i have a Dataframe like this.
My column names are F02283_L1_S49_L006, F02284_L1_S50_L006 etc. which is totally fine.
But my row names are 1,2,3,4 etc. and i dont want that. Instead of that i want ENSG00000182199, ENSG00000157870 etc.
Im new in R and i have to process this dataframe. I will my dataframe as a matrix, so they muss contain just numeric values. How can i do that with R?
Related
This probably has a simple fix, but I'm relatively new to using R and could use some assistance.
The toy data I'm using for a gene network analysis has rows that look like this:
whereas the data that I've uploaded has rows that look like this:
.
The code I'm using refers to the row names to map on as gene names. I am able to successfully run this analysis, however, the output I end up with has lists of row numbers where there should be lists of gene names.
Is there a simple way that I can convert my data into the toy data format so that the row names are gene names instead of numbers?
I have a data table and I would like to create a vector that cointains all the unique values from that column. After that, I would like to download it and use it in excel.
The column from which I want to derive the vector is "Estacao":
I have a dataframe with over 100 columns. Post implementation of certain conditions, I need a subset of the dataframe with the columns that are listed in a separate array.
The array has 50 entries with 2 columns. The first column has the selected variable names and the second column has some associated values.
I wish to build a new data frame with just the variables mentioned in the the first column of the separate array. Could you please point me as to how to proceed?
Try this:
library(dplyr)
iris <- iris %>% select(contains(dataframe_with_names$names))
In R you can use square brackets [rows, columns] to select specific rows or specific columns. (Leaving either blank selects all).
If you had a vector of column names you wanted to keep called important_columns you could select only those columns with:
myData[,important_columns]
In your case the vector of column names is actually a column in your array. So you select that column and use it as your vector:
myData[, array$names]
I have two lists and a dataframe. The columns in the dataframe have the same names as the entries in the list. The dataframe has other columns as well, other than the ones specified in the lists
category.list <- c('Reserve_Book','choicepriv_and_points','Latency_freeze_load','signin','gift_card','mystery_gift','credit_card','call_support','account')
crosstab.list <- c('browser','OS','Device','comment_cat','comment_focus','recommend')
Now, how do I iterate through the elements in the list and use them to access the dataframe columns?
Below is the code, I am trying but I am getting errors while trying to access the dataframe column via the iterator variable.
for (i in category.list){
for (j in crosstab.list){
ftable(dataframe[j]~dataframe[i])
}
}
Specifically to your question, your dataframe references need to specify both which columns are desired and which rows.
ftable(dataframe[j]~dataframe[i])
needs to be
ftable(dataframe[,j]~dataframe[,i])
Note the addition of commas
I have a data set (df2) with 400 columns and thousands of rows. The columns all have different names but all have either 'typeP' or 'typeR' at the end of their names. They are not ordered sequentially (eg. P,P,P,P,R,R,R,R) but randomly (P,P,R,R,R,P,R,P etc). I want to create a new data frame with just those columns whose names have 'type P' in their names.
I'm very new to R and so far I have only managed to find the positions of those columns using: grep("typeP",colnames(df2)). Any help would be appreciated!
After we get the index, we can use that to subset the initial dataset
df3 <- df2[grep("typeP",colnames(df2))]