r

问题 I am trying to render the following table in a RMD file: \begin{table}[] \centering \caption{My caption} \label{my-label} \begin{tabular}{|l|} \hline \\ \hline \end{tabular} \end{table} So far no success. Is there any fundamental reason why rmarkdown cannot compile LaTeX enviroments (other than equations) to HTML? 回答1: In a markdown document, the expected input markup language is (r)markdown. You should not expect pandoc to automatically recognize arbitrarily mixed markup languages. LaTeX

问题 I am trying to render the following table in a RMD file: \begin{table}[] \centering \caption{My caption} \label{my-label} \begin{tabular}{|l|} \hline \\ \hline \end{tabular} \end{table} So far no success. Is there any fundamental reason why rmarkdown cannot compile LaTeX enviroments (other than equations) to HTML? 回答1: In a markdown document, the expected input markup language is (r)markdown. You should not expect pandoc to automatically recognize arbitrarily mixed markup languages. LaTeX

问题 I have two dataframes. one ( txt.df ) has a column with a text I want to extract phrases from ( text ). The other ( wrd.df ) has a column with the phrases ( phrase ). both are big dataframes with complex texts and strings but lets say: txt.df <- data.frame(id = c(1, 2, 3, 4, 5), text = c("they love cats and dogs", "he is drinking juice", "the child is having a nap on the bed", "they jump on the bed and break it", "the cat is sleeping on the bed")) wrd.df <- data.frame(label = c('a', 'b', 'c',

问题 I’ve discovered this morning that bulk of timestamp formats in R seem to be posix.ct class based, which seems to be risky for use with nano second timestamps due to rounding and accumulation errors. Is this true? If so, What packages and processing steps are needed to safely import timestamps in nano seconds precision - probably from csv files? (Preferably staying with packages within tidyverse) Output Visual tools used currently are ggplot2 , plotly, and d3 回答1: We wrote a package for that:

问题 I posted a question some days ago and while the solution seems to be working on RStudio in Windows (but takes forever and sometimes spits out no results), I keep getting an error of long vectors not supported when I run the same code with 30 CPUs on a HPC. Any ideas why? Here is a sample of the data: > head(forfuzzy) # A tibble: 6 x 3 grantee_name grantee_city grantee_state <chr> <chr> <chr> 1 (ICS)2 MAINE CHAPTER CLEARWATER FL 2 (SUFFOLK COUNTY) VANDERBILT~ CENTERPORT NY 3 1 VOICE TREKKING A

问题 I'm setting up tests. I operate on test-data, then want to assure that the right value showed up in a cell in a tibble. I think there's a more concise way to this. Using the example band_instruments library(tidyverse) test_that("Musicians play instruments", { expect_equal(band_instruments %>% filter(name == "Paul") %>% pull("plays"), "bass") expect_equal({band_instruments %>% filter(name == "Keith")}$plays, "guitar") }) This works, but it's too long, too wordy. What's the most concise, yet

问题 I adapted a heatmap plot for a confusion matrix from this answer. However I would like to twist it. In the diagonal (from top left to bottom right) are the matches (correct classifications). My aim would be, to plot this diagonal in a yellow color palette. And mismatches (so all tiles except those in the diagonal) in a red color palette. In my plot.cm function I can get the diagonal with cm_d$diag <- cm_d$Prediction == cm_d$Reference # Get the Diagonal cm_d$ndiag <- cm_d$Prediction != cm_d

问题 Reading the description of glm in R it is not clear to me what the difference is between specifying a model offset in the formula, or using the offset argument. In my model I have a response y, that should be divided by an offset term w, and for simplicity lets assume we have the covariate x. I use log link. What is the difference between glm(log(y)~x+offset(-log(w))) and glm(log(y)~x,offset=-log(w)) 回答1: The two ways are identical. This can be seen in the documentation (the bold part): this

问题 I have a whole list of misspelling and I would like to change the all in one go. Is there an easy way to do so without writing a massive ifelse statement? vegas <- c("North Las Vegas","N Las Vegas", "LAS VEGAS", "Las vegas","N. Las Vegas", "las vegas", "Las Vegas", "Las Vegas ", "South Las Vegas", "La Vegas", "Las Vegas, NV", "LasVegas", "110 Las Vegas", "C Las Vegas", "Henderson and Las vegas", "las Vegas", "Las Vegas & Henderson", "Las Vegas East", "Las Vegas Nevada", "Las Vegas NV", "Las

问题 I have a whole list of misspelling and I would like to change the all in one go. Is there an easy way to do so without writing a massive ifelse statement? vegas <- c("North Las Vegas","N Las Vegas", "LAS VEGAS", "Las vegas","N. Las Vegas", "las vegas", "Las Vegas", "Las Vegas ", "South Las Vegas", "La Vegas", "Las Vegas, NV", "LasVegas", "110 Las Vegas", "C Las Vegas", "Henderson and Las vegas", "las Vegas", "Las Vegas & Henderson", "Las Vegas East", "Las Vegas Nevada", "Las Vegas NV", "Las