Annual exceedance probability scale in ggplot
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I've got a dataframe df of calculated Annual exceedance probability (AEP) of daily maximum liquid precipitation (P):
df <- tibble::tribble(
~AEP, ~P,
0.001, 299.0973209,
0.01, 254.7226534,
0.03, 233.0298722,
0.05, 223.9571177,
0.1, 211.2898816,
0.3, 190.5075232,
0.5, 182.3294549,
1, 170.5569051,
3, 148.9113334,
5, 138.991102,
10, 125.4449161,
20, 110.1408306,
25, 104.74124,
30, 100.2363357,
40, 92.15268627,
50, 85.75477796,
60, 79.55311702,
70, 73.44249835,
75, 70.21061223,
80, 66.79821521,
90, 58.54507042,
95, 52.44861458,
97, 48.86357489,
99, 43.12184627,
99.5, 39.72675936,
99.7, 37.5826596,
99.9, 33.91759317
)
All I need is to create a specific scale with equal distance between breaks in the middle and increasing on both ends. A perfect example from book is here:
All I was able to create by myself (based on code chuncks from this gist) return me a mess of labels:
library(dplyr)
library(scales)
library(ggplot2)
df %>%
ggplot(aes(x = AEP, y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = df$AEP,
labels = str_c(df$AEP,'%'),
expand = c(0.001,0.001)) +
theme_grey(base_size = 12)
r ggplot2 probability
asked Nov 16 '18 at 15:45
atsyplenkovatsyplenkov
305211
add a comment |
0
I've got a dataframe df of calculated Annual exceedance probability (AEP) of daily maximum liquid precipitation (P):
df <- tibble::tribble(
~AEP, ~P,
0.001, 299.0973209,
0.01, 254.7226534,
0.03, 233.0298722,
0.05, 223.9571177,
0.1, 211.2898816,
0.3, 190.5075232,
0.5, 182.3294549,
1, 170.5569051,
3, 148.9113334,
5, 138.991102,
10, 125.4449161,
20, 110.1408306,
25, 104.74124,
30, 100.2363357,
40, 92.15268627,
50, 85.75477796,
60, 79.55311702,
70, 73.44249835,
75, 70.21061223,
80, 66.79821521,
90, 58.54507042,
95, 52.44861458,
97, 48.86357489,
99, 43.12184627,
99.5, 39.72675936,
99.7, 37.5826596,
99.9, 33.91759317
)
All I need is to create a specific scale with equal distance between breaks in the middle and increasing on both ends. A perfect example from book is here:
All I was able to create by myself (based on code chuncks from this gist) return me a mess of labels:
library(dplyr)
library(scales)
library(ggplot2)
df %>%
ggplot(aes(x = AEP, y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = df$AEP,
labels = str_c(df$AEP,'%'),
expand = c(0.001,0.001)) +
theme_grey(base_size = 12)
r ggplot2 probability
asked Nov 16 '18 at 15:45
atsyplenkovatsyplenkov
305211
How exactly do you want to determine the breaks? Is taking all the AEP values from your data what you want, or just the first try at it?
– camille
Nov 16 '18 at 16:09
@camille I want all AEP values to be breaks or at least as on the example (e.g. without 0.001)
– atsyplenkov
Nov 16 '18 at 16:12
I'm not familiar with the context of this type of data or chart. Can you explain how the breaks in your chart differ from those in the example? Where do the example's breaks come from?
– camille
Nov 16 '18 at 16:37
@camille the distance between breaks should not be the same. On both ends where the probability is below 20 (or higher than 80) it is logscale. I know that in Python it's possible to do with 'probscale', for example (matplotlib.org/mpl-probscale)
– atsyplenkov
Nov 17 '18 at 7:27
add a comment |
0
0
0
I've got a dataframe df of calculated Annual exceedance probability (AEP) of daily maximum liquid precipitation (P):
df <- tibble::tribble(
~AEP, ~P,
0.001, 299.0973209,
0.01, 254.7226534,
0.03, 233.0298722,
0.05, 223.9571177,
0.1, 211.2898816,
0.3, 190.5075232,
0.5, 182.3294549,
1, 170.5569051,
3, 148.9113334,
5, 138.991102,
10, 125.4449161,
20, 110.1408306,
25, 104.74124,
30, 100.2363357,
40, 92.15268627,
50, 85.75477796,
60, 79.55311702,
70, 73.44249835,
75, 70.21061223,
80, 66.79821521,
90, 58.54507042,
95, 52.44861458,
97, 48.86357489,
99, 43.12184627,
99.5, 39.72675936,
99.7, 37.5826596,
99.9, 33.91759317
)
All I need is to create a specific scale with equal distance between breaks in the middle and increasing on both ends. A perfect example from book is here:
All I was able to create by myself (based on code chuncks from this gist) return me a mess of labels:
library(dplyr)
library(scales)
library(ggplot2)
df %>%
ggplot(aes(x = AEP, y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = df$AEP,
labels = str_c(df$AEP,'%'),
expand = c(0.001,0.001)) +
theme_grey(base_size = 12)
r ggplot2 probability
asked Nov 16 '18 at 15:45
atsyplenkovatsyplenkov
305211
I've got a dataframe df of calculated Annual exceedance probability (AEP) of daily maximum liquid precipitation (P):
df <- tibble::tribble(
~AEP, ~P,
0.001, 299.0973209,
0.01, 254.7226534,
0.03, 233.0298722,
0.05, 223.9571177,
0.1, 211.2898816,
0.3, 190.5075232,
0.5, 182.3294549,
1, 170.5569051,
3, 148.9113334,
5, 138.991102,
10, 125.4449161,
20, 110.1408306,
25, 104.74124,
30, 100.2363357,
40, 92.15268627,
50, 85.75477796,
60, 79.55311702,
70, 73.44249835,
75, 70.21061223,
80, 66.79821521,
90, 58.54507042,
95, 52.44861458,
97, 48.86357489,
99, 43.12184627,
99.5, 39.72675936,
99.7, 37.5826596,
99.9, 33.91759317
)
All I need is to create a specific scale with equal distance between breaks in the middle and increasing on both ends. A perfect example from book is here:
All I was able to create by myself (based on code chuncks from this gist) return me a mess of labels:
library(dplyr)
library(scales)
library(ggplot2)
df %>%
ggplot(aes(x = AEP, y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = df$AEP,
labels = str_c(df$AEP,'%'),
expand = c(0.001,0.001)) +
theme_grey(base_size = 12)
r ggplot2 probability
r ggplot2 probability
asked Nov 16 '18 at 15:45
atsyplenkovatsyplenkov
305211
asked Nov 16 '18 at 15:45
atsyplenkovatsyplenkov
305211
asked Nov 16 '18 at 15:45
atsyplenkovatsyplenkov
305211
asked Nov 16 '18 at 15:45
atsyplenkovatsyplenkov
305211
asked Nov 16 '18 at 15:45
atsyplenkovatsyplenkov
305211
305211
How exactly do you want to determine the breaks? Is taking all the AEP values from your data what you want, or just the first try at it?
– camille
Nov 16 '18 at 16:09
@camille I want all AEP values to be breaks or at least as on the example (e.g. without 0.001)
– atsyplenkov
Nov 16 '18 at 16:12
I'm not familiar with the context of this type of data or chart. Can you explain how the breaks in your chart differ from those in the example? Where do the example's breaks come from?
– camille
Nov 16 '18 at 16:37
@camille the distance between breaks should not be the same. On both ends where the probability is below 20 (or higher than 80) it is logscale. I know that in Python it's possible to do with 'probscale', for example (matplotlib.org/mpl-probscale)
– atsyplenkov
Nov 17 '18 at 7:27
add a comment |
How exactly do you want to determine the breaks? Is taking all the AEP values from your data what you want, or just the first try at it?
– camille
Nov 16 '18 at 16:09
@camille I want all AEP values to be breaks or at least as on the example (e.g. without 0.001)
– atsyplenkov
Nov 16 '18 at 16:12
I'm not familiar with the context of this type of data or chart. Can you explain how the breaks in your chart differ from those in the example? Where do the example's breaks come from?
– camille
Nov 16 '18 at 16:37
@camille the distance between breaks should not be the same. On both ends where the probability is below 20 (or higher than 80) it is logscale. I know that in Python it's possible to do with 'probscale', for example (matplotlib.org/mpl-probscale)
– atsyplenkov
Nov 17 '18 at 7:27
How exactly do you want to determine the breaks? Is taking all the AEP values from your data what you want, or just the first try at it?
– camille
Nov 16 '18 at 16:09
How exactly do you want to determine the breaks? Is taking all the AEP values from your data what you want, or just the first try at it?
– camille
Nov 16 '18 at 16:09
@camille I want all AEP values to be breaks or at least as on the example (e.g. without 0.001)
– atsyplenkov
Nov 16 '18 at 16:12
@camille I want all AEP values to be breaks or at least as on the example (e.g. without 0.001)
– atsyplenkov
Nov 16 '18 at 16:12
I'm not familiar with the context of this type of data or chart. Can you explain how the breaks in your chart differ from those in the example? Where do the example's breaks come from?
– camille
Nov 16 '18 at 16:37
I'm not familiar with the context of this type of data or chart. Can you explain how the breaks in your chart differ from those in the example? Where do the example's breaks come from?
– camille
Nov 16 '18 at 16:37
@camille the distance between breaks should not be the same. On both ends where the probability is below 20 (or higher than 80) it is logscale. I know that in Python it's possible to do with 'probscale', for example (matplotlib.org/mpl-probscale)
– atsyplenkov
Nov 17 '18 at 7:27
@camille the distance between breaks should not be the same. On both ends where the probability is below 20 (or higher than 80) it is logscale. I know that in Python it's possible to do with 'probscale', for example (matplotlib.org/mpl-probscale)
– atsyplenkov
Nov 17 '18 at 7:27
add a comment |
1 Answer
1
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oldest
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I found an answer in one of the Stack Overflow's questions.
To create desired scale we need to apply a qnorm quantile function to all AEP values (or other x values), e.g.
df %>%
ggplot(aes(x = qnorm(AEP/100), # transform to quantiles
y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = qnorm(df$AEP/100), #transform
labels = df$AEP,
expand = c(0.035,0.035)) +
theme_bw(base_size = 12)
answered Nov 17 '18 at 11:10
atsyplenkovatsyplenkov
305211
add a comment |
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1 Answer
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1 Answer
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I found an answer in one of the Stack Overflow's questions.
To create desired scale we need to apply a qnorm quantile function to all AEP values (or other x values), e.g.
df %>%
ggplot(aes(x = qnorm(AEP/100), # transform to quantiles
y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = qnorm(df$AEP/100), #transform
labels = df$AEP,
expand = c(0.035,0.035)) +
theme_bw(base_size = 12)
answered Nov 17 '18 at 11:10
atsyplenkovatsyplenkov
305211
add a comment |
0
I found an answer in one of the Stack Overflow's questions.
To create desired scale we need to apply a qnorm quantile function to all AEP values (or other x values), e.g.
df %>%
ggplot(aes(x = qnorm(AEP/100), # transform to quantiles
y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = qnorm(df$AEP/100), #transform
labels = df$AEP,
expand = c(0.035,0.035)) +
theme_bw(base_size = 12)
answered Nov 17 '18 at 11:10
atsyplenkovatsyplenkov
305211
add a comment |
0
0
0
I found an answer in one of the Stack Overflow's questions.
To create desired scale we need to apply a qnorm quantile function to all AEP values (or other x values), e.g.
df %>%
ggplot(aes(x = qnorm(AEP/100), # transform to quantiles
y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = qnorm(df$AEP/100), #transform
labels = df$AEP,
expand = c(0.035,0.035)) +
theme_bw(base_size = 12)
answered Nov 17 '18 at 11:10
atsyplenkovatsyplenkov
305211
I found an answer in one of the Stack Overflow's questions.
To create desired scale we need to apply a qnorm quantile function to all AEP values (or other x values), e.g.
df %>%
ggplot(aes(x = qnorm(AEP/100), # transform to quantiles
y = P)) +
geom_point() +
geom_line() +
scale_y_continuous(name = "Precipitation (P), mm",
labels = scales::comma,
breaks = seq(0, 300, 50)) +
scale_x_continuous(name = "AEP, %",
breaks = qnorm(df$AEP/100), #transform
labels = df$AEP,
expand = c(0.035,0.035)) +
theme_bw(base_size = 12)
answered Nov 17 '18 at 11:10
atsyplenkovatsyplenkov
305211
answered Nov 17 '18 at 11:10
atsyplenkovatsyplenkov
305211
answered Nov 17 '18 at 11:10
atsyplenkovatsyplenkov
305211
answered Nov 17 '18 at 11:10
atsyplenkovatsyplenkov
305211
305211
add a comment |
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How exactly do you want to determine the breaks? Is taking all the AEP values from your data what you want, or just the first try at it?
– camille
Nov 16 '18 at 16:09
@camille I want all AEP values to be breaks or at least as on the example (e.g. without 0.001)
– atsyplenkov
Nov 16 '18 at 16:12
I'm not familiar with the context of this type of data or chart. Can you explain how the breaks in your chart differ from those in the example? Where do the example's breaks come from?
– camille
Nov 16 '18 at 16:37
@camille the distance between breaks should not be the same. On both ends where the probability is below 20 (or higher than 80) it is logscale. I know that in Python it's possible to do with 'probscale', for example (matplotlib.org/mpl-probscale)
– atsyplenkov
Nov 17 '18 at 7:27