similar to: prettyR 25% quartile, 75% quartile

#is there a way to get NA in the table of descriptive statistics instead of the function stopping Thank you in advance #data x.f <- structure(list(Site = structure(c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("BC", "HC", "RM119", "RM148", "RM179", "RM185",

Why am I getting a wrong result for quartiles? here is my code: > cbiomass = c(910, 1058, 929, 1103, 1056, 1022, 1255, 1121, 1111, 1192, > 1074, 1415) > summary(cbiomass) > IQR(cbiomass) The result R gives me is: For the summary > Min. 1st Qu. Median Mean 3rd Qu. Max. 910 1048 1088 1104 1139 1415 For IQR > 91.25 ********* The true Q1 is 1039

Hi R users, The following code worked well to summarize four data groups in a dataframe for three variables (t_depth, t_alt_count, t_alt_ratio), 12 columns of summary, see attached. However, after running another 2000 lines of R codes using functions from more than 10 other R libraries, then it only generated one column of summary. Do you know why? Thank you, Yuan Chun Ding summary_anno1148ft

I am sorry that I know I should provide a dataset that allows to replicate my problem. It is a research dataset and quite large, so I can not share. Both Bert and Tim guessed my problem correctly. I also thought about the conflicting issue between different packages and function masking. I just hope to that someone has similar experience, so providing me suggestion. For conflicting issue,

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) )

Hello, Inline. ?s 19:03 de 12/06/2024, Yuan Chun Ding via R-help escreveu: > I am sorry that I know I should provide a dataset that allows to replicate my problem. > > It is a research dataset and quite large, so I can not share. > > Both Bert and Tim guessed my problem correctly. I also thought about the conflicting issue between different packages and function masking. > I

Hello everyone. I am new to R, so please bear with me. I am trying to find an easy way to export descriptive statistics and other information about my data frame to a LaTeX format. I have found the describe function in PrettyR to be very helpful in producing results in the exact format I'm looking for. However, the value of the describe function is a LIST (rather than a data frame) which I

Hi Rui, Thank you very much! Yes, I verified using real data, it worked correctly as expected after adding tidyr:: to the pivot_longer function and dplyr:: to the group_by and summarize Function. I did not know how to assign the tidyr and dplyr to the three functions because I do not really understand well the three functions and just got the code from a google search. I also tried your

Hi all, I have finally gotten the prettyR package going (many thanks to Kurt Hornik for his patience). prettyR is a set of functions that allows the user to produce HTML output from R scripts. Given an R script that runs properly, an HTML listing complete with embedded graphics can be produced simply by passing the script to the core function htmlize (Phillipe Grosjean has not only offered

Hi Everybody I need to create a lot of frequency tables with frequencies and percentages (and cumilative freq and % as well) for a report. freq() in prettyR give more or less what I need. I am trying to export the result of freq() to html but the html doesn't look look the console output. See the following example library(prettyR) library(Hmisc) x <- matrix(sample(1:3, 12,

Hi folks, Both plotrix and prettyR have been updated. plotrix now has the "Matlab" style dotplot that was requested not long ago on the list, provided by that intrepid duo, Barry Rowlingson and Rolf Turner. Mike Cheetham has contributed an implementation of the Piper diagram, also in response to a request. prettyR has finally learned how to display SPSS value labels in the xtab and

I sometimes think people on this list are quite rude to posters. I'm afraid I'm likely to join in with some rudeness? 1. "Here is some code that works but also doesn't" is probably not going to get you an answer 2. I provide no information about the data it works on or doesn't 3. I tell you I'm using a load of dependencies, but don't tell you what 4. I refer to

Hello, I've been trying to use odfWeave and prettyR packages to create documents with both text and graphs, but so far I haven't been very lucky... With the function R2html () in prettyR package, when I try to use a source file, which works perfectly if I run it directly form R using "source("file")", it works for the first part and then it makes a mess, i guess

Hello, I'm currently using a simple plot to visualize some mean values. I'm having ~200 datapoints on the x-axis, each has 10 records. I'm currently plotting only the mean value of each of the datapoints. What I need is a way to visualize the quartiles/error/whatever of these points. I thought about boxplots, but I have to many points on the xaxis - it would be impossible to see

Hi all, does anyone have any practical examples of how this command can be used in prettyR? add.value.labels(x,value.labels) I mean, can we have the SPSS style of using numbers and/or labels if we want to? Thank you for your time Jason Dr. Iasonas Lamprianou Assistant Professor (Educational Research and Evaluation) Department of Education Sciences European University-Cyprus P.O. Box 22006

When data contains both factor and numeric variables, how to get quartiles for all numeric variables? n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <- factor(sample(c('a','b','c'),n,replace=TRUE)) x6 <- factor(1*(x5=='a' | x5=='c')) data1 <- cbind(x1,x2,x3,x4,x5,x6) data

Dear list, I have a problem with freq from prettyR. Please have a look at my syntax with a litte example: library(prettyR) #Version 1 test.df<-data.frame(q1=sample(1:4,8,TRUE), gender=sample(c("f","m"),8,TRUE)) test.df freq(test.df) #No error message #Version 2 test.df<-data.frame(gender=sample(c("f","m"),8,TRUE), q1=sample(1:4,8,TRUE)) test.df

Greetings R users, My goal is to generate quartile groups of each variable in my data set. I would like each experiment to have its designated group added as a subsequent column. I can accomplish this individually with the following code: brks <- with(data_variables, cut2(var2, g=4)) #I don't want the actual numbers, I need a numbered group data$test1=factor(brks,

Hi, I have a simple question about quartiles in R, especially how they are calculated using the boxplot. Quartiles (.25 and .75) in boxplot are different from the summary function and also don't match with the 9 types in the quantile function. See attachment for details. Can you give me the details on how the boxplot function does calculate these values? Cheers, Rene Brinkhuis

I have data that looks like lake,loglength,logweight 1,2.369215857,1.929418926 1,2.426511261,2.230448921 1,2.434568904,2.298853076 1,2.437750563,2.298853076 1,2.442479769,2.230448921 1,2.445604203,2.356025857 ... 102,2.722633923,3.310268367 102,2.781755375,3.502153893 102,2.836324116,3.683407299 102,2.802773725,3.583312152 102,2.790285164,3.546419267 102,2.806179974,3.599118565