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 plot is showing an increase in the number of girls being baptized from 1629 to 1710.

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

Exercise 3

Simply, the number of boys baptized increased since 1660 and it became a positive linear.

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

Exercise 4

Response: Range: 1940 2002; Dimensions: 63 3; and Variable Names: “year,” “boys,” and “girls”

range(present$year)

## [1] 1940 2002

Exercise 5

No, these counts are different to Arbuthnot’s. Specifically, these data are in millions and ranges from 1940-2002. Meanwhile, Arbuthnot’s data are in thousands and ranges from 1629-1710.

arbuthnot$girls + arbuthnot$boys

##  [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

present$girls + present$boys

##  [1] 2360399 2513427 2808996 2936860 2794800 2735456 3288672 3699940 3535068
## [10] 3559529 3554149 3750850 3846986 3902120 4017362 4047295 4163090 4254784
## [19] 4203812 4244796 4257850 4268326 4167362 4098020 4027490 3760358 3606274
## [28] 3520959 3501564 3600206 3731386 3555970 3258411 3136965 3159958 3144198
## [37] 3167788 3326632 3333279 3494398 3612258 3629238 3680537 3638933 3669141
## [46] 3760561 3756547 3809394 3909510 4040958 4158212 4110907 4065014 4000240
## [55] 3952767 3899589 3891494 3880894 3941553 3959417 4058814 4025933 4021726

range(present$year)

## [1] 1940 2002

range(arbuthnot$year)

## [1] 1629 1710

Exercise 6

Over time, the proportion of boys had increased up until the 1960s. Then, declined in the late 1970s. Then, increased again in the 1980s and continued to through 2002 (note: there is another decline in the 1990s).

Arbuthnot’s observation does, in fact, hold up. Arbuthnot’s observation is true.

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

plot (present$boys,type="l")

present$boys > present$girls #Arbuthnot’s observation

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE

Exercise 7

present <- present %>%
        mutate(total = boys + girls)
present <- present %>%
        arrange(desc(total))
head(present, 3)

## # A tibble: 3 × 4
##    year    boys   girls   total
##   <dbl>   <dbl>   <dbl>   <dbl>
## 1  1961 2186274 2082052 4268326
## 2  1960 2179708 2078142 4257850
## 3  1957 2179960 2074824 4254784