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

The number of girls being baptized generally increases over the years.

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

Exercise 3

Generally, the proportion of boys born is significantly higher than that of the girls as most of the ratios of boys/total is higher than 0.5, meaning that it’s more than half.

total<-arbuthnot$boys + arbuthnot$girls
arbuthnot <- arbuthnot %>%
  mutate(boy_ratio = boys / total)
ggplot(data = arbuthnot, aes(x = year, y =boy_ratio)) + 
  geom_point()

Exercise 4

Present includes years 1940-2002. The dimensions are 63 rows x 3 columns. The variable 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

Arbuthnot’s data set varies significantly on the time frame it includes (1629-1710), but other than that the magnitude of the columns is similar as they both keep track of boys, girls and year.

glimpse(arbuthnot)

## Rows: 82
## Columns: 4
## $ year      <int> 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, …
## $ boys      <int> 5218, 4858, 4422, 4994, 5158, 5035, 5106, 4917, 4703, 5359, …
## $ girls     <int> 4683, 4457, 4102, 4590, 4839, 4820, 4928, 4605, 4457, 4952, …
## $ boy_ratio <dbl> 0.5270175, 0.5215244, 0.5187705, 0.5210768, 0.5159548, 0.510…

Exercise 6

The proportion of boys born over time according to the ‘present’ dataset it generally above 0.5 with occasional dips into below 0.5. This is about the same as the ratio seen in the ‘arbuthnot’ but the ‘present’ dataset seems more uniform than the ‘artbuthnot’ dataset.

total<-present$boys + present$girls
present <- present %>%
  mutate(boy_ratio = boys / total)
ggplot(data = present, aes(x = year, y =boy_ratio)) + 
  geom_point()

Exercise 7

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

present %>%
  arrange(desc(total))

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