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Using variables in SAS User defined function


Using variables in SAS User defined function

By : Govind Prajapati
Date : November 22 2020, 02:59 PM
like below fixes the issue The macro processor evaluates before the results are passed onto base SAS for processing.
Since your program uses this macro logic.
code :
lencheck = %length(testvar);
lencheck = 7 ;


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MySQL user-defined variables compared to MSSQL-server user-defined variables

MySQL user-defined variables compared to MSSQL-server user-defined variables


By : user2840194
Date : March 29 2020, 07:55 AM
I hope this helps . There's not an "in-the-box" way that I'm aware of. I'd assume you're writing scripts or procedures. I'd recommend that you validate all variables where appropriate. Then, you can raise an error (using SIGNAL) when the variable is null. You have to code it, regretfully, but it should do what you want, and you should get an error back to your application that you can then parse.
http://dev.mysql.com/doc/refman/5.5/en/signal.html
code :
CREATE PROCEDURE foo (VAL INT)
BEGIN
    IF val IS NULL THEN
        SIGNAL SQLSTATE '45000'
        SET MESSAGE_TEXT = 'An error occurred';
    END IF;
END
user defined variables in a function in r

user defined variables in a function in r


By : mamun
Date : March 29 2020, 07:55 AM
I wish this helpful for you The only thing that changes is how you get the independent variables
If the user specifies them, then use that character vector directly
code :
# creating mock data
data <- data.frame(col1 = numeric(0), col2 = numeric(0), col3 = numeric(0), col4 = numeric(0))

# the function
lmformula <- function (data, DepVariable, IndepVariable, VariableList = TRUE) {
  if (!VariableList) {
    IndepVariable <- names(data)[!names(data) %in% DepVariable]
  }
  f <- as.formula(paste(DepVariable,"~", paste(IndepVariable, collapse = '+')))
  return (f)
}

# working examples
lmformula(data = data, DepVariable = "col1", VariableList = FALSE)
lmformula(data = data, DepVariable = "col1", IndepVariable = c("col2", "col3"), VariableList = TRUE)
user defined function variables are getting overwritten somewhere

user defined function variables are getting overwritten somewhere


By : vamsi varma
Date : March 29 2020, 07:55 AM
With these it helps Hello I have a user defined function lambda , into which I am passing age (integer), gender (integer) and a file (BMI).. When I call it I get some value, which is wrong. But when I execute each statements in the function I get the right answer. Strange thing is , There are several (potential) issues:
code :
lambda <-function(ag, gnd, ibmi) {
  require(MASS)
  sel <- ibmi[ibmi$age==ag & ibmi$gender == gnd,]
  out <- boxcox(bmi~1, data = sel)
  rn <- range(out$x[out$y > max(out$y)-qchisq(0.95,1)/2])
  return((rn[1] + rn[2])/2)
}

lambda(6,2,bmi)
#[1] -0.4040404

sel <- subset(bmi, age==6 & gender == 2)
out <- boxcox(lm(sel$bmi~1))
rn <- range(out$x[out$y > max(out$y)-qchisq(0.95,1)/2])
(rn[1] + rn[2])/2
#[1] -0.4040404
How to make function variables defined by user?

How to make function variables defined by user?


By : joshua jerezo
Date : March 29 2020, 07:55 AM
like below fixes the issue So I have code like this: , Try this for start:
code :
a = input("Town:\n")
b = int(input("Days:\n"))
c = int(input("Money:\n"))

print (trip_cost(a, b, c))
Use an apply function with user defined function that adds variables to data frame

Use an apply function with user defined function that adds variables to data frame


By : Antonio Marotta
Date : March 29 2020, 07:55 AM
hope this fix your issue I have defined a function which will dynamically create new variables in a data frame. For this function the input is a string which is then pasted with other strings to create variable names that already exist in the data frame which are then compared using case_when within mutate. The output of the function is the data frame with the new variable appended to the end. I want to apply this function to a vector of inputs, and create multiple new columns in the data frame. I have used the iris data set to create a function very similar to what I am doing. , Slight change in the way you want to structure the output -
code :
func <- function(x) {
  a <- paste0("Sepal", x)
  b <- paste0("Petal", x)
  x1 <- iris %>% 
    mutate(
      !!(paste0("Compare.", x)) :=
        case_when(
          a > b ~ "Sepal",
          a < b ~ "Petal",
          TRUE ~ "Equal"
        )
    )
  return(x1[[paste0('Compare.',x)]])
}

inputVector <- c("Length", "Width")
op <- iris
op[,paste0('Compare.',inputVector)] <- lapply(inputVector, func)
> head(op)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species Compare.Length Compare.Width
1          5.1         3.5          1.4         0.2  setosa          Sepal         Sepal
2          4.9         3.0          1.4         0.2  setosa          Sepal         Sepal
3          4.7         3.2          1.3         0.2  setosa          Sepal         Sepal
4          4.6         3.1          1.5         0.2  setosa          Sepal         Sepal
5          5.0         3.6          1.4         0.2  setosa          Sepal         Sepal
6          5.4         3.9          1.7         0.4  setosa          Sepal         Sepal
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