glimpse(arbuthnot)
## Rows: 82
## Columns: 3
## $ year  <int> 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639…
## $ boys  <int> 5218, 4858, 4422, 4994, 5158, 5035, 5106, 4917, 4703, 5359, 5366…
## $ girls <int> 4683, 4457, 4102, 4590, 4839, 4820, 4928, 4605, 4457, 4952, 4784…

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

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

Based on the graph above we can see that early on the is a slight increase in the number of girls baptized before taking a dramatic drop after 1640. We can see that the lower number of girls baptized continues until 1660 where the trend takes a dramatic increase. Throughout the 1660s and 1670s we can see that there is an overall increasing trend of baptisms occurring even with a sharp drop in the mid 1660s.

This upward trend continues throughout the 1680s until around the 1690s where we begin to see the number of baptisms level off. In the years after the leveling off of baptisms there are some drops and increases in the number of girls that were baptized. Which could be a factor of less births or less people having girls. Overall, from the start of the data collection to the end we can see an overall increase in the number of girls baptized.

Exercise 3

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

We can see here that over time there is somewhat of a positive linear increase in the proportion of boys baptized mainly since 1660. Before that we see the proportion of boys being baptized decreasing. We can assume that the proportion of boys being baptized runs parallel to the number of boys being born. For example, we can assume that when the number of boys being baptized increases than the number of boys that are being born increases and vice versa.

Exercise 4

range(present$year)
## [1] 1940 2002
dim(present)
## [1] 63  3
colnames(present)
## [1] "year"  "boys"  "girls"

The years included in the data set: 1940 to 2002. The dimensions of the data set: 63 rows, 3 columns. The column names of the data set: year, boys, girls.

Exercise 5

arbuthnot$girls + arbuthnot$boys
##  [1]  9901  9315  8524  9584  9997  9855 10034  9522  9160 10311 10150 10850
## [13] 10670 10370  9410  8104  7966  7163  7332  6544  5825  5612  6071  6128
## [25]  6155  6620  7004  7050  6685  6170  5990  6971  8855 10019 10292 11722
## [37]  9972  8997 10938 11633 12335 11997 12510 12563 11895 11851 11775 12399
## [49] 12626 12601 12288 12847 13355 13653 14735 14702 14730 14694 14951 14588
## [61] 14771 15211 15054 14918 15159 13632 13976 14861 15829 16052 15363 14639
## [73] 15616 15687 15448 11851 16145 15369 16066 15862 15220 14928
present$girls + present$boys
##  [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

The counts of the present data frame are much larger than those in Arbuthnot’s. While Arbuthnot’s counts are in the thousands those in the present data frame are in the millions. This marks a huge difference between Arbuthnot’s data frame and the present data frame.

range(arbuthnot$year)
## [1] 1629 1710
range(present$year)
## [1] 1940 2002

Additionally, we can see that they are not of similar magnitude. Not only from the counts but also from the number of years the data sets take into account. While Arbuthnot’s ranges from 1629-1710 (~ 82 years) the present data frame ranges from 1940-2002 (~ 63 years). While Arbuthnot’s spans more years the counts are lower than the present data frame which spanned 20 years less.

Exercise 6

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

We can see that overtime the proportion of boys increased until the 1960s were they begin to decline. Through the 1960s and early 1970s we see this decline hold steady. Then, in the late 1970s we see the proportion of boys increase again until the 1990s where the proportion declines again before taking a slight increase to the year 2000. Then it seems to almost level off in 2001.

present$boys > present$girls #Arbuthnot's Observation
##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE

Based off the information above we can conclude that Arbuthnot’s observation is true. Therefore, we can conclude that boys were being born in great proportion than girls in the U.S.

Exercise 7

present <- present %>%
  mutate(total = boys + girls)
present <- present %>%
  arrange(desc(total))
head(present, 3)
## # A tibble: 3 × 4
##    year    boys   girls   total
##   <dbl>   <dbl>   <dbl>   <dbl>
## 1  1961 2186274 2082052 4268326
## 2  1960 2179708 2078142 4257850
## 3  1957 2179960 2074824 4254784

Based on the present data frame we see the most total number of births took place in 1961 in the U.S.