Geom_vline(aes(xintercept = grp.mean, color = sex), # Change fill color by sex and add mean lineĪ + geom_density(aes(fill = sex), alpha = 0.4) + use scale_fill_manual() for changing area fill colors.use scale_color_manual() or scale_colour_manual() for changing line color.Data: mu, which contains the mean values of weights by sex (computed in the previous section). Add vertical mean lines using geom_vline().Change areas fill and add line color by groups (sex):.# Change y axis to count instead of densityĪ + geom_density(aes(y =. Geom_vline(aes(xintercept = mean(weight)), Add a vertical line corresponding to the mean value of the weight variable ( geom_vline()): Lab.pos = "in", lab.font = list(color = "white"), Alternative solution to easily create a pie chart: use the function ggpie():.Geom_text(aes(y = lab.ypos, label = prop), color = "white")+ Geom_bar(width = 1, stat = "identity", color = "white") + Ggplot(df, aes(x = "", y = prop, fill = cut)) + Create the pie charts using ggplot2 verbs.Mutate(prop = round(counts*100/sum(counts), 1), To put the labels in the center of pies, we’ll use cumsum(prop) - 0.5*prop as label position. compute the position of the text labels as the cumulative sum of the proportion.compute the proportion (counts/total) of each category.This important to compute the y coordinates of labels. Arrange the grouping variable ( cut) in descending order.Pie chart is just a stacked bar chart in polar coordinates. Geom_text(aes(label = counts), vjust = -0.3) + Adjust the position of the labels by using hjust (horizontal justification) and vjust (vertical justification). geom_bar() with option stat = "identity" is used to create the bar plot of the summary output as it is.dplyr package used to summarise the data.
ggplot(diamonds, aes(cut)) +Ĭompute the frequency of each category and add the labels on the bar plot:
#ADD FREQUENCY AXIS TO ROSE DIAGRAM R CODE#
The R code below creates a bar plot visualizing the number of elements in each category of diamonds cut. The column cut contains the quality of the diamonds cut (Fair, Good, Very Good, Premium, Ideal). Contains the prices and other attributes of almost 54000 diamonds. Key arguments: alpha, color, fill, linetype and sizeĭemo data set: diamonds.Plot types: Bar plot of the count of group levels.