I am trying to read in a csv file (exported from survey monkey).
I have tried survey <- read.csv("Survey Item Evaluation2.csv", header=TRUE, stringsAsFactors = FALSE)
I ran skim(survey), which shows it is reading in as characters.
str(survey) output: data.frame: 623obs. of 68 variables. G1 (which is a survey item) reads in as chr "1" "3" "4" "1"....
How do I change those survey item variables to numeric?
The correct answer to your question is given in the first two comments by two very well respected people with a combined reputation of over 600k. I'll post their very similar answer here:
as.numeric(survey$G1)
However, that is not very good advice in my opinion. Your question should really have been:
"Why am I getting character data when I'm sure this variable should be numeric?"
To which the answer would be: "Either your not reading the data correctly (does the data start at row 3), or there is non-numeric (garbage) data among the numeric data (for example NA is entered as . or some other character), or certain people entered a , instead of a . to represent decimal point (such as nationals of Indonesia and some European countries), or they entered a thin thousand separator instead of a comma, or some other unknown reason which needs further investigation. Maybe a certain group of people enter text instead of numbers for their age (fifty instead of 50), or they put a . at the end of the data, for example 62.5. instead of 62.5 for their age (older folks were taught to always end a sentence with a period!). In these last two cases, certain groups (elderly) will have missing data and your data is then missing not at random (MNAR), a big bias in your analysis".
I see this all too often and I worry that new users of R are making terrible mistakes due being given poor advice, or because they didn't learn the basics. Importing data is the first step of analysis. It can be difficult because data files come in all shapes and sizes - there is no global standard. Data is also often entered without any quality control mechanisms. I'm glad that you added the stringsAsFactors=FALSE argument in your command to import the data. Someone gave you good advice there. But that person forgot to advise you not to trust your data, especially if it was given to you by someone else to analyse. Always check every variable carefully before the analysis. This can take time, but it can be worth the investment.
Hope that helps at least someone out there.
Related
It is hard to explain this without just showing what I have, where I am, and what I need in terms of data structure:
What structure I had:
Where I have got to with my transformation efforts:
What I need to end up with:
Notes:
I've not given actual names for anything as the data is classed as sensitive, but:
Metrics are things that can be measured- for example, the number of permanent or full-time jobs. The number of metrics is larger than presented in the test data (and the example structure above).
Each metric has many years of data (whilst trying to do the code I have restricted myself to just 3 years. The illustration of the structure is based on this test). The number of years captured will change overtime- generally it will increase.
The number of policies will fluctuate, I've just labelled them policy 1, 2 etc for sensitivity reasons and limited the number whilst testing the code. Again, I have limited the number to make it easier to check the outputs.
The source data comes from a workbook of surveys with a tab for each policy. The initial import creates a list of tibbles consisting of a row for each metric, and 4 columns (the metric names, the values for 2024, the values for 2030, and the values for 2035). I converted this to a dataframe, created a vector to be a column header and used cbind() to put this on top to get the "What structure I had" data.
To get to the "Where I have got to with my transformation efforts" version of the table, I removed all the metric columns, created another vector of metrics and used rbind() to put this as the first column.
The idea in my head was to group the data by policy to get a vector for each metric, then transpose this so that the metric became the column, and the grouped data would become the row. Then expand the data to get the metrics repeated for each year. A friend of mine who does coding (but has never used R) has suggested using loops might be a better way forward. Again, I am not sure of the best approach so welcome advice. On Reddit someone suggested using pivot_wider/pivot_longer but this appears to be a summarise tool and I am not trying to summarise the data rather transform its structure.
Any suggestions on approaches or possible tools/functions to use would be gratefully received. I am learning R whilst trying to pull this data together to create a database that can be used for analysis, so, if my approach sounds weird, feel free to suggest alternatives. Thanks
So full disclosure, I am new to R and programming in general. Because of that, it is very hard for me to search when I have problems because I am not even sure what keywords to use. I am learning, and all I am hoping for y'all to do is point me in the right direction.
I have a very large csv file that I imported into R. Around 2 million observations (don't worry, I am not planning on using all 2 million). The only problem is that the people recording the data formatted the file to record to prices as "$10.00". Because of this, R recognizes the data has a factor, and also treats each individual price as a separate variable because of the dollar sign. I would like to reformat this column as a numeric variable.
I am sure there is some way to go about reformatting this in R, the only problem is I am not sure which functions I need. Sorry for the very basic question, I have just hit a wall a figured I would reach out.
Any and all help is much appreciated!
Thank you!
We could also use sub
as.numeric(sub('\\D+', '', x))
#[1] 10.00 11.24 15.22
data
x<-c("$10.00","$11.24","$15.22")
Suppose that your data looks like this:
x<-c("$10.00","$11.24","$15.22")
You can use the substring function to trim the initial dollar sign (which will still leave you with strings) and then use as.numeric to turn it to a numeric vector.
newx<-as.numeric(substring(x,2))
will produce a vector named newx with value
c(10.00,11.24,15.22)
We tell the substring to start at the 2nd character (strings in R are 1-indexed), and then cast to numeric.
In your data frame (suppose it is called df), you can replace the column like
df$MoneyColumn <- as.numeric(substring(df$MoneyColumn,2))
I am confused. I input a .csv file in R and want to fit a linear multivariate regression model.
However, R declares all my obvious numeric variables to be factors and my categorial variables to be integers. Therefore, I cannot fit the model.
Does anyone know how to resolve this?
I know this is probably so basic. But I really need to know this. Elsewhere, I found only posts concerning how to declare factors. But this does not apply here.
Any suggestions very much appreciated!
The easiest way, imo, to handle this is to just tell R what type of data your columns contain when you read them into the workspace. For example, if you have a csv file where the first column should be characters, columns 2-21 should be numeric, and column 22 should be a factor, here's how I would read that csv file into the workspace:
Data <- read.csv("MyData.csv", colClasses=c("character", rep("numeric", 20), "factor"))
Sometimes (with certain versions of R, as Andrew points out) float entries in a CSV are long enough that it thinks they are strings and not floats. In this case, you can do the following
data <- read.csv("filename.csv")
data$some.column <- as.numeric(as.character(data$some.column))
Or you could pass stringsAsFactors=F to the read.csv call, and just apply as.numeric in the next line. That might be a bad idea though if you have a lot of data.
It's a little harder to say what's going on with the categorical variables. You might want to try just treating those as strings and see how that works. Sometimes R will treat factor vectors as being of numeric type, so this is a good first sanity check. If that doesn't work, you can also see if the regression functions in question will let you declare how the variables should be treated.
It is hard to tell without a sample of your data file and the commands that you have been using to try and work with the data, but here are some general problems that can lead to what you describe (though there could be other possibilities as well).
The read.csv and read.table (which is called by read.csv) function will try and guess the types of data when they are not told what each column should be (the colClasses argument). If everything looks like a number then it will convert to a number, but if it sees anything in the first lines that does not look like part of a number then it will read it in as character and convert to a factor. Some of the common reasons why what you think should be a number but R sees something non-numeric include: a finger slip results in a letter somewhere in the column; similar looking substitutions, O for 0 or l for 1; a comma where one is not expected, many European files use , where R expects . (but there are options to tell R what you want here) or if you use read.table without setting sep when it really is a comma separated file.
If you have a categorical variable represented by integers, then R will convert it to integers unless you tell it to make a factor. If you use as.numeric on a factor then it will return the integers used to represent the factor internally. How to convert a factor with labels that are numbers to a numeric is a question (and answer) in the FAQ.
If this does not point you in the right direction then give us a sample of your data and what commands you are using.
I'm struggling with how to best structure categorical data that's messy, and comes from a dataset I'll need to clean.
The Coding Scheme
I'm analyzing data from a university science course exam. We're looking at patterns in
student responses, and we developed a coding scheme to represent the kinds of things
students are doing in their answers. A subset of the coding scheme is shown below.
Note that within each major code (1, 2, 3) are nested non-unique sub-codes (a, b, ...).
What the Raw Data Looks Like
I've created an anonymized, raw subset of my actual data which you can view here.
Part of my problem is that those who coded the data noticed that some students displayed
multiple patterns. The coders' solution was to create enough columns (reason1, reason2,
...) to hold students with multiple patterns. That becomes important because the order
(reason1, reason2) is arbitrary--two students (like student 41 and student 42 in my
dataset) who correctly applied "dependency" should both register in an analysis, regardless of
whether 3a appears in the reason column or the reason2 column.
How Can I Best Structure Student Data?
Part of my problem is that in the raw data, not all students display the same
patterns, or the same number of them, in the same order. Some students may do just one
thing, others may do several. So, an abstracted representation of example students might
look like this:
Note in the example above that student002 and student003 both are coded as "1b", although I've deliberately shown the order as different to reflect the reality of my data.
My (Practical) Questions
Should I concatenate reason1, reason2, ... into one column?
How can I (re)code the reasons in R to reflect the multiplicity for some students?
Thanks
I realize this question is as much about good data conceptualization as it is about specific features of R, but I thought it would be appropriate to ask it here. If you feel it's inappropriate for me to ask the question, please let me know in the comments, and stackoverflow will automatically flood my inbox with sadface emoticons. If I haven't been specific enough, please let me know and I'll do my best to be clearer.
Make it "long":
library(reshape)
dnow <- read.csv("~/Downloads/catsample20100504.csv")
dnow <- melt(dnow, id.vars=c("Student", "instructor"))
dnow$variable <- NULL ## since ordering does not matter
subset(dnow, Student%in%c(41,42)) ## see the results
What to do next will depend on the kind of analysis you would like to do. But the long format is the useful for irregular data such as yours.
you should use ddply from plyr and split on all of the columns if you want to take into account the different reasons, if you want to ignore them don't use those columns in the split. You'll need to clean up some of the question marks and extra stuff first though.
x <- ddply(data, c("split_column1", "split_column3" etc),
summarize(result_df, stats you want from result_df))
What's the (bigger picture) question you're attempting to answer? Why is this information interesting to you?
Are you just trying to find patterns such as 'if the student does this, then they also likely do this'?
Something I'd consider if that's the case - split the data set into smaller random samples for your analysis to reduce the risk of false positives.
Interesting problem though!
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.