I think my problem is less difficult than it seems to me right now:
In R, I have a data frame with several columns. Two of them are called "PlotId" and "Landuse":
PlotId Landuse
---------------------
000AEG01 Wiese
000AEG02 Weide
000AEG03 Maehweide
000AEG04 ...
"PlotId" contains 50 rows with values from "000AEG01" to "000AEG50", "Landuse" contains three levels: "Wiese", "Weide" and "Maehweide".
For another problem, which I try to solve, I simply need to get the "Landuse"-value for the corresponding "PlotId"-row. SO, I need a command that gives me the information: "Plot 000AEG01 corresponds to Landuse Wiese." And so on for all the other rows, so I probably need to write a loop for that. This information, I would like to get as an object, which I can use then within another loop. I hope, you get what I mean and can help me!
Thanks a lot in advance!
Related
I am trying to categorize genes with multiple GO descriptors into bins based on what those GO descriptors are related to. I have dataframe A which contains the raw data associated with a list of geneIDs (>500,000) and their associated GO descriptors and dataframe B which classifies these GO descriptors into larger groups.
Example of dataframe A
dfA
Example of dataframe B
dfB
Ideally, the final output would reference the entire list and generate a new column in dataframe A classifying the GeneIDs into the GO_Category's associated with its specific GO_IDs -- bonus points if it removes duplicate hits on the GO_Categorys.
Looking something like this...
Example of Ideal Solution
However, I know that the ideal solution might be difficult to obtain, and I already have dataframe B listed out based on the unique GO_Categories so a solution like this might be easier to obtain.
Example of Acceptable Solution
So far I have struggled with getting any command to search for partial strings using a list from another dataframe with the goal of returning all matches.
I have had partial success with the acceptable solution approach and using:
dfA <- dfA %>%
mutate(GO_Cat_1 = c('No', 'Yes')[1+str_detect(dfA$GO_IDs, as.character(dfB$GO_IDs))])
The solution seems okay, however, it does return an error along the lines of
problem with mutate() column GO_Cat_1.
i GO_Cat_1 = ...[].
i longer object length is not a multiple of shorter object length
I have also tried to look into applying grepl/grep - but struggled to feed it a list of terms to look for partial string matches in dfA.
Any assistance is greatly appreciated!
I have a small problem, which I don't think is too hard, but I couldn't find any answer here (maybe I phrased my research wrong so please excuse me if the question has already been asked!)
I am importing data from an excel sheet which is split in two columns as in the following picture:
Now, I am trying to import all the data in the second column to my R script, but by splitting it into different vectors: one vector for category A, one for category B, etc... by keeping the data points in the order they are in the file (because as it happens, they are in chronological order).
Now, the categories each have a different number of elements, however, they are ordered alphabetically (ie you'll never find an A in the B's, for example). So I guess that makes it easier, but I'm still a novice with R and I don't really know how to proceed without getting really messy with the code and I know there's probably a simple way of doing it.
Does anyone have an idea on how to treat this nicely please? :)
We can use split in base R to return a list of vectors of 'Data' based on the unique values in 'Category'
lst1 <- split(df1$Data, df1$Category)
I need to subset my data depending on the content of one factor variable.
I tried to do it with subset:
new <- subset(data, original$Group1=="SALAD")
data is already a subset from a bigger data frame, in original I have the factor variable which should identify the wanted rows.
This works perfectly for one level of the factor variable, but (and I really don´t understand why!!) when I do it with the other factor level "BREAD" it creates the data frame but says "no data available" - so it is empty. I´ve imported the data from SPSS, if this matters. I´ve already checked the factor levels, but the naming should be right!
Would be really grateful for help, I spent 3 hours on this problem and wasn´t able to find a solution.
I´ve also tried other ways to subset my data (e.g. split), but I want a data frame as output.
Do you have advice in general, what is the best way to subset a data frame if I want e.g. 3 columns of this data frame and these should be extracted depending on the level of a factor (most Code examples are only for one or all columns..)
The entire point of the subset function (as I understand it) is to look inside the data frame for the right variable - so you can type
subset(data, var1 == "value")
instead of
data[data$var1 == "value,]
Please correct me anyone if that is incorrect.
Now, in you're case, you are explicitly taking Group1 from the data frame original and using that to subset data - which you say is a subset of original. Based on this, I see no reason to believe (and every reason not to believe) that the elements of original$Group1 will align with the rows of data. If Group1 is defined within data, why not just use the copy defined there - which is aligned correctly? If not, you need to be very explicit about what you are trying to accomplish, so that you can ensure that things are aligned correctly.
I was having a really hard time describing what I need in the Title, so I apologize ahead of time if that makes absolutely no sense.
If I have a CSV that has 2 columns, one with a persons name and a second column with a numeric value I need to find the duplicates in the names column then add the numeric values for that person together to get a total number in a new CSV.
This is a very simplified version of the real CSV
Name,Number
Dog,1
Cat,2
Fish,1
Dog,3
Dog,2
Cat,2
Fish,1
Given the information above, what I would like to be able to produce is this:
Name,Number
Dog,6
Cat,4
Fish,2
I really don't have any idea how to get there or if it's possible with PowerShell. I can only get as far as using group-object to group by name, but I have no clue how to add the columns after that.
The biggest problem I'm coming across with my research on this is that most if not all the results I get when googling involve adding new columns to a csv and not performing the mathematical calculation.
I finally got it
$csvfile = import-csv c:\csvfile.csv
$csvfile | group name | select name,#{Name="Totals";Expression={($_.group | Measure-Object -sum number).sum}}
Credit goes to:
http://www.hanselman.com/blog/ParsingCSVsAndPoorMansWebLogAnalysisWithPowerShell.aspx
First off, this may be the wrong Forum for this question, as it's pretty darn R+Bioconductor specific. Here's what I have:
library('GEOquery')
GDS = getGEO('GDS785')
cd4T = GDS2eSet(GDS)
cd4T <- cd4T[!fData(cd4T)$symbol == "",]
Now cd4T is an ExpressionSet object which wraps a big matrix with 19794 rows (probesets) and 15 columns (samples). The final line gets rid of all probesets that do not have corresponding gene symbols. Now the trouble is that most genes in this set are assigned to more than one probeset. You can see this by doing
gene_symbols = factor(fData(cd4T)$Gene.symbol)
length(gene_symbols)-length(levels(gene_symbols))
[1] 6897
So only 6897 of my 19794 probesets have unique probeset -> gene mappings. I'd like to somehow combine the expression levels of each probeset associated with each gene. I don't care much about the actual probe id for each probe. I'd like very much to end up with an ExpressionSet containing the merged information as all of my downstream analysis is designed to work with this class.
I think I can write some code that will do this by hand, and make a new expression set from scratch. However, I'm assuming this can't be a new problem and that code exists to do it, using a statistically sound method to combine the gene expression levels. I'm guessing there's a proper name for this also but my googles aren't showing up much of use. Can anyone help?
I'm not an expert, but from what I've seen over the years everyone has their own favorite way of combining probesets. The two methods that I've seen used the most on a large scale has been using only the probeset which has the largest variance across the expression matrix and the other being to take the mean of the probesets and creating a meta-probeset out of it. For smaller blocks of probesets I've seen people use more intensive methods involving looking at per-probeset plots to get a feel for what's going on ... generally what happens is that one probeset turns out to be the 'good' one and the rest aren't very good.
I haven't seen generalized code to do this - as an example we recently realized in my lab that a few of us have our own private functions to do this same thing.
The word you are looking for is 'nsFilter' in R genefilter package. This function assign two major things, it looks for only entrez gene ids, rest of the probesets will be filtered out. When an entrez id has multiple probesets, then the largest value will be retained and the others removed. Now you have unique entrez gene id mapped matrix. Hope this helps.