DATA 606 Lab0_Herold
DATA606 - Lab0 by Zachary Herold
Arbuthnot Data
EXERCISE1: What command would you use to extract just the counts of girls baptized? Try it!
ANSWER: arbuthnot$girls
require(RCurl)## Loading required package: RCurl## Loading required package: bitopsarbuthnot <- data.frame(read.csv(text = getURL("https://raw.githubusercontent.com/ZacharyHerold/chinafundnews/master/arbuthnot.csv")))
arbuthnot$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 7288EXERCISE2: Is there an apparent trend in the number of girls baptized over the years? How would you describe it?
ANSWER: The trend is generally upward with a quick recovery following the 1640-1660 bottom
plot(x = arbuthnot$year, y = arbuthnot$girls, type = "l")
EXERCISE3. Now, make a plot of the proportion of boys over time. What do you see?
ANSWER: The proportion wavers, but is always north of 50%, with a slight downward trend in the mean.
arbuthnot$boyratio <- arbuthnot$boys / (arbuthnot$boys + arbuthnot$girls)
plot(x = arbuthnot$year, y = arbuthnot$boyratio, type = "l")
Present-Day Data
These data come from a report by the Centers for Disease Control [http://www.cdc.gov/nchs/data/nvsr/nvsr53/nvsr53_20.pdf]
present <-
structure(list(year = c(1940, 1941, 1942, 1943, 1944, 1945, 1946,
1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957,
1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968,
1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979,
1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,
1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
2002), boys = c(1211684, 1289734, 1444365, 1508959, 1435301,
1404587, 1691220, 1899876, 1813852, 1826352, 1823555, 1923020,
1971262, 2001798, 2059068, 2073719, 2133588, 2179960, 2152546,
2173638, 2179708, 2186274, 2132466, 2101632, 2060162, 1927054,
1845862, 1803388, 1796326, 1846572, 1915378, 1822910, 1669927,
1608326, 1622114, 1613135, 1624436, 1705916, 1709394, 1791267,
1852616, 1860272, 1885676, 1865553, 1879490, 1927983, 1924868,
1951153, 2002424, 2069490, 2129495, 2101518, 2082097, 2048861,
2022589, 1996355, 1990480, 1985596, 2016205, 2026854, 2076969,
2057922, 2057979), girls = c(1148715, 1223693, 1364631, 1427901,
1359499, 1330869, 1597452, 1800064, 1721216, 1733177, 1730594,
1827830, 1875724, 1900322, 1958294, 1973576, 2029502, 2074824,
2051266, 2071158, 2078142, 2082052, 2034896, 1996388, 1967328,
1833304, 1760412, 1717571, 1705238, 1753634, 1816008, 1733060,
1588484, 1528639, 1537844, 1531063, 1543352, 1620716, 1623885,
1703131, 1759642, 1768966, 1794861, 1773380, 1789651, 1832578,
1831679, 1858241, 1907086, 1971468, 2028717, 2009389, 1982917,
1951379, 1930178, 1903234, 1901014, 1895298, 1925348, 1932563,
1981845, 1968011, 1963747)), .Names = c("year", "boys", "girls"
), row.names = c(NA, 63L), class = "data.frame")
str(present)## 'data.frame': 63 obs. of 3 variables:
## $ year : num 1940 1941 1942 1943 1944 ...
## $ boys : num 1211684 1289734 1444365 1508959 1435301 ...
## $ girls: num 1148715 1223693 1364631 1427901 1359499 ...QUESTION1: What years are included in this data set? What are the dimensions of the data frame and what are the variable or column names?
ANSWER: The data covers 63 years, from 1940 to 2002. There are 63 cases with 3 variables, or a dimension of 63x3. The column names are “year”, “boys”, and “girls”.
print(c(min(present$year),max(present$year)))## [1] 1940 2002dim(present)## [1] 63 3names(present)## [1] "year" "boys" "girls"QUESTION2: How do these counts compare to Arbuthnot’s? Are they on a similar scale?
ANSWER: The quantity of new births is two orders of scale higher than the baptisms logged in the arbuthnot data (1.0 x 10^6 vs. 1.0 x 10^4)
print(c(max(present$boys + present$girls),max(arbuthnot$boys + arbuthnot$girls)))## [1] 4268326 16145QUESTION3: Make a plot that displays the boy-to-girl ratio for every year in the dataset. What do you see? Does Arbuthnot’s observation about boys being born in greater proportion than girls hold up in the U.S.? Include the plot in your response.
ANSWER: From this plot, we observe values all greater than 1.0 for all points in the time series, indicating a higher share of boys than girls. The ratio tracks downward closer to equality in numbers over the 60 year period.
present$bgratio <- present$boys / present$girls
plot (x = present$year, y = present$bgratio, type = "l")
QUESTION4: In what year did we see the most total number of births in the U.S.?
ANSWER: The max number of children were born in 1961 (4.27 million)
print(present$year[present$boys + present$girls == max(present$boys + present$girls)])## [1] 1961