I am trying to merge a data.frame and a column from another data.frame, but have so far been unsuccessful.
My first data.frame [Frequencies] consists of 2 columns, containing 47 upper/ lower case alpha characters and their frequency in a bigger data set. For example purposes:
Character<-c("A","a","B","b")
Frequency<-(100,230,500,420)
The second data.frame [Sequences] is 93,000 rows in length and contains 2 columns, with the 47 same upper/ lower case alpha characters and a corresponding qualitative description. For example:
Character<-c("a","a","b","A")
Descriptor<-c("Fast","Fast","Slow","Stop")
I wish to add the descriptor column to the [Frequencies] data.frame, but not the 93,000 rows! Rather, what each "Character" represents. For example:
Character<-c("a")
Frequency<-c("230")
Descriptor<-c("Fast")
Following can also be done:
> merge(adf, bdf[!duplicated(bdf$Character),])
Character Frequency Descriptor
1 a 230 Fast
2 A 100 Fast
3 b 420 Stop
4 B 500 Slow
Why not:
df1$Descriptor <- df2$Descriptor[ match(df1$Character, df2$Character) ]
Related
I have a data frame, "Customers_list", as follows:
Customer_No Entry_Time Exit_Time(Adjusted)
123 1 2
295 3 5
337 5 4
and another list of matrices (named "Customers_details"), where each matrix correspond to each unique customer number. The individual matrices have time as rows, and customers' details as columns.
I would like to subset the customers from the list of matrices, based on Exit_Time(Adjusted) >= Entry_Time.
I have tried:
Customers_details[noquote(paste("c(", paste(sprintf("'%d'",subset(Customers_list[,"Customer_No"],"Exit_Time(Adjusted)" >= "Entry_Time"),collapse=","), ")", sep=""))]
But it does not seem to work, even though the printed output of the paste function gives me:
c('123','295')
Whereas if I had typed the code Customers_details[c('123','295')] directly, I would have managed to subset the 2 relevant matrices.
I am working with two CSV files. They are formatted like this:
File 1
able,2
gobble,3
highway,3
test,6
zoo,10
File 2
able,6
gobble,10
highway,3
speed,7
test,8
upper,3
zoo,10
In my program I want to do the following:
Create a keyword list by combining the values from two CSV files and keeping only unique keywords
Compare that keyword list to each individual CSV file to determine the maximum number of occurences of a given keyword, then append that information to the keyword list.
The first step I have done already.
I am getting confused by R reading things as vectors/factors/data frames etc...and "coercion to lists". For example in my files given above, the maximum occurrence for the word "gobble" should be 10 (its value is 3 in file 1 and 10 in file 2)
So basically two things need to happen. First, I need to create a column in "keywords" that holds information about the maximum number of occurrences of a word from the CSV files. Second, I need to populate that column with the maximum value.
Here is my code:
# Read in individual data sets
keywordset1=as.character(read.csv("set1.csv",header=FALSE,sep=",")$V1)
keywordset2=as.character(read.csv("set2.csv",header=FALSE,sep=",")$V1)
exclude_list=as.character(read.csv("exclude.csv",header=FALSE,sep=",")$V1)
# Sort, capitalize, and keep unique values from the two keyword sets
keywords <- sapply(unique(sort(c(keywordset1, keywordset2))), toupper)
# Keep keywords greater than 2 characters in length (basically exclude in at etc...)
keywords <- keywords[nchar(keywords) > 2]
# Keep keywords that are not in the exclude list
keywords <- setdiff(keywords, sapply(exclude_list, toupper))
# HERE IS WHERE I NEED HELP
# Compare the read keyword list to the master keyword list
# and keep the frequency column
key1=read.csv("set1.csv",header=FALSE,sep=",")
key1$V1=sapply(key1[[1]], toupper)
keywords$V2=key1[which(keywords[[1]] %in% key1$V1),2]
return(keywords)
The reason that your last commmand fails is that you try to use the $ operator on a vector. It only works on lists or data frames (which are a special case of lists).
A remark regarding toupper (and many other functions in R): it works on vectors, such that you don't need to use sapply. toupper(c(keywordset1, keywordset2)) is perfectly fine.
But I would like to propose an entirely different solution to your problem. First, I create the data as follows:
keywords1 <- read.table(text="able,2
gobble,3
highway,3
test,6
zoo,10",sep=",",stringsAsFactors=FALSE)
keywords2 <- read.table(text="gobble,10
highway,3
speed,7
test,8
upper,3
zoo,10",sep=",",stringsAsFactors=FALSE)
Note that I use stringsAsFactors=FALSE. This prevents read.table from converting characters to factors, such that there is no need to call as.character later.
The next steps are to capitalize the keyword columns in both tables. At the same time, I put both tables in a list. This is often a good way to simplify calculations in R, because you can use lapply to apply a function on all the list elements. Then I put both tables into a single table.
keyword_list <- lapply(list(keywords1,keywords2),function(kw)
transform(kw,V1=toupper(V1)))
keywords_all <- do.call(rbind,keyword_list)
The next step is to sort the data frame in decreasing order by the number in the second column:
keywords_sorted <- keywords_all[order(keywords_all$V2,decreasing=TRUE),]
keywords_sorted looks as follows:
V1 V2
5 ZOO 10
6 GOBBLE 10
11 ZOO 10
9 TEST 8
8 SPEED 7
4 TEST 6
2 GOBBLE 3
3 HIGHWAY 3
7 HIGHWAY 3
10 UPPER 3
1 ABLE 2
As you notice, some keywords appear only once and for those that appear twice, the first appearance is the one you want to keep. There is a function in R that can be used to extract exactly these elements: duplicated() (run ?duplicated to learn more). Basically, the function returns TRUE, if an element appears for the at least second time in a vector. These are the elements you don't want. To convert TRUE to FALSE (and vice versa), you use the operator !. So the following gives your desired result:
keep <- !duplicated(keywords_sorted$V1)
keywords_max <- keywords_sorted[keep,]
V1 V2
5 ZOO 10
6 GOBBLE 10
9 TEST 8
8 SPEED 7
3 HIGHWAY 3
10 UPPER 3
1 ABLE 2
I have a R dataframe like this one:
a<-c(1,2,3,4,5)
b<-c(6,7,8,9,10)
df<-data.frame(a,b)
colnames(df)<-c("a","b")
df
a b
1 1 6
2 2 7
3 3 8
4 4 9
5 5 10
I would like to get the 1st, 2nd, 3rd AND 5th row of the column a, so 1 2 3 5, by selecting rows by their number.
I have tried df$a[1:3,5] but I get Error in df$a[1:3, 5] : incorrect number of dimensions.
What DOES work is c(df$a[1:3],df$a[5]) but I was wondering if there was an easier way to achieve this with R?
Your data frame has two dimensions (rows and columns). When you use the square brackets to extract values, R expects everything prior to the comma to indicate the rows desired, and everything after the comma to indicate the columns desired (see: ?[). Hence, df[1:3,5] means rows 1 through 3, from column 5. To turn your desired rows into a single vector, you need to concatenate (i.e., c(1:3,5)). That would all go before the comma, the column indicator, 1 or "a", would go after the comma. Thus, df[c(1:3,5), 1] is what you need.
For alternative answer (that might be more appropriate to a dataframe with many more columns), df[c(1:3, 5), "a"] as suggested by #Mamoun Benghezal would also get it done!
I have a data object signal in R with 40,000+ rows (named variables) of numeric values and 200+ columns (samples). For every row of each column, I want to subtract the value for the row named background for that column.
The code below can be used to create an example signal object in R. With the example, for column A, the background value of 4 is to be subtracted from the values of channelNo1 to 3. Similarly, for column B, the value of 6 is to be subtracted. And so on. What is the simplest way to achieve this in R?
text <- textConnection('
A B C
channelNo1 12 22 32
channelNo2 13 21 33
channelNo3 12 21 30
background 4 6 8
')
signal <- read.table(text, header = TRUE)
close(text)
typeof(signal)
# returns 'list'
class(signal)
# returns 'data.frame'
Elements in an R matrix are oriented by column (check out matrix(1:12, nrow=3) and signal - signal[4,] is not doing what you think -- check out column B, where the second and third values should be the same (and equal to 15). You could write
as.data.frame(Map("-", signal, as.vector(signal[4,])))
(I think this would be relatively efficient) but since the data really seem to be a matrix (i.e., a rectangle of homogeneous type) it makes a lot more sense to manipulate it as a matrix
m = as.matrix(signal)
sweep(m, 2, m[4,], "-")
I printed out the summary of a column variables as such:
summary(document$subject)
A,B,C,D,E,F,.. are the subjects belonging to a column of a data.frame where A,B,C,...appear many times in the column, and the summary above shows the number of times (frequency) these subjects have appeared in the file. Also, the term "OTHER" refers to those subjects which have appeared only once in the file, I also need to assign "1" to these subjects.
There are so many different subjects that it's difficult to list out all of them if we use command "c".
I want to build up a new column (or data.frame) and then assign these corresponding numbers (scores) to the subjects. Ideally, it will become this in the file:
A 198
B 113
C 96
D 69
A 198
E 65
F 62
A 198
C 113
BZ 21
BC 1
CJ 1
...
I wonder what command I should use to take the scores/values from the summary table and then build a new column to assign these values to the corresponding subjects in the file.
Plus, since it's a summary table printed by R, I don't know how to build it into a table in a file, or take out the values and subject names from the table. I also wonder how I could find out the subject names which appeared only once in the file, so that the summary table added them up into "OTHER".
Your question is hard to interpret without a reproducible example. Please take a look this threat for tips on how to do that:
How to make a great R reproducible example?
Having said that, here is how I interpret your question. You have two data frames, one with a score per subject and another with the subjects multiple times in a column:
Sum <- data.frame(subject=c("A","B"),score=c(1,2))
foo <- data.frame(subject=c("A","B","A"))
> Sum
subject score
1 A 1
2 B 2
> foo
subject
1 A
2 B
3 A
You can then use match() to match the subjects in one data frame to the other and create the new variable in the second data frame:
foo$score <- Sum$score[match(foo$subject, Sum$subject)]
> foo
subject score
1 A 1
2 B 2
3 A 1