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I have a dataframe comprising two columns, 'host', and 'date'; which describes a series of cyber attacks against a number of different servers on specific dates over a seven month period.

Here's what the data looks like,

> china_atks %>% head(100) host date 1 groucho-oregon 2013-03-03 2 groucho-oregon 2013-03-03 ... 46 groucho-singapore 2013-03-03 48 groucho-singapore 2013-03-04 ...

Where 'groucho-oregon', 'groucho-signapore', etc., is the hostname of the server targeted by an attack.

There are around 190,000 records, spanning 03/03/2013 to 08/09/2013, e.g.

> unique(china_atks$date) [1] "2013-03-03" "2013-03-04" "2013-03-05" "2013-03-06" "2013-03-07" "2013-03-08" "2013-03-09" [8] "2013-03-10" "2013-03-11" "2013-03-12" "2013-03-13" "2013-03-14" "2013-03-15" "2013-03-16" [15] "2013-03-17" "2013-03-18" "2013-03-19" "2013-03-20" "2013-03-21" "2013-03-22" "2013-03-23" ...

I'd like to create a multi-line time series chart that visualises how many attacks each individual server received each day over the range of dates, but I can't figure out how to pass the data to ggplot to achieve this. There are nine unique hostnames, and so the chart would show nine lines.

Thanks!

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  • You dont have the number of attacks in the data, if yes where is it? Commented Apr 1, 2018 at 19:43
  • Each row appears to be a separate observation, so the number of attacks could be calculated by summarising this. Commented Apr 1, 2018 at 19:47
  • No the number of attacks will have to be counted in the actual data frame itself -- should this be my first step? Commented Apr 1, 2018 at 19:48
  • stackoverflow.com/q/22767893/7347699 Commented Apr 1, 2018 at 19:49
  • 1
    Please add more dates to the sample data above. Commented Apr 1, 2018 at 19:52

2 Answers 2

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3

Here's one way to do this.

First Summarize the count frequency by date.

library(plyr) df <- plyr::count(da,c("host", "date"))

Then Do the plotting.

ggplot(data=df, aes(x=date, y=freq, group=1)) + geom_line(aes(color = host))

Data

 da <- structure(list(host = structure(1:4, .Label = c("groucho-eu", "groucho-oregon", "groucho-singapore", "groucho-tokyo"), class = "factor"), date = structure(c(1L, 1L, 1L, 1L), .Label = "2013-03-03", class = "factor"), freq = c(1L, 4L, 2L, 1L)), .Names = c("host", "date", "freq" ), row.names = c(NA, -4L), class = "data.frame")
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5 Comments

This is perfect, thanks! I had to change 'group=1' to 'group=host' for a smooth line between the points, otherwise it comes out as a sort of bar chart.
Good one. You can use dplyr which is next iteration of plyr.
@MKR Thanks! Could have done dc <- da %>% group_by(host,date) %>% dplyr::summarise(freq = n()) but I think it would have been a bit longer.
Absolutely correct!! But i meant you could have used dplyr::count
@MKR makes sense :)
3

ggplot2 library is capable of performing statistics. Hence, an option could be to let ggplot handle count/frequency. This should draw multiple lines (one for each group)

ggplot(df, aes(x=Date, colour = host, group = host)) + geom_line(stat = "count")

Note: Make sure host is converted to factor to have discrete color for lines.

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