Lab 1: Intro to R

library(tidyverse)
library(openintro)

Exercise 1

Extract counts of girls baptized

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

Data Visualization

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

Plot with line

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

Exercise 2

Apparent trend in the number of girls baptized

# Adding new variable to the data frame

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

# Plot for the trend

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

# Create two new variables on data frame

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

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

Exercise 3

Plot proportion of boys over time

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

Add a data frame with logical data

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

Find min and max values of columns

arbuthnot %>%
  summarise(min = min(boys), max = max(boys))

## # A tibble: 1 x 2
##     min   max
##   <int> <int>
## 1  2890  8426

Exercise 4

Explore new data frame

# Show years included in the 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

The present data set includes years from 1940 to 2002.

# Dimensions of data frame
dim.data.frame(present)

## [1] 63  3

# Retrieve columns names
colnames(present)

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

Exercise 5

Compare Arbuthnot’s and present data set counts.

# Arbuthnot's dim

arbuthnot$year

##  [1] 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643
## [16] 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658
## [31] 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673
## [46] 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688
## [61] 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703
## [76] 1704 1705 1706 1707 1708 1709 1710

# Arbuthnot dim

dim.data.frame(arbuthnot)

## [1] 82  7

Arbuthnot data set includes year from 1629 to 1710. Thus, Arbuthnot data set has 81 different years while The present data set has 62 different years.

They are not of a similar magnitude. Arbuthnot data set is 19 years longer than present data set.

Exercise 6

Proportion of boys over time.

# Create 2 new variables in present data set

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

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

# Plot proportion of boys born over time for present data set

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

The proportion of born over time declines from 1940 to late 60’s then increases a bit between late 60’s to mid 70’s, then start decreasing again. Overall, we can say that the proportion of boys over time decreases.

Boys to girls ratio in US

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

present$more_boys

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE

As we can see, that is true. Arbuthnot’s observation about boys being born in greater proportion than girls holds up in the U.S as well.

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

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

Exercise 7

Most number of birth in U.S

present %>%
  arrange(desc(total))

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

As we can see from the above, the most total number of births in U.S was in 1961.