R语言做条形图时候，离散变量和连续型变量的区别

1）条形图

条形图或许是最常用图形，常用来展示分类(different categories on the x-axis)和数值(numeric values on the y-axis)之间的关系。sometimes the bar heights represent counts of cases in the data set, and sometimes they represent values in the data set(有时条形图高度代表数据集中的频数(count),有时候代表数据集中的值(values),这个要牢牢的记在心中，否则会产生疑惑).

By default, geom_bar uses stat="count" which makes the height of the bar proportion to the number of cases in each group (即频数or if the weight aethetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use stat="identity" and map a variable to the y aesthetic.

1.1）以BOD数据框中的数据为例

1.2) 当x为(连续型或数字变量):one bar at each possible x value between the minimum and the maximum

ggplot(BOD, aes(x=Time, y=demand)) + geom_bar(stat="identity")

1.3) 当x为分类变量时候：having one bar at each actual x value,

# Convert Time to a discrete (categorical) variable with factor()
ggplot(BOD, aes(x=factor(Time), y=demand)) + geom_bar(stat="identity")

1.4）默认情况下，条形图是灰黑色，用颜色参数 fill,可以调节参数.同时, 默认情况下outline around the fill也没有颜色.  可以用 colour参数来调节外框的颜色。

ggplot(pg_mean, aes(x=group, y=weight)) +geom_bar(stat="identity", fill="lightblue", colour="black")

1.5)  通过将分组变量(这个变量必须是分类变量(categorical) 而不是连续变量(continuous variables)）mapping到fill，从而构建分组条形图，同时必须使用position="dodge",来避免水平叠加，否则将会得到堆叠图，（You can produce a grouped bar plot by mapping that variable to fill, which represents the fill color of the bars.You must also use position="dodge", which tells the bars to “dodge” each other horizontally; if you don’t, you’ll end up with a stacked bar plot）。

library(gcookbook) # For the data set

ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) +geom_bar(stat="identity",position="dodge")

ggplot(cabbage_exp, aes(x=Date, y=Weight, fill=Cultivar)) +geom_bar(stat="identity")