Ggplot2 - there is a way to redefine global aesthetic displays when reusing geological layers

Question

By assigning the ggplot () object to a variable, you can easily reuse the object and make several graph options with changes on geomagnetic layers without redundant code for each graph. However, I was wondering if there is a way to reuse geometry layers when sharing global aesthetic mappings.

One use case is that I want to make several graphs with the same geometric representations, but I want to change the variable associated with one of the dimensions. Another use case is that I want to make two graphs in which data comes from two different data frames.

An intuitive way: 1) to save the combination of geometry layers in a variable without assigning a ggplot () object; or 2) to redefine the data and aesthetics of an existing ggplot () object in a variable by adding another ggplot () object. Doing any of these things causes errors (for 1- "a non-numeric argument for a binary operator, for 2 -" I don't know how to add o to the plot ").

For example, suppose in the following graph I want to reuse the gg variable, but reassign the x variable to something else in the data frame:

dsamp <- diamonds[sample(nrow(diamonds), 1000), ] gg <- (ggplot(data = dsamp, aes(x = carat, y = price, color = clarity)) + geom_point() + facet_wrap(~ cut)) print(gg) 

In practice, plot definitions can be longer than three lines, so this starts to annoy the code.

Answer 1

Variable variables related to aesthetics and graph related data are simple. Using the gg that you define in the question, use aes yourself to change the aesthetics:

 gg + aes(x=table, y=depth) 

To change the data used for the chart, use the %+% operator.

 dsamp2 <- head(diamonds, 100) gg %+% dsamp2 

Answer 2

As joran mentions, I guess ... but:

you can do one of two things, edit the ggplot2 object (a bad idea) or wrap a graph in a function.

allows you to use the following data and graph:

 dat <- data.frame(x=1:10, y=10:1, z=1, a=letters[1:2], b=letters[3:4]) # p <- ggplot(dat, aes_string(x=xvar, y=yvar, color=colorvar)) + geom_point() 

Notice that I used aes_string , so I can pass variables, not column names.

 xvar <- 'y' yvar <- 'z' colorvar <- 'a' p <- ggplot(dat, aes_string(x=xvar, y=yvar, color=colorvar)) + geom_point() 

Below is the p structure, and I will leave it to you to sort it. Instead, wrap ggplot in a function:

 plotfun <- function(DF, xvar, yvar, colorvar) { ggplot(DF, aes_string(x=xvar, y=yvar, color=colorvar)) + geom_point() } p <- plotfun(dat, 'z', 'x', 'a') p 

---

 str(p) List of 8 $ data :'data.frame': 10 obs. of 5 variables: ..$ x: int [1:10] 1 2 3 4 5 6 7 8 9 10 ..$ y: int [1:10] 10 9 8 7 6 5 4 3 2 1 ..$ z: num [1:10] 1 1 1 1 1 1 1 1 1 1 ..$ a: chr [1:10] "a" "b" "a" "b" ... ..$ b: chr [1:10] "c" "d" "c" "d" ... $ layers :List of 1 ..$ :Classes 'proto', 'environment' <environment: 0x34d5628> $ scales :Reference class 'Scales' [package "ggplot2"] with 1 fields ..$ scales: list() ..and 20 methods, of which 9 are possibly relevant: .. add, clone, find, get_scales, has_scale, initialize, input, n, non_position_scales $ mapping :List of 3 ..$ x : symbol y ..$ y : symbol x ..$ colour: symbol a $ options :List of 1 ..$ labels:List of 3 .. ..$ x : chr "z" .. ..$ y : chr "x" .. ..$ colour: chr "a" $ coordinates:List of 1 ..$ limits:List of 2 .. ..$ x: NULL .. ..$ y: NULL ..- attr(*, "class")= chr [1:2] "cartesian" "coord" $ facet :List of 1 ..$ shrink: logi TRUE ..- attr(*, "class")= chr [1:2] "null" "facet" $ plot_env :<environment: R_GlobalEnv> - attr(*, "class")= chr "ggplot"