<Three ways to make a histogram>
I will make a histogram from data 'airqulity' which is built in the ggplot2 package.
histogram is a graphical display of distribution of data which consists of nonoverlapping bins.
Generally, y-axis of histograms which is the height of each bin in the histogram is proportional to frequencies of the number of cases of the unit of the variable in x-axis.
The graphs below represents the distribution of Temperature of New York, May to September 1973.
> table(airquality$Month)
5 6 7 8 9
31 30 31 31 30
1. hist()
> hist(airquality$Temp) |
> hist(airquality$Temp,breaks=5) |
|
|
2. qplot()
>library(ggplot2) > qplot(airquality$Temp) |
> qplot(airquality$Temp,binwidth=5) |
|
|
3. ggplot()+geom_histogram()
>library(ggplot2) > ggplot(data=airquality, aes(x=Temp))+geom_histogram() |
>ggplot(data=airquality,aes(x=Temp))+geom_histogram(binwidth=5) |
|
|
<Two ways to adjust the width of boxes when using geom_histogram()>
> ggplot(data=airquality,aes(x=Temp))+geom_histogram()
The number of bins in geom_histogram() is 30 in normal.
<two ways to make 9 bins in the graph>
> ggplot(data=airquality,aes(x=Temp))+geom_histogram(binwidth=5) |
> ggplot(data=airquality,aes(x=Temp))+geom_histogram(binwidth=diff(range(airquality$Temp)/8)) In this way, you can have 9 bins in total. when dividing bins with the range of the graph, The number of bins becomes the number used as a division of the range +1. For this case, you divide the range of airquality$Temp in 8 times, so you have 9 bins in total. |
|
|
