melt

I have data that looks like this: sample start end gene coverage X 1 10 A 5 X 11 20 A 10 Y 1 10 A 5 Y 11 20 A 10 X 1 10 B 5 X 11 20 B 10 Y 1 10 B 5 Y 11 20 B 10 I added additional columns: data$length <- (data$end - data$start + 1) data$ct_lt <- (data$length * data$coverage) I reformated my data using dcast: casted <- dcast(data, gene ~ sample, value.var = "coverage", fun.aggregate = mean) So my new data looks like this: gene X Y A 10.00000 10.00000 B 38.33333 38.33333 This is the correct data format I desire, but I would like to fun.aggregate differently. Instead, I would like to take a

This question already has an answer here: Converting multiple boolean columns to single factor column 4 answers Trying to melt or collapse a dataframe with multiple boolean columns into a two column database with an id column and a column for the collapsed values BUT each value results in a new row. Example beginning: A S1 S2 S3 S4 1 ex1 1 0 0 0 2 ex2 0 1 0 0 3 ex3 0 0 1 0 4 ex4 1 1 0 0 5 ex5 0 1 0 1 6 ex6 0 1 0 0 7 ex7 1 1 1 0 8 ex8 0 1 1 0 9 ex9 0 0 1 0 10 ex10 1 0 0 0 Desired output: A Type ex1 S1 ex2 S2 ex3 S3 ex4 S1 ex4 S2 ex5 S2 ex5 S4 ex6 S2 ex7 S1 ex7 S2 ex7 S3 ex8 S2 ex8 S3 ex9 S3

This is indeed a duplicate for this question r-split-string-using-tidyrseparate , but I cannot use the MWE for my purpose, because I do not know how to adjust the regular Expression. I basically want the same thing, but split the variable after the last underscore. Reason: I have data where some columns show up several times for the same factor/type. I figured I can melt the data separate the value variable before the type string and spread it out again to a wide format with less columns. My Problem is that my variable names have different several underscores and I would like to learn how to

I'm trying to convert a data frame from wide to long format using the melt formula. The challenge is that I have multiple column names that are labeled the same. When I use the melt function, it drops the values from the repeat column. I have read similar questions and it was advised to use the reshape function, however I was not able to get it work. To reproduce my starting data frame: conversion.id<-c("1", "2", "3") interaction.num<-c("1","1","1") interaction.num2<-c("2","2","2") conversion.id<-as.data.frame(conversion.id) interaction.num<-as.data.frame(interaction.num) interaction.num2<-as

import pandas as pd mydata = [{'ID' : '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016'}, {'ID' : '20', 'Entry Date': '10/10/2016', 'Exit Date': '18/10/2016'}] mydata2 = [{'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '10/10/2016'}, {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '11/10/2016'}, {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '12/10/2016'}, {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date': '15/10/2016', 'Date': '13/10/2016'}, {'ID': '10', 'Entry Date': '10/10/2016', 'Exit Date':

Recently I am doing all my data manipulations using dplyr and it is an excellent tool for that. However I am unable to melt or cast a data frame using dplyr. Is there any way to do that? Right now I am using reshape2 for this purpose. I want 'dplyr' solution for: require(reshape2) data(iris) dat <- melt(iris,id.vars="Species") The successor to reshape2 is tidyr . The equivalent of melt() and dcast() are gather() and spread() respectively. The equivalent to your code would then be library(tidyr) data(iris) dat <- gather(iris, variable, value, -Species) If you have magrittr imported you can use