dplyr Package in R Programming

The dplyr package in R Programming Language is a structure of data manipulation that provides a uniform set of verbs, helping to resolve the most frequent data manipulation hurdles.

The dplyr Package in R performs the steps given below quicker and in an easier fashion:

• By limiting the choices the focus can now be more on data manipulation difficulties.
• There are uncomplicated “verbs”, functions present for tackling every common data manipulation and the thoughts can be translated into code faster.
• There are valuable backends and hence waiting time for the computer reduces.

Important Verb Functions

dplyr package provides various important functions that can be used for Data Manipulation. These are: 

• filter() Function: For choosing cases and using their values as a base for doing so.

R

# Create a data frame with missing data
d < - data.frame(name=c("Abhi", "Bhavesh",
                        "Chaman", "Dimri"),
                 age=c(7, 5, 9, 16),
                 ht=c(46, NA, NA, 69),
                 school=c("yes", "yes", "no", "no"))
d

# Finding rows with NA value
d % > % filter(is.na(ht))

# Finding rows with no NA value
d % > % filter(! is.na(ht))

Output:

# A tibble: 4 x 4
  name      age    ht school
        
1 Abhi        7    46 yes   
2 Bhavesh     5    NA yes   
3 Chaman      9    NA no    
4 Dimri      16    69 no

# A tibble: 2 x 4
  name      age    ht school
        
1 Bhavesh     5    NA yes   
2 Chaman      9    NA no

# A tibble: 2 x 4
  name    age    ht school
      
1 Abhi      7    46 yes   
2 Dimri    16    69 no

• arrange(): For reordering of the cases.

R

# Create a data frame with missing data
d <- data.frame( name = c("Abhi", "Bhavesh", "Chaman", "Dimri"),
                 age = c(7, 5, 9, 16),
                 ht = c(46, NA, NA, 69),
                 school = c("yes", "yes", "no", "no") )

# Arranging name according to the age
d.name<- arrange(d, age)
print(d.name)

Output:

# A tibble: 4 x 4
  name      age    ht school
        
1 Bhavesh     5    NA yes   
2 Abhi        7    46 yes   
3 Chaman      9    NA no    
4 Dimri      16    69 no   

• select() and rename(): For choosing variables and using their names as a base for doing so.

R

# Create a data frame with missing data
d < - data.frame(name=c("Abhi", "Bhavesh",
                        "Chaman", "Dimri"),
                 age=c(7, 5, 9, 16),
                 ht=c(46, NA, NA, 69),
                 school=c("yes", "yes", "no", "no"))

# startswith() function to print only ht data
select(d, starts_with("ht"))

# -startswith() function to print
# everything except ht data
select(d, -starts_with("ht"))

# Printing column 1 to 2
select(d, 1: 2)

# Printing data of column
# heading containing 'a'
select(d, contains("a"))

# Printing data of column
# heading which matches 'na'
select(d, matches("na"))

Output:

# A tibble: 4 x 1
     ht
  
1    46
2    NA
3    NA
4    69

# A tibble: 4 x 3
  name      age school
       
1 Abhi        7 yes   
2 Bhavesh     5 yes   
3 Chaman      9 no    
4 Dimri      16 no

# A tibble: 4 x 2
  name      age
     
1 Abhi        7
2 Bhavesh     5
3 Chaman      9
4 Dimri      16

# A tibble: 4 x 2
  name      age
     
1 Abhi        7
2 Bhavesh     5
3 Chaman      9
4 Dimri      16

# A tibble: 4 x 1
  name   
    
1 Abhi   
2 Bhavesh
3 Chaman 
4 Dimri

• mutate() and transmute(): Addition of new variables which are the functions of prevailing variables.

R

# Create a data frame with missing data
d <- data.frame( name = c("Abhi", "Bhavesh",
                          "Chaman", "Dimri"),
                 age = c(7, 5, 9, 16),
                 ht = c(46, NA, NA, 69),
                 school = c("yes", "yes", "no", "no") )

# Calculating a variable x3 which is sum of height
# and age printing with ht and age
mutate(d, x3 = ht + age)

# Calculating a variable x3 which is sum of height
# and age printing without ht and age
transmute(d, x3 = ht + age)

Output:

 
# A tibble: 4 x 5
  name      age    ht school    x3
         
1 Abhi        7    46 yes       53
2 Bhavesh     5    NA yes       NA
3 Chaman      9    NA no        NA
4 Dimri      16    69 no        85

# A tibble: 4 x 1
     x3
  
1    53
2    NA
3    NA
4    85
> 

• summarise(): Condensing various values to one value.

R

# Create a data frame with missing data
d <- data.frame( name = c("Abhi", "Bhavesh",
                          "Chaman", "Dimri"),
                 age = c(7, 5, 9, 16),
                 ht = c(46, NA, NA, 69),
                 school = c("yes", "yes", "no", "no") )

# Calculating mean of age
summarise(d, mean = mean(age))

# Calculating min of age
summarise(d, med = min(age))

# Calculating max of age
summarise(d, med = max(age))

# Calculating median of age
summarise(d, med = median(age))

Output:

# A tibble: 1 x 1
      mean
     
1     9.25

# A tibble: 1 x 1
    med
  
1     5

# A tibble: 1 x 1
    med
  
1    16

# A tibble: 1 x 1
    med
  
1     8

• sample_n() and sample_frac(): For taking random specimens.

R

# Create a data frame with missing data
d <- data.frame( name = c("Abhi", "Bhavesh",
                          "Chaman", "Dimri"),
                 age = c(7, 5, 9, 16),
                 ht = c(46, NA, NA, 69),
                 school = c("yes", "yes", "no", "no") )

# Printing three rows
sample_n(d, 3)

# Printing 50 % of the rows
sample_frac(d, 0.50)

Output:

# A tibble: 3 x 4
  name      age    ht school
        
1 Abhi        7    46 yes   
2 Bhavesh     5    NA yes   
3 Chaman      9    NA no 

# A tibble: 2 x 4
  name      age    ht school
        
1 Dimri      16    69 no    
2 Bhavesh     5    NA yes