Lab report

Load data:

rm(list = ls())
arbuthnot <- read.csv("arbuthnot.csv")

Exercise 1:

12*7-45
## [1] 39
n <- 25 
sqrt(n)
## [1] 5

Exercise 2:

scores <- c(88, 92, 79, 95, 84)
length(scores)
## [1] 5
mean(scores)
## [1] 87.6

Exercise 3:

arbuthnot <- read.csv("arbuthnot.csv")
dim(arbuthnot)
## [1] 82  3
names(arbuthnot)
## [1] "year"  "boys"  "girls"
range(arbuthnot)
## [1] 1629 8426

Written answer:

Exercise 4:

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 5:

plot(x = arbuthnot$year, y = arbuthnot$boys, main = "Boys baptized in London, 1629-1710", xlab = "Year", ylab = "Number of Boys baptized")

Written answer:there seems to be a sharo increase in the number of boys babtized after the year 1660.

Exercise 6:

arbuthnot$boys / (arbuthnot$boys + arbuthnot$girls)
##  [1] 0.5270175 0.5215244 0.5187705 0.5210768 0.5159548 0.5109082 0.5088698
##  [8] 0.5163831 0.5134279 0.5197362 0.5286700 0.5085714 0.5126523 0.5265188
## [15] 0.5093518 0.5067868 0.5080341 0.5260366 0.5177305 0.5139059 0.5285837
## [22] 0.5149679 0.5322023 0.5254569 0.5192526 0.5197885 0.5218447 0.5202837
## [29] 0.5080030 0.5116694 0.5357262 0.5342132 0.5361942 0.5206108 0.5257482
## [36] 0.5153557 0.5128359 0.5199511 0.5134394 0.5220493 0.5274422 0.5232975
## [43] 0.5155076 0.5128552 0.5105507 0.5158214 0.5144798 0.5284297 0.5087122
## [50] 0.5212285 0.5083822 0.5096910 0.5108199 0.5060426 0.5142178 0.5152360
## [57] 0.5080788 0.5155165 0.5174905 0.5132301 0.5147925 0.5199527 0.5089677
## [64] 0.5095857 0.5063659 0.5123973 0.5196766 0.5135590 0.5093183 0.5249190
## [71] 0.5149385 0.5176583 0.5188268 0.5119526 0.5026541 0.5158214 0.5181790
## [78] 0.5174052 0.5215362 0.5194175 0.5151117 0.5117899
total <- (arbuthnot$boys + arbuthnot$girls)
plot(x = arbuthnot$year, y = total, main="Total baptisms in London, 1629-1710")

Written answer: The proportion is slightly above 0.5.

Exercise 7:

sum(arbuthnot$boys < arbuthnot$girls)
## [1] 0
arbuthnot$year[which.max(arbuthnot$girls)]
## [1] 1705

Written answer:The year 1705 was the bussiest year for female baptisms.

On your own:

present <- read.csv("present.csv")

1: [6 pts]

dim(present)
## [1] 63  3
str(present)
## 'data.frame':    63 obs. of  3 variables:
##  $ year : int  1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 ...
##  $ boys : int  1211684 1289734 1444365 1508959 1435301 1404587 1691220 1899876 1813852 1826352 ...
##  $ girls: int  1148715 1223693 1364631 1427901 1359499 1330869 1597452 1800064 1721216 1733177 ...
head(present)
##   year    boys   girls
## 1 1940 1211684 1148715
## 2 1941 1289734 1223693
## 3 1942 1444365 1364631
## 4 1943 1508959 1427901
## 5 1944 1435301 1359499
## 6 1945 1404587 1330869
range(present$year)
## [1] 1940 2002

Written answer:The modern data has 63 rows and 3 columns. The columns are the years in which boys and girls were born from 1940 to 2002. A single column compares the amount of boys and girls born in a single year.

2: [6 pts]

present$boys + present$girls
##  [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
min(present$boys + present$girls)
## [1] 2360399
max(present$boys + present$girls)
## [1] 4268326

Written answer:The largest yearly total is 2360399 while the largest yearly total is 4021726. Even the smallest yearly total of the recent data is significantly larger than the largest yearly total of the previous data sets. This suggest more birth were occuring in the 20th century than previous centuries.

3: [8 pts]

total <- present$boys + present$girls
plot(x = present$year, y = total, type = "l", main = "Total births in the US, 1940-2002", xlab = "Year", ylab = "Total births")

Written answer: The trend of the plot seems to be slowly falling before rising again. There’s a noticable decline in births between 1970 and 1980.

4: [8 pts]

present$year[which.max(total)]
## [1] 1961
total[which.max(total)]
## [1] 4268326

Written answer: The year 1961 is the year with the most births by having a total of 426326 births.

5: [12 pts]

present$boys / (present$boys + present$girls)
##  [1] 0.5133386 0.5131376 0.5141926 0.5138001 0.5135613 0.5134745 0.5142562
##  [8] 0.5134883 0.5131024 0.5130881 0.5130778 0.5126891 0.5124173 0.5130027
## [15] 0.5125423 0.5123716 0.5125011 0.5123550 0.5120462 0.5120713 0.5119269
## [22] 0.5122088 0.5117064 0.5128408 0.5115250 0.5124656 0.5118474 0.5121866
## [29] 0.5130068 0.5129073 0.5133154 0.5126337 0.5124973 0.5127013 0.5133340
## [36] 0.5130513 0.5127982 0.5128057 0.5128266 0.5126110 0.5128692 0.5125792
## [43] 0.5123372 0.5126648 0.5122425 0.5126849 0.5124035 0.5121951 0.5121931
## [50] 0.5121286 0.5121179 0.5112054 0.5121992 0.5121845 0.5116894 0.5119398
## [57] 0.5114951 0.5116337 0.5115255 0.5119072 0.5117182 0.5111665 0.5117154
total <- present$boys / (present$boys + present$girls)
plot(x = present$year, y = total, type = "l", main = "Total male births in the US, 1940-2002", xlab = "Year", ylab = "Total births")

sum(present$boys > present$girls)
## [1] 63

Written answer:There more males births in 63 of the years which is odd because going from 1940 to 2002 should only add up to 62 years. This could mean there’s a small error like an extra row that’s gone unaccounted for, but nonetheless; the data seems to imply that in every year, there was a greater proportion of boys born in the recent US. This a sharp contrast to historical Britain where in every year, there was a greater proportion of girls born every year.

Teamwork report

Team member Attendance Author Contribution %
William Aranda Yes Yes 20%
Mohammad Kaleed Yes No 20%
Elsa Rodriguez Yes No 20%
Ryan Penor Yes No 20%
Ishta Patel Yes No 20%
Total 100% 100%