daRoll | R Documentation |
This data set contains the voting records for the Healthy Forests Restoration Act in 2003, as used in Sun (2006). The characteristics of individual congressmen are also included. There are 537 observations and 22 variables.
state | state name for a congressman |
district | district for a congressman; 0 for senators |
name | Family name of a congressman |
voteMay | voting record in May 2003 in the House; 1 if yes, 0 if no, and NA if not voted |
voteNov | voting record in Nov 2003 in both the House and Senate |
RepParty | Dummy equals one if Republican |
East | Regional dummy for 11 northeastern states |
West | Regional dummy for 11 western states |
South | Regional dummy for 13 southern states |
PopDen | Population density - 1000 persons per km2 |
PopRural | Population density per km2 |
Edu | Percentage of population over 25 with a Bachelor's degree |
Income | Median family income ($1,000) |
FYland | Percentage of federal lands in total forestlands 2002 |
Size | Value of shipments of forest industry 1997 (million dollars) |
ContrFY | Contribution from forest firms (1,000 dollars) |
ContrEN | Contribution from environmental groups (1,000 dollars) |
Sex | Dummy equals one if male |
Lawyer | Dummy equals one if lawyer |
Member | Dummy equals one if a committee member for the HFRA |
Year | Number of years in the position |
Chamber | Dummy equals one if House and zero if Senate |
data(daRoll)
A data frame object with 537 rows and 22 variables. This is a cross-sectional dataset that are generating from merging several raw datasets.
This is the combinded final data set used in the study of Sun (2006).
See Table 1 in Sun (2006) for detail.
Sun, C. 2006. A roll call analysis of the Healthy Forests Restoration Act and constituent interests in fire policy. Forest Policy and Economics 9(2):126-138.
glm
; maBina
.
# generate four datasets used in Sun (2006)
data(daRoll)
xn <- c('RepParty', 'East', 'West', 'South', 'PopDen',
'PopRural', 'Edu', 'Income', 'FYland', 'Size',
'ContrFY', 'ContrEN', 'Sex', 'Lawyer', 'Member', 'Year', 'Chamber')
f1 <- daRoll[!is.na(daRoll$voteMay), c('voteMay', xn)]
f2 <- daRoll[!is.na(daRoll$voteNov) & daRoll$Chamber == 1, c('voteNov', xn)]
f3 <- daRoll[!is.na(daRoll$voteNov), c('voteNov', xn)]
f4 <- daRoll[!is.na(daRoll$voteNov) & daRoll$RepParty == 0, c('voteNov', xn)]
rownames(f1) <- 1:nrow(f1); rownames(f2) <- 1:nrow(f2)
rownames(f3) <- 1:nrow(f3); rownames(f4) <- 1:nrow(f4)
colnames(f1)[1] <- colnames(f2)[1] <- 'Vote'
colnames(f3)[1] <- colnames(f4)[1] <- 'Vote'
dim(f1); dim(f2); dim(f3); dim(f4)
tail(f3)
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