Passing Parameters to Ggplot

passing parameters to ggplot

One solution would be to pass x and y as string names of columns in data frame DS.

f <- function(DS, x, y) {    
  ggplot(DS, aes_string(x = x, y = y)) + geom_point()  
}

And then call the function as:

f(cars, "speed", "dist")

However, it seems that you don't want that? Can you provide an example why you would need different functionality? Is it because you don't want to have the arguments in the same data frame?

R - How to pass variables to ggplot

Using of aes_string is deprecated, you can use sym with !! :

library(ggplot2)
library(rlang)

scatterWithRegLine = function(df, xColName, yColName, regMethod, showSe, showFullrange, size, yAxisLim) {
  
  ggplot(df, aes(x = !!sym(xColName), y = !!sym(yColName))) + 
    geom_point() +
    geom_smooth(method= regMethod, se=showSe, fullrange=showFullrange, size=size) + ylim(yAxisLim)
}

and then call it with

scatterWithRegLine(mtcars, "mpg", "wt", "loess", TRUE, FALSE, 1, c(0,5))

How to use a variable to specify column name in ggplot

Note: the solution in this answer is "soft-deprecated". See the answer below using .data[[ for the currently preferred method.

You can use aes_string:

f <- function( column ) {
    ...
    ggplot( rates.by.groups, aes_string(x="name", y="rate", colour= column,
                                        group=column ) )
}

as long as you pass the column to the function as a string (f("majr") rather than f(majr) ). Also note that we changed the other columns, "name" and "rate", to be strings.

If for whatever reason you'd rather not use aes_string, you could change it to (the somewhat more cumbersome):

    ggplot( rates.by.groups, aes(x=name, y=rate, colour= get(column),
                                        group=get(column) ) )

Passing an extra parameter to a custom geom in ggplot2

There is a relatively easy way, and that is to pass the extra parameter as argument to the panel drawing function. Here is a simple example that would work with your geom_simple_point():

GeomSimplePoint <- ggproto(
  "GeomSimplePoint", 
  GeomPoint,
  extra_params = c("na.rm", "showpoints"),
  draw_panel = function(data, panel_params, 
                        coord, na.rm = FALSE, showpoints = TRUE) {
    if (showpoints) {
      return(GeomPoint$draw_panel(data, panel_params, coord, na.rm = na.rm))
    } else {
      return(zeroGrob())
    }
  }
)

As an aside, if you want to replicate behaviour of an already existing geom, such as geom_point(), it is easier to set your own ggproto object to inherit from that geom's ggproto object. That way, the default aes, required aes and other arguments that you specify in ggproto are automatically copied/inherited from that geom and you don't have to manually specify all of them.

R: How to pass parameters to ggplot geom_ within a function?

I like MSM's approach, but if you want to be able to add my_geom_y to a ggplot you've already made, this is an alternative that might suit what you're after:

library(ggplot2)
x <- 1:100

my_geom_y <- function(yy, colour = "black"){
  list(
    geom_line(mapping = aes(y= yy), 
              col = colour), 
              data = data.frame(x, yy),
    geom_point(mapping = aes(y = yy), 
               col = colour, 
               data = data.frame(x, yy))
  )
 }

ggplot(mapping = aes(x))  + 
  my_geom_y(x, "red") + 
  my_geom_y(dlnorm(x), "blue") +
  my_geom_y((x^1.1), "black") +
  my_geom_y(x/2, "yellow")

Passing extra parameters into the stat function

When you sepcify stat="summary", it still needs to know how to summarize your values. The default is to use the mean with standard errors. But you want medians with min and max values. You can write your own summary function

median_min_max <- function(x) {
  data.frame(y=median(x), ymin=min(x), ymax=max(x))
}

And then pass that to your pointrange layer via the fun.data= parameter.

ggplot(data = diamonds) +
  geom_pointrange(
    mapping = aes(x = cut, y = depth),
    stat = "summary", fun.data = median_min_max
  )

This will give you a plot that matches the one you created from your summarized data.

ggplot does have a median_hilow summary function but that uses the Hmisc::smedian.hilow function which uses quantiles based on a confidence inteval rather than min/max values.

R- pass argument to ggplot in function

Based on the function arguments, it seems like convert is flip. Instead of using get to modify the 'x' argument, we can do this with mutate after converting the arguments to quosures

myfunction<- function(mydata, x, y, convert = FALSE){
    x <- enquo(x)
    y <- enquo(y)
    xnew <- quo_name(x)
if (convert) {
   mydata %>%
         mutate(!! (xnew) := 1- !!(x)) %>%
         ggplot(., aes_string(xnew, quo_name(y))) +
               geom_line() + 
               xlab(paste0("1 - ", xnew))
   
} else {

     ggplot(mydata, aes_string(xnew, quo_name(y))) +
             geom_line()

    }
    
}

myfunction(df1, x1, x2, convert = TRUE)

-output

myfunction(df1, x1, x2, convert = FALSE)

-output

data

df1 <- structure(list(x1 = c(0.1, 0.3, 0.4), x2 = c(0.2, 0.5, 0.6), 
x3 = c(0.3, 0.7, 0.8), x4 = c(0.4, 0.9, 0.2)), .Names = c("x1", 
"x2", "x3", "x4"), class = "data.frame", row.names = c(NA, -3L
))

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