When I integrate tables and figures in a document using knitr, adding the code makes it more reproducible and interesting.
Often a combination of dplyr and ggvis can make a plot that has relatively legible code (using the magrittr pipe operator %>).
mtcars %>%
group_by(cyl, am) %>%
summarise( weight = mean(wt) ) %>%
ggvis(x=~am, y=~weight, fill=~cyl) %>%
layer_bars()
The problem is that the ggvis plot:
does not look quite as as pretty as the ggplot2 plot (I know, factoring of cyl):
However, for ggplot2 we need:
mtcars %>%
group_by(am, cyl) %>%
summarise( weight = mean(wt) ) %>%
ggplot( aes(x=am, y=weight, fill=cyl) ) +
geom_bar(stat='identity')
My problem is that this switches from %>% to + for piping. I know this is a very minor itch, but I would much prefer to use:
mtcars %>%
group_by(am, cyl) %>%
summarise( weight = mean(wt) ) %>%
ggplot( aes(x=am, y=weight, fill=cyl) ) %>%
geom_bar(stat='identity')
Is there a way to modify the behaviour of ggplot2 so that this would work?
ps. I don't like the idea of using magrittr's add() since this again make the code more complicated to read.
Since it would be too long to expand in the comments, and based on your answer I am not sure if you tried the bit of code I provided and it didn't work or you tried previously and didn't manage
geom_barw<-function(DF,x,y,fill,stat){
require(ggplot2)
p<-ggplot(DF,aes_string(x=x,y=y,fill=fill)) + geom_bar(stat=stat)
return(p)
}
library(magrittr)
library(dplyr)
library(ggplot2)
mtcars %>%
group_by(cyl, am) %>%
summarise( weight = mean(wt) ) %>%
geom_barw(x='am', y='weight', fill='cyl', stat='identity')
This works for me with:
dplyr_0.4.2 ggplot2_2.1.0 magrittr_1.5
Of course geom_barw could be modified so you don't need to use the quotes anymore.
EDIT: There should be more elegant and safer way with lazy (see the lazyeval package), but a very quick adaptation would be to use substitute (as pointed by Axeman - however without the deparse part):
geom_barw<-function(DF,x,y,fill,stat){
require(ggplot2)
x<-substitute(x)
y<-substitute(y)
fill<-substitute(fill)
p<- ggplot(DF,aes_string(x=x,y=y,fill=fill))
p<- p + geom_bar(stat=stat)
return(p)
}
Related
Boxplot in ggplot
df %>%
mutate(Bezettingsgraad = Bezetting_gem / Capaciteit *100 ) %>%
group_by(Stadion)
Code for the boxplot
df %>%
mutate(Bezettingsgraad = Bezetting_gem / Capaciteit *100 ) %>%
group_by(Provincie) %>%
ggplot(Provincie, aes(x=Provincie, y=Bezetting_gem, color=dose)) +
geom_boxplot()
In the image you see in yellow the rows that are being used
Error
Before the mapping aesthetics you have included the variable Provincie in the place where your data should be . Besides you are already piping your data into your ggplot call via the %>% operator.
Try deleting Provincie
I would like to make four boxplots side-by-side using ggplot2, but I am struggling to find an explanation that suits my purposes.
I am using the well-known Iris dataset, and I simply want to make a chart that has boxplots of the values for sepal.length, sepal.width, petal.length, and petal.width all next to one another. These are all numerical values.
I feel like this should be really straightforward but I am struggling to figure this one out.
Any help would be appreciated.
Try this. The approach would be to selecting the numeric variables and with tidyverse functions reshape to long in order to sketch the desired plot. You can use facet_wrap() in order to create a matrix style plot or avoid it to have only one plot. Here the code (Two options):
library(tidyverse)
#Data
data("iris")
#Code
iris %>% select(-Species) %>%
pivot_longer(everything()) %>%
ggplot(aes(x=name,y=value,fill=name))+
geom_boxplot()+
facet_wrap(.~name,scale='free')
Output:
Or if you want all the data in one plot, you can avoid the facet_wrap() and use this:
#Code 2
iris %>% select(-Species) %>%
pivot_longer(everything()) %>%
ggplot(aes(x=name,y=value,fill=name))+
geom_boxplot()
Output:
This is a one-liner using reshape2::melt
ggplot(reshape2::melt(iris), aes(variable, value, fill = variable)) + geom_boxplot()
In base R, it can be done more easily in a one-liner
boxplot(iris[-5])
Or using ggboxplot from ggpubr
library(ggpubr)
library(dplyr)
library(tidyr)
iris %>%
select(-Species) %>%
pivot_longer(everything()) %>%
ggboxplot(x = 'name', fill = "name", y = 'value',
palette = c("#00AFBB", "#E7B800", "#FC4E07", "#00FABA"))
I often combine dplyr with ggplot2 in wrapper functions for analysis. As I am moving to the new NSE / SE paradigm of v.0.7.1 with tidyeval, I am struggling to get this combination to work. I found that ggplot does not understand unquoted quosers (yet). The following does not work:
example_func <- function(col) {
col <- enquo(col)
mtcars %>% count(!!col) %>%
ggplot(aes((!!col), n)) +
geom_bar(stat = "identity")
}
example_func(cyl)
# Error in !col : invalid argument type
I currently use the following work-around. But I assume there must be a better way.
example_func2 <- function(col) {
col <- enquo(col)
mtcars %>% count(!!col) %>%
ggplot(aes_string(rlang::quo_text(col), "n")) +
geom_bar(stat = "identity")
}
Please show me what the best way to combine these two. Thanks!
If you are already handling quosures it's easier to use aes_ which accepts inputs quoted as a formula: aes_(col, ~n).
This bit of code solves your problem:
library(tidyverse)
example_func <- function(col) {
col <- enquo(col)
mtcars %>% count(!!col) %>%
ggplot(aes_(col, ~n)) +
geom_bar(stat = "identity")
}
example_func(cyl)
There seem to be two ways of thinking about this.
Approach 1: Separation of concerns.
I like my plotting stuff to be very much separate from my wrangling stuff. Also, you can name your group which feels like the easiest method to solve your problem [although you do loose the original column name]. So one method of solving what you're trying to do can be via;
library(tidyverse)
concern1_data <- function(df, col) {
group <- enquo(col)
df %>%
group_by(group = !!group) %>%
summarise(n = n())
}
concern2_plotting <- function(df){
ggplot(data=df) +
geom_bar(aes(group, n), stat = "identity")
}
mtcars %>%
concern1_data(am) %>%
concern2_plotting()
This achieves what you're trying to do more or less and keeps concerns apart (which deserves a mention).
Approach 2: Accept and Wait
Thing is: tidyeval is not yet implemented in ggplot2.
- Colin Fay from link
I think this is support that is currently not in ggplot2 but I can't imagine that ggplot2 won't get this functionality. It's just not there yet.
I would like to extract some plotting code using a functional sequence as described in http://www.r-bloggers.com/magrittr-1-5/. However, it does not work
require(magrittr); require(ggplot2); require(dplyr)
plot_me <- . %>% (ggplot(aes(Sepal.Width, Sepal.Length)) + geom_point())
iris %>% plot_me
When trying this, R gives the following error
Error: ggplot2 doesn't know how to deal with data of class uneval
Doing the same using simple piping works nicely:
iris %>% ggplot(aes(Sepal.Width, Sepal.Length)) + geom_point()
What's wrong with my functional sequence/code?
I can't really explain why, but the following works.
(It might be because of the use of { instead of ( to control the order of computation inside the pipe).
library(magrittr)
library(ggplot2)
plot_me <- . %>% {ggplot(., aes(Sepal.Width, Sepal.Length)) + geom_point()}
iris %>% plot_me
Trying to replicate the ggplot function position="fill" in ggvis. I use this handy function all the time in the presentation of results. Reproducible example successfully performed in ggplot2 + the ggvis code. Can it be done using the scale_numeric function?
library(ggplot2)
p <- ggplot(mtcars, aes(x=factor(cyl), fill=factor(vs)))
p+geom_bar()
p+geom_bar(position="fill")
library(ggvis)
q <- mtcars %>%
ggvis(~factor(cyl), fill = ~factor(vs))%>%
layer_bars()
# Something like this?
q %>% scale_numeric("y", domain = c(0,1))
I think that to do this sort of thing with ggvis you have to do the heavy data reshaping lifting before sending it to ggvis. ggplot2's geom_bar handily does a lot of calculations (counting things up, weighting them, etc) for you that you need to do explicitly yourself in ggvis. So try something like the below (there may be more elegant ways):
mtcars %>%
mutate(cyl=factor(cyl), vs=as.factor(vs)) %>%
group_by(cyl, vs) %>%
summarise(count=length(mpg)) %>%
group_by(cyl) %>%
mutate(proportion = count / sum(count)) %>%
ggvis(x= ~cyl, y = ~proportion, fill = ~vs) %>%
layer_bars()