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.
str(iris) summary(iris)
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
iris[,1]
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)