similar to: bug (?) in [.data.frame with matrix-like indexing

Hi, I'm wondering why indexing a data frame by row name doesn't work with [[. It works with [: > sw <- swiss[1:5,1:2] > sw["Moutier", "Agriculture"] [1] 36.5 but not with [[: > sw[["Moutier", "Agriculture"]] Error in .subset2(.subset2(x, ..2), ..1) : subscript out of bounds The problem is really with the row name (and not

Hi, Hope you are well. I was wondering if there is a function at C level that is equivalent to mtcars$carb or .subset2(mtcars, "carb"). If I have the index of the column then the answer would be VECTOR_ELT(df, asInteger(idx)) but I was wondering if there is a way to do it directly from the name of the column without having to loop over columns names to find the index? Thank you Best

Below is a version of [.data.frame that is faster for subscripting rows of large data frames; it avoids calling duplicated(rows) if there is no need to check for duplicate row names, when: i is logical attr(x, "dup.row.names") is not NULL (S+ compatibility) i is numeric and negative i is strictly increasing "[.data.frame" <- function (x, i, j,

Dear all, I have searched the forums for an answer - and there is plenty of questions along the same line - but none of the paproaches shown worked to my problem: I have a data frame that I get from a csv: summarystats<-as.data.frame(read.csv(file=f_summary)); where I have the columns Dataset, Class, Type, Category,.. Problem1: I want to find a subset of this frame, based on values in

Hello, I'm fairly new to R. I'm a chemist, not a programmer so please bear with me. I have a large data.frame that I want to break down (subset) into smaller data.frames for analysis. I would like to give the data.frames descriptive names which I have stored in a character vector. My original thought was that I want the subsets to show up as individual objects, but haveing them stored

Hi R People: Several of you pointed out that using "tapply" on a data frame will work on the iris data frame. I'm still having a problem. The iris data frame has 150 rows, 5 variables. The first 4 are numeric, while the last is a factor, which has the Species names. I can use tapply for 1 variable at a time: >tapply(iris[,1],iris[,5],mean) setosa versicolor virginica

"[.ggobiDataset" <- function(x, ..., drop=FALSE) { x <- as.data.frame(x) NextMethod("[", x) } "[[.ggobiDataset" <- function(x, ..., drop=FALSE) { x <- as.data.frame(x) NextMethod("[[", x) } "$.ggobiDataset" <- function(x, ..., drop=FALSE) { x <- as.data.frame(x) NextMethod("$", x) } > class(x) [1]

As a fourth option, I wonder if the first author could fork the package? Presumably, appropriately cited, a fork is permitted by the license under which it was released. Then the original package, by both authors, still exists (and a final version could point to the new one) and the new package, citing the previous version appropriately, is by a single author. The page of CRAN's policies

In R prior to 2.6.0 with matrizes as well as with data.frames it is possible to do: a[2, c(TRUE, FALSE)] In R 2.6.0 the mixed indexing works only for matrizes. Is this the intention, a bug, or did I get something wrong? Best regards Meinhard Meinhard Ploner Althingstrasse 15 39031 Bruneck P.IVA 0234 058 0212 Tel. +39 340 8937861 www.data-ploner.com [[alternative HTML version

Hi, In the code below I create a small data.frame (dat) and then cut it into different groups using CutList. The lists in CutList allow to me choose whatever columns I want from dat and allow me to cut it into any number of groups by changing the lists. It seems to work OK but when I'm done I have a variable number of data frames what I need to do further operations on and I don't know

Hello. I'm currently trying to wrap up data frames into OCaml via OCaml-R, and I'm having trouble with data frame subsetting: > # x#column 1;; > Erreur dans (function(x, i, exact) if (is.matrix(i)) as.matrix(x)[[i]] else .subset2(x, : > l'?l?ment 1 est vide ; > la partie de la liste d'arguments de 'is.matrix' en cours d'?valuation ?tait : > (i)

Hi, Here seems to be a corner case in which format() fails: > format(data.frame()) Error in .subset2(x, i, exact = exact) : subscript out of bounds > sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-pc-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8

I'm trying to learn to use manova(), and don't understand why none of the following work: > data(iris) > fit <- manova(~ Species, data=iris) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : incompatible dimensions > fit <- manova(iris[,1:4] ~ Species, data=iris) Error in model.frame(formula, rownames, variables, varnames, extras,

Hello R Users and Developers, I have a basic question about how R works. Over the past few years I have struggled when I try to generate a new data frame that I believe should contain numeric data in some columns and character data in others only to find everything converted to character data. Is there a general method to create data frames that contain the data in the desired format: numbers

Hi R-devel, I am facing a situation where the number of options I would like to propose to the user is somewhat big (and could easily increase more and more as I will code up a little more - even coming to a point where an user should be able to implement his own options). What we have to handle options is the couple: options(par=value) and getOption("par") I was aking myselft what

Dear all, I am encountering problems with the application of ddply within the body of a self-defined function. The script is the following: moncostcarmoto <- function(costtype){ costaux_result <- data.frame() for (purp in PURPcount){for (per in PERcount){ costcarin =

In using cor(data.frame), it is annoying that you have to explicitly filter out non-numeric columns, and when you don't, the error message is misleading: > cor(iris) Error in cor(iris) : missing observations in cov/cor In addition: Warning message: In cor(iris) : NAs introduced by coercion It would be nicer if stats:::cor() did the equivalent *itself* of the following for a data.frame:

Hi Hilmar and Ivan, I have used your code examples to write a blog post about this topic, which has figures that show the asymptotic time complexity of the various approaches, https://tdhock.github.io/blog/2023/df-partial-match/ The asymptotic complexity of partial matching appears to be quadratic O(N^2) whereas the other approaches are asymptotically faster: linear O(N) or log-linear O(N log N).

Hello, I have been banging my head against the wall trying to figure out this seemingly simple problem with no success. I'm hoping that one or some of you can help. Here is the code I am trying to use: #importing data data.file <-read.csv("/file/location", header=TRUE, sep = ",") #selecting a subset of data based on variable "Sample" data.subset1 <-

Full_Name: Sebastian Meyer Version: 2.10.0 OS: x86_64-pc-linux-gnu Submission from: (NULL) (77.135.124.96) When using model.frame (model.frame.default) on an intercept-only model and with na.action=na.fail SPECIFIED, i.e. data(iris) model.frame( ~ 1, data = iris, na.action = na.fail) one gets the following error: Error in complete.cases(object) : negative length vectors are not