I am interested in writing a function where it takes a data frame as an input and returns an html page via knitr as an output based on the information in the data frame.
Here is sort of the psuedocode of the function that I wanted to write:
htmlOuput <- function(Df) {
newDf<-someManipulation(Df)
meltedDf<-melt(Df)
g<-ggplot(meltedDf)
return (html(g)) # This is the part that I am not sure about
}
Is there a way to output an html page as a function output via knitr ?
After some research I found that calling an rmarkdown file to render within a function to be the best option.
htmlOuput <- function(Df,meta = NULL, cacheable = NA) {
rmarkdown::render('./report.rmd',params=list(output_file = report.html))
}
Where the report.rmd will contain the manipulation of the data frame
newDf<-someManipulation(Df)
meltedDf<-melt(Df)
g<-ggplot(meltedDf)
g
I guess you took the hard way.
An easy approach would be to use htmlTable
I have used that to export to html and it is easy to use.
Related
I'm completely new to using quarto. But one thing I wonder is if there is any chance to use a dataframe created "on the fly" in the quarto document in an observable (ojs) chunk, instead of maybe writing it out as CSV and reading it in? I guess there is no way at all, but you never know:)
So something like this:
df = data.frame(
x = 1:10,
y = 2:11
)
{
// somehow get access to the df
}
Use the ojs_define() function to make data processed in Python or R available to {ojs} cells (this function should be called in the R or Python cell).
See more here in the Quarto Documentation.
I have an R function from a package that I need to pass a file path as an argument but it's expecting a csv and my file is an xlsx.
I've looked at the code for the function an it is using read.csv to load the file but unfortunately I can't make any changes to the package.
Is there a good way to read in the xlsx and pass it to the function without writing it to a csv and having the function read it back in?
I came across the text argument for read.csv here:
Is there a way to use read.csv to read from a string value rather than a file in R?
This seems like might be part way there but as I said I am unable to alter the function.
Maybe you could construct your own function checking if the file is xlsx, and in this case create a temporary csv file, feed it to your function, and delete it. Something like
yourfunction = function(path){
read.csv(path)
head(path)
}
library(readxl)
modified_function = function(path){
if(grepl{"\\.xlsx",path}){
tmp <- read_xlsx(path)
tmp_path <- paste0(gsub("\\.xlsx","",path),"_tmp.csv")
write.csv(tmp,file = tmp_path)
output <- yourfunction(tmp_path)
file.remove(tmp_path)
}else{
output <- yourfunction(path)
}
return(output)
}
If it is of help, here you can see how to modify only one function of a package: How to modify a function of a library in a module
I can see the entire data frame in the console. Is there any possible way or any function to view data frame in the R-Console (Editing similar to that of Excel) so that I should be able to edit the data manually?
S3 method for class 'data.frame'
You can use:
edit(name, factor.mode = c("character", "numeric"),
edit.row.names = any(row.names(name) != 1:nrow(name)), ...)
Example:
edit(your_dataframe)
You can go through in detail with the help of this link - Here
You really can use edit() or view().
But maybe, if you dataset isn't big enough, if you prefer to use Excel, you can use this function below:
library(xlsx)
view.excel<-function(inputDF,nrows=5000){
if (class(inputDF)!="data.frame"){
stop("ERROR: <inputDF> class is not \"data.frame\"")
}
if(nrow(inputDF)>5000 & nrows!=-1){
inputDF=inputDF[1:nrows,]
}
tempPath=tempfile(fileext='.xlsx')
write.xlsx(inputDF,tempPath)
system(paste0('open ',tempPath))
return(invisible(tempPath))
}
I've defined this function to help me with some tasks in R...
Basically, you only need to pass a DataFrame to the function as a parameter. The function by default display a maximum of 5000 rows (you can set the parameter nrows = -1 to view all the rows, but it may be slow).
This function opens your DataFrame in Excel and returns the path where your temporary view was saved. If you wanna save and load your temporary view, after changing something directly with Excel, you can load again your data frame with:
# Open a view in excel
tempPath <- view.excel(initialDF, nrows=-1)
# Load the file of the Excel View in the new DataFrame modifiedDF
modifiedDF <- read.xlsx(tempPath)
This function may works well in Linux, Windows or Mac.
You can view the dataframe with View():
View(df)
As #David Arenburg says, you can also open your dataframe in an editable view, but be warned this is slow:
edit(df)
For updates/changes to affect the dataframe use:
df <- edit(df)
Since a lot of people are using (and developing in) RStudio and Shiny nowadays, things have become far more convenient for R users.
You should look at the rhandsontable package.
There is also very nice Shiny implementation of rhandsontable, from a blog I stumbled upon: https://stla.github.io/stlapblog/posts/shiny_editTable.html. It's not using the console, but it is anyway super slick:
(A few years later) This may be worth trying if you use RStudio: It seems to support all data types. I did not use it extensively but helped me ocassionally:
https://cran.r-project.org/web/packages/editData/README.html
It shows an editing dialog by default. If your dataframe is big you can browse to http://127.0.0.1:7212 while the dialog is being shown, to get a resizable editing view.
You can view and edit a dataframe using with the fix() function:
# Open the mtcars dataframe for editing:
fix(mtcars)
# Edit and close.
# This produces the same result:
mtcars <- edit(mtcars)
# But it is a longer command to write.
Is there an equivalent to the unix less command that can be used within the R console?
There is also page() which displays a representation of an object in a pager, like less.
dat <- data.frame(matrix(rnorm(1000), ncol = 10))
page(dat, method = "print")
Not really. There are the commands
head() and tail() for showing the beginning and end of objects
print() for explicitly showing an object, and just its name followed by return does the same
summary() for concise summary that depends on the object
str() for its structure
and more. An equivalent for less would be a little orthogonal to the language and system. Where the Unix shell offers you less to view the content of a file (which is presumed to be ascii-encoded), it cannot know about all types.
R is different in that it knows about the object types which is why summary() -- as well as the whole modeling framework -- are more appropriate.
Follow-up edit: Another possibility is provided by edit() as well as edit.data.frame().
I save the print output to a file and then read it using an editor or less.
Type the following in R
sink("Routput.txt")
print(varname)
sink()
Then in a shell:
less Routput.txt
If the file is already on disk, then you can use file.show
You might like my little toy here:
short <- function(x=seq(1,20),numel=4,skipel=0,ynam=deparse(substitute(x))) {
ynam<-as.character(ynam)
#clean up spaces
ynam<-gsub(" ","",ynam)
#unlist goes by columns, so transpose to get what's expected
if(is.list(x)) x<-unlist(t(x))
if(2*numel >= length(x)) {
print(x)
}
else {
frist=1+skipel
last=numel+skipel
cat(paste(ynam,'[',frist,'] thru ',ynam,'[',last,']\n',sep=""))
print(x[frist:last])
cat(' ... \n')
cat(paste(ynam,'[',length(x)-numel-skipel+1,'] thru ', ynam, '[', length(x)-skipel,']\n',sep=""))
print(x[(length(x)-numel-skipel+1):(length(x)-skipel)])
}
}
blahblah copyright by me, not Disney blahblah free for use, reuse, editing, sprinkling on your Wheaties, etc.
can anybody help me through the URL optimization in R.
Actually I need to get multiple organization related CSV file from URL.
I have to just change the one parameter in url to get that particular organization CSV table.
So mainly I was writing a function to extract that.
Example:
data<-function("bank"){table<-read.csv(url("*://.....=string.....")}
can you guide me on this.
Is that what you are looking for?
Data <- function(string){
table <- read.csv(url(paste("https://.....=", string, "...", sep="")))
# ...
}
# then you can change the parameter with function call
# (also dynamically using apply or a loop )
Data(string="bank")