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

The data indicates an upward trend in the # of baptisms for girls.

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

Exercise 3

The ratio of boys to girls seems to have generally remained the same over time, with the # of boys outnumbering girls each year.

# Insert code for Exercise 3 here
# Create the new columns
arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)
arbuthnot <- arbuthnot %>%
  mutate(boy_to_girl_ratio = boys / girls)

# Plot the data
ggplot(data = arbuthnot, aes(x = year, y = boy_to_girl_ratio)) +     geom_line()

Exercise 4

The data set ranges from 1940 to 2002. The data frame has 63 rows and 3 columns, and the variable names are: year, boys and girls.

# Insert code for Exercise 4 here
glimpse(present)

## Rows: 63
## Columns: 3
## $ year  <dbl> 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1...
## $ boys  <dbl> 1211684, 1289734, 1444365, 1508959, 1435301, 1404587, 1691220...
## $ girls <dbl> 1148715, 1223693, 1364631, 1427901, 1359499, 1330869, 1597452...

Exercise 5

The present day dataset includes significantly higher figures.

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

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

present %>% summarize(min = min(boys), max = max(boys))

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

Exercise 6

In difference to the Arbhuthnot dataset, the ratio of boys to girls in the ‘Present’ dataset seems to be decreasing over time.

# Insert code for Exercise 6 here
# Create the new columns
present <- present %>% mutate(total = boys + girls)
present <- present %>% mutate(boy_ratio = boys / total)

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

Exercise 7

The most total number of births in the U.S. occurred in 1961, with 4,268,326 births.

# Insert code for Exercise 7 here
present[c(1,4)] %>%
    arrange(desc(total))

## # A tibble: 63 x 2
##     year   total
##    <dbl>   <dbl>
##  1  1961 4268326
##  2  1960 4257850
##  3  1957 4254784
##  4  1959 4244796
##  5  1958 4203812
##  6  1962 4167362
##  7  1956 4163090
##  8  1990 4158212
##  9  1991 4110907
## 10  1963 4098020
## # ... with 53 more rows