Election results for the 2010 U.S. House of Represenatives races

Usage

houserace10

Format

A data frame with 435 observations on the following 24 variables.

id: Unique identifier for the race, which does not overlap with other 2010 races (see govrace10 and senaterace10)
state: State name
abbr: State name abbreviation
num: District number for the state
name1: Name of the winning candidate
perc1: Percentage of vote for winning candidate (if more than one candidate)
party1: Party of winning candidate
votes1: Number of votes for winning candidate
name2: Name of candidate with second most votes
perc2: Percentage of vote for candidate who came in second
party2: Party of candidate with second most votes
votes2: Number of votes for candidate who came in second
name3: Name of candidate with third most votes
perc3: Percentage of vote for candidate who came in third
party3: Party of candidate with third most votes
votes3: Number of votes for candidate who came in third
name4: Name of candidate with fourth most votes
perc4: Percentage of vote for candidate who came in fourth
party4: Party of candidate with fourth most votes
votes4: Number of votes for candidate who came in fourth
name5: Name of candidate with fifth most votes
perc5: Percentage of vote for candidate who came in fifth
party5: Party of candidate with fifth most votes
votes5: Number of votes for candidate who came in fifth

Source

MSNBC.com, retrieved 2010-11-09.

Details

This analysis in the Examples section was inspired by and is similar to that of Nate Silver's district-level analysis on the FiveThirtyEight blog in the New York Times: https://fivethirtyeight.com/features/2010-an-aligning-election/

Examples


hr <- table(houserace10[, c("abbr", "party1")])
nr <- apply(hr, 1, sum)

pr <- prrace08[prrace08$state != "DC", c("state", "p_obama")]
hr <- hr[as.character(pr$state), ]
(fit <- glm(hr ~ pr$p_obama, family = binomial))
#> 
#> Call:  glm(formula = hr ~ pr$p_obama, family = binomial)
#> 
#> Coefficients:
#> (Intercept)   pr$p_obama  
#>      -5.726        0.103  
#> 
#> Degrees of Freedom: 49 Total (i.e. Null);  48 Residual
#> Null Deviance:	    107.4 
#> Residual Deviance: 48.72 	AIC: 138.7

x1 <- pr$p_obama[match(houserace10$abbr, pr$state)]
y1 <- (houserace10$party1 == "Democrat") + 0
g <- glm(y1 ~ x1, family = binomial)


x <- pr$p_obama[pr$state != "DC"]
nr <- apply(hr, 1, sum)
plot(x, hr[, "Democrat"] / nr,
  pch = 19, cex = sqrt(nr), col = "#22558844",
  xlim = c(20, 80), ylim = c(0, 1),
  xlab = "Percent vote for Obama in 2008",
  ylab = "Probability of Democrat winning House seat"
)
X <- seq(0, 100, 0.1)
lo <- -5.6079 + 0.1009 * X
p <- exp(lo) / (1 + exp(lo))
lines(X, p)
abline(h = 0:1, lty = 2, col = "#888888")