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

There is an overall increasing trend since around 1660. We could see a decreasing trend before that between 1640 and 1660

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

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

Exercise 3

We see a slightly decreasing trend in the proportion of boy to the total.

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(method=lm)

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

Exercise 4

Information goes from years 1940 to 2002. Dimensions of dataframe are 63 x 3. Column names are year, boys, girls

data('present', package='openintro')
min_year <- present %>% 
     summarize(min = min(year))
min_year

## # A tibble: 1 x 1
##     min
##   <dbl>
## 1  1940

max_year <- present %>% 
     summarize(max = max(year))
max_year

## # A tibble: 1 x 1
##     max
##   <dbl>
## 1  2002

nrow(present)

## [1] 63

ncol(present)

## [1] 3

colnames(present)

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

Exercise 5

I took the mean of the total in both datasets and the oberved its ratio. The observed ratio was over 300 times higher for the present dataset vs arbuthnot.

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

mean(present$total) / mean(arbuthnot$total)

## [1] 321.5869

Exercise 6

For the present dataset the proportion of boys to total has a decreasing trend. This is different to what we observed in the arbuthnot data where the boys trend was upwards.

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

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

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

Exercise 7

By sorting the totals and then taking the first row in th sorted dataframe, the year with largest total was 1961

sorted_present <- present %>%
    arrange(desc(total))
sorted_present$year[1]

## [1] 1961