Lab 1: Intro to R

library(tidyverse)
library(openintro)

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

To identify trends in the number of girls baptized over time, a line plot would be an effective visualization tool. In this representation, the x-axis would denote the years, while the y-axis would correspond to the number of female baptisms. The observed trend reveals a noticeable decline in baptismal rates for girls, beginning around 1640 and continuing through 1710, indicating a potential demographic or societal shift during this period.

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

Exercise 3

To effectively visualize the trend, a line plot would be an appropriate choice, with the x-axis representing the years and the y-axis depicting the proportion of male births. The observed trend indicates that baptismal rates for boys consistently exceed those for girls, with male births accounting for more than 50% of the total over the given time period.

arbuthnot$boys + arbuthnot$girls

##  [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

arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)
arbuthnot <- arbuthnot %>%
  mutate(boy_ratio = boys / total)

ggplot(data = arbuthnot, aes(x = year, y = boy_ratio)) + 
  geom_line(color = "purple", linewidth = 1)

Exercise 4

This newly acquired dataset consists of 63 observations across three distinct variables. The dataset encompasses a temporal range of 62 years, spanning from 1940 to 2002, providing a comprehensive view of trends over this extended period.

data('present', package='openintro')
arbuthnot %>%
  summarize(min = min(boys), max = max(boys))

## # A tibble: 1 × 2
##     min   max
##   <int> <int>
## 1  2890  8426

range(present$year)

## [1] 1940 2002

dim(present)

## [1] 63  3

colnames(present)

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

Exercise 5

To effectively compare both datasets, it is necessary to perform a count on each, utilizing the minimum, maximum, and summary functions to analyze birth trends for both boys and girls. Based on this analysis, the report indicates that during the observed time period, there was a general increase in birth rates over time.

arbuthnot %>%
  summarize(min_arb_boys = min(boys), max_arb_boys = max(boys),
            min_arb_girls =min(girls), max_arb_girls= max(girls),
            min_arb_total= min(min_arb_boys + min_arb_girls),
            max_arb_total= max(max_arb_boys + max_arb_girls)
            )

## # A tibble: 1 × 6
##   min_arb_boys max_arb_boys min_arb_girls max_arb_girls min_arb_total
##          <int>        <int>         <int>         <int>         <int>
## 1         2890         8426          2722          7779          5612
## # ℹ 1 more variable: max_arb_total <int>

present %>%
  summarize(min_pres_boys = min(boys), max_pres_boys = max(boys),
            min_pres_girls =min(girls), max_pres_girls= max(girls),
            min_pres_total= min(min_pres_boys + min_pres_girls),
            max_pres_total= max(max_pres_boys + max_pres_girls)
            )

## # A tibble: 1 × 6
##   min_pres_boys max_pres_boys min_pres_girls max_pres_girls min_pres_total
##           <dbl>         <dbl>          <dbl>          <dbl>          <dbl>
## 1       1211684       2186274        1148715        2082052        2360399
## # ℹ 1 more variable: max_pres_total <dbl>

Exercise 6

The overall trend in male birth rates over time has remained consistent, showing no significant deviations or anomalies in the observed patterns.

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

ggplot(data = present, aes(x = year, y = boy_ratio)) + 
  geom_line( color ="orange", linewidth = 1)

Exercise 7

The year with the highest volume of recorded data was 1961. To provide a clearer analysis, I applied a filter to isolate this year and display the corresponding maximum total value.

present<- present %>%
  mutate(total = boys + girls)
present %>%
  arrange(desc(total))

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

present$year[which.max(present$boys + present$girls)]

## [1] 1961

present %>%
  filter(year == 1961)

## # A tibble: 1 × 5
##    year    boys   girls   total boy_ratio
##   <dbl>   <dbl>   <dbl>   <dbl>     <dbl>
## 1  1961 2186274 2082052 4268326     0.512

Final Conclusion

This analysis explores historical baptism data (1629–1710) from John Arbuthnot and compares it with modern U.S. birth records (1940–2002). The key findings include:

1. Declining Baptism Rates for Girls (1629–1710)
   • The number of girls baptized steadily declined over time.
   • This could be due to population changes, societal shifts, or data recording practices.
2. More Boys Than Girls Are Born Historically and Today
   • Arbuthnot originally observed that more boys than girls were baptized.
     
   • This trend remains consistent in modern U.S. birth records, with the proportion of boys consistently above 50%.
3. The U.S. Birth Rate Is Much Higher Than Historical Data
   • While Arbuthnot’s dataset records thousands of births per year,
     the present dataset records millions—a result of population growth.
4. Most Births Recorded in 1961 (Baby Boom Era)
   • The highest number of births was recorded in 1961, reflecting the post-war Baby Boom.

Key Takeaways

• The findings confirm historical birth trends hold true today.
• Population growth and societal changes impact birth counts and reporting.