Install Packages

#install.packages("tidyverse")
#install.packages("openintro")

Load Packages

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.1.1     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(openintro)
## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdata

Explore Data

arbuthnot
## # A tibble: 82 x 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
## # ... with 72 more rows

Explore with Glimpse

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~

Boys Data

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

Exercise 1

What command would you use to extract just the counts of girls baptized? Try it out in the console!

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

Is there an apparent trend in the number of girls baptized over the years? How would you describe it? (To ensure that your lab report is comprehensive, be sure to include the code needed to make the plot as well as your written interpretation.)

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

The graph shows a drop in births in 1640, and then an increase since 1660

Exercise 3

Now, generate a plot of the proportion of boys born over time. What do you see?

Determine total

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

Create ratio of boys compared to total

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

Plot ratio

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

The proportion of boys has been decreasing since 1660

Present Data

present
## # A tibble: 63 x 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
## # ... with 53 more rows

Exercise 4

What years are included in this data set? What are the dimensions of the data frame? What are the variable (column) names?

Years: 1940-2002

dim(present)
## [1] 63  3

Columns: years, boys, girls, total, boy_ratio

Exercise 5

How do these counts compare to Arbuthnot’s? Are they of a similar magnitude?

They are greater; Arbuthnot’s numbers were in the thousands, Present is in millions

Exercise 6

Make a plot that displays the proportion of boys born over time. 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. Hint: You should be able to reuse your code from Exercise 3 above, just replace the name of the data frame.

Determine total

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

Create ratio of boys compared to total

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

Plot ratio

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

Boys are still in greater proportion however the ratio is going down.

Exercise 7

In what year did we see the most total number of births in the U.S.? Hint: First calculate the totals and save it as a new variable. Then, sort your dataset in descending order based on the total column. You can do this interactively in the data viewer by clicking on the arrows next to the variable names. To include the sorted result in your report you will need to use two new functions. First we use arrange() to sorting the variable. Then we can arrange the data in a descending order with another function, desc(), for descending order. The sample code is provided below.

present %>%
  arrange(desc(total))
## # A tibble: 63 x 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
## # ... with 53 more rows

1961 had the most total births in the U.S.