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Asked: 2026-05-27T19:38:40+00:00

I have CSV data of a log for 24 hours that looks like this:

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I have CSV data of a log for 24 hours that looks like this:

svr01,07:17:14,'u1@user.de','8.3.1.35'
svr03,07:17:21,'u2@sr.de','82.15.1.35'
svr02,07:17:30,'u3@fr.de','2.15.1.35'
svr04,07:17:40,'u2@for.de','2.1.1.35'

I read the data with tbl <- read.csv("logs.csv")

How can I plot this data in a histogram to see the number of hits per hour?
Ideally, I would get 4 bars representing hits per hour per srv01, srv02, srv03, srv04.

Thank you for helping me here!

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1 Answer

  1. Editorial Team

    An example dataset:

    dat = data.frame(server = paste("svr", round(runif(1000, 1, 10)), sep = ""),
                 time = Sys.time() + sort(round(runif(1000, 1, 36000))))

    The trick I use is to create a new variable which only specifies in which hour the hit was recorded:

    dat$hr = strftime(dat$time, "%H")

    Now we can use some plyr magick:

    hits_hour = count(dat, vars = c("server","hr"))

    And create the plot:

    ggplot(data = hits_hour) + geom_bar(aes(x = hr, y = freq, fill = server), stat="identity", position = "dodge")

    Which looks like:

    enter image description here

    I don’t really like this plot, I’d be more in favor of:

    ggplot(data = hits_hour) + geom_line(aes(x = as.numeric(hr), y = freq)) + facet_wrap(~ server, nrow = 1)

    Which looks like:

    enter image description here

    Putting all the facets in one row allows easy comparison of the number of hits between the servers. This will look even better when using real data instead of my random data.

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