I have 5 dataframes with different subsets of variables. For example, the subset of the 5 A-Variables appear in dataframe 1 and 5. The subset of the 7 B-Variables appear in dataframe 1 and 4 and so on. A different number of persons did one of the 5 test-versions (thats why I have 5 dataframes)
Now, I want to merge the dataframes together. The colums shall have all variables of all dataframes. When a variable appeared in two dataframes, the values should be merged and appear in one column at the end. For all persons who did not see a variable because it was in another test, a "NA" should be in there at the end..
Do you guys have an idea?
Thank you very much in advance!
You'll probably need to do some combination of inner_join(), left_join(), right_join() etc.
Check this out, should have what you need... It's difficult to know exactly what you need without seeing the data.
Related
I have a dataframe that has 23 columns of various parameters defining a patient which I extracted using dplyr from a larger dataframe after pivoting it such that each of the parameters forms the columns of the new dataframe.
Now I am facing an issue. I am getting a lot of rows for the same patient. For each parameter, one of the rows shows the required value and the rest is denoted as NA. So if the same patient is repeated, say 10 times, in every parameter column there is one row with the actual value and the rest is NA.
How do I remove these NAs and gather the information that is scattered in this manner?
I want the 1 and 2 to be on the same row. All the rows seen in this image of dataframe are of the same person.
I have a large set of data and I'm trying to group different rows together. I will know how to group the rows by using an ID. In the dataset, these IDs are sequential.
For example,
So what I want to do is iterate through this set of data and then place the data contained in these rows into a vector of vectors for processing later. The data contained in these rows of identical ID are going to be compared with one another to categorize the groupings.
I would like my data structure to look like something like this.
1 -> 1 -> 1
|
V
2 -> 2
So row 1 would contain only data from 1 type of ID, then the next row in the vector would be a vector of another type of ID. How would I go about doing this in R? In C++ it would just be a vector of vectors but I haven't been able to figure out how to do the same in R.
Is this even the right way to be approaching this problem? Is there a better way to do what I'm trying to do?
You would want to work with Data Frames rather than simple matrices. Have a look as the Documentation R-tutor Data.Frames.
It is doable. Best!
I need to extract the columns from a dataset without header names.
I have a ~10000 x 3 data set and I need to plot the first column against the second two.
I know how to do it when the columns have names ~ plot(data$V1, data$V2) but in this case they do not. How do I access each column individually when they do not have names?
Thanks
Why not give them sensible names?
names(data)=c("This","That","Other")
plot(data$This,data$That)
That's a better solution than using the column number, since names are meaningful and if your data changes to have a different number of columns your code may break in several places. Give your data the correct names and as long as you always refer to data$This then your code will work.
I usually select columns by their position in the matrix/data frame.
e.g.
dataset[,4] to select the 4th column.
The 1st number in brackets refers to rows, the second to columns. Here, I didn't use a "1st number" so all rows of column 4 are selected, i.e., the whole column.
This is easy to remember since it stems from matrix calculations. E.g., a 4x3 dimensional matrix has 4 rows and 3 columns. Thus when I want to select the 1st row of the third column, I could do something like matrix[1,3]
Here is my question,
I have a list of data.frames. It's produced by same piece of codes with different data.
All of the data.frames looks like
US 100 (not guarantee to exist in another data.frame because data is different)
CA 50
...
Is there any fast/neat way to sum over all the data.frames?
I am not sure whether I have understood your problem correctly, but here a possible solution:
Try to put all your dataframes in a list, e.g., your_list=list(df1,df2,...)
Then use total_df=do.call(rbind,your_list) to combine all dataframes (row-wise).
After that you can use ddply(total_df,"country",function (x) sum(x$value)) to aggregate the data. Here, I have assumed that US and CA stand for entries in a country column and 100 and 50 for entries in a value column.
I hope you won't find my question too silly, i did a lot of research but it seems that i can't figure how to solve this really annoying issue.
Well, i have datas for 6 participants (P) in an experiment, with 50 trials (T) per participants and 10 condition (C). So i'd like to create a dataframe in r allowing me to put these datas.
This data.frame should have 3 factors (P, T and C) and so a number of total row of (P*T*C). The difficulty for me is to create this one, since i have the datas for the 6 participant in 6 data.frame of 100 obs(T) by 10 varibles(C).
I'd like first to create the empty dataset with these factors, and then copy the values of the 6 data.set according to the factors P, T and C.
Any help would be greatly appreciated, i'm novice in r.
Thank you.
OK; First we create one big dataframe for all participants:
result<-rbind(dfrforparticipant1, dfrforparticipant2,...dfrforparticipant6) #you'll have to fill out the proper names of the original data.frames
Next, we add a column for the participant ID:
numTrials<-50 #although 100 is also mentioned in your question
result$P<-as.factor(rep(1:6, each=numTrials))
Finally, we need to go from 'wide' format to 'long' format (I'm assuming your column names holding the results for each condition are called C1, C2 etc. ; I'm also assuming your original data.frames already held a column named T to denote the trial), like this (untested, since you did not provide example data):
orgcolnames<-paste("C", 1:10, sep="")
result2<-reshape(result, varying=list(orgcolnames), v.names="val", idvar=c("T","P"), timevar="C", times=seq_along(orgcolnames), direction="long")
What you want is now in result2.