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

Is there an apparent trend in the number of girls baptized over the years? How would you describe it?

There does seem to be an apparent trend in the number of girls baptized over the years. I would describe the trend as a linear trend level to a constant number of baptisms. Over time, the trend decreases linearly then other times it increases linearly. After these linear trends occur there is some level of consistent number of baptisms.

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

Exercise 3

Now, generate a plot of the proportion of boys born over time. What do you see?

I see that most boys were born of a ratio of about 0.50 to 0.54 over time.

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_point()

Exercise 4

What years are included in this data set? What are the dimensions of the data frame? What are the variable (column) names?

Year included in data set: 1940 - 2002.

Dimension of the data: 63 x 3

Variable names: year, boys, girls

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

## [1] 63  3

Exercise 5

How do these counts compare to Arbuthnot’s? Are they of a similar magnitude?

The counts are significantly higher in present data then Arbuthnot data and therefore the magnitudes are not similar.

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

Exercise 6

Make a plot that displays the proportion of boys born over time. What do you see? Does Arbuthnot’s observation about boys being born in greater proportion than girls hold up in the U.S.? Include the plot in your response. Hint: You should be able to reuse your code from Exercise 3 above, just replace the dataframe name.

I see boys’ proportionality does change slightly over time but still in a 50% range. Therefore Arbuthnot’s observation about boys being born in greater proportion than girls does not hold up in the U.S.

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

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

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

Exercise 7

In what year did we see the most total number of births in the U.S.? Hint: First calculate the totals and save it as a new variable. Then, sort your dataset in descending order based on the total column. You can do this interactively in the data viewer by clicking on the arrows next to the variable names. To include the sorted result in your report you will need to use two new functions: arrange (for sorting). We can arrange the data in a descending order with another function: desc (for descending order). The sample code is provided below.

The year in which we saw the most total number of births in the U.S. : 1961

present %>%
  arrange(desc(total))

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