R Data Frames
A data frame is a table with rows and columns, like a sheet in Excel, and it is how R stores most real data.
Creating a Data Frame
Each column is a vector, and all columns have the same length. Unlike a matrix, different columns can have different types:
Example
students <- data.frame(
name = c("Ali", "Ayesha", "Bilal", "Sana", "Hamza"),
city = c("Karachi", "Lahore", "Karachi", "Multan", "Karachi"),
marks = c(78, 92, 65, 88, 71)
)
studentsname city marks 1 Ali Karachi 78 2 Ayesha Lahore 92 3 Bilal Karachi 65 4 Sana Multan 88 5 Hamza Karachi 71
The numbers on the left are row numbers.
Looking at a Data Frame
Example
nrow(students)
names(students)
str(students)[1] 5 [1] "name" "city" "marks" 'data.frame': 5 obs. of 3 variables: $ name : chr "Ali" "Ayesha" "Bilal" "Sana" ... $ city : chr "Karachi" "Lahore" "Karachi" "Multan" ... $ marks: num 78 92 65 88 71
| Function | What it shows |
|---|---|
head(df), tail(df) | The first or last 6 rows |
nrow(), ncol(), dim() | Number of rows and columns |
names() | Column names |
str() | Each column's type and first values |
summary() | Quick statistics for each column |
In RStudio, View(students) opens the data frame in a spreadsheet-like window. It is the easiest way to look at a big table.
Selecting Columns and Rows
Use $ for one column. Use [row, column] like a matrix:
Example
students$marks
mean(students$marks)
students[2, ][1] 78 92 65 88 71
[1] 78.8
name city marks
2 Ayesha Lahore 92Filtering Rows
Put a condition in the row position to keep only matching rows:
Example
students[students$marks > 75, ]
students[students$city == "Karachi", c("name", "marks")]name city marks 1 Ali Karachi 78 2 Ayesha Lahore 92 4 Sana Multan 88 name marks 1 Ali 78 3 Bilal 65 5 Hamza 71
subset() does the same with shorter code, because you do not need to repeat students$:
Example
subset(students, marks >= 70 & city == "Karachi")name city marks 1 Ali Karachi 78 5 Hamza Karachi 71
Adding a Column
Assign to a new column name. ifelse() picks one of two values for every row:
Example
students$result <- ifelse(students$marks >= 70, "Pass", "Fail")
studentsname city marks result 1 Ali Karachi 78 Pass 2 Ayesha Lahore 92 Pass 3 Bilal Karachi 65 Fail 4 Sana Multan 88 Pass 5 Hamza Karachi 71 Pass
Sorting Rows
order() gives the row positions in sorted order:
Example
students[order(students$marks, decreasing = TRUE), ]name city marks result 2 Ayesha Lahore 92 Pass 4 Sana Multan 88 Pass 1 Ali Karachi 78 Pass 5 Hamza Karachi 71 Pass 3 Bilal Karachi 65 Fail
Adding a Row
rbind() joins rows. The new row must have the same column names:
Example
new_row <- data.frame(name = "Zara", city = "Quetta", marks = 84, result = "Pass")
students <- rbind(students, new_row)
nrow(students)[1] 6
Summarising Data
Example
table(students$city)
aggregate(marks ~ city, data = students, FUN = mean)
Karachi Lahore Multan Quetta
3 1 1 1
city marks
1 Karachi 71.33333
2 Lahore 92.00000
3 Multan 88.00000
4 Quetta 84.00000aggregate(marks ~ city, ...) means "average the marks for each city". The ~ sign is read as "by".
Reading and Writing CSV Files
Real data usually comes from a file. Save a sheet from Excel as CSV, then:
Example
shop <- read.csv("sales.csv")
head(shop)
write.csv(students, "students.csv", row.names = FALSE)read.csv() looks for the file in your working directory. Use getwd() to see which folder that is, or choose it in RStudio from Session, Set Working Directory.
For larger projects, many R users load the dplyr package. It gives easy-to-read functions like filter(), arrange() and summarise() for the same jobs you learned here.
Exercise
Exercise
Which code keeps only the rows of the students data frame where marks are above 80?