Re: [R] Normality tests on groups of rows in a data frame, grouped based on content in other columns
Hi Dennis,
Thanks for your prompt response.
Best,
Joel
Dennis Murphy djmu...@gmail.com 30-10-2011 21:11
Hi:
Here are a few ways (untested, so caveat emptor):
# plyr package
library('plyr')
ddply(df, .(Plant, Tissue, Gene), summarise, ntest =
shapiro.test(ExpressionLevel))
# data.table package
library('data.table')
dt - data.table(df, key = 'Plant, Tissue, Gene')
dt[, list(ntest = shapiro.test(ExpressionLevel)), by = key(dt)]
# aggregate() function
aggregate(ExpressionLevel ~ Plant + Tissue + Gene, data = df, FUN =
shapiro.test)
# doBy package:
summaryBy(ExpressionLevel ~ Plant + Tissue + Gene, data = df, FUN =
shapiro.test)
There are others, too...
HTH,
Dennis
2011/10/30 Joel Fürstenberg-Hägg jo...@life.ku.dk:
Dear R users,
I have a data frame in the form below, on which I would like to make
normality tests on the values in the ExpressionLevel column.
head(df)
ID Plant Tissue Gene ExpressionLevel
1 1 p1 t1 g1 366.53
2 2 p1 t1 g2 0.57
3 3 p1 t1 g311.81
4 4 p1 t2 g1 498.43
5 5 p1 t2 g2 2.14
6 6 p1 t2 g3 7.85
I would like to make the tests on every group according to the
content of the Plant, Tissue and Gene columns.
My first problem is how to run a function for all these sub groups.
I first thought of making subsets:
group1 - subset(df, Plant==p1 Tissue==t1 Gene==g1)
group2 - subset(df, Plant==p1 Tissue==t1 Gene==g2)
group3 - subset(df, Plant==p1 Tissue==t1 Gene==g3)
group4 - subset(df, Plant==p1 Tissue==t2 Gene==g1)
group5 - subset(df, Plant==p1 Tissue==t2 Gene==g2)
group6 - subset(df, Plant==p1 Tissue==t2 Gene==g3) etc...
But that would be very time consuming and I would like to be able to
use the code for other data frames...
I have also tried to store these in a list, which I am looping
through, running the tests, something like this:
alist=list(group1, group2, group3, group4, group5, group6)
for(i in alist)
{
print(shapiro.test(i$ExpressionLevel))
print(pearson.test(i$ExpressionLevel))
print(pearson.test(i$ExpressionLevel, adjust=FALSE))
}
But, there must be an easier and more elegant way of doing this... I
found the example below at
http://stackoverflow.com/questions/4716152/why-do-r-objects-not-print-in-a-function-or-a-for-loop.
I think might be used for the printing of the results, but I do not know
how to adjust for my data frame, since the functions are applied on
several columns instead of certain rows in one column.
DF - data.frame(A = rnorm(100), B = rlnorm(100))
obj2 - lapply(DF, shapiro.test)
tab2 - lapply(obj, function(x) c(W = unname(x$statistic), p.value =
x$p.value))
tab2 - data.frame(do.call(rbind, tab2))
printCoefmat(tab2, has.Pvalue = TRUE)
Finally, I have found several different functions for testing for
normality, but which one(s) should I choose? As far as I can see in the
help files they only differ in the minimum number of samples required.
Thanks in advance!
Kind regards,
Joel
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and provide commented, minimal, self-contained, reproducible code.
[R] Normality tests on groups of rows in a data frame, grouped based on content in other columns
Dear R users,
I have a data frame in the form below, on which I would like to make normality
tests on the values in the ExpressionLevel column.
head(df)
ID Plant Tissue Gene ExpressionLevel
1 1 p1 t1 g1 366.53
2 2 p1 t1 g2 0.57
3 3 p1 t1 g311.81
4 4 p1 t2 g1 498.43
5 5 p1 t2 g2 2.14
6 6 p1 t2 g3 7.85
I would like to make the tests on every group according to the content of the
Plant, Tissue and Gene columns.
My first problem is how to run a function for all these sub groups.
I first thought of making subsets:
group1 - subset(df, Plant==p1 Tissue==t1 Gene==g1)
group2 - subset(df, Plant==p1 Tissue==t1 Gene==g2)
group3 - subset(df, Plant==p1 Tissue==t1 Gene==g3)
group4 - subset(df, Plant==p1 Tissue==t2 Gene==g1)
group5 - subset(df, Plant==p1 Tissue==t2 Gene==g2)
group6 - subset(df, Plant==p1 Tissue==t2 Gene==g3) etc...
But that would be very time consuming and I would like to be able to use the
code for other data frames...
I have also tried to store these in a list, which I am looping through, running
the tests, something like this:
alist=list(group1, group2, group3, group4, group5, group6)
for(i in alist)
{
print(shapiro.test(i$ExpressionLevel))
print(pearson.test(i$ExpressionLevel))
print(pearson.test(i$ExpressionLevel, adjust=FALSE))
}
But, there must be an easier and more elegant way of doing this... I found the
example below at
http://stackoverflow.com/questions/4716152/why-do-r-objects-not-print-in-a-function-or-a-for-loop.
I think might be used for the printing of the results, but I do not know how
to adjust for my data frame, since the functions are applied on several columns
instead of certain rows in one column.
DF - data.frame(A = rnorm(100), B = rlnorm(100))
obj2 - lapply(DF, shapiro.test)
tab2 - lapply(obj, function(x) c(W = unname(x$statistic), p.value = x$p.value))
tab2 - data.frame(do.call(rbind, tab2))
printCoefmat(tab2, has.Pvalue = TRUE)
Finally, I have found several different functions for testing for normality,
but which one(s) should I choose? As far as I can see in the help files they
only differ in the minimum number of samples required.
Thanks in advance!
Kind regards,
Joel
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Normality tests on groups of rows in a data frame, grouped based on content in other columns
Hi:
Here are a few ways (untested, so caveat emptor):
# plyr package
library('plyr')
ddply(df, .(Plant, Tissue, Gene), summarise, ntest =
shapiro.test(ExpressionLevel))
# data.table package
library('data.table')
dt - data.table(df, key = 'Plant, Tissue, Gene')
dt[, list(ntest = shapiro.test(ExpressionLevel)), by = key(dt)]
# aggregate() function
aggregate(ExpressionLevel ~ Plant + Tissue + Gene, data = df, FUN =
shapiro.test)
# doBy package:
summaryBy(ExpressionLevel ~ Plant + Tissue + Gene, data = df, FUN =
shapiro.test)
There are others, too...
HTH,
Dennis
2011/10/30 Joel Fürstenberg-Hägg jo...@life.ku.dk:
Dear R users,
I have a data frame in the form below, on which I would like to make
normality tests on the values in the ExpressionLevel column.
head(df)
ID Plant Tissue Gene ExpressionLevel
1 1 p1 t1 g1 366.53
2 2 p1 t1 g2 0.57
3 3 p1 t1 g3 11.81
4 4 p1 t2 g1 498.43
5 5 p1 t2 g2 2.14
6 6 p1 t2 g3 7.85
I would like to make the tests on every group according to the content of the
Plant, Tissue and Gene columns.
My first problem is how to run a function for all these sub groups.
I first thought of making subsets:
group1 - subset(df, Plant==p1 Tissue==t1 Gene==g1)
group2 - subset(df, Plant==p1 Tissue==t1 Gene==g2)
group3 - subset(df, Plant==p1 Tissue==t1 Gene==g3)
group4 - subset(df, Plant==p1 Tissue==t2 Gene==g1)
group5 - subset(df, Plant==p1 Tissue==t2 Gene==g2)
group6 - subset(df, Plant==p1 Tissue==t2 Gene==g3) etc...
But that would be very time consuming and I would like to be able to use the
code for other data frames...
I have also tried to store these in a list, which I am looping through,
running the tests, something like this:
alist=list(group1, group2, group3, group4, group5, group6)
for(i in alist)
{
print(shapiro.test(i$ExpressionLevel))
print(pearson.test(i$ExpressionLevel))
print(pearson.test(i$ExpressionLevel, adjust=FALSE))
}
But, there must be an easier and more elegant way of doing this... I found
the example below at
http://stackoverflow.com/questions/4716152/why-do-r-objects-not-print-in-a-function-or-a-for-loop.
I think might be used for the printing of the results, but I do not know how
to adjust for my data frame, since the functions are applied on several
columns instead of certain rows in one column.
DF - data.frame(A = rnorm(100), B = rlnorm(100))
obj2 - lapply(DF, shapiro.test)
tab2 - lapply(obj, function(x) c(W = unname(x$statistic), p.value =
x$p.value))
tab2 - data.frame(do.call(rbind, tab2))
printCoefmat(tab2, has.Pvalue = TRUE)
Finally, I have found several different functions for testing for normality,
but which one(s) should I choose? As far as I can see in the help files they
only differ in the minimum number of samples required.
Thanks in advance!
Kind regards,
Joel
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
