Columns Accessors
Since data frames have names on their columns, you should be able to extract necessary data using them.
There are several ways in R to refer to a particular column using naming. One of them is the same as in vectors and matrices: column name within square brackets (for example, data[, "col_name"]). The second way is unique for data frames - using the dollar $ sign. The syntax is data$col_name (yes, without quotation marks). For example, you can extract the column "Age" from the data frame created in the last chapter.
12345678910# Data name <- c("Alex", "Julia", "Finn") age <- c(24, 43, 32) gender <- c("M", "F", "M") # Creating a data frame test <- data.frame(name, age, gender) # Extracting the name column using two ways test[,"name"] test$name
Swipe to start coding
Let's work with the mtcars dataset. Your tasks are:
- Extract the
cylcolumn values using square brackets. - Extract the
dispcolumn values using the dollar$sign.
Solução
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Since data frames have names on their columns, you should be able to extract necessary data using them.
There are several ways in R to refer to a particular column using naming. One of them is the same as in vectors and matrices: column name within square brackets (for example, data[, "col_name"]). The second way is unique for data frames - using the dollar $ sign. The syntax is data$col_name (yes, without quotation marks). For example, you can extract the column "Age" from the data frame created in the last chapter.
12345678910# Data name <- c("Alex", "Julia", "Finn") age <- c(24, 43, 32) gender <- c("M", "F", "M") # Creating a data frame test <- data.frame(name, age, gender) # Extracting the name column using two ways test[,"name"] test$name
Swipe to start coding
Let's work with the mtcars dataset. Your tasks are:
- Extract the
cylcolumn values using square brackets. - Extract the
dispcolumn values using the dollar$sign.
Solução
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