Course Content
R Introduction: Part II
R Introduction: Part II
Logical Indexing
Good! Accessing columns by their names is convenient. Can we filter the rows we want to output?
Indeed, we can. First, we can use indices (like it was for vectors or matrices). But usually, we do not know the positions of the rows but know some conditions we want to satisfy. For example, we may want to extract data for only Males or only people older than 30. You can do it by specifying necessary conditions within square brackets. You need to use the double sign == for equality.
Assume we have data frame data and want to filter to rows having the value 30 in column age. This can be done using the following syntax: data[data$age == 30,]. Note that you put condition as the first index within the square bracket. For example, for the same training data as before, let's extract the data of people older than 30 and males only.
# Data name <- c("Alex", "Julia", "Finn") age <- c(24, 43, 32) gender <- c("M", "F", "M") # Creating a data frame test <- data.frame(name, age, gender) # People older than 30 test[test$age > 30, ] # Males only test[test$gender == 'M', ]
As you can see, that's correct.
Task
Using the mtcars dataset, extract the following data:
- The cars pass a quarter-mile in less than 16 seconds (
qseccolumn). - Cars with 6 cylinders (
cylcolumn).
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Logical Indexing
Good! Accessing columns by their names is convenient. Can we filter the rows we want to output?
Indeed, we can. First, we can use indices (like it was for vectors or matrices). But usually, we do not know the positions of the rows but know some conditions we want to satisfy. For example, we may want to extract data for only Males or only people older than 30. You can do it by specifying necessary conditions within square brackets. You need to use the double sign == for equality.
Assume we have data frame data and want to filter to rows having the value 30 in column age. This can be done using the following syntax: data[data$age == 30,]. Note that you put condition as the first index within the square bracket. For example, for the same training data as before, let's extract the data of people older than 30 and males only.
# 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) # People older than 30 test[test$age > 30, ] # Males only test[test$gender == 'M', ]
As you can see, that's correct.
Task
Using the mtcars dataset, extract the following data:
- The cars pass a quarter-mile in less than 16 seconds (
qseccolumn). - Cars with 6 cylinders (
cylcolumn).
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Logical Indexing
Good! Accessing columns by their names is convenient. Can we filter the rows we want to output?
Indeed, we can. First, we can use indices (like it was for vectors or matrices). But usually, we do not know the positions of the rows but know some conditions we want to satisfy. For example, we may want to extract data for only Males or only people older than 30. You can do it by specifying necessary conditions within square brackets. You need to use the double sign == for equality.
Assume we have data frame data and want to filter to rows having the value 30 in column age. This can be done using the following syntax: data[data$age == 30,]. Note that you put condition as the first index within the square bracket. For example, for the same training data as before, let's extract the data of people older than 30 and males only.
# Data name <- c("Alex", "Julia", "Finn") age <- c(24, 43, 32) gender <- c("M", "F", "M") # Creating a data frame test <- data.frame(name, age, gender) # People older than 30 test[test$age > 30, ] # Males only test[test$gender == 'M', ]
As you can see, that's correct.
Task
Using the mtcars dataset, extract the following data:
- The cars pass a quarter-mile in less than 16 seconds (
qseccolumn). - Cars with 6 cylinders (
cylcolumn).
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Good! Accessing columns by their names is convenient. Can we filter the rows we want to output?
Indeed, we can. First, we can use indices (like it was for vectors or matrices). But usually, we do not know the positions of the rows but know some conditions we want to satisfy. For example, we may want to extract data for only Males or only people older than 30. You can do it by specifying necessary conditions within square brackets. You need to use the double sign == for equality.
Assume we have data frame data and want to filter to rows having the value 30 in column age. This can be done using the following syntax: data[data$age == 30,]. Note that you put condition as the first index within the square bracket. For example, for the same training data as before, let's extract the data of people older than 30 and males only.
# 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) # People older than 30 test[test$age > 30, ] # Males only test[test$gender == 'M', ]
As you can see, that's correct.
Task
Using the mtcars dataset, extract the following data:
- The cars pass a quarter-mile in less than 16 seconds (
qseccolumn). - Cars with 6 cylinders (
cylcolumn).
Switch to desktop for real-world practiceContinue from where you are using one of the options belowSwitch to desktop for real-world practiceContinue from where you are using one of the options below