Lab 1: Intro to R

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.3.2

## Warning: package 'ggplot2' was built under R version 4.3.2

## Warning: package 'readr' was built under R version 4.3.2

## Warning: package 'purrr' was built under R version 4.3.2

## Warning: package 'dplyr' was built under R version 4.3.2

## Warning: package 'stringr' was built under R version 4.3.2

## Warning: package 'lubridate' was built under R version 4.3.2

library(openintro)

## Warning: package 'openintro' was built under R version 4.3.2

## Warning: package 'airports' was built under R version 4.3.2

## Warning: package 'cherryblossom' was built under R version 4.3.2

## Warning: package 'usdata' was built under R version 4.3.2

Data

arbuthnot

## # A tibble: 82 × 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
## # ℹ 72 more rows

View Boys

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

Exercise 2

There is an increasing trend in the number of girls being baptized over time. By adding a Trend Line, you can see the trend more clearly.

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

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

Exercise 3

The proportion of boys over time hovers between 51% - 53% for the most part. However, there seems to be a downward trend.

# Plot the total number of births per year
arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)

# Proportion of boys over time
arbuthnot <- arbuthnot %>%
  mutate(boy_ratio = boys / total)

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

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

Exercise 4

The years included in the present data set are 1940 - 2002. The dimensions of the data frame are 63 rows by 3 columns. The variable (column) names are “year”, “boys”, and “girls”.

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 counts of the Present data are a higher magnitude of baptisms compared to Arbuthnots.

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

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

arbuthnot %>% 
  summarise(min = min(boys),max = max(girls))

## # A tibble: 1 × 2
##     min   max
##   <int> <int>
## 1  2890  7779

Exercise 6

In the Present Data Set the proportion of boys over time seems to be decreasing, although still in a greater proportion to girls. Compared to Arbuthnot’s Data set, the observation still hold true for the time being.

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

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

ggplot(data = present, aes(x = year, y = boys_ratio)) + geom_point() + geom_smooth(method = "lm") + ggtitle("Present Data Set")

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

ggplot(data = arbuthnot, aes(x = year, y = boy_ratio)) + geom_point() + geom_smooth(method = "lm") + ggtitle("Arbuthnot Data Set")

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

Exercise 7

1961 was the year with the most total number of births in the U.S.

present %>% 
  arrange(desc(total))

## # A tibble: 63 × 5
##     year    boys   girls   total boys_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