

source("http://www.openintro.org/stat/data/arbuthnot.R")
arbuthnot

##    year boys girls
## 1  1629 5218  4683
## 2  1630 4858  4457
## 3  1631 4422  4102
## 4  1632 4994  4590
## 5  1633 5158  4839
## 6  1634 5035  4820
## 7  1635 5106  4928
## 8  1636 4917  4605
## 9  1637 4703  4457
## 10 1638 5359  4952
## 11 1639 5366  4784
## 12 1640 5518  5332
## 13 1641 5470  5200
## 14 1642 5460  4910
## 15 1643 4793  4617
## 16 1644 4107  3997
## 17 1645 4047  3919
## 18 1646 3768  3395
## 19 1647 3796  3536
## 20 1648 3363  3181
## 21 1649 3079  2746
## 22 1650 2890  2722
## 23 1651 3231  2840
## 24 1652 3220  2908
## 25 1653 3196  2959
## 26 1654 3441  3179
## 27 1655 3655  3349
## 28 1656 3668  3382
## 29 1657 3396  3289
## 30 1658 3157  3013
## 31 1659 3209  2781
## 32 1660 3724  3247
## 33 1661 4748  4107
## 34 1662 5216  4803
## 35 1663 5411  4881
## 36 1664 6041  5681
## 37 1665 5114  4858
## 38 1666 4678  4319
## 39 1667 5616  5322
## 40 1668 6073  5560
## 41 1669 6506  5829
## 42 1670 6278  5719
## 43 1671 6449  6061
## 44 1672 6443  6120
## 45 1673 6073  5822
## 46 1674 6113  5738
## 47 1675 6058  5717
## 48 1676 6552  5847
## 49 1677 6423  6203
## 50 1678 6568  6033
## 51 1679 6247  6041
## 52 1680 6548  6299
## 53 1681 6822  6533
## 54 1682 6909  6744
## 55 1683 7577  7158
## 56 1684 7575  7127
## 57 1685 7484  7246
## 58 1686 7575  7119
## 59 1687 7737  7214
## 60 1688 7487  7101
## 61 1689 7604  7167
## 62 1690 7909  7302
## 63 1691 7662  7392
## 64 1692 7602  7316
## 65 1693 7676  7483
## 66 1694 6985  6647
## 67 1695 7263  6713
## 68 1696 7632  7229
## 69 1697 8062  7767
## 70 1698 8426  7626
## 71 1699 7911  7452
## 72 1700 7578  7061
## 73 1701 8102  7514
## 74 1702 8031  7656
## 75 1703 7765  7683
## 76 1704 6113  5738
## 77 1705 8366  7779
## 78 1706 7952  7417
## 79 1707 8379  7687
## 80 1708 8239  7623
## 81 1709 7840  7380
## 82 1710 7640  7288

dim(arbuthnot)

## [1] 82  3

names(arbuthnot)

## [1] "year"  "boys"  "girls"

Exercise 1 : What command would you use to extract just the counts of girls baptized

arbuthnot$girls

##  [1] 4683 4457 4102 4590 4839 4820 4928 4605 4457 4952 4784 5332 5200 4910
## [15] 4617 3997 3919 3395 3536 3181 2746 2722 2840 2908 2959 3179 3349 3382
## [29] 3289 3013 2781 3247 4107 4803 4881 5681 4858 4319 5322 5560 5829 5719
## [43] 6061 6120 5822 5738 5717 5847 6203 6033 6041 6299 6533 6744 7158 7127
## [57] 7246 7119 7214 7101 7167 7302 7392 7316 7483 6647 6713 7229 7767 7626
## [71] 7452 7061 7514 7656 7683 5738 7779 7417 7687 7623 7380 7288

plot(x = arbuthnot$year, y = arbuthnot$girls)

plot(x = arbuthnot$year, y = arbuthnot$girls, type = "l")

Exercise 2: Is there an apparent trend in the number of girls baptized over the years? How would you describe it?

The previous graph clearly shows that the girsl baptized after the 1660s are growing, for the period of time between 1640 and 1660 there is a significant drops of girls baptized.

Exercise 3 :Now, make a plot of the proportion of boys over time. What do you see? Tip: If you use the up and down arrow keys, you can scroll through your previous commands, your so-called command history. You can also access it by clicking on the history tab in the upper right panel. This will save you a lot of typing in the future.

plot(arbuthnot$year, arbuthnot$boys / (arbuthnot$boys + arbuthnot$girls), type = "l")

Over time, I can see a variations year to year of the proportion of boys; however, the proportions over 0.535 means that boys’ baptisms went beyond the girls durin 1660s, and there is a significant drops of boys baptisms in the 1700’s. We can see below how boys outnumber girls in each year

arbuthnot$boys > arbuthnot$girls

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Values of either TRUE if that year had more boys than girls, or FALSE if that year did not.

source("http://www.openintro.org/stat/data/present.R")

What years are included in this data set? What are the dimensions of the dataframe and what are the variable or column names?

present$year

##  [1] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953
## [15] 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967
## [29] 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981
## [43] 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
## [57] 1996 1997 1998 1999 2000 2001 2002

dim(present)

## [1] 63  3

names(present)

## [1] "year"  "boys"  "girls"

How do these counts compare to Arbuthnot’s? Are they on a similar scale? Both data frames Arbuthot and Present are identical in terms of attributes and scale but in the present data frame is clear similar that contain higher numbers of births than Arbuthnot’s data frame, respectively

present$boys

##  [1] 1211684 1289734 1444365 1508959 1435301 1404587 1691220 1899876
##  [9] 1813852 1826352 1823555 1923020 1971262 2001798 2059068 2073719
## [17] 2133588 2179960 2152546 2173638 2179708 2186274 2132466 2101632
## [25] 2060162 1927054 1845862 1803388 1796326 1846572 1915378 1822910
## [33] 1669927 1608326 1622114 1613135 1624436 1705916 1709394 1791267
## [41] 1852616 1860272 1885676 1865553 1879490 1927983 1924868 1951153
## [49] 2002424 2069490 2129495 2101518 2082097 2048861 2022589 1996355
## [57] 1990480 1985596 2016205 2026854 2076969 2057922 2057979

summary(present$boys)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 1211684 1799857 1924868 1885600 2058524 2186274

present$girls

##  [1] 1148715 1223693 1364631 1427901 1359499 1330869 1597452 1800064
##  [9] 1721216 1733177 1730594 1827830 1875724 1900322 1958294 1973576
## [17] 2029502 2074824 2051266 2071158 2078142 2082052 2034896 1996388
## [25] 1967328 1833304 1760412 1717571 1705238 1753634 1816008 1733060
## [33] 1588484 1528639 1537844 1531063 1543352 1620716 1623885 1703131
## [41] 1759642 1768966 1794861 1773380 1789651 1832578 1831679 1858241
## [49] 1907086 1971468 2028717 2009389 1982917 1951379 1930178 1903234
## [57] 1901014 1895298 1925348 1932563 1981845 1968011 1963747

summary(present$girls)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 1148715 1711405 1831679 1793915 1965538 2082052

Does Arbuthnot’s observation about boys being born in greater proportion than girls hold up in the U.S.?

present$boys > present$girls

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Values of either TRUE if that year had more boys than girls, or FALSE if that year did not.

Make a plot that displays the boy-to-girl ratio for every year in the dataset. What do you see? There is a significant drops in the ratio at birth over time. Also radio at birth in boys is greater than girls in the 1940’s it went over 1.054 ratio in which more boys were born.

plot(x = present$year, y = present$boys/present$girls, type = "l")

In what year did we see the most total number of births in the U.S.?

present$tot <- present$boys + present$girls
present$tot

##  [1] 2360399 2513427 2808996 2936860 2794800 2735456 3288672 3699940
##  [9] 3535068 3559529 3554149 3750850 3846986 3902120 4017362 4047295
## [17] 4163090 4254784 4203812 4244796 4257850 4268326 4167362 4098020
## [25] 4027490 3760358 3606274 3520959 3501564 3600206 3731386 3555970
## [33] 3258411 3136965 3159958 3144198 3167788 3326632 3333279 3494398
## [41] 3612258 3629238 3680537 3638933 3669141 3760561 3756547 3809394
## [49] 3909510 4040958 4158212 4110907 4065014 4000240 3952767 3899589
## [57] 3891494 3880894 3941553 3959417 4058814 4025933 4021726

which.max(present$tot)

## [1] 22

present$year[22]

## [1] 1961

Lab0

Durley Torres-Marin

September 2, 2017