Lab 1: Intro to R

library(tidyverse)
library(openintro)

Exercise 1

What command would you use to extract just the counts of girls baptized? Try it!

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? (To ensure that your lab report is comprehensive, be sure to include the code needed to make the plot as well as your written interpretation.)

ANSWER Except for the 20 years between, roughly, 1640 and 1660 there are more births as time goes by. There are small variations around this trend.

I have searched online for an explanation on how to add a trendline to the graph (similar to what is done, for example, in Powerpoint). I have found one here: https://www.r-bloggers.com/2021/09/draw-a-trend-line-using-ggplot-quick-guide/

# Insert code for Exercise 2 here
ggplot (data = arbuthnot, aes(x = year, y= girls))+ geom_point() + geom_smooth(method=lm, se=FALSE, col= 'black')

## `geom_smooth()` using formula = 'y ~ x'

Exercise 3

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

ANSWER There are consistently, a few more boys than girls born every year. This is a relatively standard demographic result.

First I created two additional variables: the total number of births and the proportion of boys out of the total number of births.

arbuthnot <- arbuthnot %>% + mutate(total = boys + girls) arbuthnot <- arbuthnot %>% + mutate(boy_ratio = boys / total)

Then I checked the new variables were incorporated in the dataframe, using > arbuthnot$total [1] 9901 9315 8524 9584 9997 9855 10034 9522 9160 10311 10150 10850 10670 [14] 10370 9410 8104 7966 7163 7332 6544 5825 5612 6071 6128 6155 6620 [27] 7004 7050 6685 6170 5990 6971 8855 10019 10292 11722 9972 8997 10938 [40] 11633 12335 11997 12510 12563 11895 11851 11775 12399 12626 12601 12288 12847 [53] 13355 13653 14735 14702 14730 14694 14951 14588 14771 15211 15054 14918 15159 [66] 13632 13976 14861 15829 16052 15363 14639 15616 15687 15448 11851 16145 15369 [79] 16066 15862 15220 14928

and > arbuthnot$boy_ratio [1] 0.5270175 0.5215244 0.5187705 0.5210768 0.5159548 0.5109082 0.5088698 [8] 0.5163831 0.5134279 0.5197362 0.5286700 0.5085714 0.5126523 0.5265188 [15] 0.5093518 0.5067868 0.5080341 0.5260366 0.5177305 0.5139059 0.5285837 [22] 0.5149679 0.5322023 0.5254569 0.5192526 0.5197885 0.5218447 0.5202837 [29] 0.5080030 0.5116694 0.5357262 0.5342132 0.5361942 0.5206108 0.5257482 [36] 0.5153557 0.5128359 0.5199511 0.5134394 0.5220493 0.5274422 0.5232975 [43] 0.5155076 0.5128552 0.5105507 0.5158214 0.5144798 0.5284297 0.5087122 [50] 0.5212285 0.5083822 0.5096910 0.5108199 0.5060426 0.5142178 0.5152360 [57] 0.5080788 0.5155165 0.5174905 0.5132301 0.5147925 0.5199527 0.5089677 [64] 0.5095857 0.5063659 0.5123973 0.5196766 0.5135590 0.5093183 0.5249190 [71] 0.5149385 0.5176583 0.5188268 0.5119526 0.5026541 0.5158214 0.5181790 [78] 0.5174052 0.5215362 0.5194175 0.5151117 0.5117899

Then I created the graph with ggplot. This the code:

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

However, when trying to knit, I received messages saying the new variables I had just created were not there. I tried various ways (saving again, restarting the computer, etc) and I could not solve the problem. When working in the console, the variables were there and I was able to produce graphs

Also, in the graph using the code above, the y-axis was not starting at 0. As a result, although the vast majority of the values were between 0.51 and 0.53, the scatter plot seemed to show a lot of variability

Then I tried to force the y-axis to start at 0, using: ggplot(data = arbuthnot, aes(x = year, y = boy_ratio)) + geom_point() + ylim(0, 0.6)

I have found the ylim command at: https://ggplot2.tidyverse.org/reference/lims.html

I cannot paste the graph here. I will do so separately

I am not typing the command in the chunk below because it halts the knitting

# Insert code for Exercise 3 here

Exercise 4

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

ANSWER

The years range from 1940 to 2002

The original data frame has three columns and 63 observations (one for every year).

The names of the original columns are: year, boys and girls

I generated the new variables total and boy_ratio as in exercise 3, using:

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

I have used the function summary(), which I found in the textbook by Navarro (p., 135). The function provides the information below for the three original variables as well as for the two I generated.

summary(present) year boys girls total
Min. :1940 Min. :1211684 Min. :1148715 Min. :2360399
1st Qu.:1956 1st Qu.:1799857 1st Qu.:1711404 1st Qu.:3511262
Median :1971 Median :1924868 Median :1831679 Median :3756547
Mean :1971 Mean :1885600 Mean :1793915 Mean :3679515
3rd Qu.:1986 3rd Qu.:2058524 3rd Qu.:1965538 3rd Qu.:4023830
Max. :2002 Max. :2186274 Max. :2082052 Max. :4268326
boy_ratio
Min. :0.5112
1st Qu.:0.5121
Median :0.5125
Mean :0.5125
3rd Qu.:0.5130
Max. :0.5143

For the names of the columns and finding the maximum and minimum years, I have also used:

colnames(present) [1] “year” “boys” “girls” “total” “boy_ratio”

and (modifying from the example in the assignment instructions)

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

Please, notice than when trying to knit, only the three original columns appear, as I had the same problems mentioned above when tying to knit.

# Insert code for Exercise 4 here
present %>%
  summarize(min = min(year), max = max(year))

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

colnames(present)

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

Exercise 5

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

ANSWER The counts in the Arbuthnot data are in the thousands, while the data in Present are in the millions. This is to be expected as Arbuthnot’s data are from London three centuries ago and the data in Present are for the whole United States

# Insert code for Exercise 5 here

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.

ANSWER

Yes, the same pattern, slightly more boys than girls born every year, is observed.

Although I had the problems mentioned above, in the console I was able to run:

ggplot(data = present, aes(x = year, y = boy_ratio)) + geom_point() + ylim(0, 0.6)

I will paste the graph separately

Also, I am not pasting the code below as it halts the knitting

# Insert code for Exercise 6 here

Exercise 7

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

ANSWER For the period 1940-2002, the most births occurred in 1961. This is reasonable as these were the baby-boom years

I have used the command in the assignment instructions:

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

There is no need to see further rows, as they observations are ranked by total births from higher to lower

Again, apologies, I cannot include the code below as it halts the knitting

# Insert code for Exercise 7 here