Re: [R] other decriptive stats packages
> > are there any more packages that help decribe and explore data > sets package tdisplay at http://forums.cirad.fr/logiciel-R/viewtopic.php?t=2409 Details: display-packageTool Box for Data Importation and Display import Import Data From External Data Bases (Access, Excel or Dbf formats) using package RODBC displayContents of Variables of a Data frame aggstatComplement to Aggregate univar Descriptive Statistics for a Continuous variable quant Quantiles for a Continuous variable ctab Cross Tabulation (table outputs) ctab2 Cross Tabulation (data frame outputs) splitbin Splits Binomial Data into Bernoulli Data (used in functions ctab and ctab2) [[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] other decriptive stats packages
On 11/21/09, frenchcr wrote: > are there any more packages that help decribe and explore data sets > See numSummary() in Rcmdr. Liviu __ 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] other decriptive stats packages
A few more came to mind: VIM package (for exploring missing data): http://cran.r-project.org/web/packages/VIM/index.html http://bm2.genes.nig.ac.jp/RGM2/index.php?scope=name&query=VIM And the basic commands: * edit (for seeing the dataframe as in a spreadsheet) And the commands: * head (and) tail Tal Contact Details:--- Contact me: tal.gal...@gmail.com | 972-52-7275845 Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) | www.r-statistics.com/ (English) -- On Sun, Nov 22, 2009 at 3:15 PM, Tal Galili wrote: > Here is one more function for the list: > "whatis" > from the package: > "YaleToolkit" > See: > http://cran.r-project.org/web/packages/YaleToolkit/ > > > > I also like using: > ls() > ls.str() > And sometimes (for just one variable): > stem (which can be viewd as an ascii histogram) > > > Wonderful question and list, I hope for more answers. > > > Tal > > Contact > Details:--- > Contact me: tal.gal...@gmail.com | 972-52-7275845 > Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) | > www.r-statistics.com/ (English) > > -- > > > > > > On Sun, Nov 22, 2009 at 12:01 AM, frenchcr wrote: > >> >> i just found the following list, i wondered if anybody could add to this >> as i >> have to characterize a large data set and am new to R...the list below was >> so helpfulcan you add to this??? >> >> Just to forestall confusion amongst those who would like to use one of >> the functions called "describe"... >> >> Hmisc package - describe >> numeric >> name >> count of observations >> count of missing values >> count of unique values >> mean >> seven quantiles >> five lowest and highest values >> discrete (factor or numeric with <= 10 unique values) - >> as for numeric, but >> no mean, quantiles or low/high values and >> including a frequency/percent display for each value. >> >> psych package - describe >> item name >> item number >> number of valid cases >> mean >> standard deviation >> median >> mad: median absolute deviation (from the median) >> minimum >> maximum >> skew (optional) >> kurtosis (optional) >> standard error >> >> prettyR package - describe >> numeric >> name >> mean >> median >> var >> sd >> valid.n >> the above are the defaults - the user can specify the name(s) of any >> function(s) as an argument to the function to customize the display. >> factor >> name >> count for each value >> percent for each value >> modal value >> count of missing values >> logical >> name >> count of FALSE >> count of TRUE >> percent of TRUE >> count of missing values >> >> >> >> are there any more packages that help decribe and explore data sets >> >> -- >> View this message in context: >> http://old.nabble.com/other-decriptive-stats-packages-tp26460757p26460757.html >> Sent from the R help mailing list archive at Nabble.com. >> >> __ >> 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. >> > > [[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] other decriptive stats packages
Here is one more function for the list: "whatis" from the package: "YaleToolkit" See: http://cran.r-project.org/web/packages/YaleToolkit/ I also like using: ls() ls.str() And sometimes (for just one variable): stem (which can be viewd as an ascii histogram) Wonderful question and list, I hope for more answers. Tal Contact Details:--- Contact me: tal.gal...@gmail.com | 972-52-7275845 Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) | www.r-statistics.com/ (English) -- On Sun, Nov 22, 2009 at 12:01 AM, frenchcr wrote: > > i just found the following list, i wondered if anybody could add to this as > i > have to characterize a large data set and am new to R...the list below was > so helpfulcan you add to this??? > > Just to forestall confusion amongst those who would like to use one of > the functions called "describe"... > > Hmisc package - describe > numeric > name > count of observations > count of missing values > count of unique values > mean > seven quantiles > five lowest and highest values > discrete (factor or numeric with <= 10 unique values) - > as for numeric, but > no mean, quantiles or low/high values and > including a frequency/percent display for each value. > > psych package - describe > item name > item number > number of valid cases > mean > standard deviation > median > mad: median absolute deviation (from the median) > minimum > maximum > skew (optional) > kurtosis (optional) > standard error > > prettyR package - describe > numeric > name > mean > median > var > sd > valid.n > the above are the defaults - the user can specify the name(s) of any > function(s) as an argument to the function to customize the display. > factor > name > count for each value > percent for each value > modal value > count of missing values > logical > name > count of FALSE > count of TRUE > percent of TRUE > count of missing values > > > > are there any more packages that help decribe and explore data sets > > -- > View this message in context: > http://old.nabble.com/other-decriptive-stats-packages-tp26460757p26460757.html > Sent from the R help mailing list archive at Nabble.com. > > __ > 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. > [[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] other decriptive stats packages
On Sat, Nov 21, 2009 at 02:01:07PM -0800, frenchcr wrote: > > i just found the following list, i wondered if anybody could add to this as i > have to characterize a large data set and am new to R...the list below was > so helpfulcan you add to this??? > > Just to forestall confusion amongst those who would like to use one of > the functions called "describe"... > > Hmisc package - describe [...] > psych package - describe [...] > prettyR package - describe [...] > the above are the defaults - the user can specify the name(s) of any > function(s) as an argument to the function to customize the display. [...] > are there any more packages that help decribe and explore data sets I maintain the package descr, which has the following descriptive functions (in addition to a few others): freq : frequency table crosstab : cross tabulation compmeans : means comparison The three functions accept weights among their arguments. __ 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] other decriptive stats packages
i just found the following list, i wondered if anybody could add to this as i have to characterize a large data set and am new to R...the list below was so helpfulcan you add to this??? Just to forestall confusion amongst those who would like to use one of the functions called "describe"... Hmisc package - describe numeric name count of observations count of missing values count of unique values mean seven quantiles five lowest and highest values discrete (factor or numeric with <= 10 unique values) - as for numeric, but no mean, quantiles or low/high values and including a frequency/percent display for each value. psych package - describe item name item number number of valid cases mean standard deviation median mad: median absolute deviation (from the median) minimum maximum skew (optional) kurtosis (optional) standard error prettyR package - describe numeric name mean median var sd valid.n the above are the defaults - the user can specify the name(s) of any function(s) as an argument to the function to customize the display. factor name count for each value percent for each value modal value count of missing values logical name count of FALSE count of TRUE percent of TRUE count of missing values are there any more packages that help decribe and explore data sets -- View this message in context: http://old.nabble.com/other-decriptive-stats-packages-tp26460757p26460757.html Sent from the R help mailing list archive at Nabble.com. __ 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.
