R - graph frequency of observations over time with small value range

Question

I'd trying to graph the frequency of observations over time. I have a dataset where hundreds of laws are coded 0-3. I'd like to know if outcomes 2-3 are occurring more often as time progresses. Here is a sample of mock data:

Data <- data.frame(
  year = sample(1998:2004, 200, replace = TRUE),
  score = sample(1:4, 200, replace = TRUE)
)

If i plot

plot(Data$year, Data$score)

I get a checkered matrix where every single spot is filled in, but I can't tell which numbers occur more often. Is there a way to color or to change the size of each point by the number of observations of a given row/year?

A few notes may help in answering the question:

1). I don't know how to sample data where certain numbers occur more frequently than others. My sample procedure samples equally from all numbers. If there is a better way I should have created my reproducible data to reflect more observations in later years, I would like to know how.

2). this seemed like it would be best to visualize in a scatter plot, but I could be wrong. I'm open to other visualizations.

Thanks!

Answer 1

Here's how I would approach this (hope this is what you need)

Create the data (Note: when using sample in questions, always use set.seed too so it will be reproducible)

set.seed(123)
Data <- data.frame(
  year = sample(1998:2004, 200, replace = TRUE),
  score = sample(1:4, 200, replace = TRUE)
)

Find frequncies of score per year using table

Data2 <- as.data.frame.matrix(table(Data))
Data2$year <- row.names(Data2)

Use melt to convert it back to long format

library(reshape2)
Data2 <- melt(Data2, "year")

Plot the data while showing different color per group and relative size pre frequency

library(ggplot2)
ggplot(Data2, aes(year, variable, size = value, color = variable)) +
  geom_point()

Alternatively, you could use both fill and size to describe frequency, something like

ggplot(Data2, aes(year, variable, size = value, fill = value)) +
  geom_point(shape = 21)

Answer 2

Here's another approach:

ggplot(Data, aes(year)) + geom_histogram(aes(fill = ..count..)) + facet_wrap(~ score)

Each facet represents one "score" value, as noted in the title of each facet. You can easily get a feeling for the counts by looking at the hight of the bars + the colour (lighter blue indicating more counts).

---

Of course you could also do this only for the score %in% 2:3, if you don't want score 1 and 4 included. In such a case, you could do:

ggplot(Data[Data$score %in% 2:3,], aes(year)) + 
     geom_histogram(aes(fill = ..count..)) + facet_wrap(~ score)