CODEWITHSUNDEEP
rdplyrplotlyhistogram
Lynn
Lynn

Reputation: 4398

Group values into bins and then plot using plotly (R, Dplyr)

I have a dataset, df, which is telling us that X is the frequency and Category is the 'bin in which the X value belongs. So X is telling us how many times the Category occurs. (This is a small sample of the actual dataset)

Fig.1

                     Category         X
                     100              5
                     101              10
                     110              20
                     120              5
                     125              2
                     150              1

I created the above output using this code from another dataset that looked like Fig.3

 Fig. 2    df1 <- aggregate(df$gr, by=list(Category=data$Duration), FUN=length)




 Fig. 3     gr             Duration
            Outdata1        100
            Outdata2        101
            Outdata3        110
            Outdata4        120
            Outdata5        125
            Outdata6        150

Here is a sample of my plotly graph:

enter image description here

      p <- plot_ly(data = df,
         x = ~Category,
         y = ~x,
         name = "name",
         type = "bar",
         orientation = 'v'


        )%>% 
         layout(
         title = "title",
         xaxis = list(title = "Time in Seconds" , categoryorder = "ascending",tickangle = -45 ),
         yaxis = list(title = "example",
         barmode = "group"
        ))


  [![enter image description here][1]][1]

However, instead of the Category displaying as individual values, I want to group them in 'bins' like a histogram, like this:

enter image description here

So that the Category reveals bins in increments of 10, so the Category would be like: 100 110 120 130 140 150 , vs. 100 101 110 120 125 150

Here is the dput for Fig. 1

   structure(list(Category = structure(c(0, 1, 2, 3, 4, 5, 6, 7, 
  8, 9, 10, 11, 12, 13, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 
  27, 28, 29, 30, 31, 32, 33, 34, 36, 38, 39, 40, 42, 43, 44, 47, 
  48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 60, 63, 65, 66, 67, 68, 
  69, 70, 71, 72, 74, 77, 79, 80, 82, 84, 87, 89, 90, 91, 96, 97, 
  98, 103, 110, 114, 116, 124, 125, 126, 133, 134, 143, 149, 152, 
  154, 155, 157, 158, 161, 163, 164, 173, 177, 179, 183, 184, 185, 
  189, 190, 193, 196, 198, 201, 207, 211, 214, 217, 227, 229, 234, 
  235, 248, 265, 270, 285, 293, 307), class = "difftime", units = "secs"), 
  x = c(1L, 1L, 1L, 5L, 4L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 7L, 
  2L, 2L, 4L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 4L, 3L, 2L, 1L, 1L, 
  1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 4L, 2L, 2L, 4L, 
  1L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 
  1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 
  1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 
  1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
  1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L
  )), row.names = c(NA, -118L), class = "data.frame")

Upvotes: 0

Views: 546

Answers (1)

akash87
akash87

Reputation: 3994

So based on what you are doing, I did not filter for any kind of data. But using tidyverse package, here is what I would do:

dfs %>% 
mutate(newvar = as.numeric(gsub(" secs", "", Category)), 
new_cat = cut(newvar, breaks = seq(0,round(max(newvar), -1), by = 10), include.lowest = T)) %>% 
group_by(new_cat) %>% 
summarise(Counts = sum(x)) %>% 
ungroup() %>% 
ggplot(aes(x = new_cat, y = Counts)) + 
geom_bar(stat = "identity")

Upvotes: 1

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