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Data manipulation techniques refer to the process of adjusting or rearranging data to make it organized and easier to read.
Data manipulation is a crucial function for all types of operations.
If you want to perform any kind of analysis like customer behavior, trend identification, prediction, etc… need to re-arrange the data in the way you need it.
As such, data manipulation techniques provides many benefits.
Data Manipulation Techniques Steps
Different steps involved in data manipulation, you’ll want to understand the general steps of operations.
- You need a database, which is created from your data sources.
- Need to cleanse your data, with data manipulation, you can clean, rearrange and restructure data.
- Develop the data frame for further analysis.
- Then analyze the data, to make all of this information and produce meaningful insights.
In this tutorial, we are going to explain data manipulation with dplyr package.
dplyr is the next iteration of plyr, focusing on only data frames. The main advantages of dplyr package is speed.
Exploratory data analysis in R
Load Library
library(dplyr) library(readr)
Getting Data
mydata <- read_csv("D:/RStudio/Map/charts.csv")
In this dataset contains 327387 observations and 5 variables. You can download the dataset from here.
Linear Discriminant Analysis in R
Piping %>%
head(mydata, 10)
date order Title Name week
1 8/4/1958 1 Poor Little Fool Ricky Nelson 1
2 8/4/1958 2 Patricia Perez Prado And His Orchestra 1
3 8/4/1958 3 Splish Splash Bobby Darin 1
4 8/4/1958 4 Hard Headed Woman Elvis Presley With The Jordanaires 1
5 8/4/1958 5 When Kalin Twins 1
6 8/4/1958 6 Rebel-'rouser Duane Eddy His Twangy Guitar And The Rebels 1
7 8/4/1958 7 Yakety Yak The Coasters 1
8 8/4/1958 8 My True Love Jack Scott 1
9 8/4/1958 9 Willie And The Hand Jive The Johnny Otis Show 1
10 8/4/1958 10 Fever Peggy Lee 1
mydata %>% head(10)
10 %>% head(mydata, .)
date order Title Name week
1 8/4/1958 1 Poor Little Fool Ricky Nelson 1
2 8/4/1958 2 Patricia Perez Prado And His Orchestra 1
3 8/4/1958 3 Splish Splash Bobby Darin 1
4 8/4/1958 4 Hard Headed Woman Elvis Presley With The Jordanaires 1
5 8/4/1958 5 When Kalin Twins 1
6 8/4/1958 6 Rebel-'rouser Duane Eddy His Twangy Guitar And The Rebels 1
7 8/4/1958 7 Yakety Yak The Coasters 1
8 8/4/1958 8 My True Love Jack Scott 1
9 8/4/1958 9 Willie And The Hand Jive The Johnny Otis Show 1
10 8/4/1958 10 Fever Peggy Lee 1
In dplyr the column operations are handled based on a select and mutate functions.
Select
mydata %>% select(date, order, Title, Name, 'week')
Select function used for selecting the columns
date order Title Name week
1 8/4/1958 1 Poor Little Fool Ricky Nelson 1
2 8/4/1958 2 Patricia Perez Prado And His Orchestra 1
3 8/4/1958 3 Splish Splash Bobby Darin 1
4 8/4/1958 4 Hard Headed Woman Elvis Presley With The Jordanaires 1
5 8/4/1958 5 When Kalin Twins 1
6 8/4/1958 6 Rebel-'rouser Duane Eddy His Twangy Guitar And The Rebels 1
7 8/4/1958 7 Yakety Yak The Coasters 1
8 8/4/1958 8 My True Love Jack Scott 1
9 8/4/1958 9 Willie And The Hand Jive The Johnny Otis Show 1
10 8/4/1958 10 Fever Peggy Lee 1
mydata %>%
select(date:Name, weeks_popular='week')
date order Title Name weeks_popular
1 8/4/1958 1 Poor Little Fool Ricky Nelson 1
2 8/4/1958 2 Patricia Perez Prado And His Orchestra 1
3 8/4/1958 3 Splish Splash Bobby Darin 1
4 8/4/1958 4 Hard Headed Woman Elvis Presley With The Jordanaires 1
5 8/4/1958 5 When Kalin Twins 1
6 8/4/1958 6 Rebel-'rouser Duane Eddy His Twangy Guitar And The Rebels 1
7 8/4/1958 7 Yakety Yak The Coasters 1
8 8/4/1958 8 My True Love Jack Scott 1
9 8/4/1958 9 Willie And The Hand Jive The Johnny Otis Show 1
10 8/4/1958 10 Fever Peggy Lee 1
mydata %>%
select(-'order')
date Title Name week
1 8/4/1958 Poor Little Fool Ricky Nelson 1
2 8/4/1958 Patricia Perez Prado And His Orchestra 1
3 8/4/1958 Splish Splash Bobby Darin 1
4 8/4/1958 Hard Headed Woman Elvis Presley With The Jordanaires 1
5 8/4/1958 When Kalin Twins 1
6 8/4/1958 Rebel-'rouser Duane Eddy His Twangy Guitar And The Rebels 1
7 8/4/1958 Yakety Yak The Coasters 1
8 8/4/1958 My True Love Jack Scott 1
9 8/4/1958 Willie And The Hand Jive The Johnny Otis Show 1
10 8/4/1958 Fever Peggy Lee 1
Mutate
mydata %>%
select(date:Name, weeks_popular='week') %>%
mutate(is_collab = grepl('Featuring', Name)) %>%
select(Name, is_collab, everything())
Name is_collab date order Title weeks_popular
1 Ricky Nelson FALSE 8/4/1958 1 Poor Little Fool 1
2 Perez Prado And His Orchestra FALSE 8/4/1958 2 Patricia 1
3 Bobby Darin FALSE 8/4/1958 3 Splish Splash 1
4 Elvis Presley With The Jordanaires FALSE 8/4/1958 4 Hard Headed Woman 1
5 Kalin Twins FALSE 8/4/1958 5 When 1
6 Duane Eddy His Twangy Guitar And The Rebels FALSE 8/4/1958 6 Rebel-'rouser 1
7 The Coasters FALSE 8/4/1958 7 Yakety Yak 1
8 Jack Scott FALSE 8/4/1958 8 My True Love 1
9 The Johnny Otis Show FALSE 8/4/1958 9 Willie And The Hand Jive 1
10 Peggy Lee FALSE 8/4/1958 10 Fever 1
Naïve Bayes Classification in R
In dplyr row operations are handled based on filter, distinct and arrange functions
Filter
mydata %>%
select(date:Name, weeks_popular='week') %>%
filter(weeks_popular >= 20, Name == 'Drake' | Name == 'Taylor Swift')
date order Title Name weeks_popular
1 2/3/2007 43 Tim McGraw Taylor Swift 20
2 8/4/2007 39 Teardrops On My Guitar Taylor Swift 20
3 8/11/2007 33 Teardrops On My Guitar Taylor Swift 21
4 8/18/2007 34 Teardrops On My Guitar Taylor Swift 22
5 8/25/2007 38 Teardrops On My Guitar Taylor Swift 23
6 9/1/2007 49 Teardrops On My Guitar Taylor Swift 24
7 9/8/2007 50 Teardrops On My Guitar Taylor Swift 25
8 12/15/2007 44 Teardrops On My Guitar Taylor Swift 26
9 12/22/2007 30 Teardrops On My Guitar Taylor Swift 27
10 12/29/2007 24 Teardrops On My Guitar Taylor Swift 28
distinct
mydata %>% select(date:Name, weeks_popular='week') %>% filter(Name == 'Jack Scott') %>% distinct(Title)
This function can be used to keep only unique/distinct rows from a data frame.
Title 1 My True Love 2 Leroy 3 With Your Love 4 Geraldine 5 Goodbye Baby 6 Save My Soul 7 I Never Felt Like This 8 The Way I Walk 9 There Comes A Time 10 What In The World's Come Over You 11 Burning Bridges 12 Oh, Little One 13 Cool Water 14 It Only Happened Yesterday 15 Patsy 16 Is There Something On Your Mind 17 A Little Feeling (Called Love) 18 My Dream Come True 19 Steps 1 And 2
In dplyr group operations are handled based on group_by, summarise and count functions.
Group_by & Summarise
mydata %>%
select(date:Name, weeks_popular='week') %>%
filter(Name == 'Kalin Twins') %>%
group_by(Title) %>%
summarise(total_weeks_popular = max(weeks_popular))
Title total_weeks_popular
1 Forget Me Not 15
2 When 9
Arrange
mydata %>%
select(date:Name, weeks_popular='week') %>%
filter(Name == 'Drake') %>%
group_by(Title) %>%
summarise(total_weeks_popular = max(weeks_popular)) %>%
arrange(desc(total_weeks_popular), Title) %>%
head(10)
Title total_weeks_popular
1 God's Plan 36
2 Hotline Bling 36
3 Controlla 26
4 Fake Love 25
5 Headlines 25
6 Nice For What 25
7 Best I Ever Had 24
8 In My Feelings 22
9 Nonstop 22
10 Started From The Bottom 22
Count
mydata %>%
select(date:Name, weeks_popular='week') %>%
count(Name) %>%
arrange(desc(n))
Name n
1 Taylor Swift 1021
2 Elton John 889
3 Madonna 857
4 Kenny Chesney 758
5 Drake 742
6 Tim McGraw 731
7 Keith Urban 673
8 Stevie Wonder 659
9 Rod Stewart 657
10 Mariah Carey 621
Conclusion
Based on dplyr package data can easily be modified the way we want and that too very easily.
tidyverse in R complete tutorial
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