Setting the limits on the # coordinate system performs a visual zoom. # For changing x or y axis limits **without** dropping data # observations use. #> Warning: Removed 30 rows containing missing values (`geom_line()`). Then, specify 'freex' in your scales argument within facetgrid to get rid of your empty space in your faceted plot. This way you don't have to modify your original dataframe. p + lims (x = c ( Sys.Date ( ) - 30, NA ), y = c ( 10, 20 ) ) #> `geom_smooth()` using method = 'loess' and formula = 'y ~ x' #> Warning: Removed 30 rows containing non-finite values (`stat_smooth()`). 2 Answers Sorted by: 13 For your plot, simply use which to specify that you only want to use the subset of the dataframe containing non-zero proportions. I guess you wanted to have 6 different group values for each time point, but now the group variable just loops over, and you have: 1 30 0.1 0.3162278 1. set.seed ( 1 ) last_month `geom_smooth()` using method = 'loess' and formula = 'y ~ x' # Setting the limits with the scale discards all data outside the range. # This is useful if you want to match scales across different plots small 4 ) ggplot ( small, aes ( mpg, wt, colour = factor ( cyl ) ) ) + geom_point ( ) + lims (colour = c ( "4", "6", "8" ) ) ggplot ( big, aes ( mpg, wt, colour = factor ( cyl ) ) ) + geom_point ( ) + lims (colour = c ( "4", "6", "8" ) ) # There are two ways of setting the axis limits: with limits or # with coordinate systems. # You can also supply limits that are larger than the data. The function geompoint() adds a layer of points to your plot, which creates a scatterplot. In this case the outliers just get in the way and make it look like there are more points than there should be. Tell me about it in the comments, in case you have additional. The reason I want to hide outliers is because I am also plotting jittered points with geomjitter. Remember, the aes () function enables us to specify the 'variable mappings. Next, inside the ggplot2 () function, we're calling the aes () function. We do this with the syntax data scatterdata. # with automatic lower limit ggplot ( mtcars, aes ( mpg, wt ) ) + geom_point ( ) + xlim ( NA, 20 ) #> Warning: Removed 14 rows containing missing values (`geom_point()`). You complete your graph by adding one or more layers to ggplot(). At this point you should have learned how to delete missing data from a ggplot2 pot in R. Inside of the ggplot2 () function, we're telling ggplot that we'll be plotting data in the scatterdata dataframe. Jittering is particularly useful for small datasets with at least one discrete position. # reverse scale ggplot ( mtcars, aes ( mpg, wt ) ) + geom_point ( ) + xlim ( 20, 15 ) #> Warning: Removed 19 rows containing missing values (`geom_point()`). Jitter points to avoid overplotting Source: R/position-jitter.r Counterintuitively adding random noise to a plot can sometimes make it easier to read. # Zoom into a specified area ggplot ( mtcars, aes ( mpg, wt ) ) + geom_point ( ) + xlim ( 15, 20 ) #> Warning: Removed 19 rows containing missing values (`geom_point()`).
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