Data Frame in R Language

Please load the required data set before running the commands given below in R FAQs related to the data frame. As an example for R FAQs about data frame in R, we are assuming the iris data set is available already in R. At R prompt write data(iris).

Naming/ Renaming Columns in a Data Frame

Question: How do you name or rename a column in a data frame?
Answer: Suppose you want to change/ rename the 3rd column of the data frame, then on R prompt write

names (iris)[,3] <- "new_name"

Suppose you want to change the second and third columns of the data frame

names(irisi)[c(2,4)] <- c("A", "D")

Note that names(iris) command can be used to find the names of each column in a data frame.

Question: How you can determine the column information of a data frame such as the “names, type, missing values” etc.?
Answer: There are two built-in functions in R to find the information about columns of a data frame.

Data Frame in R Language

Exporting a Data Frame in R

Question: How a data frame can be exported in R so that it can be used in other statistical software?
Answer: Use the write.csv command to export the data in comma-separated format (CSV).

write.csv(iris, "iris.csv", row.names = FALSE)

Question: How one can select a particular row or column of a data frame?
Answer: The easiest way is to use the indexing notation []

Suppose you want to select the first column only, then at the R prompt, write


Suppose we want to select the first column and also want to put the content in a new vector, then

new <- iris[,1]

Suppose you want to select different columns, for example, columns 1, 3, and 5, then

newdata <- iris[, c(1, 3, 5)]

Suppose you want to select a first and third row, then

iris[c(1,2), ]

Dealing with Missing values in a Data Frame

Question: How do you deal with missing values in a data frame?
Answer: In R language it is easy to deal with missing values. Suppose you want to import a file named “file.csv” that contains missing values represented by a “.” (period), then on the R prompt write

data <- read.csv("file.csv", na.string = ".")

If missing values are represented as “NA” values then write

dataset <- read.csv("file.csv", na.string = "NA")

For the case of built-in data such (here iris), use

data <- na.omit(iris)

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