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

There is an apparent upward trend in the number of baptized girls per year, there was a marked decline between 1640 and 1660 and several drops along the way, the most marked one in the early 1700s. The overall trend is that more girls get baptized each year,

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

Exercise 3

When we plot to see the proportion of newborns that were boys we see that, for these years, the proportion of boys ranges between 51 and 53% which seems to indicate that more boys than girls were born during these years.

# Add a total column to the data
arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)

#Calculate the boy to girl ratio per year
arbuthnot <- arbuthnot %>%
  mutate(boy_to_girl_ratio = boys / girls)

#calculate the boy ratio per year
arbuthnot <- arbuthnot %>%
  mutate(boy_ratio = boys / total)

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

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

Exercise 4

The present data goes from 1941 to 2002. We have three variables: year, boys and girls.

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

## # A tibble: 1 x 2
##   minYear maxYear
##     <dbl>   <dbl>
## 1    1940    2002

glimpse(present)

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

Exercise 5

The present numbers are of a significant greater magnitude than the ones in arbuthnot. The arbuthnot dataframe is also longer as it spans about 20 more years.

arbuthnot$total 

##  [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$boys+present$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

Exercise 6

It appears that arbuthnot observation holds true that boys are born in a greater proportion than girls. Even tho the ratio is declining over time, boys are born about 51% of the time.

# Insert code for Exercise 6 here
# Add a total column to the data
present <- present %>%
  mutate(total = boys + girls)

#Calculate the boy to girl ratio per year
present <- present %>%
  mutate(boy_to_girl_ratio = boys / girls)

#calculate the boy ratio per year
present <- present %>%
  mutate(boy_ratio = boys / total)

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

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

Exercise 7

In 1961 we saw the greatest total number of births.

# I calculated the total in the previous exercise.
# If we need to calculate it here, simply uncomment the next two lines 
# present <- present %>%
#  mutate(total = boys + girls)
present %>%
  arrange(desc(total))

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