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It is not clear what it means to test if two data frames are "value equal" but to test if the values are the same, here is an example of two non-identical dataframes with equal values: a <- data.frame(x = 1:10) b <- data.frame(y = 1:10) To test if all values are equal: all(a == b) # TRUE
(=) is a Assignment operator while (==) is a Equal to operator. (=) is used for assigning the values from right to left while (==) is used for showing equality between values. Example:
55. I'm trying to compare two numbers in R as a part of a if-statement condition: In this particular instance, a = 0.58 and b = 0.08... and yet (a-b) >= 0.5 is false. I'm aware of the dangers of using == for exact number comparisons, and this seems related: all.equal((a - b), 0.5) is true. The only solution I can think of is to have two ...
Add a less than or equal to symbol to R string. 0. add symbol to just one axis label in R. 161.
The operators <- and = assign into the environment in which they are evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.
From here: The operators <- and = assign into the environment in which they are evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions. answered Feb 16, 2010 at 8:56.
cut_number(): Makes n groups with (approximately) equal numbers of observation; cut_interval(): Makes n groups with equal range; cut_width(): Makes groups of width; My go-to is cut_number() because this uses evenly spaced quantiles for binning observations. Here's an example with skewed data.
I need to filter/subset a dataframe using values in two columns to remove them. In the examples I want to keep all the rows that are not equal (!=) to both replicate "1" and treatment "a". However, either subset and filter functions remove all replicate 1 and all treatment a. I could solve it by using which and then indexing, but it is not the ...
sum is used to add elements; nrow is used to count the number of rows in a rectangular array (typically a matrix or data.frame); length is used to count the number of elements in a vector. You need to apply these functions correctly. Let's assume your data is a data frame named "dat". Correct solutions:
R doesn't expect further input past your first evaluation, unless you feed it a Boolean & for vectors. You may want to modify this, but here's one attempt at a functional programming approach: testEqual <- function(x, y) ifelse(x == y, x, FALSE) all(!!Reduce(testEqual, list(1:10, 1:10))) # True.