Lab 1: Intro to R

library(tidyverse)
library(openintro)

Exercise 1

arbuthnot

## # A tibble: 82 × 3
##     year  boys girls
##    <int> <int> <int>
##  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
## # ℹ 72 more rows

arbuthnot$boys

##  [1] 5218 4858 4422 4994 5158 5035 5106 4917 4703 5359 5366 5518 5470 5460 4793
## [16] 4107 4047 3768 3796 3363 3079 2890 3231 3220 3196 3441 3655 3668 3396 3157
## [31] 3209 3724 4748 5216 5411 6041 5114 4678 5616 6073 6506 6278 6449 6443 6073
## [46] 6113 6058 6552 6423 6568 6247 6548 6822 6909 7577 7575 7484 7575 7737 7487
## [61] 7604 7909 7662 7602 7676 6985 7263 7632 8062 8426 7911 7578 8102 8031 7765
## [76] 6113 8366 7952 8379 8239 7840 7640

arbuthnot$girls

##  [1] 4683 4457 4102 4590 4839 4820 4928 4605 4457 4952 4784 5332 5200 4910 4617
## [16] 3997 3919 3395 3536 3181 2746 2722 2840 2908 2959 3179 3349 3382 3289 3013
## [31] 2781 3247 4107 4803 4881 5681 4858 4319 5322 5560 5829 5719 6061 6120 5822
## [46] 5738 5717 5847 6203 6033 6041 6299 6533 6744 7158 7127 7246 7119 7214 7101
## [61] 7167 7302 7392 7316 7483 6647 6713 7229 7767 7626 7452 7061 7514 7656 7683
## [76] 5738 7779 7417 7687 7623 7380 7288

Exercise 2

There is a steady but slight incline of girl baptisms from 1629-1640, at which point the baptisms of girls takes a moderate dip from 1640 to around 1650. Just before 1660 a sharp incline in girl baptisms begins and persists until about 1683 where the frequency of girl baptisms tapers off into a flat line. The outlier on the far right near the graph’s center indicates a sharp drop in girl baptisms in 1704 by nearly 1,950 baptisms. However, the frequency of girl baptisms quickly rebounds, showing an incline back up into the mid-7000s by the next year.

ggplot(data = arbuthnot, aes(x= year, y = girls)) +
  geom_point()

ggplot(data = arbuthnot, aes(x = year, y = girls)) +
  geom_line()

ggplot(data = arbuthnot, aes(x = year, y = boys)) +
  geom_point()

arbuthnot <- arbuthnot |>
  mutate(total = boys + girls)

ggplot(data = arbuthnot, aes(x = year, y = total)) +
  geom_line()

5218 / 4683

## [1] 1.114243

arbuthnot <- arbuthnot |>
  mutate(boy_to_girl_ratio = boys / girls)

5218 / (5218 + 4683)

## [1] 0.5270175

arbuthnot <- arbuthnot |>
  mutate(boy_ratio = boys / total)

Exercise 3

The scatterplot of the proportion of boys born over time appears very scattered (pun intended). The proportion of boys born to girls born seems relatively balanced throughout the years. Although, the number of boys born is greater than girls every year from 1629-1710, even if only slightly.

ggplot(data = arbuthnot, aes(x = year, y = boy_ratio)) +
  geom_point()

arbuthnot <- arbuthnot |>
  mutate(more_boys = boys > girls)

Exercise 4

The “present” dataset includes years 1940-2002. The data frame dimensions are 63 rows x 3 columns, and the variable(column) names are: year, boys, and girls.

present

## # A tibble: 63 × 3
##     year    boys   girls
##    <dbl>   <dbl>   <dbl>
##  1  1940 1211684 1148715
##  2  1941 1289734 1223693
##  3  1942 1444365 1364631
##  4  1943 1508959 1427901
##  5  1944 1435301 1359499
##  6  1945 1404587 1330869
##  7  1946 1691220 1597452
##  8  1947 1899876 1800064
##  9  1948 1813852 1721216
## 10  1949 1826352 1733177
## # ℹ 53 more rows

Exercise 5

The counts between the “arbuthnot” and “present” datasets are vastly different. The number of boy and girl births in London from 1629-1710 are in the mere thousands, while the births of boys and girls in the United States from 1940-2002 are in the millions. The start difference in the magnitude of these numbers are of course, significantly attributed to the fact that London is just one city in the country of England where the US is an entire country. Another factor we can look into is the global birthrate trends of each time period.

ggplot(data = present, aes(x = year, y = girls)) +
  geom_point()

ggplot(data = present, aes(x = year, y = girls)) +
  geom_line()

ggplot(data = present, aes(x = year, y = boys)) +
  geom_point()

ggplot(data = present, aes(x = year, y = boys)) +
  geom_line()

present$boys

##  [1] 1211684 1289734 1444365 1508959 1435301 1404587 1691220 1899876 1813852
## [10] 1826352 1823555 1923020 1971262 2001798 2059068 2073719 2133588 2179960
## [19] 2152546 2173638 2179708 2186274 2132466 2101632 2060162 1927054 1845862
## [28] 1803388 1796326 1846572 1915378 1822910 1669927 1608326 1622114 1613135
## [37] 1624436 1705916 1709394 1791267 1852616 1860272 1885676 1865553 1879490
## [46] 1927983 1924868 1951153 2002424 2069490 2129495 2101518 2082097 2048861
## [55] 2022589 1996355 1990480 1985596 2016205 2026854 2076969 2057922 2057979

present$girls

##  [1] 1148715 1223693 1364631 1427901 1359499 1330869 1597452 1800064 1721216
## [10] 1733177 1730594 1827830 1875724 1900322 1958294 1973576 2029502 2074824
## [19] 2051266 2071158 2078142 2082052 2034896 1996388 1967328 1833304 1760412
## [28] 1717571 1705238 1753634 1816008 1733060 1588484 1528639 1537844 1531063
## [37] 1543352 1620716 1623885 1703131 1759642 1768966 1794861 1773380 1789651
## [46] 1832578 1831679 1858241 1907086 1971468 2028717 2009389 1982917 1951379
## [55] 1930178 1903234 1901014 1895298 1925348 1932563 1981845 1968011 1963747

present$boys + present$girls

##  [1] 2360399 2513427 2808996 2936860 2794800 2735456 3288672 3699940 3535068
## [10] 3559529 3554149 3750850 3846986 3902120 4017362 4047295 4163090 4254784
## [19] 4203812 4244796 4257850 4268326 4167362 4098020 4027490 3760358 3606274
## [28] 3520959 3501564 3600206 3731386 3555970 3258411 3136965 3159958 3144198
## [37] 3167788 3326632 3333279 3494398 3612258 3629238 3680537 3638933 3669141
## [46] 3760561 3756547 3809394 3909510 4040958 4158212 4110907 4065014 4000240
## [55] 3952767 3899589 3891494 3880894 3941553 3959417 4058814 4025933 4021726

present <- present |>
  mutate(total = boys + girls)

ggplot(data = present, aes(x = year, y = total)) +
  geom_line()

Exercise 6

The plot below shows that Arbuthnot’s observation of boys being born in greater proportion than girls also holds up in the US even though the frequency of births in London vs the US are at vastly different magnitudes.

present <- present |>
  mutate(boy_ratio = boys / total)

ggplot(data = present, aes(x = year, y = boy_ratio)) +
  geom_point()

present <- present |>
  mutate(more_boys = boys > girls)

Exercise 7

In 1961 we saw the most total number of births in the U.S. at a whopping 4,268,326.

present |>
  arrange(desc(total))

## # A tibble: 63 × 6
##     year    boys   girls   total boy_ratio more_boys
##    <dbl>   <dbl>   <dbl>   <dbl>     <dbl> <lgl>    
##  1  1961 2186274 2082052 4268326     0.512 TRUE     
##  2  1960 2179708 2078142 4257850     0.512 TRUE     
##  3  1957 2179960 2074824 4254784     0.512 TRUE     
##  4  1959 2173638 2071158 4244796     0.512 TRUE     
##  5  1958 2152546 2051266 4203812     0.512 TRUE     
##  6  1962 2132466 2034896 4167362     0.512 TRUE     
##  7  1956 2133588 2029502 4163090     0.513 TRUE     
##  8  1990 2129495 2028717 4158212     0.512 TRUE     
##  9  1991 2101518 2009389 4110907     0.511 TRUE     
## 10  1963 2101632 1996388 4098020     0.513 TRUE     
## # ℹ 53 more rows

present <- present |>
  mutate(total = boys + girls)