Looping over variables in ggplot to create a grid of density distributions for each variable
I want to create a grid of density distribution plots, with a dashed vertical line at the mean, for multiple variables I have in a dataset. Using mtcars dataset as an example, the code for a single variable plot would be:
ggplot(mtcars, aes(x = mpg)) + geom_density() + geom_vline(aes(xintercept =
mean(mpg)), linetype = "dashed", size = 0.6)
I am unclear about how I alter this to make it loop over specified variables in my dataset and produce a grid with the plots of each one. It seems like it would involve some combination of adding facet_grid and the "vars" argument but I have tried a number of combinations with no success.
It seems like in all the examples I can find online, facet_grid splits the plots by subsets of a variable, while keeping the same x and y for each plot, but I want to have the plot of x vary in each graph and the y is the density of values.
In trying to solve this, it is also my understanding that the new release of ggplot includes something involving "quasiquotation" which may help solve my problem (https://www.tidyverse.org/articles/2018/07/ggplot2-tidy-evaluation/) but again, I couldn't quite figure out how to apply the examples provided here to my own issue.
r loops ggplot2 facet-grid
asked Nov 26 '18 at 16:57
Ella WindElla Wind
155
add a comment |
I want to create a grid of density distribution plots, with a dashed vertical line at the mean, for multiple variables I have in a dataset. Using mtcars dataset as an example, the code for a single variable plot would be:
ggplot(mtcars, aes(x = mpg)) + geom_density() + geom_vline(aes(xintercept =
mean(mpg)), linetype = "dashed", size = 0.6)
I am unclear about how I alter this to make it loop over specified variables in my dataset and produce a grid with the plots of each one. It seems like it would involve some combination of adding facet_grid and the "vars" argument but I have tried a number of combinations with no success.
It seems like in all the examples I can find online, facet_grid splits the plots by subsets of a variable, while keeping the same x and y for each plot, but I want to have the plot of x vary in each graph and the y is the density of values.
In trying to solve this, it is also my understanding that the new release of ggplot includes something involving "quasiquotation" which may help solve my problem (https://www.tidyverse.org/articles/2018/07/ggplot2-tidy-evaluation/) but again, I couldn't quite figure out how to apply the examples provided here to my own issue.
r loops ggplot2 facet-grid
asked Nov 26 '18 at 16:57
Ella WindElla Wind
155
2
The examples you've found withfacet_gridare all on point, you're just missing the fact that you need to transform your data slightly to make it work. You'll use something likegatherfrom tidyr to convert a set of variables (columns) into a single pair of columns: a key and a value column. Then you'll facet on the key variable, and the value variable is your new y variable.
– joran
Nov 26 '18 at 17:01
(Sorry, minor typo, I meant "new x variable", since you're plotting densities.)
– joran
Nov 26 '18 at 17:09
Wow, that seems to do the trick! Just for anyone new to R like me who wants to see the solution, I did this to get two plots for mtcars variables disp and hp:mt_density <- mtcars %>% gather(disp, hp, key = "variable", value = "value") ggplot(mt_density, aes(x = value)) + geom_density() + geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) + facet_grid(. ~ variable)
– Ella Wind
Nov 26 '18 at 17:56
add a comment |
I want to create a grid of density distribution plots, with a dashed vertical line at the mean, for multiple variables I have in a dataset. Using mtcars dataset as an example, the code for a single variable plot would be:
ggplot(mtcars, aes(x = mpg)) + geom_density() + geom_vline(aes(xintercept =
mean(mpg)), linetype = "dashed", size = 0.6)
I am unclear about how I alter this to make it loop over specified variables in my dataset and produce a grid with the plots of each one. It seems like it would involve some combination of adding facet_grid and the "vars" argument but I have tried a number of combinations with no success.
It seems like in all the examples I can find online, facet_grid splits the plots by subsets of a variable, while keeping the same x and y for each plot, but I want to have the plot of x vary in each graph and the y is the density of values.
In trying to solve this, it is also my understanding that the new release of ggplot includes something involving "quasiquotation" which may help solve my problem (https://www.tidyverse.org/articles/2018/07/ggplot2-tidy-evaluation/) but again, I couldn't quite figure out how to apply the examples provided here to my own issue.
r loops ggplot2 facet-grid
asked Nov 26 '18 at 16:57
Ella WindElla Wind
155
I want to create a grid of density distribution plots, with a dashed vertical line at the mean, for multiple variables I have in a dataset. Using mtcars dataset as an example, the code for a single variable plot would be:
ggplot(mtcars, aes(x = mpg)) + geom_density() + geom_vline(aes(xintercept =
mean(mpg)), linetype = "dashed", size = 0.6)
I am unclear about how I alter this to make it loop over specified variables in my dataset and produce a grid with the plots of each one. It seems like it would involve some combination of adding facet_grid and the "vars" argument but I have tried a number of combinations with no success.
It seems like in all the examples I can find online, facet_grid splits the plots by subsets of a variable, while keeping the same x and y for each plot, but I want to have the plot of x vary in each graph and the y is the density of values.
In trying to solve this, it is also my understanding that the new release of ggplot includes something involving "quasiquotation" which may help solve my problem (https://www.tidyverse.org/articles/2018/07/ggplot2-tidy-evaluation/) but again, I couldn't quite figure out how to apply the examples provided here to my own issue.
r loops ggplot2 facet-grid
r loops ggplot2 facet-grid
asked Nov 26 '18 at 16:57
Ella WindElla Wind
155
asked Nov 26 '18 at 16:57
Ella WindElla Wind
155
asked Nov 26 '18 at 16:57
Ella WindElla Wind
155
asked Nov 26 '18 at 16:57
Ella WindElla Wind
155
asked Nov 26 '18 at 16:57
Ella WindElla Wind
155
155
2
The examples you've found withfacet_gridare all on point, you're just missing the fact that you need to transform your data slightly to make it work. You'll use something likegatherfrom tidyr to convert a set of variables (columns) into a single pair of columns: a key and a value column. Then you'll facet on the key variable, and the value variable is your new y variable.
– joran
Nov 26 '18 at 17:01
(Sorry, minor typo, I meant "new x variable", since you're plotting densities.)
– joran
Nov 26 '18 at 17:09
Wow, that seems to do the trick! Just for anyone new to R like me who wants to see the solution, I did this to get two plots for mtcars variables disp and hp:mt_density <- mtcars %>% gather(disp, hp, key = "variable", value = "value") ggplot(mt_density, aes(x = value)) + geom_density() + geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) + facet_grid(. ~ variable)
– Ella Wind
Nov 26 '18 at 17:56
add a comment |
2
The examples you've found withfacet_gridare all on point, you're just missing the fact that you need to transform your data slightly to make it work. You'll use something likegatherfrom tidyr to convert a set of variables (columns) into a single pair of columns: a key and a value column. Then you'll facet on the key variable, and the value variable is your new y variable.
– joran
Nov 26 '18 at 17:01
(Sorry, minor typo, I meant "new x variable", since you're plotting densities.)
– joran
Nov 26 '18 at 17:09
Wow, that seems to do the trick! Just for anyone new to R like me who wants to see the solution, I did this to get two plots for mtcars variables disp and hp:mt_density <- mtcars %>% gather(disp, hp, key = "variable", value = "value") ggplot(mt_density, aes(x = value)) + geom_density() + geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) + facet_grid(. ~ variable)
– Ella Wind
Nov 26 '18 at 17:56
2
2
The examples you've found with
facet_grid are all on point, you're just missing the fact that you need to transform your data slightly to make it work. You'll use something like gather from tidyr to convert a set of variables (columns) into a single pair of columns: a key and a value column. Then you'll facet on the key variable, and the value variable is your new y variable.– joran
Nov 26 '18 at 17:01
The examples you've found with
facet_grid are all on point, you're just missing the fact that you need to transform your data slightly to make it work. You'll use something like gather from tidyr to convert a set of variables (columns) into a single pair of columns: a key and a value column. Then you'll facet on the key variable, and the value variable is your new y variable.– joran
Nov 26 '18 at 17:01
(Sorry, minor typo, I meant "new x variable", since you're plotting densities.)
– joran
Nov 26 '18 at 17:09
(Sorry, minor typo, I meant "new x variable", since you're plotting densities.)
– joran
Nov 26 '18 at 17:09
Wow, that seems to do the trick! Just for anyone new to R like me who wants to see the solution, I did this to get two plots for mtcars variables disp and hp:
mt_density <- mtcars %>% gather(disp, hp, key = "variable", value = "value") ggplot(mt_density, aes(x = value)) + geom_density() + geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) + facet_grid(. ~ variable)– Ella Wind
Nov 26 '18 at 17:56
Wow, that seems to do the trick! Just for anyone new to R like me who wants to see the solution, I did this to get two plots for mtcars variables disp and hp:
mt_density <- mtcars %>% gather(disp, hp, key = "variable", value = "value") ggplot(mt_density, aes(x = value)) + geom_density() + geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) + facet_grid(. ~ variable)– Ella Wind
Nov 26 '18 at 17:56
add a comment |
1 Answer
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Consider reshaping the data into long format than plotting with facets. Here both x and y scales are free since plot differ in magnitude across the columns.
rdf <- reshape(mtcars, varying = names(mtcars), v.names = "value",
times = names(mtcars), timevar = "variable",
new.row.names = 1:1000, direction = "long")
ggplot(rdf, aes(x = value)) + geom_density() +
geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) +
facet_grid(~variable, scales="free")
answered Nov 26 '18 at 17:56
ParfaitParfait
51.8k84470
add a comment |
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Consider reshaping the data into long format than plotting with facets. Here both x and y scales are free since plot differ in magnitude across the columns.
rdf <- reshape(mtcars, varying = names(mtcars), v.names = "value",
times = names(mtcars), timevar = "variable",
new.row.names = 1:1000, direction = "long")
ggplot(rdf, aes(x = value)) + geom_density() +
geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) +
facet_grid(~variable, scales="free")
answered Nov 26 '18 at 17:56
ParfaitParfait
51.8k84470
add a comment |
Consider reshaping the data into long format than plotting with facets. Here both x and y scales are free since plot differ in magnitude across the columns.
rdf <- reshape(mtcars, varying = names(mtcars), v.names = "value",
times = names(mtcars), timevar = "variable",
new.row.names = 1:1000, direction = "long")
ggplot(rdf, aes(x = value)) + geom_density() +
geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) +
facet_grid(~variable, scales="free")
answered Nov 26 '18 at 17:56
ParfaitParfait
51.8k84470
add a comment |
Consider reshaping the data into long format than plotting with facets. Here both x and y scales are free since plot differ in magnitude across the columns.
rdf <- reshape(mtcars, varying = names(mtcars), v.names = "value",
times = names(mtcars), timevar = "variable",
new.row.names = 1:1000, direction = "long")
ggplot(rdf, aes(x = value)) + geom_density() +
geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) +
facet_grid(~variable, scales="free")
answered Nov 26 '18 at 17:56
ParfaitParfait
51.8k84470
Consider reshaping the data into long format than plotting with facets. Here both x and y scales are free since plot differ in magnitude across the columns.
rdf <- reshape(mtcars, varying = names(mtcars), v.names = "value",
times = names(mtcars), timevar = "variable",
new.row.names = 1:1000, direction = "long")
ggplot(rdf, aes(x = value)) + geom_density() +
geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) +
facet_grid(~variable, scales="free")
answered Nov 26 '18 at 17:56
ParfaitParfait
51.8k84470
answered Nov 26 '18 at 17:56
ParfaitParfait
51.8k84470
answered Nov 26 '18 at 17:56
ParfaitParfait
51.8k84470
answered Nov 26 '18 at 17:56
ParfaitParfait
51.8k84470
51.8k84470
add a comment |
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2
The examples you've found with
facet_gridare all on point, you're just missing the fact that you need to transform your data slightly to make it work. You'll use something likegatherfrom tidyr to convert a set of variables (columns) into a single pair of columns: a key and a value column. Then you'll facet on the key variable, and the value variable is your new y variable.– joran
Nov 26 '18 at 17:01
(Sorry, minor typo, I meant "new x variable", since you're plotting densities.)
– joran
Nov 26 '18 at 17:09
Wow, that seems to do the trick! Just for anyone new to R like me who wants to see the solution, I did this to get two plots for mtcars variables disp and hp:
mt_density <- mtcars %>% gather(disp, hp, key = "variable", value = "value") ggplot(mt_density, aes(x = value)) + geom_density() + geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) + facet_grid(. ~ variable)– Ella Wind
Nov 26 '18 at 17:56