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

mean(arbuthnot$girls)

## [1] 5534.646

median(arbuthnot$girls)

## [1] 5718

Exercise 2

# Girl baptisms decreased around 1640-1645. Then, they increased steadily at 1660.

# Insert code for Exercise 2 here
ggplot(data = arbuthnot, aes(x = year, y = girls)) + 
  geom_point()

### Exercise 3

# The plot of the proportion of boys is very similar to the girls. They both decline at 1640 and start rising again in 1660.  

# ```{r plot-prop-boys-arbuthnot}
arbuthnot <- arbuthnot %>%
  mutate(boy_to_girl_ratio = boys / girls)
arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)
arbuthnot <- arbuthnot %>%
  mutate(boy_ratio = boys / total)
ggplot(data = arbuthnot, aes(x = year, y = boys)) + 
  geom_point()

Exercise 4

The years in this data set go from 1940-2002. The dimensions are 82x6. The variable (columns) names were year, boys, and girls.

# arbuthnot %>%
 present <- present
dim(arbuthnot)

## [1] 82  6

Exercise 5

Comparing to Arbuthnot, the span of years is less and the count are extremely higher than Arbuthnot’s counts.

# Insert code for Exercise 5 here

Exercise 6

There was no consitent trend on the ratio of boys to girls. The data set shows that there seems to be a downward trend in the ratio as the years go by.

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

Exercise 7

1961 was the year with the most total births.

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

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