I have some code which imports a data frame (keywordsDF), and whilst using a FOR LOOP, it (by using the colnames), creates new variables depending on how many columns there are in it:
keywordsDF = read_excel("//Users//n//Desktop//Keywords.xlsx")
keywordList = colnames(keywordsDF)
for (i in seq_along(keywordList)) {
assign(keywordList[i], keywordsDF[keywordList[i]])
}
This all works fine. However, as the columns are of a different length, it imports NAs into the data.
I would normally remove those NAs for each column like:
consumption = keywordsDF$Consumption[!is.na(keywordsDF$Consumption)]
But I am not sure how to do it in the FOR LOOP (where I don't know the column names). I.e. where do I put the !is.na? As nothing seems to work for me.
You can use keywordsDF[[keywordList[i]]] any place you would use keywordsDF$Consumption, so
# spaced to make the substitution line up
consumption = keywordsDF$Consumption [!is.na( keywordsDF$Consumption )]
assign(keywordList[i], keywordsDF[[keywordList[i]]][!is.na( keywordsDF[[keywordList[i]]] )]
But I'd encourage you not to use assign like this... a list might be nicer like kw_no_na = lapply(keywordsDF[keywordList], na.omit), then use kw_no_na$consumption or kw_no_na[["consumption"]].
You could simply use na.omit() ?
for (i in seq_along(keywordList)) {
assign(keywordList[i], na.omit(keywordsDF[keywordList[i]]))
}
Related
I am trying to change a variable in a function but even tho the function is producing the right values, when I go to use them in the next sections, R is still using the initial values.
I created a function to update my variables NetN and NetC:
Reproduction=function(NetN,NetC,cnrep=20){
if(NetC/NetN<=cnrep) {
DeltaC=NetC*p;
DeltaN=DeltaC/cnrep;
Crep=Crep+DeltaC;
Nrep=Nrep+DeltaN;
Brep=(Nrep*14+Crep*12)*2/1e6;
NetN=NetN-DeltaN; #/* Update N, C values */
NetC=NetC*(1-p)
print ("'Using C to allocate'")
}
else {
print("Using N to allocate");
DeltaN=NetN*p;
DeltaC=DeltaN*cnrep;
Nrep=Nrep+DeltaN;
Crep=Crep+DeltaC;
Brep=(Nrep*14+Crep*12)*2/1e6;
NetN=NetN*(1-p);
NetC=NetC-DeltaC;
} } return(c(NetC=NetC,NetN=NetN,NewB=NewB,Crep=Crep,Nrep=Nrep,Brep=Brep))}
When I use my function by say doing:
Reproduction(NetN=1.07149,NetC=0.0922349,cnrep=20)
I get the desired result printed out which includes:
NetC=7.378792e-02
However, when I go to use NetC in the next section of my code, R is still using NetC=0.0922349.
Can I make R update NetC without having to define a new variable?
In R, in general, functions shouldn't change things outside of the function. It's possible to do so using <<- or assign(), but this generally makes your function inflexible and very surprising.
Instead, functions should return values (which yours does nicely), and if you want to keep those values, you explicitly use <- or = to assign them to objects outside of the function. They way your function is built now, you can do that like this:
updates = Reproduction(NetN = 1.07149, NetC = 0.0922349, cnrep = 20)
NetC = updates["NetC"]
This way, you (a) still have all the other results of the function stored in updates, (b) if you wanted to run Reproduction() with a different set of inputs and compare the results, you can do that. (If NetC updated automatically, you could never see two different values), (c) You can potentially change variable names and still use the same function, (d) You can run the function to experiment/see what happens without saving/updating the values.
If you generally want to keep NetN, NetC, and cnrep in sync, I would recommend keeping them together in a named vector or list, and rewriting your function to take that list as input and return that list as output. Something like this:
params = list(NetN = 1.07149, NetC = 0.0922349, cnrep = 20)
Reproduction=function(param_list){
NetN = param_list$NetN
NetC = param_list$NetC
cnrep = param_list$cnrep
if(NetC/NetN <= cnrep) {
DeltaC=NetC*p;
DeltaN=DeltaC/cnrep;
Crep=Crep+DeltaC;
Nrep=Nrep+DeltaN;
Brep=(Nrep*14+Crep*12)*2/1e6;
NetN=NetN-DeltaN; #/* Update N, C values */
NetC=NetC*(1-p)
print ("'Using C to allocate'")
}
else {
print("Using N to allocate");
DeltaN=NetN*p;
DeltaC=DeltaN*cnrep;
Nrep=Nrep+DeltaN;
Crep=Crep+DeltaC;
Brep=(Nrep*14+Crep*12)*2/1e6;
NetN=NetN*(1-p);
NetC=NetC-DeltaC;
}
## Removed extra } and ) ??
return(list(NetC=NetC, NetN=NetN, NewB=NewB, Crep=Crep, Nrep=Nrep, Brep=Brep))
}
This way, you can use the single line params <- Reproduction(params) to update everything in your list. You can access individual items in the list with either params$Netc or params[["NetC"]].
i tried updating data in dataframe but its unable to get updating
//Initialize data and dataframe here
user_data=read.csv("train_5.csv")
baskets.df=data.frame(Sequence=character(),
Challenge=character(),
countno=integer(),
stringsAsFactors=FALSE)
/Updating data in dataframe here
for(i in 1:length((user_data)))
{
for(j in i:length(user_data))
{
if(user_data$challenge_sequence[i]==user_data$challenge_sequence[j]&&user_data$challenge[i]==user_data$challenge[j])
{
writedata(user_data$challenge_sequence[i],user_data$challenge[i])
}
}
}
writedata=function( seqnn,challng)
{
#print(seqnn)
#print(challng)
newRow <- data.frame(Sequence=seqnn,Challenge=challng,countno=1)
baskets.df=rbind(baskets.df,newRow)
}
//view data here
View(baskets.df)
I've modified your code to what I believe will work. You haven't provided sample data, so I can't verify that it works the way you want. I'm basing my attempt here on a couple of common novice mistakes that I'll do my best to explain.
Your writedata function was written to be a little loose with it's scope. When you create a new function, what happens in the function technically happens in its own environment. That is, it tries to look for things defined within the function, and then any new objects it creates are created only within that environment. R also has this neat (and sometimes tricky) feature where, if it can't find an object in an environment, it will try to look up to the parent environment.
The impact this has on your writedata function is that when R looks for baskets.df in the function and can't find it, R then turns to the Global Environment, finds baskets.df there, and then uses it in rbind. However, the result of rbind gets saved to a baskets.df in the function environment, and does not update the object of the same name in the global environment.
To address this, I added an argument to writedata that is simply named data. We can then use this argument to pass a data frame to the function's environment and do everything locally. By not making any assignment at the end, we implicitly tell the function to return it's result.
Then, in your loop, instead of simply calling writedata, we assign it's result back to baskets.df to replace the previous result.
for(i in 1:length((user_data)))
{
for(j in i:length(user_data))
{
if(user_data$challenge_sequence[i] == user_data$challenge_sequence[j] &&
user_data$challenge[i] == user_data$challenge[j])
{
baskets.df <- writedata(baskets.df,
user_data$challenge_sequence[i],
user_data$challenge[i])
}
}
}
writedata=function(data, seqnn,challng)
{
#print(seqnn)
#print(challng)
newRow <- data.frame(Sequence = seqnn,
Challenge = challng,
countno = 1)
rbind(data, newRow)
}
I'm not sure what you're programming background is, but your loops will be very slow in R because it's an interpreted language. To get around this, many functions are vectorized (which simply means that you give them more than one data point, and they do the looping inside compiled code where the loops are fast).
With that in mind, here's what I believe will be a much faster implementation of your code
user_data=read.csv("train_5.csv")
# challenge_indices will be a matrix with TRUE at every place "challenge" and "challenge_sequence" is the same
challenge_indices <- outer(user_data$challenge_sequence, user_data$challenge_sequence, "==") &
outer(user_data$challenge, user_data$challenge, "==")
# since you don't want duplicates, get rid of them
challenge_indices[upper.tri(challenge_indices, diag = TRUE)] <- FALSE
# now let's get the indices of interest
index_list <- which(challenge_indices,arr.ind = TRUE)
# now we make the resulting data set all at once
# this is much faster, because it does not require copying the data frame many times - which would be required if you created a new row every time.
baskets.df <- with(user_data, data.frame(
Sequence = challenge_sequence[index_list[,"row"]],
challenge = challenge[index_list[,"row"]]
)
Been going around for hours with this. My 1st question online on R. Trying to creat a function that contains a loop. The function takes a vector that the user submits like in pollutantmean(4:6) and then it loads a bunch of csv files (in the directory mentioned) and binds them. What is strange (to me) is that if I assign the variable id and then run the loop without using a function, it works! When I put it inside a function so that the user can supply the id vector then it does nothing. Can someone help ? thank you!!!
pollutantmean<-function(id=1:332)
{
#read files
allfiles<-data.frame()
id<-str_pad(id,3,pad = "0")
direct<-"/Users/ped/Documents/LearningR/"
for (i in id) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
}
Your function is missing a return value. (#Roland)
pollutantmean<-function(id=1:332) {
#read files
allfiles<-data.frame()
id<-str_pad(id,3,pad = "0")
direct<-"/Users/ped/Documents/LearningR/"
for (i in id) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
return(allfiles)
}
Edit:
Your mistake was that you did not specify in your function what you want to get out from the function. In R, you create objects inside of function (you could imagine it as different environment) and then specify which object you want it to return.
With my comment about accepting my answer, I meant this: (...To mark an answer as accepted, click on the check mark beside the answer to toggle it from greyed out to filled in...).
Consider even an lapply and do.call which would not need return being last line of function:
pollutantmean <- function(id=1:332) {
id <- str_pad(id,3,pad = "0")
direct_files <- paste0("/Users/ped/Documents/LearningR/", id, ".csv")
# READ FILES INTO LIST AND ROW BIND
allfiles <- do.call(rbind, lapply(direct_files, read.csv))
}
ok, I got it. I was expecting the files that are built to be actually created and show up in the environment of R. But for some reason they don't. But R still does all the calculations. Thanks lot for the replies!!!!
pollutantmean<-function(directory,pollutant,id)
{
#read files
allfiles<-data.frame()
id2<-str_pad(id,3,pad = "0")
direct<-paste("/Users/pedroalbuquerque/Documents/Learning R/",directory,sep="")
for (i in id2) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
#averaging polutants
mean(allfiles[,pollutant],na.rm = TRUE)
}
pollutantmean("specdata","nitrate",23:35)
I'm trying to construct a function that calculate some variances using the survey package. The problem is that I need to insert the name of the variable (not the values of the variables) into a specific function (svyby)
Is something like this:
myfun=function(variable) {
svyby(~variable,~subpop,design,svymean)
}
myfun(P16)
It gives me error. I also tried with
*base[,variable]*
instead of
*variable*
the problem here that base[,variable] gives me the vector with the values of the variable, but I need the name of the variable to be read in the design object. What I mean is, I need that the function insert the name like this
svyby(~P16,~subpop,design,svymean)
I will appreciate any help, thank you in advance,
Gonzalo
Looks like it needs a formula. You can paste a "~" to a string and use as.formula, like this:
myfun = function(variable) {
svyby(as.formula(paste("~", variable)),
~subpop, design, svymean)
}
And then call is like this: myfun("P16"). Note that you will need to use a quoted column name because you are treating it like a string.
Alternatively, you could have your function take a formula:
myfun2 = function(formula) {
svyby(formula,
~subpop, design, svymean)
}
And call it like this: myfun2(~P16).
I'm making a function and before it does any of the hard stuff I need it to check that all the column names listed in the 'samples' dataset are also present in the 'grids' dataset (the function maps one onto the other).
all(names(samples[expvar]) %in% names(grids))
This does that: the code within all() asks if all the names in the list ('expvar') of columns in 'samples' are also names in 'grids'. The output for a correct length=3, expvar would be TRUE TRUE TRUE. 'all' asks if all are TRUE, so the output here is TRUE. I want to make an IF statement along the lines of:
if(all(names(samples[expvar]) %in% names(grids)) = FALSE) {stop("Not all expvar column names found as column names in grids")}
No else needed, it'll just carry on. The problem is that the '= FALSE' is redundant because all() is a logically evaluable statement... is there a "carry on" function, e.g.
if(all(etc)) CARRYON else {stop("warning")}
Or, can anyone think of a way I can restructure this to make it work?
You're looking for the function stopifnot.
However you don't need to implement it as
if (okay) {
# do stuff
} else {
stop()
}
which is what you have. Instead you can do
if (!okay) {
stop()
}
# do stuff
since the lines will execute in sequential order. But, again, it might be more readable to use stopifnot, as in:
stopifnot(okay)
# do stuff
I would code it:
if(!all(...))
stop(...)
... rest of program ...