= element_line(size = 0.1, color = "grey"), Scale_y_continuous(labels = scales::comma) + Geom_text(aes(y = Price, label = Price_lbl, hjust = 0), size = 6) +Ĭoord_flip(clip = "off", expand = FALSE) + Geom_text(aes(y = 0, label = paste(Species, " ")), vjust = 0.2, hjust = 1, size = 6) + Several arguments are added to the theme() statement: p.static <- ggplot(prices, aes(rank, group = Species,įill = as.factor(Species), color = as.factor(Species))) +
Each species is colored differently which will aid the reader in interpreting the visualization. The visualization p.static below uses ggplot to create a bar graph that plots the stumpage price for each species. The development of the animated bar chart begins with creating a static visualization.
For example interpretation, ash was the lowest priced species in 20 (rank = 10) but the highest and second highest priced species in 20, respectively: Table 1: First 10 records of the Minnesota sawtimber price dataset. The first ten rows of the data (for ash species) are presented below. The following data manipulation step adds a variable rank that orders the prices of the species in the data by year after calculating a relative price ( Price_rel) comparing to the other species: prices % The prices data set includes the species, year, and average stumpage price for sawtimber in $USD MBF (thousand board feet). The data are read into R using the read_sheet() function from the googlesheets4 package: Data were compiled from Table 3 in the Minnesota DNR report. For the last ten years, the Stumpage Price Review and Price Index has reported trends in stumpage prices for different species.ĭata from 2010 through 2019 are available here for 11 primary sawtimber species sold in Minnesota. The Minnesota Department of Natural Resources has monitored statewide average prices received for stumpage sold by public agencies. We’ll load all of the libraries into R that we plan to use: library(googlesheets4)
#Install gifski r install
To start we’ll install the gganimate and gifski packages to help create the animated bar charts: install.packages("gganimate")
#Install gifski r software
R software will be used to develop these bar graphs. In this example, we’ll use animated bar graphs to see trends in prices paid for different species of sawtimber sold in Minnesota. Instead of a line graph, an animated bar chart ranks the order of the bars so that you can see how categories trend over time. These data might typically be represented by multiple lines on a graph that “cross” over time.
#Install gifski r series
This is why animated bar graphs work well to visualize time series data. My favorite is one that shows the popularity of data science software through time.Īnimated bar graphs are effective when you have a categorical variable that has a different value at multiple time periods. If you’re on social media, you’ve probably seen animated bar charts.
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