R - efficiently organize tables on condition over time - r

Question

I'd like to know how to organize a data.frame into tables on conditions over time. I have a politics data set where certain organizations take a position on a bill and whether the bill passed or failed, over the last few decades.
I know how to organize the data individually into tables, but I do it one-by-one, and its really hard to see the trends. The stackoverflow community always seems to have ingenious ways of grouping data. Here's some mock data:
Data <- data.frame(
year = sample(1998:2004, 200, replace = TRUE),
outcome = sample(0:1, 200, replace = TRUE),
biz1 = sample(-2:2, 200, replace = TRUE),
biz2 = sample(-2:2, 200, replace = TRUE),
biz3 = sample(-2:2, 200, replace = TRUE)
)
In biz, a negative number means they oppose the outcome and a positive outcome means they support it. In outcome, a zero means the law did not pass, a 1 means that it did.
I would like to use tables to see how each business has become more or less successful over time, by looking at how their positive numbers match 1s and negative numbers match 0s, compared to ever other organization (and vice verse with positive matching the number of negative numbers).
A few notes
In the data set, I have about 100 businesses as columns, so I definitely need an efficient way to make the tables without naming every single column. I can select them in a range, like 125:300, since they are ordered together.
Of course i'm open to all ideas! Feel free to list any other ways of looking at this.
If i failed to ask this question right, please let me know how I could improve it.

Answer 1

The comments above about your question being too vague are right on target. Having said that this interests me and the vagueness leaves me free to interpret...
First, I'd recode the outcome as -1 if the bill fails. Then ourtcome * bizn is in a sense a success score for that business on that legislation: positive if either a bill that the business supported passed, or if a bill that the business opposed failed. Then there are several ways to visualize the scores. Here are just a few to get you started.
# re-code outcomes
Data$outcome <- ifelse(Data$outcome==0,-1,1)
library(reshape2) # for melt(...)
library(ggplot2)
gg <- melt(Data, id=c("year","outcome"),
variable.name="business", value.name="support")
gg$score <- with(gg,outcome*support) # score represents level of success
# mean success vs. year with +/- 1 sd
ggplot(gg,aes(x=year,y=score, color=business))+
stat_summary(fun.data="mean_sdl")+
stat_summary(fun.y=mean,geom="line")+
facet_grid(business~.)
# boxplot of success scores
ggplot(gg,aes(x=factor(year),y=score))+
geom_boxplot(aes(fill=business))+
facet_grid(business~.)
# barplot of success/failure frequencies
# excludes cases where a business did not take a position pro or con
gg.bar <- aggregate(score~year+business,gg,
function(eff)c(success=sum(eff>0),failure=sum(eff<0)))
gg.bar <- data.frame(gg.bar[1:2],gg.bar$score)
ggplot(gg.bar,aes(x=factor(year)))+
geom_bar(aes(y=success,fill="success"),stat="identity")+
geom_bar(aes(y=-failure,fill="failure"),stat="identity")+
geom_hline(xintercept=0,linetype=2,color="blue")+
scale_fill_discrete(name="",breaks=c("success","failure"))+
labs(x="",y="frequency")+
facet_grid(business~.)
All of these represent rather simplistic ways of looking at the data. If this was a serious project I would probably run a principal components analysis on the businesses to identify groups of businesses that tend to support or oppose the same legislation. Then I'd run a cluster analysis on the principal components to identify groups of legislation that tend to attract the support or opposition of groups of businesses.
Another way to approach this would be to run a logistic regression on the outcomes using the support/opposition of the various businesses as predictors. This would tell you which businesses tend to be more influential.