You can set all bars to be a given color with thefill argument ofgeom_bar().

ggplot(mpg,aes(x=drv))+geom_bar(fill="blue")

Alternatively, if the colors should be based on a variable, this should be should happen in theaes() mapping.

ggplot(mpg,aes(x=drv, fill=drv))+geom_bar()

And if you want to then customize the colors, one option isscale_fill_manual(), which allows you to manually assign colors to each bar. See otherscale_fill_*() functions for more options for color choices.

ggplot(mpg,aes(x=drv, fill=drv))+geom_bar()+scale_fill_manual(values=c("purple","orange","darkblue"))

By default, thewidth of bars is0.9 (90% of the resolution of the data). You can set this argument to a lower value to get bars that are narrower with more space between them.

ggplot(mpg,aes(x=drv))+geom_bar(width=0.5)

ggplot(mpg,aes(x=drv))+geom_bar(width=0.1)

By default ggplot2 expands the axes so the geoms aren’t flush against the edges of the plot.

ggplot(mpg,aes(x=drv))+geom_bar()

To remove the spacing between the bars and the x-axis, but keep the spacing between the bars and the top of the plot, use the following.

ggplot(mpg,aes(x=drv))+geom_bar()+scale_y_continuous(expand=expansion(mult=c(0,0.05)))

To achieve the opposite, switch the values inmult. Note that the tallest bar is now flush against top of the plot.

ggplot(mpg,aes(x=drv))+geom_bar()+scale_y_continuous(expand=expansion(mult=c(0.05,0)))

To adjust spacing around the x-axis, adjust theexpand argument inscale_x_discrete(). Note that this places the bars flush against the left side and leaves some space on the right side.

ggplot(mpg,aes(x=drv))+geom_bar()+scale_x_discrete(expand=expansion(add=c(0,0.6)))

The default look of a bar plot can be achieved with the following.

ggplot(mpg,aes(x=drv))+geom_bar()+scale_x_discrete(expand=expansion(add=0.6))+scale_y_continuous(expand=expansion(mult=0.05))

In the following plot the bars have differing widths within each level ofdrv as there are differing levels ofclass represented.

ggplot(mpg,aes(x=drv, fill=class))+geom_bar(position="dodge")

You can useposition_dodge2() withpreserve = "single" to address this.

ggplot(mpg,aes(x=drv, fill=class))+geom_bar(position=position_dodge2(preserve="single"))

The following plot is useful for comparing counts but not as useful for comparing proportions, which is what you need if you want to be able to make statements like “in this sample, it’s more likely to have a two-seater car that has rear-wheel drive than an SUV that has rear-wheel drive”.

ggplot(mpg,aes(y=class, fill=drv))+geom_bar()

position = "fill" will generate a bar plot with bars of equal length and the stacks in each bar will show the proportion ofdrv for that particularclass.

ggplot(mpg,aes(y=class, fill=drv))+geom_bar(position="fill")

If you want to show percentages instead of proportions on the x-axis, you can define this inscale_x_continuous() withscales::label_percent().

ggplot(mpg,aes(y=class, fill=drv))+geom_bar(position="fill")+scale_x_continuous(name="percentage", labels=scales::label_percent(accuracy=1))

Suppose you have the following data from an opinion poll, where the numbers in the cells represent the number of responses for each party/opinion combination.

poll<-tribble(~party,~agree,~disagree,~no_opinion,"Democrat",20,30,20,"Republican",15,20,10,"Independent",10,5,0)

You can first pivot the data longer to obtain a data frame with one row per party/opinion combination and a new column,n, for the number of responses that fall into that category.

poll_longer<-poll|>pivot_longer(    cols=-party,    names_to="opinion",    values_to="n")poll_longer#> # A tibble: 9 × 3#>   party       opinion        n#>   <chr>       <chr>      <dbl>#> 1 Democrat    agree         20#> 2 Democrat    disagree      30#> 3 Democrat    no_opinion    20#> 4 Republican  agree         15#> 5 Republican  disagree      20#> 6 Republican  no_opinion    10#> 7 Independent agree         10#> 8 Independent disagree       5#> 9 Independent no_opinion     0

Then, you can pass this result toggplot() and create a bar for eachparty on they (orx, if you prefer vertical bars) axis and fill the bars in with number of responses for eachopinion.

ggplot(poll_longer,aes(y=party, fill=opinion, x=n))+geom_col()

To plot proportions (relative frequencies) instead of counts, useposition = "fill" ingeom_col().

ggplot(poll_longer,aes(y=party, fill=opinion, x=n))+geom_col(position="fill")+xlab("proportion")

Suppose you have data from a survey with three questions, where respondents select “Agree” or “Disagree” for each question.

survey<-tibble::tribble(~respondent,~q1,~q2,~q3,1,"Agree","Agree","Disagree",2,"Disagree","Agree","Disagree",3,"Agree","Agree","Disagree",4,"Disagree","Disagree","Agree")

You’ll first want to reshape these data so that each row represents a respondent / question pair. You can do this withtidyr::pivot_longer(). Then, pass the resulting longer data frame toggplot() group responses for each question together.

survey|>tidyr::pivot_longer(    cols=-respondent,    names_to="question",    values_to="response")|>ggplot(aes(x=question, fill=response))+geom_bar(position="dodge")

One option for calculating group means is usingdplyr::group_by() followed bydplyr::summarise(). Then, you can pass the resulting data frame toggplot() and plot bars usinggeom_col().

mpg|>group_by(drv)|>summarise(mean_hwy=mean(hwy))|>ggplot(aes(x=drv, y=mean_hwy))+geom_col()

Alternatively, you can usestat_summary() to let ggplot2 calculate and plot the means.

ggplot(mpg,aes(x=drv, y=hwy))+stat_summary(fun="mean", geom="bar")

In the following plot the y-axis is limited to 20 to 120, and hence the bars are not showing up.

ggplot(mpg,aes(x=drv))+geom_bar()+ylim(c(20,120))#> Warning: Removed 3 rows containing missing values or values outside the scale range#> (`geom_bar()`).

In order to obtain a bar plot with limited y-axis, you need to instead set the limits incoord_cartesian().

ggplot(mpg,aes(x=drv))+geom_bar()+coord_cartesian(ylim=c(20,110))

This is, indeed, a deceiving plot. If you’re using a bar plot to display values that could not take the value of 0, you might choose a different geom instead. For example, if you have the following data and plot.

df<-tibble::tribble(~x,~y,"A",1050,"B",1100,"C",1150)ggplot(df,aes(x=x, y=y))+geom_col()

Also suppose that you want to cut off the bars aty = 1000 since you know that the variable you’re plotting cannot take a value less than 1000, you might usegeom_point() instead.

# don't do thisggplot(df,aes(x=x, y=y))+geom_col()+coord_cartesian(ylim=c(1000,1150))

# do thisggplot(df,aes(x=x, y=y))+geom_point(size=3)