Lab 1: Intro to R

library(tidyverse)
library(openintro)

Exercise 1

Retrieving the count of baptisims performed on girls using arbuthnot$girls

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 was an increased of girl baptisms throughout the time period. Although there was a drop of girl baptisms 1640-1660, the trend still increased after the period.

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

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

Exercise 3

Using the new column boy_ratio, we can plot the proportion of boys born in the time period. Through the trend line, it appears almost as a sine wave.

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

arbuthnot <- arbuthnot %>%
  mutate(boy_to_girl_ratio = boys / girls)

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

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

Exercise 4

There is a total of 62 baptisms recorded in this data set from 1940 to 2002. There are three columns: year, girl, and boy.

data('present', package='openintro')
dim(present)

## [1] 63  3

range(present$year)

## [1] 1940 2002

colnames(present)

## [1] "year"  "boys"  "girls"

Exercise 5

The present table has a time period of 62 years compared to arbuthnot’s of 81 years. These tables will not have the same magnitude as there difference of 19 years will effect how to understand the baptism totals.

range(arbuthnot$year)

## [1] 1629 1710

Exercise 6

There appeared to be a downward trend of boy baptisms in the present data compared to the arbuthnot’s. The ratio boy baptisms outweighing the girls is not applicable in the US compared to Arbuthnot’s data.

present <- present %>%
  mutate(boy_to_girl_ratio = boys / girls)

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

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

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

mean(present$boys)>mean(present$girls)

## [1] TRUE

Exercise 7

The max amount of births in a year were 4,268,326 births in 1961

present$year[which(present$total==max(present$total))]

## [1] 1961

present %>%
  arrange(desc(total))

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