I have a data frame called data.df with various columns say col1,col2,col3....col15. The data frame does not have a specific class attribute but any attribute could be potentially used as a class variable. I would like to use an R variable called target which points to the column number to be treated as class as follows :
target<-data.df$col3
and then use that field (target) as input to several learners such as PART and J48 (from package RWeka) :
part<-PART(target~.,data=data.df,control=Weka_control(M=200,R=FALSE))
j48<-J48(target~.,data=data.df,control=Weka_control(M=200,R=FALSE))
The idea is to be able to change 'target' only once at the beginning of my R code. How can this be done?
I sometimes manage to get a lot done by using strings to reference columns. It works like this:
> df <- data.frame(numbers=seq(5))
> df
numbers
1 1
2 2
3 3
4 4
5 5
> df$numbers
[1] 1 2 3 4 5
> df[['numbers']]
[1] 1 2 3 4 5
You can then have a variable target be the name of your desired column as a string. I don't know about RWeka, but many libraries such as ggplot can take string references for columns (e.g. the aes_string parameter instead of aes).
If you ask about using references in R, it is impossible.
However, if you ask about getting a column by name not explicitly given, this is possible with [ operator, like this:
theNameOfColumnIwantToGetSummaryOf<-"col3"
summary(data.df[,theNameOfColumnIwantToGetSummaryOf])
...or like that:
myIndexOfTheColumnIwantToGetSummaryOf<-3
summary(data.df[,sprintf("col%d",myIndexOfTheColumnIwantToGetSummaryOf)])
Related
I am trying to select a column from a dataframe using a variable as a column name, with the problem that the column name is escaped. I have a couple of workarounds for doing it, which involve changing my code a bit too much, and anyway I've been looking around and I am curious if anybody knew the solution for this kind of weird case.
My dataset is actually a list of time series (which I construct after some operations), this would be a toy example.
df <- list(`01/19/17`=seq(1,10), `01/20/17`=seq(2,11))
> df
$`01/19/17`
[1] 1 2 3 4 5 6 7 8 9 10
$`01/20/17`
[1] 2 3 4 5 6 7 8 9 10 11
I don't put the escapes ` in the column names because I want to, but because they come as dates from the process I follow to construct the dataset.
If I know the column name I can access like this,
df$`01/19/17`
If I want to use a variable, looking around e.g. here I see I could rewrite it to something like this,
`$`(df, `01/19/17`)
But I cannot assign a variable like this,
> name1 <- `01/19/17`
Error: object '01/19/17' not found
and if assign it this other way I get a NULL,
> name1 <- "01/19/17"
> `$`(df, name1)
NULL
As I say there are workarounds like e.g. changing all the column names in the list of series, but I just would like to know. Thank you so much.
You can access with brackets rather than with $, even when the key is a string:
df <- list(`01/19/17`=seq(1,10), `01/20/17`=seq(2,11))
name1 <- "01/19/17"
df[[name1]]
# [1] 1 2 3 4 5 6 7 8 9 10
I am working with two CSV files. They are formatted like this:
File 1
able,2
gobble,3
highway,3
test,6
zoo,10
File 2
able,6
gobble,10
highway,3
speed,7
test,8
upper,3
zoo,10
In my program I want to do the following:
Create a keyword list by combining the values from two CSV files and keeping only unique keywords
Compare that keyword list to each individual CSV file to determine the maximum number of occurences of a given keyword, then append that information to the keyword list.
The first step I have done already.
I am getting confused by R reading things as vectors/factors/data frames etc...and "coercion to lists". For example in my files given above, the maximum occurrence for the word "gobble" should be 10 (its value is 3 in file 1 and 10 in file 2)
So basically two things need to happen. First, I need to create a column in "keywords" that holds information about the maximum number of occurrences of a word from the CSV files. Second, I need to populate that column with the maximum value.
Here is my code:
# Read in individual data sets
keywordset1=as.character(read.csv("set1.csv",header=FALSE,sep=",")$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 list of data frame (lets call that "data") that I have generated which goes something like this:
$"something.csv"
x y z
1 1 1 1
2 2 2 2
3 3 3 3
$"something else.csv"
x y z
1 1 1 1
2 2 2 2
3 3 3 3
I would like to output from the table "something.csv" one number within column x.
So far I have used:
data$"something.csv"$x[2]
This coding works and I am happy that it does, but my problem is that I want to automate this process and so i have put all the table titles into a list (filename) which goes:
[1] "something.csv", "something else.csv"
So i made a for loop which should allow me to do so but when I put in:
data$filename[1]$x[2]
it gives me back NULL.
When i print filename[1], I get [1] "something.csv" and if I type
data$"something.csv"$x[2]
I get the result I want. so if filename[1] = "something.csv", why does it not give me the same results?
I just want my code to out put the second row of column x and automate by using filename[i] in a for loop.
The way you have tried to approach the problem tries to find a column 'filename[1]' from the list, but it is not found. Hence, the NULL gets returned.
You need to use square brackets, and subset the object data. Here's an example:
# Generate data
data<-vector("list", 2)
names(data)<-c("something.csv", "something else.csv")
data[[1]]<-data.frame(x=1:3, y=1:3, z=1:3)
data[[2]]<-data.frame(x=1:3, y=1:3, z=1:3)
filename<-names(l)
# Subset the data
# The first data frame, notice the square brackets for subsetting lists!
data[[filename[1]]]
# column x
data[[filename[1]]]$x
# Second observation of x
data[[filename[1]]]$x[2]
The above uses for subsetting the names of the objects in the list. You can also use the number-based subsetting suggested by #Jeremy.
you can also use [ and [[ to call data$"something.csv"$x[2] try
data[[1]][2,1]
where [[1]] is the first list element and [2,1] is the data frame reference element
I have done lot of googling but I didn't find satisfactory solution to my problem.
Say we have data file as:
Tag v1 v2 v3
A 1 2 3
B 1 2 2
C 5 6 1
A 9 2 7
C 1 0 1
The first line is header. The first column is Group id (the data have 3 groups A, B, C) while other column are values.
I want to read this file in R so that I can apply different functions on the data.
For example I tried to read the file and tried to get column mean
dt<-read.table(file_name,head=T) #gives warnings
apply(dt,2,mean) #gives NA NA NA
I want to read this file and want to get column mean. Then I want to separate the data in 3 groups (according to Tag A,B,C) and want to calculate mean(column wise) for each group. Any help
apply(dt,2,mean) doesn't work because apply coerces the first argument to an array via as.matrix (as is stated in the first paragraph of the Details section of ?apply). Since the first column is character, all elements in the coerced matrix object will be character.
Try this instead:
sapply(dt,mean) # works because data.frames are lists
To calculate column means by groups:
# using base functions
grpMeans1 <- t(sapply(split(dt[,c("v1","v2","v3")], dt[,"Tag"]), colMeans))
# using plyr
library(plyr)
grpMeans2 <- ddply(dt, "Tag", function(x) colMeans(x[,c("v1","v2","v3")]))
I'm getting data from a MySQL table that has 2 columns (idDoc, tag) describing that the document has a given tag. When I use the data frame with
ddply(tags,1)
My objective is to group tags by id, so say I do the following steps
> x=c(1,1,2,2)
> y=c(4,5,6,7)
> data.frame(x,y)
x y
1 1 4
2 1 5
3 2 6
4 2 7
My desired output would be perhaps a list of lists (or whatever other result) that would get
1 -> c(4,5)
2 -> c(6,7)
Regards
This is kind of a shot in the dark, since when you say you want an 'association', that doesn't really precisely describe any particular R data structure, so it's unclear what form you want the output to take.
But one base R possibility would be to simply use split:
split(tags$tag, tags$idDoc)
which should returned a named list where the names come from idDoc and each list element is the tags associated with that idDoc value. There will be duplicates, though. So maybe this would work better:
tapply(tags$tag,tags$idDoc,FUN = unique)
which should return a list of unique tags for each idDoc.
(Edited: No need for the anonymous function; only need to pass unique).