How to index a loop by year in dataset - r

Question

My dataset looks like this:
Year Risk Resource Utilization Band Percent
2014 0 .25
2014 1 .19
2014 2 .17
2014 3 .31
2014 4 .06
2014 5 .01
2015 0 .23
2015 1 .21
2015 2 .19
2015 3 .31
2015 4 .06
2015 5 .31
I am attempting to compare percentage change year to year for the dataset I am working with. For example 2014 decreased 2% in 2015. So far, I have created a loop that puts each by year into bins and runs the calculation. The issue I am having is that the loop is indexing each loop as 1 so I have a bunch of repeating 1s next to my calculations. Here is the code I have been using, any help is much appreciated
Results.data <- data.frame()
head(data)
percent <- 0
baseyear <- 0
nextyear <- 0
bin <- 0
yearPlus1 <-0
bin2 <-0
percent1 <-0
percent2 <-0
percentDif <-0
for(i in 1:nrow(data))
{
percent[i] <- data$PERCENT[i]
baseyear[i] <- as.numeric(data$YEAR_RISK[i])
bin[i] <- as.numeric(data$RESOURCE_UTILIZATION_BAND[i])
#print(percent[i])
#print(baseyear[i])
#print(bin[i])
}
for (k in 1:nrow(data))
{
for (j in 1:nrow(data))
{
yearPlus1 <- as.numeric(baseyear[j])-1
firstYear <- as.numeric(baseyear[k])
bin2 <-bin[j]
bin1 <- bin[k]
percent1 <- as.numeric(percent[k])
percent2 <- as.numeric(percent[j])
if(firstYear==yearPlus1 && bin1==bin2)
{
percentDif <- percent2 - percent1
print(percentDif)
Results.data <- rbind(Results.data, c(percentDif))
}
}
}

Answer 1

If I understand your question, you can use grouping and vectorization to avoid loops. Here's an example using the dplyr package.
The code below first sorts by Year_Risk so that the data are ordered properly by time. Then we group by Resource_Utilization_Band so that we can get results separately for each level of Resource_Utilization_Band. Finally, we calculate the difference in Percent from year to year. The lag function returns the previous value in a sequence. (Instead of lag, we could have done Change = c(NA, diff(Percent)) as well.) All of these operations are chained one after the other using the dplyr chaining operator (%>%).
(Note that when I imported your data, I also changed your column names by adding underscores to make them legal R column names.)
library(dplyr)
# Year-over-year change within each Resource_Utilization_Band
# (Assuming your starting data frame is called "dat")
dat %>% arrange(Year_Risk) %>%
group_by(Resource_Utilization_Band) %>%
mutate(Change = Percent - lag(Percent))
Year_Risk Resource_Utilization_Band Percent Change
1 2014 0 0.25 NA
2 2014 1 0.19 NA
3 2014 2 0.17 NA
4 2014 3 0.31 NA
5 2014 4 0.06 NA
6 2014 5 0.01 NA
7 2015 0 0.23 -0.02
8 2015 1 0.21 0.02
9 2015 2 0.19 0.02
10 2015 3 0.31 0.00
11 2015 4 0.06 0.00
12 2015 5 0.31 0.30