I was trying to create a dataframe from the results of the colMean function, but the result would always be weird. There has been previous discussion, (see:R - creating dataframe from colMeans function) but it when I implement the solution suggested, I get an extremely long data frame. It has a lot of columns, but now only one row, instead of having one column that has many rows.
temporary<-data.matrix(tempdb[,2:5])
temp2<-(as.numeric(colMeans(temporary),na.rm=T))
trgdphts<-c(trgdphts,temp2)
This is the code that I used.
I found out the problem to be that I had to clean up the variables.
After deleting everything, then rerunning the new code, it cleaned everything up. As it turns out, the functions were being run on the stale data and the stale data was never overwritten.
Thanks to Akrun for helping out. His advice inadverdently made me discover the problem.
Related
how are you?
I have the next problem, that is very weird because the task it is very simple.
I want to filter one of my factor variables in R, but the outcome is an empty dataframe.
So my data frame is called "data_2022", if i execute this code:
sum(data_2022$CANALDEVENTA=="WEB")
The result is 2704800 that is the number of times that this filter is TRUE.
a= data_2022 %>% filter(CANALDEVENTA=="WEB")
This returns an empty data frame.
I know i am not an expert in R, but i have done the last thing a million times and i never had this error before.
Do you have a clue about whats the problem with this?
Sorry i did not make a reproducible example.
Already thank you.
you could use subset function:
a<-subset(data_2022, CANALDEVENTA=="WEB")
using tidyverse, make sure you are using the function from dplyr::filter. filter is looking for a logical expression but probably you apply it to a data.frame. Try this code too:
my_names<-c("WEB")
a<-dplyr::filter(data_2022, CANALDEVENTA %in% my_names)
Hope it works.
I have a list containing values and I want to assign it to the column properties of a table in spotfire. I am currently using a for loop to do it. Is there a better approach to this, like assigning the entire list in one go?
As mentioned previously I am doing it currently using a for loop which can be seen below:
high=c(5,2,10)
low=c(3,1,0)
for(col in 1:ncol(temp)){
attributes(temp[,col])$SpotfireColumnMetaData$limits.whatif.upper=(high[col])[1]
attributes(temp[,col])$SpotfireColumnMetaData$limits.whatif.lower=(low[col)[1]
}
}
I have also tried just to do
attributes(temp2)$SpotfireColumnData$limits.whatif.upper=high
but that didnt seem to work.
So I want the column for limits.whatif.upper to be 5 for the first row, 2 for the second, and 10 for the third. As I said this code works, but I want to see if there is a faster way of doing it, since it seems that accessing the column property every time and changing it slows down the code a lot.The columns properties already exist so I am not creating new ones with this code.
It seems that python works faster than R with column properties. So if you need to do it faster, it may be better just to transfer the data over to python and do it from there. I dont have as much expierence in R, so it may just be poorly written R code as well.
My goal of this code is to create a loop that aggregates each company's word frequency by a certain principle vector I created and adds it to a list. The problem is, after I run this, it only prints the 7 principles that I have rather than the word frequencies along side them. The word frequencies being the certain column of the FREQBYPRINC.AG data frame. Individually, running this code without the loop and just testing out a certain column, it works no problem. For some reason, the loop doesn't want to give me the correct data frames for the list. Any suggestions?
list.agg<-vector("list",ncol(FREQBYPRINC.AG)-2)
for (i in 1:14){
attach(FREQBYPRINC.AG)
list.agg[i]<-aggregate(FREQBYPRINC.AG[,i+1],by=list(Type=principle),FUN=sum,na.rm=TRUE)
}
I really wish I could help. After reading your statement, It seems that to you , you feel that the code should be working and it is not. Well maybe there exists a glitch.
Since you had previously specified list. agg as a list, you need to subset it with double square brackets. Try this one out:
list.agg<-vector("list",ncol(FREQBYPRINC.AG)-2)
for (i in 1:14){
list.agg[[i]]<-aggregate(FREQBYPRINC.AG[,i+1],by=list
(Type=principle),FUN=sum,na.rm=TRUE)}
New to R, taking a very accelerated class with very minimal instruction. So I apologize in advance if this is a rookie question.
The assignment I have is to take a specific column that has 21 levels from a dataframe, and condense them into 4 levels, using an if, or ifelse statement. I've tried what feels like hundreds of combinations, but this is the code that seemed most promising:
> b2$LANDFORM=ifelse(b2$LANDFORM=="af","af_type",
ifelse(b2$LANDFORM=="aflb","af_type",
ifelse(b2$LANDFORM=="afub","af_type",
ifelse(b2$LANDFORD=="afwb","af_type",
ifelse(b2$LANDFORM=="afws","af_type",
ifelse(b2$LANDFORM=="bfr","bf_type",
ifelse(b2$LANDFORM=="bfrlb","bf_type",
ifelse(b2$LANDFORM=="bfrwb","bf_type",
ifelse(b2$LANDFORM=="bfrwbws","bf_type",
ifelse(b2$LANDFORM=="bfrws","bf_type",
ifelse(b2$LANDFORM=="lb","lb_type",
ifelse(bs$LANDFORM=="lbaf","lb_type",
ifelse(b2$LANDFORM=="lbub","lb_type",
ifelse(b2$LANDFORM=="lbwb","lb_type","ws_type"))))))))))))))
LANDFORM is a factor, but I tried changing it to a character too, and the code still didn't work.
"ws_type" is the catch all for the remaining variables.
the code runs without errors, but when I check it, all I get is:
> unique(b2$LANDFORM)
[1] NA "af_type"
Am I even on the right path? Any suggestions? Should I bite the bullet and make a new column with substr()? Thanks in advance.
If your new levels are just the first two letters of the old ones followed by _type you can easily achieve what you want through:
#prototype of your column
mycol<-factor(sample(c("aflb","afub","afwb","afws","bfrlb","bfrwb","bfrws","lb","lbwb","lbws","wslb","wsub"), replace=TRUE, size=100))
as.factor(paste(sep="",substr(mycol,1,2),"_type"))
After a great deal of experimenting, I consulted a co-worker, and he was able to simplify a huge amount of this. Basically, I should have made a new column composed of the first two letters of the variables in LANDFORM, and then sample from that new column and replace values in LANDFORM, in order to make the ifelse() statement much shorter. The code is:
> b2$index=as.factor(substring(b2$LANDFORM,1,2))
b2$LANDFORM=ifelse(b2$index=="af","af_type",
ifelse(b2$index=="bf","bf_type",
ifelse(b2$index=="lb","lb_type",
ifelse(b2$index=="wb","wb_type",
ifelse(b2$index=="ws","ws_type","ub_type")))))
b2$LANDFORM=as.factor(b2$LANDFORM)
Thanks to everyone who gave me some guidance!
I want to read in a large ido file that had just under 110,000,000 rows and 8 columns. The columns are made up of 2 integer columns and 6 logical columns. The delimiter "|" is used in the file. I tried using read.big.matrix and it took forever. I also tried dumpDf and it ran out of RAM. I tried ff which I heard was a good package and I am struggling with errors. I would like to do some analysis with this table if I can read it in some way. If anyone has any suggestions that would be great.
Kind Regards,
Lorcan
Thank you for all your suggestions. I managed to figure out why I couldn't get the error to work. I'll give you all answers and suggestions so no one can make my stupid mistake again.
First of all, the data that was been giving to me contained some errors in it so I was doomed to fail from the start. I was unaware until a colleague came across it in another piece of software. In a column that contained integers there were some letters so that when the read.table.ff package tried to read in the data set it somehow got confused or I don't know. Whatever though I was given another sample of data, 16,000,000 rows and 8 columns with correct entries and it worked perfectly. The code that I ran is as follows and took about 30 seconds to read:
setwd("D:/data test")
library(ff)
ffdf1 <- read.table.ffdf(file = "test.ido", header = TRUE, sep = "|")
Thank you all for your time and if you have any questions about the answer feel free to ask and I will do my best to help.
Do you really need all the data for your analysis? Maybe you could aggregate your dataset (say from minute values to daily averages). This aggregation only needs to be done once, and can hopefully be done in chunks. In this way you do need to load all your data into memory at once.
Reading in chunks can be done using scan, the important arguments are skip and n. Alternatively, put your data into a database and extract the chunks in that way. You could even using the functions from the plyr package to run chunks in parallel, see this blog post of mine for an example.