I am completely new to R. I tried reading the reference and a couple of good introductions, but I am still quite confused.
I am hoping to do the following:
I have produced a .txt file that looks like the following:
area,energy
1.41155882174e-05,1.0914586287e-11
1.46893363946e-05,5.25011714434e-11
1.39244046855e-05,1.57904991488e-10
1.64155121046e-05,9.0815757601e-12
1.85202830392e-05,8.3207522281e-11
1.5256036289e-05,4.24756620609e-10
1.82107587343e-05,0.0
I have the following command to read the file in R:
tbl <- read.csv("foo.txt",header=TRUE).
producing:
> tbl
area energy
1 1.411559e-05 1.091459e-11
2 1.468934e-05 5.250117e-11
3 1.392440e-05 1.579050e-10
4 1.641551e-05 9.081576e-12
5 1.852028e-05 8.320752e-11
6 1.525604e-05 4.247566e-10
7 1.821076e-05 0.000000e+00
Now I want to store each column in two different vectors, respectively area and energy.
I tried:
area <- c(tbl$first)
energy <- c(tbl$second)
but it does not seem to work.
I need to different vectors (which must include only the numerical data of each column) in order to do so:
> prob(energy, given = area), i.e. the conditional probability P(energy|area).
And then plot it. Can you help me please?
As #Ananda Mahto alluded to, the problem is in the way you are referring to columns.
To 'get' a column of a data frame in R, you have several options:
DataFrameName$ColumnName
DataFrameName[,ColumnNumber]
DataFrameName[["ColumnName"]]
So to get area, you would do:
tbl$area #or
tbl[,1] #or
tbl[["area"]]
With the first option generally being preferred (from what I've seen).
Incidentally, for your 'end goal', you don't need to do any of this:
with(tbl, prob(energy, given = area))
does the trick.
Related
I am a newbie in R programming and seek help in analyzing the Metabolomics data - 118 metabolites with 4 conditions (3 replicates per condition). I would like to know, for each metabolite, which condition(s) is significantly different from which. Here is part of my data
> head(mydata)
Conditions HMDB03331 HMDB00699 HMDB00606 HMDB00707 HMDB00725 HMDB00017 HMDB01173
1 DMSO_BASAL 0.001289121 0.001578235 0.001612297 0.0007772231 3.475837e-06 0.0001221674 0.02691318
2 DMSO_BASAL 0.001158363 0.001413287 0.001541713 0.0007278363 3.345166e-04 0.0001037669 0.03471329
3 DMSO_BASAL 0.001043537 0.002380287 0.001240891 0.0008595932 4.007387e-04 0.0002033625 0.07426482
4 DMSO_G30 0.001195253 0.002338346 0.002133992 0.0007924157 4.189224e-06 0.0002131131 0.05000778
5 DMSO_G30 0.001511538 0.002264779 0.002535853 0.0011580857 3.639661e-06 0.0001700157 0.02657079
6 DMSO_G30 0.001554804 0.001262859 0.002047611 0.0008419137 6.350990e-04 0.0000851638 0.04752020
This is what I have so far.
I learned the first line from this post
kwtest_pvl = apply(mydata[,-1], 2, function(x) kruskal.test(x,as.factor(mydata$Conditions))$p.value)
and this is where I loop through the metabolite that past KW test
tCol = colnames(mydata[,-1])[kwtest_pvl <= 0.05]
for (k in tCol){
output = posthoc.kruskal.dunn.test(mydata[,k],as.factor(mydata$Conditions),p.adjust.method = "BH")
}
I am not sure how to manage my output such that it is easier to manage for all the metabolites that passed KW test. Perhaps saving the output from each iteration appending to excel? I also tried dunn.test package since it has an option of table or list output. However, it still leaves me at the same point. Kinda stuck here.
Moreover, should I also perform some kind of adjusted p-value, i.e FWER, FDR, BH right after KW test - before performing the posthoc test?
Any suggestion(s) would be greatly appreciated.
I have a long data frame of genes and various forms of ids for them (e.g. OMIM, Ensembl, Genatlas). I want to get the list of all SNPs that are associated with each gene. (This is the reverse of this question.)
So far, the best solution I have found is using the biomaRt package (bioconductor). There is an example of the kind of lookup I need to do here. Fitted for my purposes, here is my code:
library(biomaRt)
#load the human variation data
variation = useEnsembl(biomart="snp", dataset="hsapiens_snp")
#look up a single gene and get SNP data
getBM(attributes = c(
"ensembl_gene_stable_id",
'refsnp_id',
'chr_name',
'chrom_start',
'chrom_end',
'minor_allele',
'minor_allele_freq'),
filters = 'ensembl_gene',
values ="ENSG00000166813",
mart = variation
)
This outputs a data frame that begins like this:
ensembl_gene_stable_id refsnp_id chr_name chrom_start chrom_end minor_allele minor_allele_freq
1 ENSG00000166813 rs8179065 15 89652777 89652777 T 0.242412
2 ENSG00000166813 rs8179066 15 89652736 89652736 C 0.139776
3 ENSG00000166813 rs12899599 15 89629243 89629243 A 0.121006
4 ENSG00000166813 rs12899845 15 89621954 89621954 C 0.421126
5 ENSG00000166813 rs12900185 15 89631884 89631884 A 0.449681
6 ENSG00000166813 rs12900805 15 89631593 89631593 T 0.439297
(4612 rows)
The code works, but the running time is extremely long. For the above, it takes about 45 seconds. I thought maybe this was related to the allele frequencies, which the server perhaps calculated on the fly. But looking up the bare minimum of only the SNPs rs ids takes something like 25 seconds. I have a few thousand genes, so this would take an entire day (assuming no timeouts or other errors). This can't be right. My internet connection is not slow (20-30 mbit).
I tried looking up more genes per query. This did dot help. Looking up 10 genes at once is roughly 10 times as slow as looking up a single gene.
What is the best way to get a vector of SNPs that associated with a vector of gene ids?
If I could just download two tables, one with genes and their positions and one with SNPs and their positions, then I could easily solve this problem using dplyr (or maybe data.table). I haven't been able to find such tables.
Since you're using R, here's an idea that uses the package rentrez. It utilizes NCBI's Entrez database system and in particular the eutils function, elink. You'll have to write some code around this and probably tweak parameters, but could be a good start.
library(rentrez)
# for converting gene name -> gene id
gene_search <- entrez_search(db="gene", term="(PTEN[Gene Name]) AND Homo sapiens[Organism]", retmax=1)
geneId <- gene_search$ids
# elink function
snp_links <- entrez_link(dbfrom='gene', id=geneId, db='snp')
# access results with $links
length(snp_links$links$gene_snp)
5779
head(snp_links$links$gene_snp)
'864622690' '864622594' '864622518' '864622451' '864622387' '864622341'
I suggest you manually double-check that the number of SNPs is about what you'd expect for your genes of interest -- you may need to drill down further and limit by transcript, etc...
For multiple gene ids:
multi_snp_links <- entrez_link(dbfrom='gene', id=c("5728", "374654"), db='snp', by_id=TRUE)
lapply(multi_snp_links, function(x) head(x$links$gene_snp))
1. '864622690' '864622594' '864622518' '864622451' '864622387' '864622341'
2. '797045093' '797044466' '797044465' '797044464' '797044463' '797016353'
The results are grouped by gene with by_id=TRUE
I have a large data set in which I have to search for specific codes depending on what i want. For example, chemotherapy is coded by ~40 codes, that can appear in any of 40 columns called (diag1, diag2, etc).
I am in the process of writing a function that produces plots depending on what I want to show. I thought it would be good to specify what I want to plot in a input data frame. Thus, for example, in case I only want to plot chemotherapy events for patients, I would have a data frame like this:
Dataframe name: Style
Name SearchIn codes PlotAs PlotColour
Chemo data[substr(names(data),1,4)=="diag"] 1,2,3,4,5,6 | red
I already have a function that searches for codes in specific parts of the data frame and flags the events of interest. What i cannot do, and need your help with, is referring to a data frame (Style$SearchIn[1]) using codes in a data frame as above.
> Style$SearchIn[1]
[1] data[substr(names(data),1,4)=="diag"]
Levels: data[substr(names(data),1,4)=="diag"]
I thought perhaps get() would work, but I cant get it to work:
> get(Style$SearchIn[1])
Error in get(vars$SearchIn[1]) : invalid first argument
enter code here
or
> get(as.character(Style$SearchIn[1]))
Error in get(as.character(Style$SearchIn[1])) :
object 'data[substr(names(data),1,5)=="TDIAG"]' not found
Obviously, running data[substr(names(data),1,5)=="TDIAG"] works.
Example:
library(survival)
ex <- data.frame(SearchIn="lung[substr(names(lung),1,2) == 'ph']")
lung[substr(names(lung),1,2) == 'ph'] #works
get(ex$SearchIn[1]) # does not work
It is not a good idea to store R code in strings and then try to eval them when needed; there are nearly always better solutions for dynamic logic, such as lambdas.
I would recommend using a list to store the plot specification, rather than a data.frame. This would allow you to include a function as one of the list's components which could take the input data and return a subset of it for plotting.
For example:
library(survival);
plotFromSpec <- function(data,spec) {
filteredData <- spec$filter(data);
## ... draw a plot from filteredData and other stuff in spec ...
};
spec <- list(
Name='Chemo',
filter=function(data) data[,substr(names(data),1,2)=='ph'],
Codes=c(1,2,3,4,5,6),
PlotAs='|',
PlotColour='red'
);
plotFromSpec(lung,spec);
If you want to store multiple specifications, you could create a list of lists.
Have you tried using quote()
I'm not entirely sure what you want but maybe you could store the things you're trying to get() like
quote(data[substr(names(data),1,4)=="diag"])
and then use eval()
eval(quote(data[substr(names(data),1,4)=="diag"]), list(data=data))
For example,
dat <- data.frame("diag1"=1:10, "diag2"=1:10, "other"=1:10)
Style <- list(SearchIn=c(quote(data[substr(names(data),1,4)=="diag"]), quote("Other stuff")))
> head(eval(Style$SearchIn[[1]], list(data=dat)))
diag1 diag2
1 1 1
2 2 2
3 3 3
4 4 4
5 5 5
6 6 6
I have a list of boroughs and a list of localities (like this one). Each locality lies in exactly one borough. What's the best way to store this kind of hierarchical structure in R, considerung that I'd like to have a convenient and readable way of accessing these, and using this list to accumulate data on the locality-level to the borough level.
I've come up with the following:
localities <- list("Mitte" = c("Mitte", "Moabit", "Hansaviertel", "Tiergarten", "Wedding", "Gesundbrunnen",
"Friedrichshain-Kreuzberg" = c("Friedrichshain", "Kreuzberg")
)
But I am not sure if this is the most elegant and accessible way.
If I wanted to assign additional information on the localitiy-level, I could do that by replacing the c(...) by some other call, like rbind(c('0201', '0202'), c("Friedrichshain", "Kreuzberg")) if I wanted to add additional information to the borough-level (like an abbreviated name and a full name for each list), how would I do this?
Edit: For example, I'd like to condense a table like this into a borough-wise version.
Hard to know without having a better view on how you intend to use this, but I would strongly recommend moving away from a nested list structure to a data frame structure:
library(reshape2)
loc.df <- melt(localities)
This is what the molten data looks like:
value L1
1 Mitte Mitte
2 Moabit Mitte
3 Hansaviertel Mitte
4 Tiergarten Mitte
5 Wedding Mitte
6 Gesundbrunnen Mitte
7 Friedrichshain Friedrichshain-Kreuzberg
8 Kreuzberg Friedrichshain-Kreuzberg
You can then use all the standard data frame and other computations:
loc.df$population <- sample(100:500, nrow(loc.df)) # make up population
tapply(loc.df$population, loc.df$L1, mean) # population by borough
gives mean population by Borough:
Friedrichshain-Kreuzberg Mitte
278.5000 383.8333
For more complex calculations you can use data.table and dplyr
You can extract all of this data directly into a data.frame using the XML library.
library(XML)
theurl <- "http://en.wikipedia.org/wiki/Boroughs_and_localities_of_Berlin#List_of_localities"
tables<-readHTMLTable(theurl)
boroughs<-tables[[1]]$Borough
localities<-tables[c(3:14)]
names(localities) <- as.character(boroughs)
all<-do.call("rbind", localities)
#Roland, I think you will find data frames superior to lists for the reasons cited earlier, but also because there is other data on the web page you reference. Loading to a data frame will make it easy to go further if you wish. For example, making comparisons based on population density or other items provided "for free" on the page will be a snap from a data frame.
I have two sources of clinical procedure billing information that I have added together (with rbind). In each row there is a CPT field and a CPT.description field that supplys a brief explanation. However, the descriptions are slightly different from the two sources. I want to be able to combine them. That way, if different words or abbreviations are used, then I can just do a string search to find what I am looking for.
So lets make up a simplified representation of a data table that I was able to generate.
cpt <- c(23456,23456,10000,44555,44555)
description <- c("tonsillectomy","tonsillectomy in >12 year old","brain transplant","castration","orchidectomy")
cpt.desc <- data.frame(cpt,description)
And here is what I want to get to.
cpt.wanted <- c(23456,10000,44555)
description.wanted <- c("tonsillectomy; tonsillectomy in >12 year old","brain transplant","castration; orchidectomy")
cpt.desc.wanted <- data.frame(cpt.wanted,description.wanted)
I have tried using functions such as unstack and then lapply(list,paste) but that is not pasting the elements of each list. I also tried reshape but there was no categorical variable to differentiate first or second version of description or even in some cases a third. The really annoying part is I had a similar problem a few months or years ago and someone helped me either on stackoverflow or on r-help and for the life of me I cannot find it.
So the underlying problem is, imagine that I have a spreadsheet in front of me. I need to do a vertical merge (paste) of two or maybe even three description cells who have the same CPT code in the adjacent column.
What buzzwords should I have been using to search for a solution to this problem.
Thank you so much for your help.
sapply( sapply(unique(cpt), function(x) grep(x, cpt) ),
# creates sets of index vectors as a list
function(x) paste(description[x], collapse=";") )
# ... and this pastes each set of selected items from "description" vector
[1] "tonsillectomy;tonsillectomy in >12 year old"
[2] "brain transplant"
[3] "castration;orchidectomy"
Here is an approach that uses plyr.
library("plyr")
cpt.desc.wanted <- ddply(cpt.desc, .(cpt), summarise,
description.wanted = paste(unique(description), collapse="; "))
which gives
> cpt.desc.wanted
cpt description.wanted
1 10000 brain transplant
2 23456 tonsillectomy; tonsillectomy in >12 year old
3 44555 castration; orchidectomy