I just hope to learn how to make a simple statistical summary of the random numbers fra row 1 to 5 in R. (as shown in picture).
And then assign these rows to a single variable.
enter image description here
Hope you can help!
When you type something like 3 on a single line and ask R to "run" it, it doesn't store that anywhere -- it just evaluates it, meaning that it tries to make sense out of whatever you've typed (such as 3, or 2+1, or sqrt(9), all of which would return the same value) and then it more or less evaporates. You can think of your lines 1 through 5 as behaving like you've used a handheld scientific calculator; once you type something like 300 / 100 into such a calculator, it just shows you a 3, and then after you have executed another computation, that 3 is more or less permanently gone.
To do something with your data, you need to do one of two things: either store it into your environment somehow, or to "pipe" your data directly into a useful function.
In your question, you used this script:
1
3
2
7
6
summary()
I don't think it's possible to repair this strategy in the way that you're hoping -- and if it is possible, it's not quite the "right" approach. By typing the numbers on individual lines, you've structured them so that they'll evaluate individually and then evaporate. In order to run the summary() function on those numbers, you will need to bind them together inside a single vector somehow, then feed that vector into summary(). The "store it" approach would be
my_vector <- c(1, 3, 7, 2, 6)
summary(my_vector)
The importance isn't actually the parentheses; it's the function c(), which stands for concatenate, and instructs R to treat those 5 numbers as a collective object (i.e. a vector). We then pass that single object into my_vector().
To use the "piping" approach and avoid having to store something in the environment, you can do this instead (requires R 4.1.0+):
c(1, 3, 7, 2, 6) |> summary()
Note again that the use of c() is required, because we need to bind the five numbers together first. If you have an older version of R, you can get a slightly different pipe operator from the magrittr library instead that will work the same way. The point is that this "binding" part of the process is an essential part that can't be skipped.
Now, the crux of your question: presumably, your data doesn't really look like the example you used. Most likely, it's in some separate .csv file or something like that; if not, hopefully it is easy to get it into that format. Assuming this is true, this means that R will actually be able to do the heavy lifting for you in terms of formatting your data.
As a very simple example, let's say I have a plain text file, my_example.txt, whose contents are
1
3
7
2
6
In this case, I can ask R to parse this file for me. Assuming you're using RStudio, the simplest way to do this is to use the File -> Import Dataset part of the GUI. There are various options dealing with things such as headers, separators, and so forth, but I can't say much meaningful about what you'd need to do there without seeing your actual dataset.
When I import that file, I notice that it does two things in my R console:
my_example <- read.table(...)
View(my_example)
The first line stores an object (called a "data frame" in this case) in my environment; the second shows a nice view of how it's rendered. To get the summary I wanted, I just need to extract the vector of numbers I want, which I see from the view is called V1, which I can do with summary(my_example$V1).
This example is probably not helpful for your actual data set, because there are so many variations on the theme here, but the theme itself is important: point R at a file, as it to render an object, then work with that object. That's the approach I'd recommend instead of typing data as lines within an R script, as it's much faster and less error-prone.
Hopefully this will get you pointed in the right direction in terms of getting your data into R and working with it.
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I am learning R through tutorials, but I have difficulties in "how to read" R code, which in turn makes it difficult to write R code. For example:
dir.create(file.path("testdir2","testdir3"), recursive = TRUE)
vs
dup.names <- as.character(data.combined[which(duplicated(as.character(data.combined$name))), "name"])
While I know what these lines of code do, I cannot read or interpret the logic of each line of code. Whether I read left to right or right to left. What strategies should I use when reading/writing R code?
dup.names <- as.character(data.combined[which(duplicated(as.character(data.combined$name))), "name"])
Don't let lines of code like this ruin writing R code for you
I'm going to be honest here. The code is bad. And for many reasons.
Not a lot of people can read a line like this and intuitively know what the output is.
The point is you should not write lines of code that you don't understand. This is not Excel, you do not have but 1 single line to fit everything within. You have a whole deliciously large script, an empty canvas. Use that space to break your code into smaller bits that make a beautiful mosaic piece of art! Let's dive in~
Dissecting the code: Data Frames
Reading a line of code is like looking at a face for familiar features. You can read left to right, middle to out, whatever -- as long as you can lock onto something that is familiar.
Okay you see data.combined. You know (hope) it has rows and columns... because it's data!
You spot a $ in the code and you know it has to be a data.frame. This is because only lists and data.frames (which are really just lists) allow you to subset columns using $ followed by the column name. Subset-by the way- just means looking at a portion of the overall. In R, subsetting for data.frames and matrices can be done using single brackets[, within which you will see [row, column]. Thus if we type data.combined[1,2], it would give you the value in row 1 of column 2.
Now, if you knew that the name of column 2 was name you can use data.combined[1,"name"] to get the same output as data.combined$name[1]. Look back at that code:
dup.names <- as.character(data.combined[which(duplicated(as.character(data.combined$name))), "name"])
Okay, so now we see our eyes should be locked on data.combined[SOMETHING IS IN HERE?!]) and slowly be picking out data.combined[ ?ROW? , Oh the "name" column]. Cool.
Finding those ROW values!
which(duplicated(as.character(data.combined$name)))
Anytime you see the which function, it is just giving you locations. An example: For the logical vector a = c(1,2,2,1), which(a == 1) would give you 1 and 4, the location of 1s in a.
Now duplicated is simple too. duplicated(a) (which is just duplicated(c(1,2,2,1))) will give you back FALSE FALSE TRUE TRUE. If we ran which(duplicated(a)) it would return 3 and 4. Now here is a secret you will learn. If you have TRUES and FALSES, you don't need to use the which function! So maybe which was unnessary here. And also as.character... since duplicated works on numbers and strings.
What You Should Be Writing
Who am I to tell you how to write code? But here's my take.
Don't mix up ways of subsetting: use EITHER data.frame[,column] or data.frame$column...
The code could have been written a little bit more legibly as:
dupes <- duplicated(data.combined$name)
dupe.names <- data.combines$name[dupes]
or equally:
dupes <- duplicated(data.combined[,"name"])
dupe.names <- data.combined[dupes,"name"]
I know this was lengthy but I hope it helps.
An easier way to read any code is to break up their components.
dup.names <-
as.character(
data.combined[which(
duplicated(
as.character(
data.combined$name
)
)
), "name"]
)
For each of the functions - those parts with rounded brackets following them e.g. as.character() you can learn more about what they do and how they work by typing ?as.character in the console
Square brackets [] are use to subset data frames, which are stored in your environment (the box to the upper right if you're using R within RStudio contains your values as well as any defined functions). In this case, you can tell that data.combined is the name that has been given to such a data frame in this example (type ?data.frame to find out more about data frames).
"Unwrapping" long lines of code can be daunting at first. Start by breaking it down into parenthesis , brackets, and commas. Parenthesis directly tacked onto a word indicate a function, and any commas that lie within them (unless they are part of another nested function or bracket) separate arguments which contain parameters that modify the way the function behaves. We can reduce your 2nd line to an outer function as.character and its arguments:
dup.names <- as.character(argument_1)
Just from this, we know that dup.names will be assigned a value with the data type "character" off of a single argument.
Two functions in the first line, file.path() and dir.create(), contain a comma to denote two arguments. Arguments can either be a single value or specified with an equal sign. In this case, the output of file.path happens to perform as argument #1 of dir.create().
file.path(argument_1,argument_2)
dir.create(argument_1,argument_2)
Brackets are a way of subsetting data frames, with the general notation of dataframe_object[row,column]. Within your second line is a dataframe object, data.combined. You know it's a dataframe object because of the brackets directly tacked onto it, and knowing this allows you to that any functions internal to this are contributing to subsetting this data frame.
data.combined[row, column]
So from there, we can see that the internal functions within this bracket will produce an output that specifies the rows of data.combined that will contribute to the subset, and that only columns with name "name" will be selected.
Use the help function to start to unpack these lines by discovering what each function does, and what it's arguments are.
So I'm trying to manipulate a simple Qualtrics CSV, and I want to use colSums on certain columns of data, given a certain filter.
For example: within the .csv file called data, I want to get the sum of a few columns, and print them with certain labels (say choice1, choice2 etc). That is easy enough by itself:
firstqn<-data.frame(choice1=data$Q7_2,choice2=data$Q7_3,choice3=data$Q7_4);
secondqn<-data.frame(choice1=data$Q8_6,choice2=data$Q8_7,choice3=data$Q8_8)
print colSums(firstqn); print colSums(secondqn)
The problem comes when I want to repeat the above steps with different filters, - say, only the rows where gender==2.
The only way I know how is to create a new dataset data2 and replace data$ with data2$ in every line of the above code, such as:
data2<-(data[data$Q2==2,])
firstqn<-data.frame(choice1=data2$Q7_2,choice2=data2$Q7_3,choice3=data2$Q7_4);
however i have 6 choices for each of 5 questions and am planning to apply about 5-10 different filters, and I don't relish the thought of copy/pasting data2 and `data3' etc hundreds of times.
So my question is: Is there any way of getting R to reference data by default without using data$ in front of every variable name?
I can probably use attach() to achieve this, but i really don't want to:
data2<-(data[data$Q2==2,])
attach(data2)
firstqn<-data.frame(choice1=Q7_2,choice2=Q7_3,choice3=Q7_4);
detach(data2)
is there a command like attach() that would allow me to avoid using data$ in front of every variable, for a specified amount of code? Then whenever I wanted to create a new filter, I could just copy/paste the same code and change the first command (defining a new dataset).
I guess I'm looking for some command like with(data2, *insert multiple commands here*)
Alternatively, if anyone has a better way to do the above in an entirely different way please enlighten me - i'm not very proficient at R (yet).
I basically want to be capable to call columns from inside a for loop (in reality two nested for loops), using past() and i (j..) value of the loop to access
my data frames columns wise in a flexible manner.
#for the showcase I use the standard cars example
r1 <- cars
r2 <- cars
# in case there are more data to consider I would want to add, ore remove further with out changing the rest
# here I am entering the "dimension" of what I want to compare for the showcase its only one
num_r <- 2 #total number of reactors in the experiment
for( i in 1:num_r)
{
# shoud create proxie variable to be processed further
assign(paste("proxi_r",i,sep="", colapse="") , do.call("matrix",
list(get(paste("r",i,"$speed",sep="", colapse="" )))))
# further operations of gluing and arranging data follow so they fit tests formatting requirements
}
which gives me:
Error in get(paste("r", i, "$speed", sep = "", colapse = "")) :
object 'r1$speed' not found
but when typ r1$speed it obviously exists??
Sofare I searched "R object dont exist inside loop", "using paste() to acces variables inside loop", "foor loops and objects","do.call inside loops" ....and similar...
Is there anything to circumvent get() so I don’t have to look into the topic of environments, so I can keep the flexibility of my loops so I don’t have re-edit my script every time I have a changed the experimental configuration, which is really time consuming and allows a lot of errors to sneak inside.
The size of the data have crashed excel with extensive use of excel macros, which everyone in the lab here is using, several times :) , so there is no going back to the convort zone.
I am now trying to dig into R programming with a R statics book, and a lot of googling and reading tutorials, so please forgive my naive approach, and my lousy English.
I would be very thankful for any tips, as I feel sort of stuck right now.
This is a common confusion. You've created an object name "r1$speed" , i.e. a complete character string. This is not the same as the object r1 subsetted by $speed .
Try using get(paste('r',i,collapse='',sep=''))$speed
I wrote a function in R - called "filtre": it takes a dataframe, and for each line it says whether it should go in say bin 1 or 2. At the end, we have two data frames that sum up to the original input, and corresponding respectively to all lines thrown in either bin 1 or 2. These two sets of bin 1 and 2 are referred to as filtre1 and filtre2. For convenience the values of filtre1 and filtre2 are calculated but not returned, because it is an intermediary thing in a bigger process (plus they are quite big data frame). I have the following issue:
(i) When I later on want to use filtre1 (or filtre2), they simply don't show up... like if their value was stuck within the function, and would not be recognised elsewhere - which would oblige me to copy the whole function every time I feel like using it - quite painful and heavy.
I suspect this is a rather simple thing, but I did search on the web and did not find the answer really (I was not sure of best key words). Sorry for any inconvenience.
Thxs / g.
It's pretty hard to know the optimum way of achieve what you want as you do not provide proper example, but I'll give it a try. If your variables filtre1 and filtre2 are defined inside of your function and you do not return them, of course they do not show up on your environment. But you could just return the classification and make filtre1 and filtre2 afterwards:
#example data
df<-data.frame(id=1:20,x=sample(1:20,20,replace=TRUE))
filtre<-function(df){
#example function, this could of course be done by bins<-df$x<10
bins<-numeric(nrow(df))
for(i in 1:nrow(df))
if(df$x<10)
bins[i]<-1
return(bins)
}
bins<-filtre(df)
filtre1<-df[bins==1,]
filtre2<-df[bins==0,]
Many intro R books and guides start off with the practice of attaching a data.frame so that you can call the variables by name. I have always found it favorable to call variables with $ notation or square bracket slicing [,2]. That way I can use multiple data.frames without confusing them and/or use iteration to successively call columns of interest. I noticed Google recently posted coding guidelines for R which included the line
1) attach: avoid using it
How do people feel about this practice?
I never use attach. with and within are your friends.
Example code:
> N <- 3
> df <- data.frame(x1=rnorm(N),x2=runif(N))
> df$y <- with(df,{
x1+x2
})
> df
x1 x2 y
1 -0.8943125 0.24298534 -0.6513271
2 -0.9384312 0.01460008 -0.9238312
3 -0.7159518 0.34618060 -0.3697712
>
> df <- within(df,{
x1.sq <- x1^2
x2.sq <- x2^2
y <- x1.sq+x2.sq
x1 <- x2 <- NULL
})
> df
y x2.sq x1.sq
1 0.8588367 0.0590418774 0.7997948
2 0.8808663 0.0002131623 0.8806532
3 0.6324280 0.1198410071 0.5125870
Edit: hadley mentions transform in the comments. here is some code:
> transform(df, xtot=x1.sq+x2.sq, y=NULL)
x2.sq x1.sq xtot
1 0.41557079 0.021393571 0.43696436
2 0.57716487 0.266325959 0.84349083
3 0.04935442 0.004226069 0.05358049
I much prefer to use with to obtain the equivalent of attach on a single command:
with(someDataFrame, someFunction(...))
This also leads naturally to a form where subset is the first argument:
with(subset(someDataFrame, someVar > someValue),
someFunction(...))
which makes it pretty clear that we operate on a selection of the data. And while many modelling function have both data and subset arguments, the use above is more consistent as it also applies to those functions who do not have data and subset arguments.
The main problem with attach is that it can result in unwanted behaviour. Suppose you have an object with name xyz in your workspace. Now you attach dataframe abc which has a column named xyz. If your code reference to xyz, can you guarantee that is references to the object or the dataframe column? If you don't use attach then it is easy. just xyz refers to the object. abc$xyz refers to the column of the dataframe.
One of the main reasons that attach is used frequently in textbooks is that it shortens the code.
"Attach" is an evil temptation. The only place where it works well is in the classroom setting where one is given a single dataframe and expected to write lines of code to do the analysis on that one dataframe. The user is unlikely to ever use that data again once the assignement is done and handed in.
However, in the real world, more data frames can be added to the collection of data in a particular project. Furthermore one often copies and pastes blocks of code to be used for something similar. Often one is borrowing from something one did a few months ago and cannot remember the nuances of what was being called from where. In these circumstances one gets drowned by the previous use of "attach."
Just like Leoni said, with and within are perfect substitutes for attach, but I wouldn't completely dismiss it. I use it sometimes, when I'm working directly at the R prompt and want to test some commands before writing them on a script. Especially when testing multiple commands, attach can be a more interesting, convenient and even harmless alternative to with and within, since after you run attach, the command prompt is clear for you to write inputs and see outputs.
Just make sure to detach your data after you're done!
I prefer not to use attach(), as it is far too easy to run a batch of code several times each time calling attach(). The data frame is added to the search path each time, extending it unnecessarily. Of course, good programming practice is to also detach() at the end of the block of code, but that is often forgotten.
Instead, I use xxx$y or xxx[,"y"]. It's more transparent.
Another possibility is to use the data argument available in many functions which allows individual variables to be referenced within the data frame. e.g., lm(z ~ y, data=xxx).
While I, too, prefer not to use attach(), it does have its place when you need to persist an object (in this case, a data.frame) through the life of your program when you have several functions using it. Instead of passing the object into every R function that uses it, I think it is more convenient to keep it in one place and call its elements as needed.
That said, I would only use it if I know how much memory I have available and only if I make sure that I detach() this data.frame once it is out of scope.
Am I making sense?