Lab 1: Intro to R

library(tidyverse)
library(openintro)
data(arbuthnot)
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…

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

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

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

Exercise 2

There appears to be an upward trend associated with the number of girls baptized over the time period. However, this is based upon data that depicts overall numbers and not necessarily an increased percentage as compared to boys. Further analysis is needed to confirm if there is a percentage change over the same period.

ggplot(arbuthnot, aes(x=year, y=girls)) + geom_point() +geom_line() + geom_smooth(method = "lm")

## `geom_smooth()` using formula = 'y ~ x'

Exercise 3

In this graph, it appears that the percentage of boys being baptized over the period is decreasing over time. Conversely, this would mean that the percentage of girls being baptized is increasing over the same time period. However, the ratio of boys is still above 50%, or more than half the babies.

arbuthnot <- arbuthnot |>  mutate(total = boys + girls)
arbuthnot <-  arbuthnot |> mutate (boy_to_girl_ratio = boys/girls)
arbuthnot <-  arbuthnot |> mutate (boy_ratio = boys/total)
ggplot(arbuthnot, aes(x = year, y = boy_ratio)) + geom_point() + geom_line() + geom_smooth(method="lm")

## `geom_smooth()` using formula = 'y ~ x'

Exercise 4

Years included in the data set is 1940 through 2002. There are 63 observations (rows), and 3 variables (columns). The variable names are year, boys and girls.

data(present)
present <- present |>  mutate(total = boys + girls)
present <-  present |> mutate (boy_to_girl_ratio = boys/girls)
present <-  present |> mutate (boy_ratio = boys/total)

Exercise 5

Completing a comparison of the two time periods, we see that the more recent data set has a vastly larger number of babies being born, with the minimum being ~419 times larger, and the maximum ~260 times larger, than the number being tracked by Arbuthnot.

arbuthnot |>  summarise(min = min(boys), max = max(boys))

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

present |> summarise(min = min(boys), max = max(boys))

## # A tibble: 1 × 2
##       min     max
##     <dbl>   <dbl>
## 1 1211684 2186274

Exercise 6

A plot of the “present” data indicates a continuing downward trend of boy babies being born over the period; however, we do not have data to analyze for the intervening period from 1711 through 1940. There may be a period where the trend may have reversed and there may have been more girls than boys proportionately. But all things remaining stable, there has been a decreasing trend of males being born than females, but males proportionately remain above 50% of the children being born.

ggplot(present, aes(x = year, y = boy_ratio)) + geom_point() + geom_line() + geom_smooth(method="lm")

## `geom_smooth()` using formula = 'y ~ x'

Exercise 7

It appears that the year with the most total number of births in the US was 1961, with 4,268,326 births. The least total number of births over the same time period was 2,360,399 occuring in 1940.

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

## # A tibble: 63 × 6
##     year    boys   girls   total boy_to_girl_ratio boy_ratio
##    <dbl>   <dbl>   <dbl>   <dbl>             <dbl>     <dbl>
##  1  1961 2186274 2082052 4268326              1.05     0.512
##  2  1960 2179708 2078142 4257850              1.05     0.512
##  3  1957 2179960 2074824 4254784              1.05     0.512
##  4  1959 2173638 2071158 4244796              1.05     0.512
##  5  1958 2152546 2051266 4203812              1.05     0.512
##  6  1962 2132466 2034896 4167362              1.05     0.512
##  7  1956 2133588 2029502 4163090              1.05     0.513
##  8  1990 2129495 2028717 4158212              1.05     0.512
##  9  1991 2101518 2009389 4110907              1.05     0.511
## 10  1963 2101632 1996388 4098020              1.05     0.513
## # ℹ 53 more rows