Lab 1: Intro to R

library(tidyverse)
library(openintro)

#arbuthnot
#glimpse(arbuthnot)

Exercise 1

arbuthnot$girls #returns vector

##  [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

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

?ggplot

Exercise 2

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

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

The number of girls being baptized had a gneral trend of rising from 1629 to 1640 before a sharp and steady decline through to 1650, where less than 3000 girls were baptized. From 1660 there was a sharp rise again, from which point there was an overall trend of more girls being baptized through to 1710.

arbuthnot$boys + arbuthnot$girls

##  [1]  9901  9315  8524  9584  9997  9855 10034  9522  9160 10311 10150 10850
## [13] 10670 10370  9410  8104  7966  7163  7332  6544  5825  5612  6071  6128
## [25]  6155  6620  7004  7050  6685  6170  5990  6971  8855 10019 10292 11722
## [37]  9972  8997 10938 11633 12335 11997 12510 12563 11895 11851 11775 12399
## [49] 12626 12601 12288 12847 13355 13653 14735 14702 14730 14694 14951 14588
## [61] 14771 15211 15054 14918 15159 13632 13976 14861 15829 16052 15363 14639
## [73] 15616 15687 15448 11851 16145 15369 16066 15862 15220 14928

arbuthnot2 <- arbuthnot%>%
  mutate(total = boys+girls)%>%
  mutate(ratio = boys/girls)%>%
  mutate(boy_ratio = boys/total, 
          girl_ratio = girls/total)

Exercise 3

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

# Insert code for Exercise 3 here
ggplot(data = arbuthnot2, aes(x = year, y = boy_ratio))+
  geom_line()

Boys have for the most part made up more than half of the number of babies baptized, though the ratio has seemed to be stable for the most part between 1629 and 1710, ranging between 0.5 and 0.53.

arbuthnot2 <- arbuthnot2 %>%
  mutate(more_boys = boys > girls)

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

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

Exercise 4

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

glimpse(present)

## Rows: 63
## Columns: 3
## $ year  <dbl> 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950…
## $ boys  <dbl> 1211684, 1289734, 1444365, 1508959, 1435301, 1404587, 1691220, 1…
## $ girls <dbl> 1148715, 1223693, 1364631, 1427901, 1359499, 1330869, 1597452, 1…

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

## # A tibble: 1 × 2
##     min   max
##   <dbl> <dbl>
## 1  1940  2002

The column names are year, boys, girls, and the dataset includes data from the years 1940 to 2002

Exercise 5

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

present <- present%>%
  mutate(total = boys+girls)
max(arbuthnot2$total)

## [1] 16145

max(present$total)

## [1] 4268326

These counts are of a much higher magnitude than Arbuthnot’s.

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.

present <- present%>%
  mutate(boys_ratio = boys/total,
         ratio = boys/girls)
ggplot(data = present, aes(x = year, y = boys_ratio))+
  geom_line()

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

No, there seem to be more girls being born than boys over time.

Exercise 7

In what year did we see the most total number of births in the U.S.?

present%>%
  filter(total == 4268326)

## # A tibble: 1 × 6
##    year    boys   girls   total boys_ratio ratio
##   <dbl>   <dbl>   <dbl>   <dbl>      <dbl> <dbl>
## 1  1961 2186274 2082052 4268326      0.512  1.05

present%>%
  arrange(-total)

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

1961 saw the most total number of births in the U.S