On Your Own

In the previous few pages, you recreated some of the displays and preliminary analysis of Arbuthnot’s baptism data. Your assignment involves repeating these steps, but for present day birth records in the United States. Load up the present day data with the following command.

source("more/present.R")

The data are stored in a data frame called present.

What years are included in this data set?

present$year
##  [1] 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953
## [15] 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967
## [29] 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981
## [43] 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
## [57] 1996 1997 1998 1999 2000 2001 2002

What are the dimensions of the data frame?

    dim(present)
## [1] 63  3

What are the variable or column names?

names(present)
## [1] "year"  "boys"  "girls"

How do these counts compare to Arbuthnot’s? Are they on a similar scale?

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

Make a plot that displays the boy-to-girl ratio for every year in the data

set. 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.
plot(present$year, (present$boys/(present$boys + present$girls)),type = 'l')

In what year did we see the most total number of births in the U.S.? You can

refer to the help files or the R reference card 
[http://cran.r-project.org/doc/contrib/Short-refcard.pdf](http://cran.r-project.org/doc/contrib/Short-refcard.pdf)
to find helpful commands.
subset(present$year, present$boys + present$girls == max(present$boys + present$girls)) 
## [1] 1961

These data come from a report by the Centers for Disease Control

http://www.cdc.gov/nchs/data/nvsr/nvsr53/nvsr53_20.pdf. Check it out if you would like to read more about an analysis of sex ratios at birth in the United States.

That was a short introduction to R and RStudio, but we will provide you with more functions and a more complete sense of the language as the course progresses. Feel free to browse around the websites for R and RStudio if you’re interested in learning more, or find more labs for practice at http://openintro.org.

This is a product of OpenIntro that is released under a Creative Commons Attribution-ShareAlike 3.0 Unported. This lab was adapted for OpenIntro by Andrew Bray and Mine Çetinkaya-Rundel from a lab written by Mark Hansen of UCLA Statistics.