Lab 1: Intro to R

library(tidyverse)
library(openintro)

The data set

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

We can view the data by typing its name into the console.

arbuthnot

## # A tibble: 82 × 3
##     year  boys girls
##    <int> <int> <int>
##  1  1629  5218  4683
##  2  1630  4858  4457
##  3  1631  4422  4102
##  4  1632  4994  4590
##  5  1633  5158  4839
##  6  1634  5035  4820
##  7  1635  5106  4928
##  8  1636  4917  4605
##  9  1637  4703  4457
## 10  1638  5359  4952
## # … with 72 more rows

The following code displays the dimensions of this data frame as well as the names of the variables and the first few observations by

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…

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

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

Exercise 1

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

sum(arbuthnot$girls)

## [1] 453841

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

Exercise 2

Question: Is there an apparent trend in the number of girls baptized over the years? How would you describe it?

Answer: The number of girls baptized increased each year since 1660

5218 + 4683

## [1] 9901

to see the total number of baptisms in 1629. We could repeat this once for each year, but there is a faster way. If we add the vector for baptisms for boys to that of girls, R will compute all sums simultaneously.

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

Adding a new variable to the data frame

We’ll be using this new vector to generate some plots, so we’ll want to save it as a permanent column in our data frame.

You can make a line plot of the total number of baptisms per year with the command

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

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

5218 / 4683

## [1] 1.114243

arbuthnot <- arbuthnot %>%
  mutate(boy_to_girl_ratio = boys / girls)

5218 / (5218 + 4683)

## [1] 0.5270175

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

Exercise 3

Question: Generate a plot of the proportion of boys born over time. What do you see?

Answer: The number of boys baptized had a sudden drop in 1950 however the number bof baptized steadily increased each year after 1660 with a brief drop in baptism in 1972

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

ggplot(data = prop_boys , aes(x = year, y = total)) + geom_line() + geom_point()

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

Exercise 4

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

print('Years included in the  present day birth records in the United States data set')

## [1] "Years included in the  present day birth records in the United States data set"

present$year

##  [1] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954
## [16] 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969
## [31] 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
## [46] 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
## [61] 2000 2001 2002

print('Dimensions of the data frame')

## [1] "Dimensions of the data frame"

dim(present)

## [1] 63  3

print('Variable (column) names')

## [1] "Variable (column) names"

colnames(present)

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

Exercise 5

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

Answer: The two data sets being compared are not of the same magnitude. The years being compare are different. Arbuthnot’s being in the 16th and 17th centuries while present is in the 19th and 20th centuries. Present’s counts also exceeds arbuthnot’s by far.

print('arbuthnot counts')

## [1] "arbuthnot counts"

sapply(arbuthnot, range)

##      year boys girls total boy_to_girl_ratio boy_ratio
## [1,] 1629 2890  2722  5612          1.010673 0.5026541
## [2,] 1710 8426  7779 16145          1.156075 0.5361942

print('present day birth record counts')

## [1] "present day birth record counts"

sapply(present, range)

##      year    boys   girls
## [1,] 1940 1211684 1148715
## [2,] 2002 2186274 2082052

Exercise 6

Question: 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. Hint: You should be able to reuse your code from Exercise 3 above, just replace the dataframe name.

Answer: There was a steady increase in births from 1940 to 1960. Arbuthnot’s observation about boys being born in greater proportion than girls hold up in the U.S is true.

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

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

Exercise 7

Question: In what year did we see the most total number of births in the U.S.? Hint: First calculate the totals and save it as a new variable. Then, sort your dataset in descending order based on the total column. You can do this interactively in the data viewer by clicking on the arrows next to the variable names. To include the sorted result in your report you will need to use two new functions: arrange (for sorting). We can arrange the data in a descending order with another function: desc (for descending order). The sample code is provided below.

Answer: 1961 was the year with most births in the US

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