Hi David,
You "just" need to learn how to subset your data.frame, see functions
like ?subset and ?"[", as well as a good guide to understand the subtleties!
Some graphic functions also have a built-in argument to subset within
the function (e.g. argument 'subset' in 'plot.formula'), although
> On Nov 29, 2017, at 9:20 AM, Jyoti Bhogal wrote:
>
> The following R commands were written:
>> help.search("factorial")
>> data(npk)
>> npk
>> coef(npk.aov)
>
> In the output of coef command, please explain me the interpretation of
> coefficients of block1 to block 6 in this 2^3 confounded f
Hi again,
Typo in the last email. Should read "about 40 standard deviations".
Jim
On Thu, Nov 30, 2017 at 10:54 AM, Jim Lemon wrote:
> Hi Robert,
> People want different levels of automation in the software they use.
> What concerns many of us is the desire for the function
> "figure-out-what-th
Hi Robert,
People want different levels of automation in the software they use.
What concerns many of us is the desire for the function
"figure-out-what-this-data-is-import-it-and-get-rid-of-bad-values".
Such users typically want something that justifies its use by being
written by someone who seem
On 11/29/2017 05:47 PM, Tóth Dénes wrote:
Hi Martin,
On 11/29/2017 10:46 PM, Martin Morgan wrote:
On 11/29/2017 04:15 PM, Tóth Dénes wrote:
Hi,
A benchmarking study with an additional (data.table-based) solution.
I don't think speed is the right benchmark (I do agree that
correctness is!)
Hi Martin,
On 11/29/2017 10:46 PM, Martin Morgan wrote:
On 11/29/2017 04:15 PM, Tóth Dénes wrote:
Hi,
A benchmarking study with an additional (data.table-based) solution.
I don't think speed is the right benchmark (I do agree that correctness
is!).
Well, agree, and sorry for the wording.
Reading in the data from the file
x <- read.csv( "ExampleData.csv", header = TRUE, stringsAsFactors = FALSE )
Subsetting as you want
x <- x[ x$Location != "MW01", ]
This selects all rows where the value in column 'Location' is not equal to
"MW01". The comma after that ensures that all
On 11/29/2017 04:15 PM, Tóth Dénes wrote:
Hi,
A benchmarking study with an additional (data.table-based) solution.
I don't think speed is the right benchmark (I do agree that correctness
is!).
For the R-help list, maybe something about least specialized R knowledge
required would be appro
Hi,
A benchmarking study with an additional (data.table-based) solution.
Enjoy! ;)
Cheers,
Denes
--
## packages ##
library(dplyr)
library(data.table)
library(IRanges)
library(microbenchmark)
## prepare example dataset ###
## use Be
Say I have a dataset that looks like
LocationYear GW_Elv
MW011999 546.63
MW021999 474.21
MW031999 471.94
MW041999466.80
MW012000545.90
MW022000546.10
The whole dataset is at http://doylesdartden.com
The following R commands were written:
>help.search("factorial")
>data(npk)
>npk
>coef(npk.aov)
In the output of coef command, please explain me the interpretation of
coefficients of block1 to block 6 in this 2^3 confounded factorial experiment.
Thanks.
[[alternative HTML version deleted
A) This list is a general interest list on the R language... you have posed
your question as if you were looking for domain experts such as you might be
more likely to find on the Bioconductor mailing list.
B) Example is not reproducible. [1][2][3]
C) Just because your data don't have missing
Sorry forgot to use plain text format, hope this time it works:
Hi, I am trying to using SAMseq() to analyze my RNA-seq experiment (2 genes
x 550 samples) with survival endpoint. It quickly give the following error:
> library(samr)
Loading required package: impute
Loading required package: m
Hi, I am trying to using SAMseq() to analyze my RNA-seq experiment (2 genes
x 550 samples) with survival endpoint. It quickly give the following error:
> library(samr)Loading required package: imputeLoading required package:
> matrixStats
Attaching package: ‘matrixStats’
The following objects
Christopher,
OK, well what about a range of functions in an R package that
automatically, with very little syntax, pulls in data from a variety of
formats (CSV, SQLite, and so on) and converts them to an R data frame. You
seem to be pointing to something like that.
Something like that, in some for
Oh Crap! I mistakenly replied onlist. PLEASE IGNORE -- these are only my
ignorant opinions.
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, Nov 29,
Great question. What do I want? I want my co-workers to stop using Excel
spreadsheets for data entry, storage, and sharing! I want them to
understand the value of data discipline. But alas . . . .
I work in a county health department in the US. Between dplyr, stringr,
grep, grepl, and the base R r
I don't think my view is of interest to many, so offlist.
I reject this:
" I would consider data analysis work to be three stages: data preparation,
statistical analysis, and producing the report."
For example, there is no such thing as "outliers" -- data to be removed as
part of cleaning/prepar
R has a very wide audience, clinical research, astronomy, psychology, and
so on and so on.
I would consider data analysis work to be three stages: data preparation,
statistical analysis, and producing the report.
This regards the process of getting the data ready for analysis and
reporting, sometim
Dear Larry,
Have a look at https://github.com/inbo/rstable That is a dockerfile
with a stable version of R and a set of packages.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE F
> On 29 Nov 2017, at 15:28, Larry Martell wrote:
>
> I have a R script that I call from python using rpy2. It uses dplyr, doBy,
> and ggplot2. The script has install.packages commands for these 3 packages.
> Even thought the packages are already installed it still downloads,
> builds, and insta
Dear Larry
As far as your first question is concerned I think one of require or
requireNamespace may be what you need.
Michael
On 29/11/2017 14:28, Larry Martell wrote:
I have a R script that I call from python using rpy2. It uses dplyr, doBy,
and ggplot2. The script has install.packages com
I have a R script that I call from python using rpy2. It uses dplyr, doBy,
and ggplot2. The script has install.packages commands for these 3 packages.
Even thought the packages are already installed it still downloads,
builds, and installs them, which is very time consuming. Is there a way to
have
Since you only provide pseudo-code I will give a guess as to the source of
the problem.
It is easy to get "burned" by use of the ifelse statement. Its results have
the same "shape" as the first argument.
My suggestion is to try replacing ifelse by a standard
if ( ) {
} else {
}
HTH,
Eric
Dear all,
I am using the DeSolve Package to simulate a system dynamics model. At the
problematic point in the model, I basically want to decide how many products
shall be produced to be sold. In order to determine the amount a basic
forecasting model of using the average of the last 12 time pe
(This time with the r-help in the recipients...)
Be careful when mixing lme4 and lmerTest together -- lmerTest extends
and changes the behavior of various lme4 functions.
From the help page for lme4-anova (?lme4::anova.merMod)
> ‘anova’: returns the sequential decomposition of the contribut
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