Function for calculate quantile and qnorm for QQ plot in R

Question

My data:

Subject Test1 Test2 Test3 Test4  
    1   8   7   1   6  
    2   9   5   2   5  
    3   6   2   3   8  
    4   5   3   1   9  
    5   8   4   5   8  
    6   7   5   6   7  
    7   10  2   7   2  
    8   12  6   8   1

mydata <- read.csv("myData.csv", header = TRUE)
mydataframe <- data.frame(mydata)

I did the following function to be applied to each column variable of my data frame, which contains 4 columns:

qqfunc <- function(df,df_var) {    
          y <- quantile(df$df_var, c(0.25, 0.75))     
          x <- qnorm( c(0.25, 0.75))       
          slope <- diff(y) / diff(x)      
          int <- y[1] - slope * x[1]      
          ggplot() + aes(sample=df$df_var) + stat_qq(distribution=qnorm) +   
          geom_abline(intercept=int, slope=slope) + ylab("QQ")    
}  

When I run

qqfunc(mydataframe, Test1)

appears the Warning Message:

Removed 1 rows containing missing values (geom_abline).

As result, the QQ Plot doesn't appear in pdf output file. I am not sure if the problem is in the parsing or in the function ggplot().

PS:
1. Curiously, if I run these following commands outside the function, it works:

y <- quantile(mydataframe$Test1, c(0.25, 0.75)) # Find the 1st and 3rd quartiles  
x <- qnorm( c(0.25, 0.75)) # Find the matching normal values on the x-axis
slope <- diff(y) / diff(x) # Compute the line slope
int <- y[1] - slope * x[1] # Compute the line intercept # Generate normal q-q plot   
ggplot() + aes(sample=mydataframe$Test1) + stat_qq(distribution=qnorm) + 
  geom_abline(intercept=int, slope=slope) + ylab("QQ Test1")  

2.If I run these commands:

qqfunc <- function(df, df_var) {   
  y <- quantile(df[[df_var]], c(0.25, 0.75))   
  x <- qnorm( c(0.25, 0.75))  
  slope <- diff(y) / diff(x)  
  int <- y[1] - slope * x[1]  
  ggplot() + aes(sample=df[[df_var]]) + stat_qq(distribution=qnorm) + 
    geom_abline(intercept=int, slope=slope) + ylab("QQ")   
}
qqfunc(mydataframe, Test1)  

Error message:

Error in (function(x, i, exact) if (is.matrix(i)) as.matrix(x)[[i]] else .subset2(x, : object 'Test1' not found

FULL CODE:

library(Hmisc)  
library(ggplot2)  
library(boot)  
library(polycor)  
library(ggm)  
library(gdata)  
library(readxl)  
library(csvread)  
library (plyr)  
library(psych)  
library(mice)  
library(VIM)  
library(ez)   
library(reshape)   
library(multcomp)  
library(nlme)  
library(pastecs)  
library(WRS2)  
library(dplyr)  

mydata <- read.csv("mydata.csv", header = TRUE) # CSV  
mydataframe <- data.frame(mydata)  

y <- quantile(mydataframe$Test1, c(0.25, 0.75)) # Find the 1st and 3rd quartiles   
x <- qnorm( c(0.25, 0.75)) # Find the matching normal values on the x-axis   
slope <- diff(y) / diff(x) # Compute the line slope   
int <- y[1] - slope * x[1] # Compute the line intercept # Generate normal q-q plot   
ggplot() + aes(sample=mydataframe$Test1) + stat_qq(distribution=qnorm) + geom_abline(intercept=int, slope=slope) + ylab("QQ Test 1") 

qqfunc <- function(df, df_var) {     
         y <- quantile(df[[df_var]], c(0.25, 0.75))   
         x <- qnorm( c(0.25, 0.75))   
         slope <- diff(y) / diff(x)   
         int <- y[1] - slope * x[1]   
         ggplot() + aes(sample=df[[df_var]]) + stat_qq(distribution=qnorm) + 
           geom_abline(intercept=int, slope=slope) + ylab("QQ")   
}
qqfunc(mydataframe, Test1) 

Answer 1

Works with me. You should have followed my suggestion.
And the suggestion of @Tung to post a sample dataset. Since you have not, here is the complete working code.

library(ggplot2)

qqfunc <- function(df, df_var) {    
          y <- quantile(df[[df_var]], c(0.25, 0.75))     
          x <- qnorm( c(0.25, 0.75))       
          slope <- diff(y) / diff(x)      
          int <- y[1] - slope * x[1]      
          ggplot() + aes(sample=df[[df_var]]) + stat_qq(distribution=qnorm) +   
              geom_abline(intercept=int, slope=slope) + ylab("QQ")    
}  

set.seed(3551)    # Make the results reproducible
n <- 100
mydataframe <- data.frame(X = rnorm(n))

column_variable <- "X"

qqfunc(mydataframe, column_variable)