Lab 1: Intro to R

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.3.3

## Warning: package 'ggplot2' was built under R version 4.3.3

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## Warning: package 'lubridate' was built under R version 4.3.3

library(openintro)

## Warning: package 'openintro' was built under R version 4.3.3

## Warning: package 'airports' was built under R version 4.3.3

## Warning: package 'cherryblossom' was built under R version 4.3.3

## Warning: package 'usdata' was built under R version 4.3.3

Exercise 1

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

Starting in the late 17th century (1660s onward), there appears to be an upward trend in the number of girls baptized.

# Insert code for Exercise 2 here
ggplot(data = arbuthnot, aes(x = year, y = girls)) +
  geom_line()

Exercise 3

The plot reveals fluctuations in the proportion of boys born throughout the analyzed period. These fluctuations appear to be cyclical, with highs and lows occurring every few decades.

# Insert code for Exercise 3 here
arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)
arbuthnot<-arbuthnot%>%
  mutate(proportion_boys = boys / total)
ggplot(data= arbuthnot,aes(x= year, y= proportion_boys))+
          geom_line()

Exercise 4

The dimensions of the data frame and years included in the present data set

# Insert code for Exercise 4 here
present$year

##  [1] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954
## [16] 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969
## [31] 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
## [46] 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
## [61] 2000 2001 2002

glimpse(present)

## Rows: 63
## Columns: 3
## $ year  <dbl> 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950…
## $ boys  <dbl> 1211684, 1289734, 1444365, 1508959, 1435301, 1404587, 1691220, 1…
## $ girls <dbl> 1148715, 1223693, 1364631, 1427901, 1359499, 1330869, 1597452, 1…

Exercise 5

The magnitude of the present data set counts compare to Arbuthnot’s are much higher. More boys and girs were observed in the present data frame

# Insert code for Exercise 5 here
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

# count of girls
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

Exercise 6

Like Arbuthnot’s observation, the proportion of boys born in the US has significantly fluctuated over time. In both observations, there were slightly more boys than girls.

# Insert code for Exercise 6 here
present <- present %>%
  mutate(total = boys + girls)
present<-arbuthnot%>%
  mutate(proportion_boys = boys / total)
ggplot(data= present,aes(x= year, y= proportion_boys))+
          geom_line()

Exercise 7

The highest number of births in the U.S. was observed in 1705, with a total of 16,145 boys and girls being born.

# Insert code for Exercise 7 here
present <- present %>%
  mutate(total = boys + girls)
 present%>%
   arrange(desc(total))

## # A tibble: 82 × 5
##     year  boys girls total proportion_boys
##    <int> <int> <int> <int>           <dbl>
##  1  1705  8366  7779 16145           0.518
##  2  1707  8379  7687 16066           0.522
##  3  1698  8426  7626 16052           0.525
##  4  1708  8239  7623 15862           0.519
##  5  1697  8062  7767 15829           0.509
##  6  1702  8031  7656 15687           0.512
##  7  1701  8102  7514 15616           0.519
##  8  1703  7765  7683 15448           0.503
##  9  1706  7952  7417 15369           0.517
## 10  1699  7911  7452 15363           0.515
## # ℹ 72 more rows