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I am learning geom_bar on section 3.7 of r4ds.had.co.nz. I run a code like this:
library(ggplot2) ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1)) Then I have this plot: 
The point is, if I exclude the "group = 1" part:
library(ggplot2) ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y = ..prop..)) The plot will be wrong, 
But if I replace group = 1 by group = 2 or group = "x", the plot still looks correct. So I don't quite understand the meaning of group = 1 here and how to use it.
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In the right figure, aesthetic mapping is included in ggplot(..., aes(..., color = factor(year)) . It displays data points of different years with different colors as expected. It also further split each drv group into factor(year) subgroups.
By default, geom_bar uses stat="count" which makes the height of the bar proportion to the number of cases in each group (or if the weight aethetic is supplied, the sum of the weights).
In the above example, we've overridden the default count value by specifying stat = "identity" . This indicates that R should use the y-value given in the ggplot() function. Notice that bar graphs use the fill argument instead of the color argument to color-code each cut category.
geom_col makes the height of the bar from the values in dataset.
You want to plot means and error bars for a dataset. To make graphs with ggplot2, the data must be in a data frame, and in “long” (as opposed to wide) format. If your data needs to be restructured, see this page for more information. The examples below will the ToothGrowth dataset.
There are two ways in which ggplot2 creates groups implicitly: If x or y are categorical variables, the rows with the same level form a group. If aesthetic mapping, such as color, shape, and fill, map to categorical variables, they subset the data into groups.
The group aesthetic is by default set to the interaction of all discrete variables in the plot. This choice often partitions the data correctly, but when it does not, or when no discrete variable is used in the plot, you will need to explicitly define the grouping structure by mapping group to a variable that has a different value for each group.
Each group has its own boxplot. In the right figure, aesthetic mapping is included in ggplot (..., aes (..., color = factor (year)). It displays data points of different years with different colors as expected. It also further split each drv group into factor (year) subgroups.
Group will help the plot to look at the specific rows that contain the specific cut and the proportion is found with respect to the whole database as in proportion of an ideal cut in the whole dataset.
If group is not used, the proportion is calculated with respect to the data that contains that field and is ultimately going to be 100% in any case. For instance, The proportion of an ideal cut in the ideal cut specific data will be 1.
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group="whatever" is a "dummy" grouping to override the default behavior, which (here) is to group by cut and in general is to group by the x variable. The default for geom_bar is to group by the x variable in order to separately count the number of rows in each level of the x variable. For example, here, the default would be for geom_bar to return the number of rows with cut equal to "Fair", "Good", etc.
However, if we want proportions, then we need to consider all levels of cut together. In the second plot, the data are first grouped by cut, so each level of cut is considered separately. The proportion of Fair in Fair is 100%, as is the proportion of Good in Good, etc. group=1 (or group="x", etc.) prevents this, so that the proportions of each level of cut will be relative to all levels of cut.
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