Multiple qqplots on one gragh and single abline ggplot2 R
Borrowing from the example QQ plot: More than two data, how can one possibly implement it in ggplot2? I need one abline only.
Sample data:
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
sim1...sim6 should have different colors.
All suggestions are welcome.
r ggplot2
asked Nov 23 '18 at 20:26
code123code123
7481629
add a comment |
Borrowing from the example QQ plot: More than two data, how can one possibly implement it in ggplot2? I need one abline only.
Sample data:
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
sim1...sim6 should have different colors.
All suggestions are welcome.
r ggplot2
asked Nov 23 '18 at 20:26
code123code123
7481629
add a comment |
Borrowing from the example QQ plot: More than two data, how can one possibly implement it in ggplot2? I need one abline only.
Sample data:
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
sim1...sim6 should have different colors.
All suggestions are welcome.
r ggplot2
asked Nov 23 '18 at 20:26
code123code123
7481629
Borrowing from the example QQ plot: More than two data, how can one possibly implement it in ggplot2? I need one abline only.
Sample data:
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
sim1...sim6 should have different colors.
All suggestions are welcome.
r ggplot2
r ggplot2
asked Nov 23 '18 at 20:26
code123code123
7481629
asked Nov 23 '18 at 20:26
code123code123
7481629
asked Nov 23 '18 at 20:26
code123code123
7481629
asked Nov 23 '18 at 20:26
code123code123
7481629
asked Nov 23 '18 at 20:26
code123code123
7481629
7481629
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Straightforward in ggplot2 with stat_qq and reshaping your data from wide to long.
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot <- dat %>%
gather(variable, value, -Observed) %>%
ggplot(aes(sample = value, color = variable)) +
geom_abline() +
stat_qq()
# All in one
plot
It might be beneficial if you look at making facets or small multiples along your comparison variable.
# Facets!
plot +
facet_wrap(~variable)
If you're looking to provide your own observed, then rather than being fancy, let qqplot do the heavy lifting but set plot.it = FALSE and it will return you a list of x/y coords for the qq plot. A little iteration with purrr::map_dfr, and you can do:
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1 = rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot_data <- map_dfr(names(dat)[-1], ~as_tibble(qqplot(dat[[.x]], dat$Observed, plot.it = FALSE)) %>%
mutate(id = .x))
ggplot(plot_data, aes(x, y, color = id)) +
geom_point() +
geom_abline() +
facet_wrap(~id)
Created on 2018-11-25 by the reprex package (v0.2.1)
answered Nov 23 '18 at 20:44
Jake KauppJake Kaupp
5,64721428
Awesome! Thank you @Jake Kaupp
– code123
Nov 23 '18 at 20:49
stat_qqandgeom_qqseem to seem to be designed to compare one or more vectorse.g. sim1-6 vectorto a theoretical distribution, I couldn't see how to use it to compare two different vectors,e.g each of sim1 to sim6 compared against Observed?How can I do exactly what I specified in the link under my question but inggplot2? Thank you.
– code123
Nov 25 '18 at 18:44
This is where you would conduct your comparison outside of ggplot, then just usegeom_pointto plot your expected vs observed.
– Jake Kaupp
Nov 25 '18 at 21:33
I guess an example with the sample data will shed more light on the problem better?
– code123
Nov 25 '18 at 21:43
1
Updated the answer to address providing your own observed values.
– Jake Kaupp
Nov 26 '18 at 1:12
|
show 1 more comment
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1 Answer
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Straightforward in ggplot2 with stat_qq and reshaping your data from wide to long.
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot <- dat %>%
gather(variable, value, -Observed) %>%
ggplot(aes(sample = value, color = variable)) +
geom_abline() +
stat_qq()
# All in one
plot
It might be beneficial if you look at making facets or small multiples along your comparison variable.
# Facets!
plot +
facet_wrap(~variable)
If you're looking to provide your own observed, then rather than being fancy, let qqplot do the heavy lifting but set plot.it = FALSE and it will return you a list of x/y coords for the qq plot. A little iteration with purrr::map_dfr, and you can do:
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1 = rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot_data <- map_dfr(names(dat)[-1], ~as_tibble(qqplot(dat[[.x]], dat$Observed, plot.it = FALSE)) %>%
mutate(id = .x))
ggplot(plot_data, aes(x, y, color = id)) +
geom_point() +
geom_abline() +
facet_wrap(~id)
Created on 2018-11-25 by the reprex package (v0.2.1)
answered Nov 23 '18 at 20:44
Jake KauppJake Kaupp
5,64721428
Awesome! Thank you @Jake Kaupp
– code123
Nov 23 '18 at 20:49
stat_qqandgeom_qqseem to seem to be designed to compare one or more vectorse.g. sim1-6 vectorto a theoretical distribution, I couldn't see how to use it to compare two different vectors,e.g each of sim1 to sim6 compared against Observed?How can I do exactly what I specified in the link under my question but inggplot2? Thank you.
– code123
Nov 25 '18 at 18:44
This is where you would conduct your comparison outside of ggplot, then just usegeom_pointto plot your expected vs observed.
– Jake Kaupp
Nov 25 '18 at 21:33
I guess an example with the sample data will shed more light on the problem better?
– code123
Nov 25 '18 at 21:43
1
Updated the answer to address providing your own observed values.
– Jake Kaupp
Nov 26 '18 at 1:12
|
show 1 more comment
Straightforward in ggplot2 with stat_qq and reshaping your data from wide to long.
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot <- dat %>%
gather(variable, value, -Observed) %>%
ggplot(aes(sample = value, color = variable)) +
geom_abline() +
stat_qq()
# All in one
plot
It might be beneficial if you look at making facets or small multiples along your comparison variable.
# Facets!
plot +
facet_wrap(~variable)
If you're looking to provide your own observed, then rather than being fancy, let qqplot do the heavy lifting but set plot.it = FALSE and it will return you a list of x/y coords for the qq plot. A little iteration with purrr::map_dfr, and you can do:
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1 = rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot_data <- map_dfr(names(dat)[-1], ~as_tibble(qqplot(dat[[.x]], dat$Observed, plot.it = FALSE)) %>%
mutate(id = .x))
ggplot(plot_data, aes(x, y, color = id)) +
geom_point() +
geom_abline() +
facet_wrap(~id)
Created on 2018-11-25 by the reprex package (v0.2.1)
answered Nov 23 '18 at 20:44
Jake KauppJake Kaupp
5,64721428
Awesome! Thank you @Jake Kaupp
– code123
Nov 23 '18 at 20:49
stat_qqandgeom_qqseem to seem to be designed to compare one or more vectorse.g. sim1-6 vectorto a theoretical distribution, I couldn't see how to use it to compare two different vectors,e.g each of sim1 to sim6 compared against Observed?How can I do exactly what I specified in the link under my question but inggplot2? Thank you.
– code123
Nov 25 '18 at 18:44
This is where you would conduct your comparison outside of ggplot, then just usegeom_pointto plot your expected vs observed.
– Jake Kaupp
Nov 25 '18 at 21:33
I guess an example with the sample data will shed more light on the problem better?
– code123
Nov 25 '18 at 21:43
1
Updated the answer to address providing your own observed values.
– Jake Kaupp
Nov 26 '18 at 1:12
|
show 1 more comment
Straightforward in ggplot2 with stat_qq and reshaping your data from wide to long.
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot <- dat %>%
gather(variable, value, -Observed) %>%
ggplot(aes(sample = value, color = variable)) +
geom_abline() +
stat_qq()
# All in one
plot
It might be beneficial if you look at making facets or small multiples along your comparison variable.
# Facets!
plot +
facet_wrap(~variable)
If you're looking to provide your own observed, then rather than being fancy, let qqplot do the heavy lifting but set plot.it = FALSE and it will return you a list of x/y coords for the qq plot. A little iteration with purrr::map_dfr, and you can do:
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1 = rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot_data <- map_dfr(names(dat)[-1], ~as_tibble(qqplot(dat[[.x]], dat$Observed, plot.it = FALSE)) %>%
mutate(id = .x))
ggplot(plot_data, aes(x, y, color = id)) +
geom_point() +
geom_abline() +
facet_wrap(~id)
Created on 2018-11-25 by the reprex package (v0.2.1)
answered Nov 23 '18 at 20:44
Jake KauppJake Kaupp
5,64721428
Straightforward in ggplot2 with stat_qq and reshaping your data from wide to long.
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1= rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot <- dat %>%
gather(variable, value, -Observed) %>%
ggplot(aes(sample = value, color = variable)) +
geom_abline() +
stat_qq()
# All in one
plot
It might be beneficial if you look at making facets or small multiples along your comparison variable.
# Facets!
plot +
facet_wrap(~variable)
If you're looking to provide your own observed, then rather than being fancy, let qqplot do the heavy lifting but set plot.it = FALSE and it will return you a list of x/y coords for the qq plot. A little iteration with purrr::map_dfr, and you can do:
library(tidyverse)
set.seed(10)
dat <- data.frame(Observed = rnorm(20), sim1 = rnorm(20), sim2 = rnorm(20),sim3 = rnorm(20),sim4 = rnorm(20),sim5 = rnorm(20),sim6 = rnorm(20))
plot_data <- map_dfr(names(dat)[-1], ~as_tibble(qqplot(dat[[.x]], dat$Observed, plot.it = FALSE)) %>%
mutate(id = .x))
ggplot(plot_data, aes(x, y, color = id)) +
geom_point() +
geom_abline() +
facet_wrap(~id)
Created on 2018-11-25 by the reprex package (v0.2.1)
answered Nov 23 '18 at 20:44
Jake KauppJake Kaupp
5,64721428
edited Nov 26 '18 at 1:11
answered Nov 23 '18 at 20:44
Jake KauppJake Kaupp
5,64721428
answered Nov 23 '18 at 20:44
Jake KauppJake Kaupp
5,64721428
answered Nov 23 '18 at 20:44
Jake KauppJake Kaupp
5,64721428
5,64721428
Awesome! Thank you @Jake Kaupp
– code123
Nov 23 '18 at 20:49
stat_qqandgeom_qqseem to seem to be designed to compare one or more vectorse.g. sim1-6 vectorto a theoretical distribution, I couldn't see how to use it to compare two different vectors,e.g each of sim1 to sim6 compared against Observed?How can I do exactly what I specified in the link under my question but inggplot2? Thank you.
– code123
Nov 25 '18 at 18:44
This is where you would conduct your comparison outside of ggplot, then just usegeom_pointto plot your expected vs observed.
– Jake Kaupp
Nov 25 '18 at 21:33
I guess an example with the sample data will shed more light on the problem better?
– code123
Nov 25 '18 at 21:43
1
Updated the answer to address providing your own observed values.
– Jake Kaupp
Nov 26 '18 at 1:12
|
show 1 more comment
Awesome! Thank you @Jake Kaupp
– code123
Nov 23 '18 at 20:49
stat_qqandgeom_qqseem to seem to be designed to compare one or more vectorse.g. sim1-6 vectorto a theoretical distribution, I couldn't see how to use it to compare two different vectors,e.g each of sim1 to sim6 compared against Observed?How can I do exactly what I specified in the link under my question but inggplot2? Thank you.
– code123
Nov 25 '18 at 18:44
This is where you would conduct your comparison outside of ggplot, then just usegeom_pointto plot your expected vs observed.
– Jake Kaupp
Nov 25 '18 at 21:33
I guess an example with the sample data will shed more light on the problem better?
– code123
Nov 25 '18 at 21:43
1
Updated the answer to address providing your own observed values.
– Jake Kaupp
Nov 26 '18 at 1:12
Awesome! Thank you @Jake Kaupp
– code123
Nov 23 '18 at 20:49
Awesome! Thank you @Jake Kaupp
– code123
Nov 23 '18 at 20:49
stat_qq and geom_qq seem to seem to be designed to compare one or more vectors e.g. sim1-6 vector to a theoretical distribution, I couldn't see how to use it to compare two different vectors, e.g each of sim1 to sim6 compared against Observed? How can I do exactly what I specified in the link under my question but in ggplot2? Thank you.– code123
Nov 25 '18 at 18:44
stat_qq and geom_qq seem to seem to be designed to compare one or more vectors e.g. sim1-6 vector to a theoretical distribution, I couldn't see how to use it to compare two different vectors, e.g each of sim1 to sim6 compared against Observed? How can I do exactly what I specified in the link under my question but in ggplot2? Thank you.– code123
Nov 25 '18 at 18:44
This is where you would conduct your comparison outside of ggplot, then just use
geom_point to plot your expected vs observed.– Jake Kaupp
Nov 25 '18 at 21:33
This is where you would conduct your comparison outside of ggplot, then just use
geom_point to plot your expected vs observed.– Jake Kaupp
Nov 25 '18 at 21:33
I guess an example with the sample data will shed more light on the problem better?
– code123
Nov 25 '18 at 21:43
I guess an example with the sample data will shed more light on the problem better?
– code123
Nov 25 '18 at 21:43
1
1
Updated the answer to address providing your own observed values.
– Jake Kaupp
Nov 26 '18 at 1:12
Updated the answer to address providing your own observed values.
– Jake Kaupp
Nov 26 '18 at 1:12
|
show 1 more comment
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