Thanks. That did it!
And I get it now--in your original example, aes(x = x, y = Freq), x
refers to the column name in as.data.frame(table(x)), not the x
vector(?) you created.
Aren
On Sun, Jan 1, 2012 at 4:44 PM, Joshua Wiley wrote:
> Sorry, that was probably a really confusing example...too ma
Sorry, that was probably a really confusing example...too many xs
floating around.
set.seed(10)
rawdata <- sample(0:23, 1, TRUE, prob = sin(0:23)+1)
## do theis step first for your data
tableddata <- as.data.frame(table(rawdata))
## use these names in ggplot
colnames(tableddata)
require(ggpl
This is helpful, although I can't seem to adapt it to my own data.
If I run your sample as is, I do get the nice graphs.
However, this doesn't work:
(Assume you already have a data frame "dallas" with 2057980 rows. It
has column "offense_hour", and each row has a value between 0 and 23,
inclusive
Hi Aren,
I was busy thinking about how to make what you wanted, and I missed
that you were working with hours from a day. That being the case, you
may think about a circular graph. The attached plots show two
different ways of working with the same data.
Cheers,
Josh
set.seed(10)
x <- sample(
On Sun, Jan 1, 2012 at 5:29 AM, peter dalgaard wrote:
> Exactly. If what you want is a barplot, make a barplot; histograms are for
> continuous data. Just remember that you may need to set the levels
> explicitly in case of empty groups: barplot(table(factor(x,levels=0:23))).
> (This is irrel
On Jan 1, 2012, at 07:40 , Joshua Wiley wrote:
> If you just want a plot of the frequencies at each hour why not just call
> barplot on the output of table? Histograms create bins and count in those,
> which doesn't sound like what you're after.
>
Exactly. If what you want is a barplot, make
If you just want a plot of the frequencies at each hour why not just call
barplot on the output of table? Histograms create bins and count in those,
which doesn't sound like what you're after.
Cheers,
Josh
On Dec 31, 2011, at 21:37, jim holtman wrote:
> Fast fingers; notice that there is s
Fast fingers; notice that there is still a problem in the counts; I
was only looking at the last.
Happy New Year -- up too late.
On Sun, Jan 1, 2012 at 12:33 AM, jim holtman wrote:
> Here is a test I ran and looks fine, but then I created the data, so
> it might have something to do with your d
Here is a test I ran and looks fine, but then I created the data, so
it might have something to do with your data:
> x <- sample(0:23, 10, TRUE)
> a <- hist(x, breaks = 24)
> a[1:5]
$breaks
[1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
$counts
[1] 8262 4114 418
Hi,
I think you're not understanding quite what's going on with hist. Reread the
help, and take a look at this small example. The solution I'd use is the last
item.
> x <- rep(1:10, times=1:10)
> table(x)
x
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
>
>
> hist(x, plot=FALSE, righ
I have two large datasets (156K and 2.06M records). Each row has the
hour that an event happened, represented by an integer from 0 to 23.
R's histogram is combining some data.
Here's the command I ran to get the histogram:
> histinfo <- hist(crashes$hour, right=FALSE)
Here's histinfo:
> histinfo
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