Data frames ---From STHDA

A data frame is like a matrix but can have columns with different types (numeric, character, logical).
Rows are observations (individuals) and columns are variables.

Create a data frame

using the function data.frame(), as follow:

friends_data <- data.frame(name = c("A","B","C","D"),
                          age = c(25,27 ,26,29),
                          height = c(180, 170, 185, 169),
                          married = c(T,F,F,T)
)# Create a data frame
friends_data # Print

is.data.frame(friends_data) #To check whether a data is a data frame, use the is.data.frame() function. Returns TRUE if the data is a data frame.

data.frame() is.data.frame()

col1 <- c(5, 6, 7, 8, 9)# Numeric vectors
col2 <- c(2, 4, 5, 9, 8)# Numeric vectors
col3 <- c(7, 3, 4, 8, 7)# Numeric vectors
my_data <- cbind(col1, col2, col3)# Combine the vectors by column
my_data

is.data.frame(my_data)

class() as.data.frame()

The object “friends_data” is a data frame, but not the object “my_data”. We can convert-it to a data frame using the as.data.frame() function:

class(my_data)# What is the class of my_data? --> matrix

my_data2 <- as.data.frame(my_data)# Convert it as a data frame

class(my_data2)# Convert it as a data frame

As described in matrix section, you can use the function t() to transpose a data frame:

t(friends_data)

t()

Subset a data frame

To select just certain columns from a data frame, you can either refer to the columns by name or by their location (i.e., column 1, 2, 3, etc.).

1. Positive indexing by name and by location
   Select rows/columns by positive indexing---Select by row/column names

# Access the data in 'name' column
# dollar sign is used  ***$***
friends_data$name

# or use this
friends_data[, 'name']

# Subset columns 1 and 3
friends_data[ , c(1, 3)]

positive indexing:$ ,"colname",c(1,2,3,4:5)

2. Negative indexing
   Exclude rows/columns by negative indexing

# Exclude column 1
friends_data[, -1]

Negative indexing: -1,c(-1,-2)

3. Index by characteristics
   Selection by logical: T F

#We want to select all friends with age >= 27.

friends_data$age >= 27# Identify rows that meet the condition,return  lodgic, 
 #TRUE specifies that the row contains a value of age >= 27.else ,FALSE, not

friends_data[friends_data$age >= 27, ]# Select the rows that meet the condition

#The R code above, tells R to get all rows from friends_data where age >= 27, and then to return all the columns.

#If you don’t want to see all the column data for the selected rows but are just interested in displaying, for example, friend names and age for friends with age >= 27, you could use the following R code:


friends_data[friends_data$age >= 27,  c(1, 2)]# Use column locations
# Or use column names
friends_data[friends_data$age >= 27, c("name", "age")]

logical indexing

indexing: positive,negative,logic

a. If you’re finding that your selection statement is starting to be inconvenient, you can --put your row and column selections into variables first---, such as:
b. Then you can select the rows and columns with those variables:

age27 <- friends_data$age >= 27
cols <- c("name", "age")

friends_data[age27, cols]

index by variables

4. function :subset()
   It’s also possible to use the function subset() as follow.
   subset()

# Select friends data with age >= 27
subset(friends_data, age >= 27)

function :subset()

5. function: attach() and detach().
   Another option is to use the functions attach() and detach().
   The function attach() takes a data frame and makes its columns accessible by simply giving their names.
   used as follow:

# Attach a data frame
attach(friends_data)
# === Data manipulation ====
friends_data[age>=27, ]
# === End of data manipulation ====
# Detach the data frame
detach(friends_data)

functions attach() and detach().

Extend a data frame

a. $ #Add new column in a data frame

# Add group column to friends_data
friends_data$group <- friend_groups
friends_data

$

variable$colname

b. It’s also possible to use the functions cbind() and rbind() to extend a data frame.

cbind(friends_data, group = friend_groups)

cbind(df1,df2)

Calculations with data frame or matrix

With numeric data frame, you can use the function
rowSums(),
colSums(),
colMeans(),
rowMeans()
and apply() as described in matrix section.
rowSums() and colSums() functions: Compute the total of each row and the total of each column, respectively.
It’s also possible to perform simple operations on matrice. For example, the following R code multiplies each element of the matrix by 2:

matrix

Note that, it’s also possible to use the function apply() to apply any statistical functions to rows/columns of matrices.
Use apply() as follow:

my_data
my_data*2
log2(my_data)#compute the log2 values
rowSums(my_data)# Total of each row
colSums(my_data)# Total of each column
  
 #apply(X, MARGIN, FUN) #X: your data matrix #MARGIN: possible values are 1 (for rows) and 2 (for columns) #FUN: the function to apply on rows/columns

apply(my_data, 1, mean) # Compute row means
apply(my_data, 1, median)# Compute row medians
apply(my_data, 2, mean)# Compute column means

numeric matrix row col calculations