![]() mutate_at(vars(matches("_DT$")),funs(as.Date(as.character(. It does not come as part of a package, rather it is a native command of R that you can directly use. Not just for characters but any data type, whenever you are converting to numeric, you can use the as.numeric () command. In my case, this turns all my YYYYMMDD columns, which were read as numbers, into dates. Probably one of the easiest ways to do this on R is by using the as.numeric () command. ![]() Mutate_at(vars(matches("dbl|num|qty")),funs(as.numeric))Īnother generally-related comment - if you have all your date columns with matchable names, and consistent formats, this is powerful. dat %>% mutate_at(vars(matches("fac|fctr|fckr")),funs(factor)) %>% I was pretty excited about figuring out mutate_at + grep, because now one line can work on lots of columns.ĮDIT - now I see matches() in among the select_helpers, which handles regex, so now I like this. Because it is a factor variable, it will already be recognized as categorical rather than quantitative. lm will treat the variable as quantitative. Mutate_at(vars(starts_with("dbl")),funs(as.numeric))īut mutate_at can take column numbers instead of a vars() argument, and after reading through this page, and looking at the alternatives, I ended up using mutate_at but with grep to capture many different kinds of column names at once (unless you always have such obvious column names!) dat %>% mutate_at(grep("^(fac|fctr|fckr)",colnames(.)),funs(factor)) %>% 1 Are you sure you need to do this The factor class stores the data as integer internally, but that does not mean stats procedures e.g. ![]() dat %>% mutate_at(vars(starts_with("fac")),funs(factor)) %>% Note the order of the arguments is reversed, compared to mutate_each, and vars() uses select() like semantics, which I interpret to mean the ?select_helpers functions. The one most similar to what mentions in his comment is probably using mutate_at. # apply function (change encoding) to all character columnsĭat %>% mutate(across(where(is.character),įunction(x))ĮDIT - The syntax of this answer has been deprecated, loki's updated answer is more appropriate.įrom the bottom of the ?mutate_each (at least in dplyr 0.5) it looks like that function, as in discimus's answer, will be deprecated and replaced with more flexible alternatives mutate_if, mutate_all, and mutate_at. For reference, I will add the respective pendants to the examples in the original answer (see below): # convert all factor to characterĭat %>% mutate(across(where(is.factor), as.character)) The flow will always be triggered daily until my colleagues replies to my email with options.As also pointed out in Eric's answer, mutate_ has been superseded by a combination of mutate() and across(). P.S according to your question, this flow would be running once per day via a separate scheduled email sender so it shouldnt be a problem stopping the flow. I hope this explanation helps even slightly! Hope to hear from you soon. As you look into this example, the Integer.parseInt(s.trim()) method is used to change from the string s to the integer i in this line of code: int. Data Type conversion is the process of converting one type of data to another type of data. ![]() Below attached are the flow explanation that I am trying to do and basically after retrieving the "Counter" value in Sharepoint, Automate will increase that value by +1 and convert it back from integer to string and update it into Sharepoint accordingly. ![]() The problem with Sharepoint is that it does not recognize Numbers as Integers so I had to convert from a "Single Line of Text" format (Used to store Counter) to Integer with the use of some Variable/Compose formula. The purpose of this specific flow is that if my colleagues did not respond within 1 day (I am using scheduled automate flow for this to be performed every 24 hours), the Status column would still be "Check" and the Counter column will increase from the default value of 1 to 2 and so on, increasing the value every time that they did not respond to my emails (So I can track how long they have not been responding to my emails and taking actions accordingly if it exceeds a certain limit) until they responded to me would then the Counter stop. The default value of the email option when not replied would be "Check" (As seen from the Status column). I will try to explain clearer in this post.įor the main objective of this flow, I want to send an automated email to my colleagues with options "Yes" and "No" for them to respond to. ![]()
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