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

After plotting the line graph below we can see that the number of girls baptized over the years increased.In particular, you see a general increase in girls getting baptized beginning from the year 1660.

#plot line graph to visualize the trend of the number of girls baptized over the years
plot(arbuthnot$year, arbuthnot$girls, type="l", xlab="Year",
  ylab="Number of Girls Baptized") 

Exercise 3

From the graph generated below, the proportion of boys baptized over time, I see that the highest proportion of boys were baptized between the years: 1657 and 1662. We see an upward trend starting to emerge from the mid to late 1650’s till about the early 1660’s after which the trend begins to go down.

#We use piping to (%>%) to assign the mutate function result to the data set,overriding 
#the old data set.
arbuthnot <- arbuthnot %>%
mutate(total = girls + boys)
ggplot(data = arbuthnot, aes(x = year, y = boys/total)) + geom_line()

Exercise 4

# range of years included in the data frame
range(present$year)

## [1] 1940 2002

#dimensions of the data frame
dim(present)

## [1] 63  3

# column names of the data frame
colnames(present)

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

Exercise 5

When comparing the counts of arbuthnot and present for both girls and boys it looks like the present has a much greater magnitude of births than arbuthnot. This can probably be best explained by the high increase in population over time. More concretely, if we compare girls, we see that the max of girls at arbuthnot is about 268 times less than present. The other counts follow a similiar narrative.

# finding the maximum and minimum amount of boy births for arbuthnot
arbuthnot %>%
summarize(min = min(boys), max = max(boys))

## # A tibble: 1 x 2
##     min   max
##   <int> <int>
## 1  2890  8426

# finding the maximum and minimum amount of girl births for arbuthnot
arbuthnot %>%
summarize(min = min(girls), max = max(girls))

## # A tibble: 1 x 2
##     min   max
##   <int> <int>
## 1  2722  7779

# finding the maximum and minimum amount of boy births for present
present %>%
summarize(min = min(boys), max = max(boys))

## # A tibble: 1 x 2
##       min     max
##     <dbl>   <dbl>
## 1 1211684 2186274

# finding the maximum and minimum amount of girl births for present
present %>%
summarize(min = min(girls), max = max(girls))

## # A tibble: 1 x 2
##       min     max
##     <dbl>   <dbl>
## 1 1148715 2082052

Exercise 6

We see an upward trend of births in the United States in the mid to late 1940’s.

#We use piping to (%>%) to assign the mutate function result to the data set,overriding 
#the old data set.
present <- present %>%
mutate(total = girls + boys)
ggplot(data = present, aes(x = year, y = boys/total)) + geom_line()

Exercise 7

It looks like the maximum total number of present births occurred in 1961.

#calculate and plot the total births
present <- present %>% mutate(total = boys + girls)
#sort the data set
present %>%arrange(desc(total))

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

# graph the data set
ggplot(data = present, aes(x = year, y = total)) + geom_line()