R - compare multiple columns and create new columns indicating matches - r

Question

I'd like to know how I can compare multiple columns to the values in a single column, then use those matches to create a table of differences. I have a political dataset of policy outcomes, and whether certain organizations supported or opposed those outcomes, by year. Here's some mock data:
Outcome 0 means the law never happened, outcome 1 means it happened.
For organizations, a negative number means they opposed the law and positive means they supported it:
set.seed(123)
Data <- data.frame(
year = sample(1998:2004, 200, replace = TRUE),
outcome = sample(0:1, 200, replace = TRUE),
union = sample(-1:1, 200, replace = TRUE),
chamber = sample(-1:1, 200, replace = TRUE),
pharma = sample(-1:1, 200, replace = TRUE),
gun = sample(-1:1, 200, replace = TRUE),
dem = sample(-1:1, 200, replace = TRUE),
repub = sample(-1:1, 200, replace = TRUE)
)
I would like to know how many times an organization matched the support or opposition of the union, per year.
I imagine its going to be some table like this, where a match equals 1 and otherwise -1 (there are also many NAs in the data were organizations take no position):
DATA$contra <- ifelse(DATA$union == page.bin$chamber, 1, -1)
In the dataset, there's about 50 organizations in consecutive columns. It seems unwieldy to create 50 new columns, one for each match. Even if that is the best way to do it, I don't know how to apply the function to create 50 new columns.
Eventually, I'd like to create a heatmap or a way to visualize which organizations match the union column. But, first, I think, I need some kind of table of data.
Thanks for your help!

Answer 1

When you say "I would like to know how many times an organization matched the support or opposition of the union, per year." then I'm assuming that you want the net number of agreement, i.e. that a 1/1 vote or a -1/-1 vote pairing occurred and that from that you want subtracted the number of disagreement, and do not care about the number of times one of the votes was 0.
Before running your code I used set.seed(123) so there could be reproducibility:
> head(Data)
year outcome union chamber pharma gun dem repub
1 2000 0 1 -1 0 -1 1 -1
2 2003 1 -1 1 0 0 1 -1
3 2000 1 1 -1 -1 -1 0 -1
4 2004 1 0 -1 -1 1 1 0
5 2004 0 0 -1 -1 1 0 -1
6 1998 1 0 1 1 0 1 1
> head( Data[-(1:3)] * Data[[3]])
chamber pharma gun dem repub
1 -1 0 -1 1 -1
2 -1 0 0 -1 1
3 -1 -1 -1 0 -1
4 0 0 0 0 0
5 0 0 0 0 0
6 0 0 0 0 0
This makes 1/1 and -1/-1 pairings be all ==1 and -1/1 and 1/-1 pairings ==-1 and others ==0. Now one can aggregate this by year:
> head( aggregate( Data[-(1:3)] * Data[[3]], Data[1], sum) )
year chamber pharma gun dem repub
1 1998 0 -2 1 2 6
2 1999 0 0 2 4 3
3 2000 -3 2 -3 -4 -11
4 2001 2 3 2 9 1
5 2002 0 -1 7 9 1
6 2003 0 -2 -11 5 -2
If instead you only wanted the sum of only the agreements it would be:
> aggregate( Data[-(1:3)] * Data[[3]], Data[1], function(x) {sum(x==1)} )
year chamber pharma gun dem repub
1 1998 5 4 5 7 9
2 1999 8 7 7 9 9
3 2000 5 8 5 3 3
4 2001 7 9 7 11 4
5 2002 7 6 11 12 9
6 2003 7 5 1 8 5
7 2004 4 4 9 2 4

Answer 2

Using dplyr
library(dplyr)
Data %>%
select(-outcome) %>%
group_by(year, union) %>%
mutate_each(funs(union * .)) %>%
group_by(year) %>%
summarise_each(funs(sum(. == 1)), -union)
You get:
Source: local data frame [7 x 6]
year chamber pharma gun dem repub
1 1998 5 4 5 7 9
2 1999 8 7 7 9 9
3 2000 5 8 5 3 3
4 2001 7 9 7 11 4
5 2002 7 6 11 12 9
6 2003 7 5 1 8 5
7 2004 4 4 9 2 4
Using gather() from tidyr to get data in a tall format and ggvis heatmap
library(dplyr)
library(tidyr)
library(ggvis)
Data %>%
select(-outcome) %>%
group_by(year, union) %>%
mutate_each(funs(union * .)) %>%
group_by(year) %>%
summarise_each(funs(sum(. == 1)), -union) %>%
gather(org, value, -year) %>%
mutate(org = as.factor(org), year = as.factor(year)) %>%
ggvis(~year, ~org, fill=~value) %>%
layer_rects(width = band(), height = band()) %>%
layer_text(
x = prop("x", ~year, scale = "xcenter"),
y = prop("y", ~org, scale = "ycenter"),
text:=~value, fontSize := 14, fill:="white",
baseline:="middle", align:="center") %>%
scale_nominal("x", padding = 0, points = FALSE) %>%
scale_nominal("y", padding = 0, points = FALSE) %>%
scale_nominal("x", name = "xcenter", padding = 1, points = TRUE) %>%
scale_nominal("y", name = "ycenter", padding = 1, points = TRUE) %>%
hide_legend("fill")

Answer 3

Maybe the following helps. First, you create a new data frame that contains for each organisation and each row whether the support matched the union:
match.union <- data.frame(year=Data$year,
lapply(Data[,4:ncol(Data)],function(col) col==Data$union))
It is important to add the column with the year for the next step, which is to sum up the number of agreements with the union per year:
aggregate(.~year,match.union,sum)
The output I get from this is
year chamber pharma gun dem repub
1 1998 11 9 10 9 7
2 1999 10 8 16 9 14
3 2000 8 9 8 7 12
4 2001 7 9 10 9 13
5 2002 11 12 11 13 8
6 2003 5 7 8 5 6
7 2004 13 13 15 15 10