R is a widely used programming language and statistical software for data analysis and visualization. One of the most common issues encountered while working with data is missing values. Missing values can occur due to a variety of reasons such as data entry errors, equipment malfunction, or even just the absence of data. In R, NA (Not Available) is used to represent missing values. NA can cause problems in data analysis, as functions that cannot handle missing values may produce incorrect or misleading results. To deal with this issue, R provides two functions – na.omit and na.rm – that can help replace missing values.

na.omit function in R:

The na.omit function in R is used to remove observations (rows) with missing values from a data frame. As researched by **R Programming Assignment Help** team, It returns a new data frame with the missing values removed. This function is useful when dealing with data sets that contain many missing values.

Syntax: na.omit(x)

Where, x is a data frame, matrix or a vector containing missing values.

For example, let’s consider a data frame “df” that contains missing values:

r

ID Age Gender Height Weight

1 1 23 Male NA 70

2 2 34 Female 165 62

3 3 28 Male 180 NA

4 4 NA Female 155 58

5 5 42 Male NA 78

6 6 NA Female 170 60

7 7 36 Female 163 NA

8 8 29 Male 175 80

9 9 31 Female 160 55

10 10 NA Male 172 73

We can remove the missing values using the na.omit function as follows:

bash

df_clean <- na.omit(df)

The resulting data frame “df_clean” will not contain any rows with missing values:

css

ID Age Gender Height Weight

2 2 34 Female 165 62

8 8 29 Male 175 80

9 9 31 Female 160 55

na.rm function in R:

The na.rm function in R is used to remove missing values from calculations. It is a parameter that can be used with some functions in R. The na.rm parameter is used to indicate whether or not missing values should be removed from the calculation.

Syntax: na.rm = TRUE/FALSE

Where, TRUE means remove the missing values from the calculation and FALSE means do not remove the missing values.

For example, let’s consider a vector “x” that contains missing values:

r

x <- c(2, 5, NA, 8, 10, NA, 15)

We can calculate the mean of “x” without removing the missing values as follows:

scss

mean(x)

The result will be “NA” because “x” contains missing values.

We can use the na.rm parameter to remove the missing values from the calculation as follows:

scss

mean(x, na.rm = TRUE)

The result will be 8.333333, which is the mean of the non-missing values in “x”.

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Conclusion: Missing values can cause problems in data analysis, as functions that cannot handle missing values may produce incorrect or misleading results. As considered by **Statistics Homework Help** team of experts, The na.omit function in R is used to remove observations (rows) with missing values from a data frame. The na.rm function in R is used to remove missing values from calculations