Data 606 - Lab 1

Shamecca Marshall

1. Taking a peek at the data and viewing it.

data('arbuthnot', package='openintro')

2. View the dimensions, names of the variables, and the first few observations of this data frame.

glimpse(arbuthnot)

## Rows: 82
## Columns: 3
## $ year  <int> 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639…
## $ boys  <int> 5218, 4858, 4422, 4994, 5158, 5035, 5106, 4917, 4703, 5359, 5366…
## $ girls <int> 4683, 4457, 4102, 4590, 4839, 4820, 4928, 4605, 4457, 4952, 4784…

3. Number of boys baptized each year.

arbuthnot$boys

##  [1] 5218 4858 4422 4994 5158 5035 5106 4917 4703 5359 5366 5518 5470 5460 4793
## [16] 4107 4047 3768 3796 3363 3079 2890 3231 3220 3196 3441 3655 3668 3396 3157
## [31] 3209 3724 4748 5216 5411 6041 5114 4678 5616 6073 6506 6278 6449 6443 6073
## [46] 6113 6058 6552 6423 6568 6247 6548 6822 6909 7577 7575 7484 7575 7737 7487
## [61] 7604 7909 7662 7602 7676 6985 7263 7632 8062 8426 7911 7578 8102 8031 7765
## [76] 6113 8366 7952 8379 8239 7840 7640

length(arbuthnot$boys)

## [1] 82

4.Number of girls baptized each year.

Answer: 82

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

length(arbuthnot$girls)

## [1] 82

5. Plot graph of the number of girls baptized per year

ggplot(data = arbuthnot, aes(x = year, y = girls)) + 
  geom_point(color = "hotpink1")

6. Line graph of the number of girls baptized per year

ggplot(data = arbuthnot, aes(x = year, y = girls)) + 
  geom_line(color = "hotpink3")

Question: 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: There was an increase of girls being baptized from 1660 to the 1700s causing a positive linear.

7. Using R as a big calculator

5218 + 4683

## [1] 9901

8. Calculating the total of baptisms

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

9. Adding a new variable to the data frame

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

10. Creating line graph for total number of baptisms per year

Answer: There was an increase of boys being baptized from 1660 to the 1700s causing a positive linear.

ggplot(data = arbuthnot, aes(x = year, y = total)) +
  geom_line(color ="maroon1")

11. Computing the ratio of the number of boys to the number of girls baptized in 1629.

5218 / 4683

## [1] 1.114243

arbuthnot$boys / arbuthnot$girls

##  [1] 1.114243 1.089971 1.078011 1.088017 1.065923 1.044606 1.036120 1.067752
##  [9] 1.055194 1.082189 1.121656 1.034884 1.051923 1.112016 1.038120 1.027521
## [17] 1.032661 1.109867 1.073529 1.057215 1.121267 1.061719 1.137676 1.107290
## [25] 1.080095 1.082416 1.091371 1.084565 1.032533 1.047793 1.153901 1.146905
## [33] 1.156075 1.085988 1.108584 1.063369 1.052697 1.083121 1.055242 1.092266
## [41] 1.116143 1.097744 1.064016 1.052778 1.043112 1.065354 1.059647 1.120575
## [49] 1.035467 1.088679 1.034100 1.039530 1.044237 1.024466 1.058536 1.062860
## [57] 1.032846 1.064054 1.072498 1.054359 1.060974 1.083128 1.036526 1.039092
## [65] 1.025792 1.050850 1.081931 1.055748 1.037981 1.104904 1.061594 1.073219
## [73] 1.078254 1.048981 1.010673 1.065354 1.075460 1.072132 1.090022 1.080808
## [81] 1.062331 1.048299

12. Computing the proportion of newborns that are boys in 1629.

5218 / (5218 + 4683)

## [1] 0.5270175

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

15. Generating a plot of the proportion of boys born over time. What do you see?

Answer: There was an increase in boys being born in the sixties, then decline in late seventies and an increase again in the eighties.

ggplot(data = present, aes(x = year, y = boys)) + geom_line(color = "palevioletred1") + geom_point(color = "tomato1")