I'm experimenting for quit some time with the tables of the trade.sqlite3 database. I came up with a combined table of the import and export table.
A csv-file of the table can be downloaded here :
Added a virus scan of the file as well
str of the table to get an idea how it looks like :
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 300 obs. of 4 variables:
$ year : num 2002 2002 2002 2002 2002 ...
$ category: Factor w/ 10 levels "0--Food And Live Animals",..: 1 2 3 4 5 6 7 8 9 10 ...
$ value : num 4.94e+08 2.88e+08 9.40e+08 4.96e+07 7.76e+06 ...
$ type : Factor w/ 2 levels "Exp","Imp": 1 1 1 1 1 1 1 1 1 1 ...
Now I want to plot this table using plot_ly.
I came up with the following idea :
library(plotly)
total_val <- read.csv("Total_Value")
plot_ly(data = total_val, x = total_val$year,
y = total_val$value, z = total_val$category, color = total_val$category,
stroke = total_val$category, type = "scatter3d" )
I still fight with some problems :
- I want to indicate if a marker is of type import or export.
- Using the color code as I did produces the following warning :
n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
There are 10 categorys and I need to display them all. Is there a better way to do this despite color coding?
Any other opinions to make the plot more informative are highly recommended.
following this I came up with a solution to plot with different shapes the Imp and Exp. The color issue is as easy as specifying the colors using the colors argument.
total_val$markers <- ifelse(total_val$type == "Exp", "circle", "diamond")
library(plotly)
p <- plot_ly(data = total_val, x = ~year,
y = ~value, z = ~category, color = ~category,
colors = rainbow(length(levels(total_val$category))),
text = ~paste('category:', category, '<br>value:', value, '<br>year:', year,
'<br>type:', type)) %>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'year'),
yaxis = list(title = 'Value'),
zaxis = list(title = 'Category')))
p <- plotly_build(p)
for (i in 1:length(p$x$data)) {
values <- total_val$markers[total_val$category == levels(total_val$category)[i]]
p$x$data[[i]]$marker$symbol <- values
}
p
Hope this helps!