Quantile probability (QP) plot in ggplot2
I want to make a quantile probability (QP) plot by ggplot2 where, rather than represented by a single dot, all observations of the same value are shown individually. What I try to do is a ggplot2 version of the qpplot.das function from the StatDA package.
Example: The following code:
library(StatDA)
data(chorizon)
As <- chorizon[,"As"]
qpplot.das(log10(As), qdist = qnorm, xlab = "As (ppm)", logx = TRUE, line = FALSE)
produces this plot;
StatDA::qpplot.das, where all observations (concentrations) at for instance 0.1 ppm are 'stacked'.
I am sure there is a ggplot2 way for this plot and I am very grateful for any help on this matter. My best attempt so far is:
library(ggplot2)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
ggplot() +
geom_point(aes(As), stat = "ecdf") +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
r ggplot2
asked Nov 19 '18 at 20:57
Ola A. EggenOla A. Eggen
31
add a comment |
I want to make a quantile probability (QP) plot by ggplot2 where, rather than represented by a single dot, all observations of the same value are shown individually. What I try to do is a ggplot2 version of the qpplot.das function from the StatDA package.
Example: The following code:
library(StatDA)
data(chorizon)
As <- chorizon[,"As"]
qpplot.das(log10(As), qdist = qnorm, xlab = "As (ppm)", logx = TRUE, line = FALSE)
produces this plot;
StatDA::qpplot.das, where all observations (concentrations) at for instance 0.1 ppm are 'stacked'.
I am sure there is a ggplot2 way for this plot and I am very grateful for any help on this matter. My best attempt so far is:
library(ggplot2)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
ggplot() +
geom_point(aes(As), stat = "ecdf") +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
r ggplot2
asked Nov 19 '18 at 20:57
Ola A. EggenOla A. Eggen
31
add a comment |
I want to make a quantile probability (QP) plot by ggplot2 where, rather than represented by a single dot, all observations of the same value are shown individually. What I try to do is a ggplot2 version of the qpplot.das function from the StatDA package.
Example: The following code:
library(StatDA)
data(chorizon)
As <- chorizon[,"As"]
qpplot.das(log10(As), qdist = qnorm, xlab = "As (ppm)", logx = TRUE, line = FALSE)
produces this plot;
StatDA::qpplot.das, where all observations (concentrations) at for instance 0.1 ppm are 'stacked'.
I am sure there is a ggplot2 way for this plot and I am very grateful for any help on this matter. My best attempt so far is:
library(ggplot2)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
ggplot() +
geom_point(aes(As), stat = "ecdf") +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
r ggplot2
asked Nov 19 '18 at 20:57
Ola A. EggenOla A. Eggen
31
I want to make a quantile probability (QP) plot by ggplot2 where, rather than represented by a single dot, all observations of the same value are shown individually. What I try to do is a ggplot2 version of the qpplot.das function from the StatDA package.
Example: The following code:
library(StatDA)
data(chorizon)
As <- chorizon[,"As"]
qpplot.das(log10(As), qdist = qnorm, xlab = "As (ppm)", logx = TRUE, line = FALSE)
produces this plot;
StatDA::qpplot.das, where all observations (concentrations) at for instance 0.1 ppm are 'stacked'.
I am sure there is a ggplot2 way for this plot and I am very grateful for any help on this matter. My best attempt so far is:
library(ggplot2)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
ggplot() +
geom_point(aes(As), stat = "ecdf") +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
r ggplot2
r ggplot2
asked Nov 19 '18 at 20:57
Ola A. EggenOla A. Eggen
31
asked Nov 19 '18 at 20:57
Ola A. EggenOla A. Eggen
31
asked Nov 19 '18 at 20:57
Ola A. EggenOla A. Eggen
31
asked Nov 19 '18 at 20:57
Ola A. EggenOla A. Eggen
31
asked Nov 19 '18 at 20:57
Ola A. EggenOla A. Eggen
31
31
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
This works:
library(tidyverse)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
chorizon %>%
arrange(As) %>%
mutate(prob = cumsum(As / sum(As))) %>%
ggplot(aes(As, prob)) +
geom_point(shape = 3) +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
answered Nov 19 '18 at 21:26
DiceboyTDiceboyT
1,01511
Excellent, thanks a lot, @DiceboyT!
– Ola A. Eggen
Nov 21 '18 at 22:08
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
This works:
library(tidyverse)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
chorizon %>%
arrange(As) %>%
mutate(prob = cumsum(As / sum(As))) %>%
ggplot(aes(As, prob)) +
geom_point(shape = 3) +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
answered Nov 19 '18 at 21:26
DiceboyTDiceboyT
1,01511
Excellent, thanks a lot, @DiceboyT!
– Ola A. Eggen
Nov 21 '18 at 22:08
add a comment |
This works:
library(tidyverse)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
chorizon %>%
arrange(As) %>%
mutate(prob = cumsum(As / sum(As))) %>%
ggplot(aes(As, prob)) +
geom_point(shape = 3) +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
answered Nov 19 '18 at 21:26
DiceboyTDiceboyT
1,01511
Excellent, thanks a lot, @DiceboyT!
– Ola A. Eggen
Nov 21 '18 at 22:08
add a comment |
This works:
library(tidyverse)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
chorizon %>%
arrange(As) %>%
mutate(prob = cumsum(As / sum(As))) %>%
ggplot(aes(As, prob)) +
geom_point(shape = 3) +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
answered Nov 19 '18 at 21:26
DiceboyTDiceboyT
1,01511
This works:
library(tidyverse)
pbreaks <- c(0.001, 0.01, 0.05, .10, .30, .50, .70, .90, 0.95, 0.99, 0.999)
chorizon %>%
arrange(As) %>%
mutate(prob = cumsum(As / sum(As))) %>%
ggplot(aes(As, prob)) +
geom_point(shape = 3) +
scale_y_continuous(trans = scales::probability_trans("norm"),
breaks = pbreaks,
labels = prettyNum(pbreaks*100)) +
scale_x_log10()
answered Nov 19 '18 at 21:26
DiceboyTDiceboyT
1,01511
answered Nov 19 '18 at 21:26
DiceboyTDiceboyT
1,01511
answered Nov 19 '18 at 21:26
DiceboyTDiceboyT
1,01511
answered Nov 19 '18 at 21:26
DiceboyTDiceboyT
1,01511
1,01511
Excellent, thanks a lot, @DiceboyT!
– Ola A. Eggen
Nov 21 '18 at 22:08
add a comment |
Excellent, thanks a lot, @DiceboyT!
– Ola A. Eggen
Nov 21 '18 at 22:08
Excellent, thanks a lot, @DiceboyT!
– Ola A. Eggen
Nov 21 '18 at 22:08
Excellent, thanks a lot, @DiceboyT!
– Ola A. Eggen
Nov 21 '18 at 22:08
add a comment |
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