Values_to = "Values" ) # Plot P_air <- ggplot ( air_df, aes (x = Day, y = Values, color = as.factor ( Month ), group = Month ) ) + geom_point ( ) + geom_line ( ) + labs (x = "Day of month", y = NULL ) + scale_color_brewer (palette = "Set1", Library ( tidyverse ) # Convert the original dataframe to long format air_df % pivot_longer (cols = 1 : 4, This dataset contains the daily measurements of four environmental variables, including ozone concentration, solar radiance, temperature, and wind speed, in New York from May to September 1973.įirst, let’s visualize these four variables at once using facets: We will be using the airquality dataset as our example data. Although you can set scales = "free_XXX" to allow the axes to vary by panel, it might not be as satisfying as you think, and this would require further adjustments of the individual facet panels. However, sometimes you might have facets in different units, and this might cause some problems for the appearances of the panels (e.g., the panels with large absolute axis range may “distort” the ones with small absolute range). Keep reading and you will surely get something out of it! The problemįacets are quite useful for displaying data by category/level/class. ![]() I have been dealing with this problem several times before (and I guess you too!), and so I think this would be a great topic to write about. ![]() In this post, I will share with you some useful tips for adjusting the axis scales for individual panels when you’re using ggplot facets. Welcome to my new post! I have been a bit busy lately and I feel so excited to come back to my blog.
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