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Example data:
x <- matrix(c("Stack","Stack","Stack",
"Overflow","Overflow","wolfrevO"),
nrow=3,ncol=2)
How can I check whether x[,1] contains entirely identical elements?
If x contains NAs, does this method still apply?
Thanks
752
To find unique values in a column in a data frame, use the unique() function in R. In Exploratory Data Analysis, the unique() function is crucial since it detects and eliminates duplicate values in the data.
setequal() function in R Language is used to check if two objects are equal. This function takes two objects like Vectors, dataframes, etc. as arguments and results in TRUE or FALSE, if the Objects are equal or not.
Method 2: Using length() and unique() function By using unique function if all the elements are the same then the length is 1 so by this way if the length is 1, we can say all elements in a vector are equal.
The unique() function in R is used to eliminate or delete the duplicate values or the rows present in the vector, data frame, or matrix as well. The unique() function found its importance in the EDA (Exploratory Data Analysis) as it directly identifies and eliminates the duplicate values in the data.
You count the unique elements of the column:
length(unique(x[,1]))==1
works even if there are NA's in your data.
For checking every column use:
apply(x, 2, function(a) length(unique(a))==1)
You can compare the vector's first value to the rest of the vector.
all(x[-1, 1] == x[1, 1])
# [1] TRUE
If NA values are present, then this exact method does not still apply. However, it can be easily rectified by using na.omit(). For example -
## create a vector with an NA value
x2 <- c(x[, 1], NA)
## standard check returns NA
all(x2 == x2[1])
# [1] NA
## call na.omit() to remove, then compare
all(na.omit(x2) == x2[1])
# [1] TRUE
So then, with your matrix x, this last line would become
all(na.omit(x[-1, 1]) == x[1, 1])
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