Lab 1: Intro to R

library(tidyverse)
library(openintro)

Exercise 1

Following is the vector of girls born during the years 1629-1710.

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 born in the years of 1629-1710.

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

Exercise 3

There are slightly more boys than girls born in the years of 1629-1710.

# Insert code for Exercise 3 here
arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)

arbuthnot <- arbuthnot %>%
  mutate(boy_ratio = boys / total)

ggplot(data = arbuthnot, aes(x = year, y = boy_ratio)) + 
  geom_line()

Exercise 4

Following are the dimensions of the “present” birth records in the United States.

# Insert code for Exercise 4 here
dim(present)

## [1] 63  3

Exercise 5

The ‘present’ dataset has 19 less cases (“years”) than the arbuthnot dataset.

# Insert code for Exercise 5 here
present %>% summarize(min = min(year), max(year))

## # A tibble: 1 x 2
##     min `max(year)`
##   <dbl>       <dbl>
## 1  1940        2002

arbuthnot %>% summarize(min = min(year), max(year))

## # A tibble: 1 x 2
##     min `max(year)`
##   <int>       <int>
## 1  1629        1710

cat("Present: ", + length(present$year))

## Present:  63

cat(" Arbuthnot: ", + length(arbuthnot$year))

##  Arbuthnot:  82

cat(" Present difference in cases: ", + (case_diff <- length(present$year) - length(arbuthnot$year)))

##  Present difference in cases:  -19

Exercise 6

There are slightly more boys than girls born in the years of 1940-2002. However, this is a negative trend.

# Insert code for Exercise 6 here
present <- present %>%
  mutate(total = boys + girls)

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

ggplot(data = present, aes(x = year, y = boy_ratio)) + 
  geom_line()

Exercise 7

The year 1961 had the most total number of births in the US at 4,268,326.

# Insert code for Exercise 7 here
present %>%
  arrange(desc(total))

## # A tibble: 63 x 5
##     year    boys   girls   total boy_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
## # ... with 53 more rows