splitstackshape

I have mydf data frame below. I want to split any cell that contains comma separated data and put it into rows. I am looking for a data frame similar to y below. How could i do it efficiently in few steps? Currently i am using cSplit function on one column at a time. I tried cSplit(mydf, c("name","new"), ",", direction = "long") , but that didn`t work library(splitstackshape) mydf=data.frame(name = c("AB,BW","x,y,z"), AB = c('A','B'), new=c("1,2,3","4,5,6,7")) mydf x=cSplit(mydf, c("name"), ",", direction = "long") x y=cSplit(x, c("new"), ",", direction = "long") y There are times when a for

I am working on HCUP data and this has range of values in one single column that needs to be split into multiple columns. Below is the HCUP data frame for reference : code label 61000-61003 excision of CNS 0169T-0169T ventricular shunt The desired output should be : code label 61000 excision of CNS 61001 excision of CNS 61002 excision of CNS 61003 excision of CNS 0169T ventricular shunt My approach to this problem is using the package splitstackshape and using this code library(data.table) library(splitstackshape) cSplit(hcup, "code", "-")[, list(code = code_1:code_2, by = label)] This

I've tried pretty much everything from this similar question , but I can't get the results everyone else seems to be getting. This is my problem: I have a data frame like this, listing the grades each teacher works with: > profs <- data.frame(teaches = c("1st", "1st, 2nd", "2nd, 3rd", "1st, 2nd, 3rd")) > profs teaches 1 1st 2 1st, 2nd 3 2nd, 3rd 4 1st, 2nd, 3rd I've been looking for solutions to break the teaches variable into columns, like so: teaches1st teaches2nd teaches3rd 1 1 0 0 2 1 1 0 3 0 1 1 4 1 1 1 I understand this solution involving the splitstackshape library and the apparently

Please run this script below, the following R script gives a shiny dashboard with two boxes. I want to reduce the width between two boxes and display data in the right chart. The data should be based on the on click event that we see in the ggplotly function. Also plotly can be used to do the job, I guess. I want the code to fast and efficient at the same time. ## app.R ## library(shiny) library(shinydashboard) library(bupaR) library(eventdataR) library(lubridate) library(dplyr) library(XML) library(edeaR) library(xml2) library(data.table) library(ggplot2) library(ggthemes) library(glue)