similar to: Error in unsplit() with tibbles

I get the sentiment, but this is really just bad coding (on my own part, I suspect), so we might as well just fix it... -pd > On 21 Nov 2020, at 17:42 , Marc Schwartz via R-devel <r-devel at r-project.org> wrote: > > >> On Nov 21, 2020, at 10:55 AM, Mario Annau <mario.annau at gmail.com> wrote: >> >> Hello, >> >> using the `unsplit()`

Cool - thank you Peter! @Marc: This is really not a tidyverse vs base-R debate and I personally think that they should both work together for most parts. The common environment is still R. But just to give you the full picture I also filed a bug for tibbles (https://github.com/tidyverse/tibble/issues/829). With these two fixes I think that split/unsplit would work for tibbles and users (like me)

> On Nov 21, 2020, at 10:55 AM, Mario Annau <mario.annau at gmail.com> wrote: > > Hello, > > using the `unsplit()` function with tibbles currently leads to the > following error: > >> mtcars_tb <- as_tibble(mtcars, rownames = NULL) >> s <- split(mtcars_tb, mtcars_tb$gear) >> unsplit(s, mtcars_tb$gear) > Error: Must subset rows with a valid

I was not able to reproduce this problem. I tried two environments 1. Ubuntu 14.04.5 LTS, R version 3.4.2 (same R version as yours) 2. Windows 10, same R version On Wed, Nov 8, 2017 at 9:50 AM, Zeki ?ATAV <zcatav at gmail.com> wrote: > Hello, > I've an error recently. > > ggplot(data = mtcars, aes(x= wt, y= mpg)) + geom_line() > Error: Found object is not a stat. >

Hi R Helpers, I recently tried to take advantage of the ability to download all the tickers in the S&P 500 using the functionality of tidyquant, but it threw an error. For summary, the set of commands that I ran was library(tidyquant) tq_index_options() tq_index("SP500") sessionInfo() R feedback including error message and sessionInfo are provided below. Guidance would be

try this: ########################################## library(dplyr) input <- tribble( ~station, ~from, ~to, ~record, "07EA001" , 1960 , 1960 , "QMS", "07EA001" , 1961 , 1970 , "QMC", "07EA001" , 1971 , 1971 , "QMM", "07EA001" , 1972 , 1976 , "QMC", "07EA001" , 1977 ,

Hello, I've an error recently. ggplot(data = mtcars, aes(x= wt, y= mpg)) + geom_line() Error: Found object is not a stat. > sessionInfo() R version 3.4.2 (2017-09-28) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 16.04.3 LTS Matrix products: default BLAS: /usr/lib/openblas-base/libblas.so.3 LAPACK: /usr/lib/libopenblasp-r0.2.18.so locale: [1] LC_CTYPE=tr_TR.UTF-8

Or with the Bioconductor IRanges package: df <- with(input, DataFrame(station, year=IRanges(from, to), record)) expand(df, "year") DataFrame with 24 rows and 3 columns station year record <character> <integer> <character> 1 07EA001 1960 QMS 2 07EA001 1961 QMC 3 07EA001 1962 QMC 4

Dear R Users, I have developed the following code for importing a series of zipped CSV by parallel computing. My problems are that: A) Some ZIP Files (Which contain CSVs inside) are corrupted, and cannot be opened. B) After executing parRapply I can only see the last.warning variable error, for knowing which CSV have failed in each node, but I cannot see all warnings, only 1 at a time. So: *

I get the same result as Eric? withR version 3.4.2 (2017-09-28) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 17.04 It looks like you have "tidyverse" loaded so I tried it with just ggplot2 loaded and with tidyverse loaded.? On Wednesday, November 8, 2017, 4:16:14 AM EST, Eric Berger <ericjberger at gmail.com> wrote: I was not able to reproduce this

Tienes razon de que hay una serie de paquetes que lo usan, pero me da que vienen impulsados por Rstudio desde el paquete tidyverse... Yo me resisto a usarlo. Me parece que queda mas limpio, pero no se porque no le pillo la gracia...Quizas sea el apoyo de Rstudio lo que me tire para atras... ________________________________ De: Carlos Ortega <cof en qualityexcellence.es> Enviado: miércoles,

Buenas Os queria preguntar que ventaja le veis a las tibbles frente a los data.table, ya que para mi, quitando el uso de librerias como tidyr o purrr, en el resto prefiero usar data.table. Me parece igual de potente y mucho más rápido, y no le veo las supuestas ventajas, pero quiero conocer vuestra opinion. Gracias Jesús [[alternative HTML version deleted]]

On 11/26/2017 08:42 PM, jim holtman wrote: > try this: > > ########################################## > > library(dplyr) > > input <- tribble( > ~station, ~from, ~to, ~record, > "07EA001" , 1960 , 1960 , "QMS", > "07EA001" , 1961 , 1970 , "QMC", > "07EA001" , 1971 , 1971 ,

Que es postgres y presto?? Se uqe google tiene la respuesta,.... ________________________________ De: José Luis Cañadas <canadasreche en gmail.com> Enviado: miércoles, 22 de noviembre de 2017 22:01 Para: Jesús Para Fernández Cc: Carlos Ortega; r-help-es en r-project.org Asunto: Re: [R-es] Tibble o data.table? Yo la ventaja que le veo a tibble y demás cosas del mundo tidy es la conexión a

On 11/29/2017 04:15 PM, T?th D?nes 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 appropriate? I'd say there were some 'hard' solutions -- Michael (deep

I work with a list of crypto assets daily closing prices in a xts class. Here is a limited example: asset.xts.lst <- list(BTCUSDT = structure(c(26759.63, 26862, 26852.48, 27154.15, 27973.45), dim = c(5L, 1L), index = structure(c(1697068800, 1697155200, 1697241600, 1697328000, 1697414400), tzone = "UTC", tclass = "Date"), class = c("xts", "zoo")), ETHUSDT

Arnaud, Short answer may be that the tibble data structure will not be supporting row names and you may want to simply save those names in an additional column or externally. My first thought was to simply save the names you need and then put them back on the tibble. In your code, something like this: save.names <- names(my.ret.lst) result.tib <- as_tibble_col(unlist(my.ret.lst),

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 Bert's example, with 2000 stations instead of 2 d_df <- data.frame( station =

Hi Martin, On 11/29/2017 10:46 PM, Martin Morgan wrote: > On 11/29/2017 04:15 PM, T?th D?nes 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. It was really just an exercise and not a full

Hello everyone, I use the split function splitting with the f function on a 3 columns and more than 100 000 rows data frame. Once it's split I have a list of data frames still with 3 columns and n rows. I manipulate those list elements and get a list of data frames still with 3 columns but less rows. So when I unsplit it, I get an error as I use the same factor function I used to split ( f in