13 Feb R Mean and Median
We can easily find the mean and median values from a vector or set or dataframe using the mean() and median() functions respectively. Let us learn about them with examples:
R mean
To get the mean of the values in the R programming language, use the mean() method. Let us see an example to calculate the mean:
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# A Vector # Marks of a student in various subjects marks <- c(95, 99, 90, 85, 87) # Find Mean i.e. the average marks using the mean() res <- mean(marks) res |
Output
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[1] 91.2 |
We can also trim the left and right observations using the trim parameter of the mean() method in R. The vector will be first sorted, then according to the value in the parameter, the values will get trimmed, for example trim = 0.2 would mean trim two values from the left as well right.
Here is an example:
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# A Vector # Marks of a student in various subjects marks <- c(95, 99, 90, 85, 87, 79, 91, 65, 82, 80) # Find Mean i.e. the average marks using the mean() # The trim parameter will sort and then trims the observations res <- mean(marks, trim = 0.2) res |
Output
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[1] 85.83333 |
The vector will be sorted first above:
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65, 79, 80, 82, 85, 87, 90, 91, 95, 99 |
Then, 0.2 means two observations from both the left position will get removed and then the mean gets calculated for the rest of the observations.
R median
To get the median of the values in the R programming language, use the median() method. Median is defined as the middle value of the given list. Let us see an example to calculate the median:
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# A Vector # Marks of a student in various subjects marks <- c(95, 99, 90, 85, 87) # Find Median i.e. the middle value res <- median(marks) res |
Output
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[1] 90 |
We can also see another example, wherein we can drop the missing values using the na.rm parameter. Let us see an example and first create a vector with NA values:
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# A Vector with missing values # Marks of a student in various subjects marks <- c(95, 99, 90, 85, 87, NA, 92, NA) # Find Median i.e. the middle value # We have set the na.rm parameter to TRUE to remove the missing values res <- median(marks, na.rm = TRUE) res |
Output
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[1] 91 |
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