similar to: Identify groups

Dear everybody, I have the following challenge. I have a data set with 2 subgroups, dates (days), and corresponding values (see example code below). Within each subgroup: I need to aggregate (sum) the values by week - for weeks that start on a Monday (for example, 2008-12-29 was a Monday). I find it difficult because I have missing dates in my data - so that sometimes I don't even have the

Hi, Can anyone pls help me in plotting the following data? The data-set contains company name, specification-1, specification-2. The graph would basically plot company name with specification-1 on x-axis, & specification-2 on y-axis. Simply put - company name should get classified into one of the four quardrants created by specification 1 & specification2. Thanks.

Hi. In a data set I have a variable that takes values from 1 to 14. For each subgroup of values of this variable, I would like to obtain some descriptive statistics of other variables present in the data set. I've been trying with a "for" loop but I couldn't get nothing. Could you please suggest me some lines? -- View this message in context:

Hello R list, I'm looking to do some stepwise discriminant function analysis (DFA) based on the minimization of Wilks' lambda in R to end up with a composite signature (of metals "Al","Sb","Bi","Cr","Ba") capable of discriminating 100% of the source factors (LANDUSE: "A","B","C"). The Wilks' lambda

I'm sure there's a simple way to do this, but I can only think of complicated ones. I have a number of character vectors that look something like this: "12 78 23 9 76 43 2 15 41 81 92 5(92 12) (81 78 5 76 9 41) (23 2 15 43)" I wish to get it into a list of numerical vectors like this: $Group [1] 12 78 23 9 76 43 2 15 41 81 92 5 $Subgroup1 [1] 92 12 $Subgroup2 [1] 81 78 5

Hello I'm trying to get the proportion "true" for dichotomous variable for various subgroups in a survey. This works fine, but obviously doesn't give proportions directly: svytable(~SurvYear+problem.vandal, seh.dsn, round=TRUE) problem.vandal SurvYear FALSE TRUE 1995 8906 786 1997 17164 2494 1998 17890 1921 1999 18322 1669 2001 17623 2122 ...

Hi R-masters I need yours help about a problema in one of may ongoing researchers. In my research the subjects (20 in total) answer 60 questions (20 type G, 20 type S and 20 type P). Which questions is classified about 3 factor (2 level each) and the subject score with 2 scale (not integer value is possible but rare): Val range -7 to 7 and other Car range 1 to 7. This a code to fake database

Hi. I searched the list and didn't found nothing similar to this. I simplified my example like below: #I need calculate correlation (for example) between 2 columns classified by a third one at a data.frame, like below: #number of rows nr = 10 #the third column is to enforce that I need correlation on two variables only dataf =

I have the following formula for a linear model: z <- lm(y~x + factor(a) + factor(b), data=NT2010) where a (groups) and b (Sub-groups) are categorical variables (factors), x is a continuous covariate, and y the response variable. Since b is nested within a, the formula can also be written as: z <- lm(y~x + factor(a) + factor(a)/factor(b), data=NT2010) and the same output is achieved

Hello everyone, I have a question regarding simulating based on runif.? Let say I have generated matrix A and B based on runif. Then I find mean for each matrix A and matrix B.? I want this process to be done let say 10 times. Anyone can help me.? Actually I want make the function that I can play around with the number of simulation process that I want. Thanks. Eg: a <- matrix(runif(5,1, 10))

Hi, I have a data.frame that has a categorical variable, for which I would like to look at the distribution of levels of this variable, based on a grouping of two other variables. As an example: x <- data.frame(obs=sample(c('low', 'high'),100, replace=TRUE), grp1=sample(1:10, 100, replace=TRUE), grp2=runif(100)) cut.grp1 <- cut(x$grp1, 3) cut.grp2 <- cut(x$grp2, 3)

On 1/29/2018 9:03 PM, smart hendsome via R-help wrote: > Hello everyone, > I have a question regarding simulating based on runif.? Let say I have generated matrix A and B based on runif. Then I find mean for each matrix A and matrix B.? I want this process to be done let say 10 times. Anyone can help me.? Actually I want make the function that I can play around with the number of simulation

Hello, Another way would be to use ?replicate and ?colMeans. set.seed(2511) # Make the results reproducible fun <- function(n){ f <- function(){ a <- runif(5, 1, 10) b <- runif(5, 10, 20) colMeans(cbind(a, b)) } replicate(n, f()) } fun(10) Hope this helps, Rui Barradas On 1/30/2018 8:58 AM, Daniel Nordlund wrote: > On 1/29/2018

On Fri 2015-06-12 01:52:54 -0400, Mark D. Baushke wrote: > I have communicated with Allen Roginsky on this topic and I have been given permission to post his response. > > In this message below, the 'vendor' was Darren Tucker's generated prime > that used a generator value of 5. > > -- Mark > > From: "Roginsky, Allen" <allen.roginsky at

Hi, I have data that looks like this: ASA1 ASA2 C1_C2 C M 9.0225 S S 2.4315 M C 3.4894 M S 4.5282 C M 1.3183 C S 1.3735 S C 1.0488 S M 7.948 M C 4.5827 I need to plot Boxplots for a given ASA1 (either C,S, or M) with respect to C1_C2. However, instead of one boxplot I want to plot

Hello you all from the R Help mailing list! I am working on a PowerBook with Mac Os X and use R 2.5.0. I used the distance function from the analogue package to perform a similarity analysis using the Gowers Index and weighted Variables. My variables are bivariate data and measurements as well as interval data transformed into minimum and maximum variables. I used this Code:

Dear R-ers, In my question there are no statistics involved - it's all about data manipulation in R. I am trying to write a code that should replace what's currently being done in SAS and SPSS. Or, at least, I am trying to show to my colleagues R is not much worse than SAS/SPSS for the task at hand. I've written a code that works but it's too slow. Probably because it's

Dear all, Lattice provides automatic coloring for subgroups on each panel by the simple use of a groups statement. For an application I want to change these colors to a predifined set. This works well using a panel function in stead of the default as long as there are only points in the graphs. When I set type="b" things get messed up. Any idea why? I include sample code for

Dear R-users, I want to multiple impute missing scores, but only for a few subgroups in my data (variable 'subgroups': only impute for subgroups 2 and 3). Does anyone knows how to do this in MICE? This is my script for the multiple imputation: imp <- mice(data, m=20, predictorMatrix=pred, post=post, method=c("", "", "", "",

I have a nice hierarchical table structure like this: divisions has_many groups groups belongs_to division has_many subgroups subgroups belongs_to group has_many units units belongs_to subgroup I have a report which is based on units, but i want to be able to filter the units by which subgroup, or which group, or which division. I also want to sort them by division.name,then group.name,