I have data frame which a column name starts with number, I want to delete the column with the following code, but there is error:
delete!(features, [:3SsnPorchH])
UndefVarError: SsnPorchH not defined
Your problem is that :3SsnPorchH is not correctly parsed as a symbol, but as follows:
julia> :(:3SsnPorchH)
:($(QuoteNode(3)) * SsnPorchH)
When a symbol cannot be correctly parsed, it most often works to put the "name" into parentheses:
julia> :(3SsnPorchH)
:(3SsnPorchH)
Another thing you could do is using the Symbol constructor directly:
julia> Symbol("3SsnPorchH")
Symbol("3SsnPorchH")
(But I'm not sure if that's a good idea -- maybe you lose interning then.)
That being said, it's probably a good idea to give columns a name which is a valid Julia identifier. This gives you construction using DataFrame with keyword arguments, and allows for certain macros to identify variables with columns. You'll just have an easier time.
Related
My question refers to redundant code and a problem that I've been having with a lot of my R-Code.
Consider the following:
list_names<-c("putnam","einstein","newton","kant","hume","locke","leibniz")
combined_df_putnam$fu_time<-combined_df_putnam$age*365.25
combined_df_einstein$fu_time<-combined_einstein$age*365.25
combined_df_newton$fu_time<-combined_newton$age*365.25
...
combined_leibniz$fu_time<-combined_leibniz$age*365.25
I am trying to slim-down my code to do something like this:
list_names<-c("putnam","einstein","newton","kant","hume","locke","leibniz")
paste0("combined_df_",list_names[0:7]) <- data.frame("age"=1)
paste0("combined_df_",list_names[0:7]) <- paste0("combined_df_",list_names[0:7])$age*365.25
When I try to do that, I get "target of assignment expands to non-language object".
Basically, I want to create a list that contains descriptors, use that list to create a list of dataframes/lists and use these shortcuts again to do calculations. Right now, I am copy-pasting these assignments and this has led to various mistakes because I failed to replace the "name" from the previous line in some cases.
Any ideas for a solution to my problem would be greatly appreciated!
The central problem is that you are trying to assign a value (or data.frame) to the result of a function.
In paste0("combined_df_",list_names[0:7]) <- data.frame("age"=1), the left-hand-side returns a character vector:
> paste0("combined_df_",list_names[0:7])
[1] "combined_df_putnam" "combined_df_einstein" "combined_df_newton"
[4] "combined_df_kant" "combined_df_hume" "combined_df_locke"
[7] "combined_df_leibniz"
R will not just interpret these strings as variables that should be created and be referenced to. For that, you should look at the function assign.
Similarily, in the code paste0("combined_df_",list_names[0:7])$age*365.25, the paste0 function does not refer to variables, but simply returns a character vector -- for which the $ operator is not accepted.
There are many ways to solve your problem, but I will recommend that you create a function that performs the necessary operations of each data frame. The function should then return the data frame. You can then re-use the function for all 7 philosophers/scientists.
Consider the following:
y<-c("A","B","C")
x<-z<-c(1,2,3)
names(x)<-y
"names<-"(z,y)
If you run this code, you will discover that names(x)<-y is not identical to "names<-"(z,y). In particular, one sees that names(x)<-y actually changes the names of x whereas "names<-"(z,y) returns z with its names changed.
Why is this? I was under the impression that the difference between writing a function normally and writing it as an infix operator was only one of syntax, rather than something that actually changes the output. Where in the documentation is this difference discussed?
Short answer: names(x)<-y is actually sugar for x<-"names<-"(x,y) and not just "names<-"(x,y). See the the R-lang manual, pages 18-19 (pages 23-24 of the PDF), which comes to basically the same example.
For example, names(x) <- c("a","b") is equivalent to:
`*tmp*`<-x
x <- "names<-"(`*tmp*`, value=c("a","b"))
rm(`*tmp*`)
If more familiar with getter/setter, one can think that if somefunction is a getter function, somefunction<- is the corresponding setter. In R, where each object is immutable, it's more correct to call the setter a replacement function, because the function actually creates a new object identical to the old one, but with an attribute added/modified/removed and replaces with this new object the old one.
In the case example for instance, the names attribute are not just added to x; rather a new object with the same values of x but with the names is created and linked to the x symbol.
Since there are still some doubts about why the issue is discussed in the language doc instead directly on ?names, here is a small recap of this property of the R language.
You can define a function with the name you wish (there are some restrictions of course) and the name does not impact in any way if the function is called "normally".
However, if you name a function with the <- suffix, it becomes a replacement function and allows the parser to apply the function with the mechanism described at the beginning of this answer if called by the syntax foo(x)<-value. See here that you don't call explicitely foo<-, but with a slightly different syntax you obtain an object replacement (since the name).
Although there are not formal restrictions, it's common to define getter/setter in R with the same name (for instance names and names<-). In this case, the <- suffix function is the replacement function of the corresponding version without suffix.
As stated at the beginning, this behaviour is general and a property of the language, so it doesn't need to be discussed in any replacement function doc.
In particular, one sees that names(x)<-y actually changes the names of x whereas "names<-"(z,y) returns z with its names changed.
That’s because `names<-`1 is a regular function, albeit with an odd name2. It performs no assignment, it returns a new object with the names attribute set. In fact `names<-` is a primitive function in R but it could be implemented as follows (there are shorter, better ways of writing this in R, but I want the separate steps to be explicit):
`names<-` = function (x, value) {
new = x
attr(new, 'names') = value
new
}
That is, it
… creates a new object that’s a copy of x,
… sets the names attribute on that newly created object, and
… returns the new object.
Since virtually all objects in R are immutable, this fits naturally into R’s semantics. In fact, a better name for this exact function would be with_names3. But the creators of R found it convenient to be able to write such an assignment without repeating the name of the object. So instead of writing
x = with_names(x, c('foo', 'bar'))
or
x = `names<-`(x, c('foo', 'bar'))
R allows us to write
names(x) = c('foo', 'bar')
R handles this syntax specially by internally converting it to another expression, documented in the Subset assignment section of the R language definition, as explained in the answer by Nicola.
But the gist is that names(x) = y and `names<-`(x, y) are different because … they just are. The former is a special syntactic form that gets recognised and transformed by the R parser. The latter is a regular function call, and the weird function name is a red herring: it doesn’t affect the execution whatsoever. It does the same as if the function was named differently, and you can confirm this by assigning it a different name:
with_names = `names<-`
`another weird(!) name` = `names<-`
# These are all identical:
`names<-`(x, y)
with_names(x, y)
`another weird(!) name`(x, y)
1 I strongly encourage using backtick quotes (`) instead of straight quotes (' or ") to quote R variable names. While both are allowed in some circumstances, the latter invites confusion with strings, and is conceptually bonkers. These are not strings. Consider:
"a" = "b"
"c" = "a"
Rather than copy the value of a into c, what this code actually does is set c to literal "a", because quotes now mean different things on the left- and right-hand side of assignment.
The R documentation confirms that
The preferred quote [for variable names] is the backtick (`)
2 Regular variable names (aka “identifiers” or just “names”) in R can only contain letters, digits, underscore and the dot, must start with a letter, or with a dot not followed by a digit, and can’t be reserved words. But R allows using pretty much arbitrary characters — including punctuation and even spaces! — in variable names, provided the name is backtick-quoted.
3 In fact, R has an almost-alias for this function, called setNames — which isn’t a great name, since set… implies mutating the object, but of course it doesn’t do that.
Trying to get into Julia after learning python, and I'm stumbling over some seemingly easy things. I'd like to have a function that takes strings as arguments, but uses one of those arguments as a regular expression to go searching for something. So:
function patterncount(string::ASCIIString, kmer::ASCIIString)
numpatterns = eachmatch(kmer, string, true)
count(numpatterns)
end
There are a couple of problems with this. First, eachmatch expects a Regex object as the first argument and I can't seem to figure out how to convert a string. In python I'd do r"{0}".format(kmer) - is there something similar?
Second, I clearly don't understand how the count function works (from the docs):
count(p, itr) → Integer
Count the number of elements in itr for which predicate p returns true.
But I can't seem to figure out what the predicate is for just counting how many things are in an iterator. I can make a simple counter loop, but I figure that has to be built in. I just can't find it (tried the docs, tried searching SO... no luck).
Edit: I also tried numpatterns = eachmatch(r"$kmer", string, true) - no go.
To convert a string to a regex, call the Regex function on the string.
Typically, to get the length of an iterator you an use the length function. However, in this case that won't really work. The eachmatch function returns an object of type Base.RegexMatchIterator, which doesn't have a length method. So, you can use count, as you thought. The first argument (the predicate) should be a one argument function that returns true or false depending on whether you would like to count a particular item in your iterator. In this case that function can simply be the anonymous function x->true, because for all x in the RegexMatchIterator, we want to count it.
So, given that info, I would write your function like this:
patterncount(s::ASCIIString, kmer::ASCIIString) =
count(x->true, eachmatch(Regex(kmer), s, true))
EDIT: I also changed the name of the first argument to be s instead of string, because string is a Julia function. Nothing terrible would have happened if we would have left that argument name the same in this example, but it is usually good practice not to give variable names the same as a built-in function name.
I'm having trouble working with a data table in R. This is probably something really simple but I can't find the solution anywhere.
Here is what I have:
Let's say t is the data table
colNames <- names(t)
for (col in colNames) {
print (t$col)
}
When I do this, it prints NULL. However, if I do it manually, it works fine -- say a column name is "sample". If I type t$"sample" into the R prompt, it works fine. What am I doing wrong here?
You need t[[col]]; t$col does an odd form of evaluation.
edit: incorporating #joran's explanation:
t$col tries to find an element literally named 'col' in list t, not what you happen to have stored as a value in a variable named col.
$ is convenient for interactive use, because it is shorter and one can skip quotation marks (i.e. t$foo vs. t[["foo"]]. It also does partial matching, which is very convenient but can under unusual circumstances be dangerous or confusing: i.e. if a list contains an element foolicious, then t$foo will retrieve it. For this reason it is not generally recommended for programming.
[[ can take either a literal string ("foo") or a string stored in a variable (col), and does not do partial matching. It is generally recommended for programming (although there's no harm in using it interactively).
Is it possible to get a list of declared variables with a VimL (aka VimScript) expression? I'd like to get the same set of values that will be presented for a command using -complete=expression. The goal is to augment that list for use in a user-defined command completion function.
You can use g: as a dictionary that holds all global variables, so:
let globals = keys(g:)
will give you all the names. The same applies to the other scopes: b:, s:, w:, etc. See :help internal-variables for the complete list.
You can get something similar using keys of g:, b:, t:, w: and v: dictionaries, but beware of the following facts:
There is no equivalent to this dictionaries if you want to complete options.
Some variables like count (but not g:count or l:count), b:changedtick and, maybe, others are not present in this dictionaries.
Some vim hacker may add key ### to dictionary g:, but it won't make expression g:### valid variable name (but adding 000 there will). Though g:["###"] will be a valid expression.