Median prices of single-family homes in 65 metropolitan statistical areas
Data for Statistical Insight Chapter 5
Homes
A data frame/tibble with 65 observations on the four variables
a character variable with values Akron OH,
Albuquerque NM, Anaheim CA, Atlanta GA, Baltimore
MD, Baton Rouge LA, Birmingham AL, Boston MA,
Bradenton FL, Buffalo NY, Charleston SC, Chicago
IL, Cincinnati OH, Cleveland OH, Columbia SC,
Columbus OH, Corpus Christi TX, Dallas TX,
Daytona Beach FL, Denver CO, Des Moines IA,
Detroit MI, El Paso TX, Grand Rapids MI,
Hartford CT, Honolulu HI, Houston TX,
Indianapolis IN, Jacksonville FL, Kansas City MO,
Knoxville TN, Las Vegas NV, Los Angeles CA,
Louisville KY, Madison WI, Memphis TN, Miami FL,
Milwaukee WI, Minneapolis MN, Mobile AL,
Nashville TN, New Haven CT, New Orleans LA, New
York NY, Oklahoma City OK, Omaha NE, Orlando FL,
Philadelphia PA, Phoenix AZ, Pittsburgh PA,
Portland OR, Providence RI, Sacramento CA, Salt
Lake City UT, San Antonio TX, San Diego CA, San
Francisco CA, Seattle WA, Spokane WA, St Louis MO,
Syracuse NY, Tampa FL, Toledo OH, Tulsa OK, and
Washington DC
a character variable with values Midwest, Northeast,
South, and West
a factor with levels 1994 and 2000
median house price (in dollars)
National Association of Realtors.
Kitchens, L. J. (2003) Basic Statistics and Data Analysis. Pacific Grove, CA: Brooks/Cole, a division of Thomson Learning.
tapply(Homes$price, Homes$year, mean)
tapply(Homes$price, Homes$region, mean)
p2000 <- subset(Homes, year == "2000")
p1994 <- subset(Homes, year == "1994")
## Not run:
library(dplyr)
library(ggplot2)
dplyr::group_by(Homes, year, region) %>%
summarize(AvgPrice = mean(price))
ggplot2::ggplot(data = Homes, aes(x = region, y = price)) +
geom_boxplot() +
theme_bw() +
facet_grid(year ~ .)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.