dplyr Package in R Programming

29 Nov, 2021
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.
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.
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 datad < - 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 valued % > % filter(is.na(ht)) # Finding rows with no NA valued % > % 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 datad <- 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 aged.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 datad < - 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 dataselect(d, starts_with("ht")) # -startswith() function to print# everything except ht dataselect(d, -starts_with("ht")) # Printing column 1 to 2select(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 datad <- 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 agemutate(d, x3 = ht + age) # Calculating a variable x3 which is sum of height# and age printing without ht and agetransmute(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 datad <- 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 agesummarise(d, mean = mean(age)) # Calculating min of agesummarise(d, med = min(age)) # Calculating max of agesummarise(d, med = max(age)) # Calculating median of agesummarise(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 datad <- 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 rowssample_n(d, 3) # Printing 50 % of the rowssample_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  
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