Different number of outliers with ggplot2
Can somebody explain to me why I get a different number of outliers
with the normal boxplot command and with the geom_boxplot
of ggplot2?
Here you have an example:
x <- c(280.9, 135.9, 321.4, 333.7, 0.2, 71.3, 33.0, 102.6, 126.8, 194.8, 35.5,
107.3, 45.1, 107.2, 55.2, 28.1, 36.9, 24.3, 68.7, 163.5, 0.8, 31.8, 121.4,
84.7, 34.3, 25.2, 101.4, 203.2, 194.1, 27.9, 42.5, 47.0, 85.1, 90.4, 103.8,
45.1, 94.0, 36.0, 60.9, 97.1, 42.5, 96.4, 58.4, 174.0, 173.2, 164.1, 92.1,
41.9, 130.2, 94.7, 121.5, 261.4, 46.7, 16.3, 50.7, 112.9, 112.2, 242.5, 140.6,
112.6, 31.2, 36.7, 97.4, 140.5, 123.5, 42.9, 59.4, 94.5, 37.4, 232.2, 114.6,
60.7, 27.8, 115.5, 111.9, 60.1)
data <- data.frame(x)
boxplot(data$x)
ggplot(data, aes(y=x)) + geom_boxplot()
With the boxplot
command I get the plot below with 4 outliers
.
And with ggplot2
I get the plot below with 5 outliers
.
r ggplot2 data-visualization boxplot
add a comment |
Can somebody explain to me why I get a different number of outliers
with the normal boxplot command and with the geom_boxplot
of ggplot2?
Here you have an example:
x <- c(280.9, 135.9, 321.4, 333.7, 0.2, 71.3, 33.0, 102.6, 126.8, 194.8, 35.5,
107.3, 45.1, 107.2, 55.2, 28.1, 36.9, 24.3, 68.7, 163.5, 0.8, 31.8, 121.4,
84.7, 34.3, 25.2, 101.4, 203.2, 194.1, 27.9, 42.5, 47.0, 85.1, 90.4, 103.8,
45.1, 94.0, 36.0, 60.9, 97.1, 42.5, 96.4, 58.4, 174.0, 173.2, 164.1, 92.1,
41.9, 130.2, 94.7, 121.5, 261.4, 46.7, 16.3, 50.7, 112.9, 112.2, 242.5, 140.6,
112.6, 31.2, 36.7, 97.4, 140.5, 123.5, 42.9, 59.4, 94.5, 37.4, 232.2, 114.6,
60.7, 27.8, 115.5, 111.9, 60.1)
data <- data.frame(x)
boxplot(data$x)
ggplot(data, aes(y=x)) + geom_boxplot()
With the boxplot
command I get the plot below with 4 outliers
.
And with ggplot2
I get the plot below with 5 outliers
.
r ggplot2 data-visualization boxplot
Look at the ylimits. You're essentially zooming in.
– NelsonGon
Dec 15 at 16:12
3
given that both plots show data from 200-300, and that's where the extra outlier is, this isn't a zoom issue
– IceCreamToucan
Dec 15 at 16:23
ggplot2 and base boxplot use same range (1.5), but do they use same way to calculate quantiles?
– PoGibas
Dec 15 at 16:26
(boxplot(data$x))
shows that its upper hinge is at 122.5, not 122.0 as suggested byquantile(data$x)
. This would put the end of the whisker at 242.5, which is above the 241.25 point. @dww's excellent answer demonstrates a way to mitigate this.
– r2evans
Dec 15 at 16:39
add a comment |
Can somebody explain to me why I get a different number of outliers
with the normal boxplot command and with the geom_boxplot
of ggplot2?
Here you have an example:
x <- c(280.9, 135.9, 321.4, 333.7, 0.2, 71.3, 33.0, 102.6, 126.8, 194.8, 35.5,
107.3, 45.1, 107.2, 55.2, 28.1, 36.9, 24.3, 68.7, 163.5, 0.8, 31.8, 121.4,
84.7, 34.3, 25.2, 101.4, 203.2, 194.1, 27.9, 42.5, 47.0, 85.1, 90.4, 103.8,
45.1, 94.0, 36.0, 60.9, 97.1, 42.5, 96.4, 58.4, 174.0, 173.2, 164.1, 92.1,
41.9, 130.2, 94.7, 121.5, 261.4, 46.7, 16.3, 50.7, 112.9, 112.2, 242.5, 140.6,
112.6, 31.2, 36.7, 97.4, 140.5, 123.5, 42.9, 59.4, 94.5, 37.4, 232.2, 114.6,
60.7, 27.8, 115.5, 111.9, 60.1)
data <- data.frame(x)
boxplot(data$x)
ggplot(data, aes(y=x)) + geom_boxplot()
With the boxplot
command I get the plot below with 4 outliers
.
And with ggplot2
I get the plot below with 5 outliers
.
r ggplot2 data-visualization boxplot
Can somebody explain to me why I get a different number of outliers
with the normal boxplot command and with the geom_boxplot
of ggplot2?
Here you have an example:
x <- c(280.9, 135.9, 321.4, 333.7, 0.2, 71.3, 33.0, 102.6, 126.8, 194.8, 35.5,
107.3, 45.1, 107.2, 55.2, 28.1, 36.9, 24.3, 68.7, 163.5, 0.8, 31.8, 121.4,
84.7, 34.3, 25.2, 101.4, 203.2, 194.1, 27.9, 42.5, 47.0, 85.1, 90.4, 103.8,
45.1, 94.0, 36.0, 60.9, 97.1, 42.5, 96.4, 58.4, 174.0, 173.2, 164.1, 92.1,
41.9, 130.2, 94.7, 121.5, 261.4, 46.7, 16.3, 50.7, 112.9, 112.2, 242.5, 140.6,
112.6, 31.2, 36.7, 97.4, 140.5, 123.5, 42.9, 59.4, 94.5, 37.4, 232.2, 114.6,
60.7, 27.8, 115.5, 111.9, 60.1)
data <- data.frame(x)
boxplot(data$x)
ggplot(data, aes(y=x)) + geom_boxplot()
With the boxplot
command I get the plot below with 4 outliers
.
And with ggplot2
I get the plot below with 5 outliers
.
r ggplot2 data-visualization boxplot
r ggplot2 data-visualization boxplot
edited Dec 15 at 19:00
massisenergy
547118
547118
asked Dec 15 at 16:10
Alfredo Sánchez
190112
190112
Look at the ylimits. You're essentially zooming in.
– NelsonGon
Dec 15 at 16:12
3
given that both plots show data from 200-300, and that's where the extra outlier is, this isn't a zoom issue
– IceCreamToucan
Dec 15 at 16:23
ggplot2 and base boxplot use same range (1.5), but do they use same way to calculate quantiles?
– PoGibas
Dec 15 at 16:26
(boxplot(data$x))
shows that its upper hinge is at 122.5, not 122.0 as suggested byquantile(data$x)
. This would put the end of the whisker at 242.5, which is above the 241.25 point. @dww's excellent answer demonstrates a way to mitigate this.
– r2evans
Dec 15 at 16:39
add a comment |
Look at the ylimits. You're essentially zooming in.
– NelsonGon
Dec 15 at 16:12
3
given that both plots show data from 200-300, and that's where the extra outlier is, this isn't a zoom issue
– IceCreamToucan
Dec 15 at 16:23
ggplot2 and base boxplot use same range (1.5), but do they use same way to calculate quantiles?
– PoGibas
Dec 15 at 16:26
(boxplot(data$x))
shows that its upper hinge is at 122.5, not 122.0 as suggested byquantile(data$x)
. This would put the end of the whisker at 242.5, which is above the 241.25 point. @dww's excellent answer demonstrates a way to mitigate this.
– r2evans
Dec 15 at 16:39
Look at the ylimits. You're essentially zooming in.
– NelsonGon
Dec 15 at 16:12
Look at the ylimits. You're essentially zooming in.
– NelsonGon
Dec 15 at 16:12
3
3
given that both plots show data from 200-300, and that's where the extra outlier is, this isn't a zoom issue
– IceCreamToucan
Dec 15 at 16:23
given that both plots show data from 200-300, and that's where the extra outlier is, this isn't a zoom issue
– IceCreamToucan
Dec 15 at 16:23
ggplot2 and base boxplot use same range (1.5), but do they use same way to calculate quantiles?
– PoGibas
Dec 15 at 16:26
ggplot2 and base boxplot use same range (1.5), but do they use same way to calculate quantiles?
– PoGibas
Dec 15 at 16:26
(boxplot(data$x))
shows that its upper hinge is at 122.5, not 122.0 as suggested by quantile(data$x)
. This would put the end of the whisker at 242.5, which is above the 241.25 point. @dww's excellent answer demonstrates a way to mitigate this.– r2evans
Dec 15 at 16:39
(boxplot(data$x))
shows that its upper hinge is at 122.5, not 122.0 as suggested by quantile(data$x)
. This would put the end of the whisker at 242.5, which is above the 241.25 point. @dww's excellent answer demonstrates a way to mitigate this.– r2evans
Dec 15 at 16:39
add a comment |
1 Answer
1
active
oldest
votes
ggplot and boxplot use slightly different methods to calculate the statistics. From ?geom_boxplot
we can see
The lower and upper hinges correspond to the first and third quartiles
(the 25th and 75th percentiles). This differs slightly from the method
used by the boxplot() function, and may be apparent with small
samples. See boxplot.stats() for for more information on how hinge
positions are calculated for boxplot().
You can get ggplot to use boxplot.stats
if you want the same results
# Function to use boxplot.stats to set the box-and-whisker locations
f.bxp = function(x) {
bxp = boxplot.stats(x)[["stats"]]
names(bxp) = c("ymin","lower", "middle","upper","ymax")
bxp
}
# Function to use boxplot.stats for the outliers
f.out = function(x) {
data.frame(y=boxplot.stats(x)[["out"]])
}
To use those functions in ggplot:
ggplot(data, aes(0, y=x)) +
stat_summary(fun.data=f.bxp, geom="boxplot") +
stat_summary(fun.data=f.out, geom="point")
If you want to replicate the statistics that ggplot uses natively, these are explained in ?geom_boxplot
as follows:
ymin = lower whisker = smallest observation greater than or equal to
lower hinge - 1.5 * IQR
lower = lower hinge, 25% quantile
notchlower = lower edge of notch = median - 1.58 * IQR / sqrt(n)
middle = median, 50% quantile
notchupper = upper edge of notch = median + 1.58 * IQR / sqrt(n)
upper = upper hinge, 75% quantile
ymax = upper whisker = largest observation less than or equal to upper
hinge + 1.5 * IQR
We can calculate these accordingly:
y = sort(x)
iqr = quantile(y,0.75) - quantile(y,0.25)
ymin = y[which(y >= quantile(y,0.25) - 1.5*iqr)][1]
ymax = tail(y[which(y <= quantile(y,0.75) + 1.5*iqr)],1)
lower = quantile(y,0.25)
upper = quantile(y,0.75)
middle = quantile(y,0.5)
ggplot(data, aes(y=x)) +
geom_boxplot() +
geom_hline(aes(yintercept=c(ymin)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(ymax)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(lower)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(upper)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(middle)), color='red', linetype='dashed')
We can also extract these statistics directly from a ggplot object using ggplot_build
p <- ggplot(data, aes(y=x)) + geom_boxplot()
ggplot_build(p)$data[1:5]
# ymin lower middle upper ymax
# 1 0.2 42.5 93.05 122 232.2
Is it possible to get the stats from the geom_boxplot like in boxplot.stats()?
– Alfredo Sánchez
Dec 15 at 18:11
sure - see edits in answer to show how
– dww
Dec 15 at 19:02
Thanks @dww for answering so quickly. Just one thing, in the computation of ymin you must use >=, and in the computation of ymax <=, must'n you?
– Alfredo Sánchez
Dec 15 at 19:17
that's right - ty - corrected
– dww
Dec 15 at 19:24
add a comment |
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ggplot and boxplot use slightly different methods to calculate the statistics. From ?geom_boxplot
we can see
The lower and upper hinges correspond to the first and third quartiles
(the 25th and 75th percentiles). This differs slightly from the method
used by the boxplot() function, and may be apparent with small
samples. See boxplot.stats() for for more information on how hinge
positions are calculated for boxplot().
You can get ggplot to use boxplot.stats
if you want the same results
# Function to use boxplot.stats to set the box-and-whisker locations
f.bxp = function(x) {
bxp = boxplot.stats(x)[["stats"]]
names(bxp) = c("ymin","lower", "middle","upper","ymax")
bxp
}
# Function to use boxplot.stats for the outliers
f.out = function(x) {
data.frame(y=boxplot.stats(x)[["out"]])
}
To use those functions in ggplot:
ggplot(data, aes(0, y=x)) +
stat_summary(fun.data=f.bxp, geom="boxplot") +
stat_summary(fun.data=f.out, geom="point")
If you want to replicate the statistics that ggplot uses natively, these are explained in ?geom_boxplot
as follows:
ymin = lower whisker = smallest observation greater than or equal to
lower hinge - 1.5 * IQR
lower = lower hinge, 25% quantile
notchlower = lower edge of notch = median - 1.58 * IQR / sqrt(n)
middle = median, 50% quantile
notchupper = upper edge of notch = median + 1.58 * IQR / sqrt(n)
upper = upper hinge, 75% quantile
ymax = upper whisker = largest observation less than or equal to upper
hinge + 1.5 * IQR
We can calculate these accordingly:
y = sort(x)
iqr = quantile(y,0.75) - quantile(y,0.25)
ymin = y[which(y >= quantile(y,0.25) - 1.5*iqr)][1]
ymax = tail(y[which(y <= quantile(y,0.75) + 1.5*iqr)],1)
lower = quantile(y,0.25)
upper = quantile(y,0.75)
middle = quantile(y,0.5)
ggplot(data, aes(y=x)) +
geom_boxplot() +
geom_hline(aes(yintercept=c(ymin)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(ymax)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(lower)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(upper)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(middle)), color='red', linetype='dashed')
We can also extract these statistics directly from a ggplot object using ggplot_build
p <- ggplot(data, aes(y=x)) + geom_boxplot()
ggplot_build(p)$data[1:5]
# ymin lower middle upper ymax
# 1 0.2 42.5 93.05 122 232.2
Is it possible to get the stats from the geom_boxplot like in boxplot.stats()?
– Alfredo Sánchez
Dec 15 at 18:11
sure - see edits in answer to show how
– dww
Dec 15 at 19:02
Thanks @dww for answering so quickly. Just one thing, in the computation of ymin you must use >=, and in the computation of ymax <=, must'n you?
– Alfredo Sánchez
Dec 15 at 19:17
that's right - ty - corrected
– dww
Dec 15 at 19:24
add a comment |
ggplot and boxplot use slightly different methods to calculate the statistics. From ?geom_boxplot
we can see
The lower and upper hinges correspond to the first and third quartiles
(the 25th and 75th percentiles). This differs slightly from the method
used by the boxplot() function, and may be apparent with small
samples. See boxplot.stats() for for more information on how hinge
positions are calculated for boxplot().
You can get ggplot to use boxplot.stats
if you want the same results
# Function to use boxplot.stats to set the box-and-whisker locations
f.bxp = function(x) {
bxp = boxplot.stats(x)[["stats"]]
names(bxp) = c("ymin","lower", "middle","upper","ymax")
bxp
}
# Function to use boxplot.stats for the outliers
f.out = function(x) {
data.frame(y=boxplot.stats(x)[["out"]])
}
To use those functions in ggplot:
ggplot(data, aes(0, y=x)) +
stat_summary(fun.data=f.bxp, geom="boxplot") +
stat_summary(fun.data=f.out, geom="point")
If you want to replicate the statistics that ggplot uses natively, these are explained in ?geom_boxplot
as follows:
ymin = lower whisker = smallest observation greater than or equal to
lower hinge - 1.5 * IQR
lower = lower hinge, 25% quantile
notchlower = lower edge of notch = median - 1.58 * IQR / sqrt(n)
middle = median, 50% quantile
notchupper = upper edge of notch = median + 1.58 * IQR / sqrt(n)
upper = upper hinge, 75% quantile
ymax = upper whisker = largest observation less than or equal to upper
hinge + 1.5 * IQR
We can calculate these accordingly:
y = sort(x)
iqr = quantile(y,0.75) - quantile(y,0.25)
ymin = y[which(y >= quantile(y,0.25) - 1.5*iqr)][1]
ymax = tail(y[which(y <= quantile(y,0.75) + 1.5*iqr)],1)
lower = quantile(y,0.25)
upper = quantile(y,0.75)
middle = quantile(y,0.5)
ggplot(data, aes(y=x)) +
geom_boxplot() +
geom_hline(aes(yintercept=c(ymin)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(ymax)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(lower)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(upper)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(middle)), color='red', linetype='dashed')
We can also extract these statistics directly from a ggplot object using ggplot_build
p <- ggplot(data, aes(y=x)) + geom_boxplot()
ggplot_build(p)$data[1:5]
# ymin lower middle upper ymax
# 1 0.2 42.5 93.05 122 232.2
Is it possible to get the stats from the geom_boxplot like in boxplot.stats()?
– Alfredo Sánchez
Dec 15 at 18:11
sure - see edits in answer to show how
– dww
Dec 15 at 19:02
Thanks @dww for answering so quickly. Just one thing, in the computation of ymin you must use >=, and in the computation of ymax <=, must'n you?
– Alfredo Sánchez
Dec 15 at 19:17
that's right - ty - corrected
– dww
Dec 15 at 19:24
add a comment |
ggplot and boxplot use slightly different methods to calculate the statistics. From ?geom_boxplot
we can see
The lower and upper hinges correspond to the first and third quartiles
(the 25th and 75th percentiles). This differs slightly from the method
used by the boxplot() function, and may be apparent with small
samples. See boxplot.stats() for for more information on how hinge
positions are calculated for boxplot().
You can get ggplot to use boxplot.stats
if you want the same results
# Function to use boxplot.stats to set the box-and-whisker locations
f.bxp = function(x) {
bxp = boxplot.stats(x)[["stats"]]
names(bxp) = c("ymin","lower", "middle","upper","ymax")
bxp
}
# Function to use boxplot.stats for the outliers
f.out = function(x) {
data.frame(y=boxplot.stats(x)[["out"]])
}
To use those functions in ggplot:
ggplot(data, aes(0, y=x)) +
stat_summary(fun.data=f.bxp, geom="boxplot") +
stat_summary(fun.data=f.out, geom="point")
If you want to replicate the statistics that ggplot uses natively, these are explained in ?geom_boxplot
as follows:
ymin = lower whisker = smallest observation greater than or equal to
lower hinge - 1.5 * IQR
lower = lower hinge, 25% quantile
notchlower = lower edge of notch = median - 1.58 * IQR / sqrt(n)
middle = median, 50% quantile
notchupper = upper edge of notch = median + 1.58 * IQR / sqrt(n)
upper = upper hinge, 75% quantile
ymax = upper whisker = largest observation less than or equal to upper
hinge + 1.5 * IQR
We can calculate these accordingly:
y = sort(x)
iqr = quantile(y,0.75) - quantile(y,0.25)
ymin = y[which(y >= quantile(y,0.25) - 1.5*iqr)][1]
ymax = tail(y[which(y <= quantile(y,0.75) + 1.5*iqr)],1)
lower = quantile(y,0.25)
upper = quantile(y,0.75)
middle = quantile(y,0.5)
ggplot(data, aes(y=x)) +
geom_boxplot() +
geom_hline(aes(yintercept=c(ymin)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(ymax)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(lower)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(upper)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(middle)), color='red', linetype='dashed')
We can also extract these statistics directly from a ggplot object using ggplot_build
p <- ggplot(data, aes(y=x)) + geom_boxplot()
ggplot_build(p)$data[1:5]
# ymin lower middle upper ymax
# 1 0.2 42.5 93.05 122 232.2
ggplot and boxplot use slightly different methods to calculate the statistics. From ?geom_boxplot
we can see
The lower and upper hinges correspond to the first and third quartiles
(the 25th and 75th percentiles). This differs slightly from the method
used by the boxplot() function, and may be apparent with small
samples. See boxplot.stats() for for more information on how hinge
positions are calculated for boxplot().
You can get ggplot to use boxplot.stats
if you want the same results
# Function to use boxplot.stats to set the box-and-whisker locations
f.bxp = function(x) {
bxp = boxplot.stats(x)[["stats"]]
names(bxp) = c("ymin","lower", "middle","upper","ymax")
bxp
}
# Function to use boxplot.stats for the outliers
f.out = function(x) {
data.frame(y=boxplot.stats(x)[["out"]])
}
To use those functions in ggplot:
ggplot(data, aes(0, y=x)) +
stat_summary(fun.data=f.bxp, geom="boxplot") +
stat_summary(fun.data=f.out, geom="point")
If you want to replicate the statistics that ggplot uses natively, these are explained in ?geom_boxplot
as follows:
ymin = lower whisker = smallest observation greater than or equal to
lower hinge - 1.5 * IQR
lower = lower hinge, 25% quantile
notchlower = lower edge of notch = median - 1.58 * IQR / sqrt(n)
middle = median, 50% quantile
notchupper = upper edge of notch = median + 1.58 * IQR / sqrt(n)
upper = upper hinge, 75% quantile
ymax = upper whisker = largest observation less than or equal to upper
hinge + 1.5 * IQR
We can calculate these accordingly:
y = sort(x)
iqr = quantile(y,0.75) - quantile(y,0.25)
ymin = y[which(y >= quantile(y,0.25) - 1.5*iqr)][1]
ymax = tail(y[which(y <= quantile(y,0.75) + 1.5*iqr)],1)
lower = quantile(y,0.25)
upper = quantile(y,0.75)
middle = quantile(y,0.5)
ggplot(data, aes(y=x)) +
geom_boxplot() +
geom_hline(aes(yintercept=c(ymin)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(ymax)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(lower)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(upper)), color='red', linetype='dashed') +
geom_hline(aes(yintercept=c(middle)), color='red', linetype='dashed')
We can also extract these statistics directly from a ggplot object using ggplot_build
p <- ggplot(data, aes(y=x)) + geom_boxplot()
ggplot_build(p)$data[1:5]
# ymin lower middle upper ymax
# 1 0.2 42.5 93.05 122 232.2
edited Dec 17 at 12:47
answered Dec 15 at 16:29
dww
14.3k22655
14.3k22655
Is it possible to get the stats from the geom_boxplot like in boxplot.stats()?
– Alfredo Sánchez
Dec 15 at 18:11
sure - see edits in answer to show how
– dww
Dec 15 at 19:02
Thanks @dww for answering so quickly. Just one thing, in the computation of ymin you must use >=, and in the computation of ymax <=, must'n you?
– Alfredo Sánchez
Dec 15 at 19:17
that's right - ty - corrected
– dww
Dec 15 at 19:24
add a comment |
Is it possible to get the stats from the geom_boxplot like in boxplot.stats()?
– Alfredo Sánchez
Dec 15 at 18:11
sure - see edits in answer to show how
– dww
Dec 15 at 19:02
Thanks @dww for answering so quickly. Just one thing, in the computation of ymin you must use >=, and in the computation of ymax <=, must'n you?
– Alfredo Sánchez
Dec 15 at 19:17
that's right - ty - corrected
– dww
Dec 15 at 19:24
Is it possible to get the stats from the geom_boxplot like in boxplot.stats()?
– Alfredo Sánchez
Dec 15 at 18:11
Is it possible to get the stats from the geom_boxplot like in boxplot.stats()?
– Alfredo Sánchez
Dec 15 at 18:11
sure - see edits in answer to show how
– dww
Dec 15 at 19:02
sure - see edits in answer to show how
– dww
Dec 15 at 19:02
Thanks @dww for answering so quickly. Just one thing, in the computation of ymin you must use >=, and in the computation of ymax <=, must'n you?
– Alfredo Sánchez
Dec 15 at 19:17
Thanks @dww for answering so quickly. Just one thing, in the computation of ymin you must use >=, and in the computation of ymax <=, must'n you?
– Alfredo Sánchez
Dec 15 at 19:17
that's right - ty - corrected
– dww
Dec 15 at 19:24
that's right - ty - corrected
– dww
Dec 15 at 19:24
add a comment |
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Look at the ylimits. You're essentially zooming in.
– NelsonGon
Dec 15 at 16:12
3
given that both plots show data from 200-300, and that's where the extra outlier is, this isn't a zoom issue
– IceCreamToucan
Dec 15 at 16:23
ggplot2 and base boxplot use same range (1.5), but do they use same way to calculate quantiles?
– PoGibas
Dec 15 at 16:26
(boxplot(data$x))
shows that its upper hinge is at 122.5, not 122.0 as suggested byquantile(data$x)
. This would put the end of the whisker at 242.5, which is above the 241.25 point. @dww's excellent answer demonstrates a way to mitigate this.– r2evans
Dec 15 at 16:39