Check out the below examples to understand how it works. This will help us to differentiate between the boxplots for the two factors. In this post we will see how to make a grouped boxplot with jittered data points with positionjitterdodge() using ggplot2 in R. Now if we have two factors then the boxplot can be created for both factor levels by passing fill argument in geom_boxplot. Grouped boxplots help visualize three variables in comparison to two variables with a simple boxplot. Round_any(x= 2.To create a boxplot, we have one factor and one numerical column and the boxplot is created for each category or levels in that factor. In some circumstances we want to plot relationships between set variables in multiple subsets of the data with. Round_any(2.4, 1, floor) # 2 # usage showing the full syntax Plotting multiple groups with facets in ggplot2. Round_any(2.4, 1, ceiling) # 3 # force rounding to the floor (lower side) Round_any(2.4, 1) # 2 # force rounding to the ceiling ( higher side) # let the function round to the nearest rounded value syntax is round_any(x, accuracy, f = round) library(plyr) We can use library(plyr) The round_any function allows to round to multiple of any number. P <- p + labs(subtitle ="We have used the log10 scale for the y axis and we can see the boxplots") P <- p + labs(subtitle ="We can not see the box plots as the y axis values have large variationsĪnd the box plots are very small and not visible clearly") ![]() P <- ggplot(movies, aes(y = votes, x = rating, group = rating)) # we want to experiment with numerical values on the x axis # Now we will use a different dataset for some more charts P # No summary function supplied, defaulting to `mean_se()` P <- p + labs(subtitle ="Showing the confidence intervals for each group") P <- p + stat_summary(aes(fun.data = weight), geom = "linerange", colour = "red", size = 2) # Warning: Ignoring unknown aesthetics: fun.data p <- p + stat_summary(fun.y = mean, geom="point",colour="darkred", size=2) # Warning: `fun.y` is deprecated. pÄata.frame(ymin = s$conf, ymax = s$conf) P <- p + stat_summary(fun.y = mean, geom="point",colour="darkred", size=2) # Warning: `fun.y` is deprecated. P <- p + labs(subtitle ="showing the mean value for each group") #p <- p + geom_jitter(shape = 2, color ="blue", size = 1) P <- p + labs(subtitle ="Hide the outlier points") P <- p + geom_boxplot(outlier.shape = NA) P <- p + labs(subtitle ="Changing the shape, colour and size for points") ![]() P <- p + geom_jitter(shape = 2, color ="blue", size = 1) P <- p + labs(subtitle ="Using geom_jitter to scatter the points and avoid overlapping points") P <- p + labs(subtitle ="showing each data point as well as box plot") ![]() P <- p + labs(subtitle ="Specifying the outliers colour and shape") P <- p + geom_boxplot(lour = "red", outlier.shape = 1) P <- p + labs(subtitle ="Reordering the position in ascending order of weight") P <- ggplot(chickwts, aes(x= reorder(feed, weight, FUN = median), y = weight, fill = feed)) P <- p + labs(subtitle ="Using fill in geom_boxplot to fill all boxes with a colour") P <- p + geom_boxplot(fill ="blue", alpha = 0.5) P <- p + labs(subtitle ="Adding the standard error bars in boxplots") P <- p + stat_boxplot(geom = 'errorbar', width = 0.2) P <- p + labs(subtitle ="Using fill command within aes to colour each box with different colours") P <- ggplot(chickwts, aes(x= feed, y = weight, fill = feed)) P <- p + labs(subtitle ="Using coord_flip to change the orientation of the axis") P <- p + labs(subtitle ="Basic boxplot showing weight for each feed type") P <- ggplot(chickwts, aes(x= feed, y = weight))
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |