dplyr in R for Data Manipulation
data03.online
2023-12-28
dplyr in R for Data Manipulation
In the realm of data analysis, mastering the art of data manipulation
is akin to wielding a powerful tool that unlocks deeper insights and
facilitates more informed decision-making. In the vast landscape of
statistical programming languages, R stands out as a preferred choice
for data manipulation tasks, and the dplyr package within R
is a key player in this arena.
Read the complete article and download the code: Data Manipulation I dplyr cheat sheet using R.
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Understanding the Basics
To embark on our journey with dplyr, let’s first
acquaint ourselves with the basics. Imagine you have a dataset, say the
classic Iris dataset. You load it into R, and here comes the magic of
dplyr to streamline your data manipulation process.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosaNow, let’s delve into some fundamental dplyr functions
showcased in the provided code.
Filtering Data with dplyr
One of the common tasks in data analysis is filtering rows based on
certain conditions. With dplyr, this becomes intuitive and
efficient. For instance, let’s filter rows where the Sepal.Length is
greater than 5.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 5.4 3.9 1.7 0.4 setosa
## 3 5.4 3.7 1.5 0.2 setosa
## 4 5.8 4.0 1.2 0.2 setosa
## 5 5.7 4.4 1.5 0.4 setosa
## 6 5.4 3.9 1.3 0.4 setosa
## 7 5.1 3.5 1.4 0.3 setosa
## 8 5.7 3.8 1.7 0.3 setosa
## 9 5.1 3.8 1.5 0.3 setosa
## 10 5.4 3.4 1.7 0.2 setosaHere, we introduce the concept of Aggregate count, which is crucial in understanding the distribution of data.
Creating New Variables
With dplyr, creating new variables is a breeze. Let’s
double the values of Sepal.Length and store them in a new variable,
NewVar.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species NewVar
## 1 5.1 3.5 1.4 0.2 setosa 10.2
## 2 4.9 3.0 1.4 0.2 setosa 9.8
## 3 4.7 3.2 1.3 0.2 setosa 9.4
## 4 4.6 3.1 1.5 0.2 setosa 9.2
## 5 5.0 3.6 1.4 0.2 setosa 10.0
## 6 5.4 3.9 1.7 0.4 setosa 10.8
## 7 4.6 3.4 1.4 0.3 setosa 9.2
## 8 5.0 3.4 1.5 0.2 setosa 10.0
## 9 4.4 2.9 1.4 0.2 setosa 8.8
## 10 4.9 3.1 1.5 0.1 setosa 9.8This operation aligns with the idea of introducing a New
variable in R, showcasing the flexibility of
dplyr in augmenting your dataset.
Selecting Specific Columns
In many scenarios, you might only be interested in specific columns.
dplyr simplifies this process.
## Sepal.Length Species
## 1 5.1 setosa
## 2 4.9 setosa
## 3 4.7 setosa
## 4 4.6 setosa
## 5 5.0 setosa
## 6 5.4 setosa
## 7 4.6 setosa
## 8 5.0 setosa
## 9 4.4 setosa
## 10 4.9 setosaHere, we touch upon the concept of Create variable, emphasizing the importance of selectively choosing variables based on your analytical goals.
Arranging Data
Sorting your data can provide valuable insights. With
dplyr, arranging data becomes a straightforward task.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 4.3 3.0 1.1 0.1 setosa
## 2 4.4 2.9 1.4 0.2 setosa
## 3 4.4 3.0 1.3 0.2 setosa
## 4 4.4 3.2 1.3 0.2 setosa
## 5 4.5 2.3 1.3 0.3 setosa
## 6 4.6 3.1 1.5 0.2 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 4.6 3.6 1.0 0.2 setosa
## 9 4.6 3.2 1.4 0.2 setosa
## 10 4.7 3.2 1.3 0.2 setosaThis aligns with the broader theme of Analyzing data in R, showcasing how ordered data can facilitate a deeper understanding.
Summarizing Data
Summarizing data is a crucial step in exploratory data analysis.
Let’s calculate the mean of Sepal.Length using dplyr.
## Mean_Sepal_Length
## 1 5.843333This encapsulates the essence of data exploration, a fundamental aspect of Data analysis.
Grouping and Aggregating Data
Grouping data based on certain variables and performing aggregate
functions is a common practice. dplyr simplifies this
process.
## # A tibble: 3 × 2
## Species Mean_Sepal_Length
## <fct> <dbl>
## 1 setosa 5.01
## 2 versicolor 5.94
## 3 virginica 6.59Here, we touch upon the concept of EDA (Exploratory Data Analysis), highlighting the importance of examining data variations across different groups.
Advanced Data Manipulation Techniques
Selecting Specific Rows
dplyr offers versatile functions for selecting specific
rows based on conditions. Let’s explore some of these
functionalities.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosaThis operation aligns with the broader theme of Data
wrangling, showcasing how dplyr facilitates
the restructuring of your data for better analysis.
Random Sampling
Random sampling is a crucial technique in statistical analysis.
dplyr provides an elegant solution.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 6.7 3.3 5.7 2.1 virginica
## 2 6.0 2.2 4.0 1.0 versicolor
## 3 6.4 2.8 5.6 2.1 virginica
## 4 5.8 2.7 4.1 1.0 versicolor
## 5 5.8 2.8 5.1 2.4 virginicaThis concept ties into the broader theme of dplyr, emphasizing the versatility of the package in handling diverse data manipulation tasks.
Top N Rows
Selecting the top N rows based on a specific variable is another
common task. Let’s explore this using dplyr.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 7.7 3.8 6.7 2.2 virginica
## 2 7.7 2.6 6.9 2.3 virginica
## 3 7.7 2.8 6.7 2.0 virginica
## 4 7.9 3.8 6.4 2.0 virginica
## 5 7.7 3.0 6.1 2.3 virginicaThis task aligns with the broader theme of Normalize data, showcasing how selecting top rows can be valuable in certain normalization scenarios.
Creating New Variables with transmute
The transmute function in dplyr allows you
to create new variables while preserving existing ones. Let’s explore
this functionality.
## NewVar
## 1 10.2
## 2 9.8
## 3 9.4
## 4 9.2
## 5 10.0
## 6 10.8
## 7 9.2
## 8 10.0
## 9 8.8
## 10 9.8This aligns with the broader theme of Create
variable, showcasing the flexibility of dplyr
in augmenting your dataset.
Conditional Case Operations
Handling conditional cases is a common task in data manipulation.
Let’s explore this using dplyr and the
case_when function.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species Size
## 1 5.1 3.5 1.4 0.2 setosa Large
## 2 4.9 3.0 1.4 0.2 setosa Small
## 3 4.7 3.2 1.3 0.2 setosa Small
## 4 4.6 3.1 1.5 0.2 setosa Small
## 5 5.0 3.6 1.4 0.2 setosa Small
## 6 5.4 3.9 1.7 0.4 setosa Large
## 7 4.6 3.4 1.4 0.3 setosa Small
## 8 5.0 3.4 1.5 0.2 setosa Small
## 9 4.4 2.9 1.4 0.2 setosa Small
## 10 4.9 3.1 1.5 0.1 setosa SmallThis aligns with the broader theme of **[Statistics](https://www.data03.online/2023/06/Statistics-A-Guide-from-Basics-to-M
achine-Learning.html)**, showcasing how conditional operations can be applied to enhance the interpretability of your data.
Scaling Numeric Columns
Scaling numeric columns is a common preprocessing step in data
analysis. Let’s explore this using dplyr and the
scale function.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 -0.8976739 3.5 -1.335752 0.2 setosa
## 2 -1.1392005 3.0 -1.335752 0.2 setosa
## 3 -1.3807271 3.2 -1.392399 0.2 setosa
## 4 -1.5014904 3.1 -1.279104 0.2 setosa
## 5 -1.0184372 3.6 -1.335752 0.2 setosa
## 6 -0.5353840 3.9 -1.165809 0.4 setosa
## 7 -1.5014904 3.4 -1.335752 0.3 setosa
## 8 -1.0184372 3.4 -1.279104 0.2 setosa
## 9 -1.7430170 2.9 -1.335752 0.2 setosa
## 10 -1.1392005 3.1 -1.279104 0.1 setosaThis operation aligns with the broader theme of RStudio,
showcasing how dplyr seamlessly integrates with other R
tools for comprehensive data analysis.
Conditional Case Operations with if_else
Another approach to handle conditional cases is using
if_else in dplyr. Let’s explore this
functionality.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species Size
## 1 5.1 3.5 1.4 0.2 setosa Big
## 2 4.9 3.0 1.4 0.2 setosa Small
## 3 4.7 3.2 1.3 0.2 setosa Small
## 4 4.6 3.1 1.5 0.2 setosa Small
## 5 5.0 3.6 1.4 0.2 setosa Small
## 6 5.4 3.9 1.7 0.4 setosa Big
## 7 4.6 3.4 1.4 0.3 setosa Small
## 8 5.0 3.4 1.5 0.2 setosa Small
## 9 4.4 2.9 1.4 0.2 setosa Small
## 10 4.9 3.1 1.5 0.1 setosa SmallThis aligns with the broader theme of Setwd, showcasing how conditional operations can be applied to enhance the interpretability of your data.
Selecting Numeric Columns and Filtering Rows
Selecting numeric columns and filtering rows based on specific
conditions is a common task. Let’s explore this using
dplyr.
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 7.0 3.2 4.7 1.4
## 2 6.4 3.2 4.5 1.5
## 3 6.9 3.1 4.9 1.5
## 4 5.5 2.3 4.0 1.3
## 5 6.5 2.8 4.6 1.5
## 6 5.7 2.8 4.5 1.3
## 7 6.3 3.3 4.7 1.6
## 8 6.6 2.9 4.6 1.3
## 9 5.2 2.7 3.9 1.4
## 10 5.9 3.0 4.2 1.5This aligns with the broader theme of Word
count, showcasing how dplyr enables the
selection and manipulation of specific types of variables.
Applying Functions to Numeric Columns
Applying functions to numeric columns is a common practice in data
analysis. Let’s explore this using dplyr and the
mutate_all function.
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 1.629241 1.252763 0.3364722 -1.6094379
## 2 1.589235 1.098612 0.3364722 -1.6094379
## 3 1.547563 1.163151 0.2623643 -1.6094379
## 4 1.526056 1.131402 0.4054651 -1.6094379
## 5 1.609438 1.280934 0.3364722 -1.6094379
## 6 1.686399 1.360977 0.5306283 -0.9162907
## 7 1.526056 1.223775 0.3364722 -1.2039728
## 8 1.609438 1.223775 0.4054651 -1.6094379
## 9 1.481605 1.064711 0.3364722 -1.6094379
## 10 1.589235 1.131402 0.4054651 -2.3025851This operation aligns with the broader theme of R
timing, showcasing how dplyr supports
efficient data transformations.
Arranging All Columns in Descending Order
Arranging all columns in descending order is a useful operation for
visual inspection. Let’s explore this using dplyr.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 7.9 3.8 6.4 2.0 virginica
## 2 7.7 3.8 6.7 2.2 virginica
## 3 7.7 3.0 6.1 2.3 virginica
## 4 7.7 2.8 6.7 2.0 virginica
## 5 7.7 2.6 6.9 2.3 virginica
## 6 7.6 3.0 6.6 2.1 virginica
## 7 7.4 2.8 6.1 1.9 virginica
## 8 7.3 2.9 6.3 1.8 virginica
## 9 7.2 3.6 6.1 2.5 virginica
## 10 7.2 3.2 6.0 1.8 virginicaThis aligns with the broader theme of Data
Analysis: Concepts, Techniques, & Real-World Insights,
showcasing how dplyr enables comprehensive data
exploration.
Summarizing Numeric Columns
Summarizing numeric columns is a common exploratory step in data
analysis. Let’s explore this using dplyr and the
summarize_all function.
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.843333 3.057333 3.758 1.199333This aligns with the broader theme of Unlock
the Power of Data: Your Beginner’s Guide to Statistics,
showcasing how dplyr supports efficient data
exploration.
Grouping by Multiple Variables
Grouping data by multiple variables is a powerful feature of
dplyr. Let’s explore this using the group_by
and summarize functions.
## # A tibble: 48 × 3
## # Groups: Species [3]
## Species Petal.Length count
## <fct> <dbl> <int>
## 1 setosa 1 1
## 2 setosa 1.1 1
## 3 setosa 1.2 2
## 4 setosa 1.3 7
## 5 setosa 1.4 13
## 6 setosa 1.5 13
## 7 setosa 1.6 7
## 8 setosa 1.7 4
## 9 setosa 1.9 2
## 10 versicolor 3 1
## # ℹ 38 more rowsThis aligns with the broader theme of Descriptive
Analysis, showcasing how dplyr facilitates
complex grouping operations.
Extracting Distinct Values Across All Columns
Extracting distinct values across all columns is a useful operation
in data analysis. Let’s explore this using dplyr and the
distinct_all function.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosaThis aligns with the broader theme of RStudio
Documentation: Your Essential Guide to Descriptive
Statistics, showcasing how dplyr enhances the
exploration of dataset characteristics.
Counting Total Number of Rows
Counting the total number of rows is a fundamental operation. Let’s
explore this using dplyr and the count
function.
## n
## 1 150This aligns with the broader theme of Descriptive
Statistics, showcasing how dplyr simplifies
basic counting operations.
Renaming All Columns with a Prefix
Renaming columns is often necessary for clarity. Let’s explore this
using dplyr and the rename_all function.
## New_Sepal.Length New_Sepal.Width New_Petal.Length New_Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
## 7 4.6 3.4 1.4 0.3
## 8 5.0 3.4 1.5 0.2
## 9 4.4 2.9 1.4 0.2
## 10 4.9 3.1 1.5 0.1
## 11 5.4 3.7 1.5 0.2
## 12 4.8 3.4 1.6 0.2
## 13 4.8 3.0 1.4 0.1
## 14 4.3 3.0 1.1 0.1
## 15 5.8 4.0 1.2 0.2
## 16 5.7 4.4 1.5 0.4
## 17 5.4 3.9 1.3 0.4
## 18 5.1 3.5 1.4 0.3
## 19 5.7 3.8 1.7 0.3
## 20 5.1 3.8 1.5 0.3
## 21 5.4 3.4 1.7 0.2
## 22 5.1 3.7 1.5 0.4
## 23 4.6 3.6 1.0 0.2
## 24 5.1 3.3 1.7 0.5
## 25 4.8 3.4 1.9 0.2
## 26 5.0 3.0 1.6 0.2
## 27 5.0 3.4 1.6 0.4
## 28 5.2 3.5 1.5 0.2
## 29 5.2 3.4 1.4 0.2
## 30 4.7 3.2 1.6 0.2
## 31 4.8 3.1 1.6 0.2
## 32 5.4 3.4 1.5 0.4
## 33 5.2 4.1 1.5 0.1
## 34 5.5 4.2 1.4 0.2
## 35 4.9 3.1 1.5 0.2
## 36 5.0 3.2 1.2 0.2
## 37 5.5 3.5 1.3 0.2
## 38 4.9 3.6 1.4 0.1
## 39 4.4 3.0 1.3 0.2
## 40 5.1 3.4 1.5 0.2
## 41 5.0 3.5 1.3 0.3
## 42 4.5 2.3 1.3 0.3
## 43 4.4 3.2 1.3 0.2
## 44 5.0 3.5 1.6 0.6
## 45 5.1 3.8 1.9 0.4
## 46 4.8 3.0 1.4 0.3
## 47 5.1 3.8 1.6 0.2
## 48 4.6 3.2 1.4 0.2
## 49 5.3 3.7 1.5 0.2
## 50 5.0 3.3 1.4 0.2
## 51 7.0 3.2 4.7 1.4
## 52 6.4 3.2 4.5 1.5
## 53 6.9 3.1 4.9 1.5
## 54 5.5 2.3 4.0 1.3
## 55 6.5 2.8 4.6 1.5
## 56 5.7 2.8 4.5 1.3
## 57 6.3 3.3 4.7 1.6
## 58 4.9 2.4 3.3 1.0
## 59 6.6 2.9 4.6 1.3
## 60 5.2 2.7 3.9 1.4
## 61 5.0 2.0 3.5 1.0
## 62 5.9 3.0 4.2 1.5
## 63 6.0 2.2 4.0 1.0
## 64 6.1 2.9 4.7 1.4
## 65 5.6 2.9 3.6 1.3
## 66 6.7 3.1 4.4 1.4
## 67 5.6 3.0 4.5 1.5
## 68 5.8 2.7 4.1 1.0
## 69 6.2 2.2 4.5 1.5
## 70 5.6 2.5 3.9 1.1
## 71 5.9 3.2 4.8 1.8
## 72 6.1 2.8 4.0 1.3
## 73 6.3 2.5 4.9 1.5
## 74 6.1 2.8 4.7 1.2
## 75 6.4 2.9 4.3 1.3
## 76 6.6 3.0 4.4 1.4
## 77 6.8 2.8 4.8 1.4
## 78 6.7 3.0 5.0 1.7
## 79 6.0 2.9 4.5 1.5
## 80 5.7 2.6 3.5 1.0
## 81 5.5 2.4 3.8 1.1
## 82 5.5 2.4 3.7 1.0
## 83 5.8 2.7 3.9 1.2
## 84 6.0 2.7 5.1 1.6
## 85 5.4 3.0 4.5 1.5
## 86 6.0 3.4 4.5 1.6
## 87 6.7 3.1 4.7 1.5
## 88 6.3 2.3 4.4 1.3
## 89 5.6 3.0 4.1 1.3
## 90 5.5 2.5 4.0 1.3
## 91 5.5 2.6 4.4 1.2
## 92 6.1 3.0 4.6 1.4
## 93 5.8 2.6 4.0 1.2
## 94 5.0 2.3 3.3 1.0
## 95 5.6 2.7 4.2 1.3
## 96 5.7 3.0 4.2 1.2
## 97 5.7 2.9 4.2 1.3
## 98 6.2 2.9 4.3 1.3
## 99 5.1 2.5 3.0 1.1
## 100 5.7 2.8 4.1 1.3
## 101 6.3 3.3 6.0 2.5
## 102 5.8 2.7 5.1 1.9
## 103 7.1 3.0 5.9 2.1
## 104 6.3 2.9 5.6 1.8
## 105 6.5 3.0 5.8 2.2
## 106 7.6 3.0 6.6 2.1
## 107 4.9 2.5 4.5 1.7
## 108 7.3 2.9 6.3 1.8
## 109 6.7 2.5 5.8 1.8
## 110 7.2 3.6 6.1 2.5
## 111 6.5 3.2 5.1 2.0
## 112 6.4 2.7 5.3 1.9
## 113 6.8 3.0 5.5 2.1
## 114 5.7 2.5 5.0 2.0
## 115 5.8 2.8 5.1 2.4
## 116 6.4 3.2 5.3 2.3
## 117 6.5 3.0 5.5 1.8
## 118 7.7 3.8 6.7 2.2
## 119 7.7 2.6 6.9 2.3
## 120 6.0 2.2 5.0 1.5
## 121 6.9 3.2 5.7 2.3
## 122 5.6 2.8 4.9 2.0
## 123 7.7 2.8 6.7 2.0
## 124 6.3 2.7 4.9 1.8
## 125 6.7 3.3 5.7 2.1
## 126 7.2 3.2 6.0 1.8
## 127 6.2 2.8 4.8 1.8
## 128 6.1 3.0 4.9 1.8
## 129 6.4 2.8 5.6 2.1
## 130 7.2 3.0 5.8 1.6
## 131 7.4 2.8 6.1 1.9
## 132 7.9 3.8 6.4 2.0
## 133 6.4 2.8 5.6 2.2
## 134 6.3 2.8 5.1 1.5
## 135 6.1 2.6 5.6 1.4
## 136 7.7 3.0 6.1 2.3
## 137 6.3 3.4 5.6 2.4
## 138 6.4 3.1 5.5 1.8
## 139 6.0 3.0 4.8 1.8
## 140 6.9 3.1 5.4 2.1
## 141 6.7 3.1 5.6 2.4
## 142 6.9 3.1 5.1 2.3
## 143 5.8 2.7 5.1 1.9
## 144 6.8 3.2 5.9 2.3
## 145 6.7 3.3 5.7 2.5
## 146 6.7 3.0 5.2 2.3
## 147 6.3 2.5 5.0 1.9
## 148 6.5 3.0 5.2 2.0
## 149 6.2 3.4 5.4 2.3
## 150 5.9 3.0 5.1 1.8
## New_Species
## 1 setosa
## 2 setosa
## 3 setosa
## 4 setosa
## 5 setosa
## 6 setosa
## 7 setosa
## 8 setosa
## 9 setosa
## 10 setosa
## 11 setosa
## 12 setosa
## 13 setosa
## 14 setosa
## 15 setosa
## 16 setosa
## 17 setosa
## 18 setosa
## 19 setosa
## 20 setosa
## 21 setosa
## 22 setosa
## 23 setosa
## 24 setosa
## 25 setosa
## 26 setosa
## 27 setosa
## 28 setosa
## 29 setosa
## 30 setosa
## 31 setosa
## 32 setosa
## 33 setosa
## 34 setosa
## 35 setosa
## 36 setosa
## 37 setosa
## 38 setosa
## 39 setosa
## 40 setosa
## 41 setosa
## 42 setosa
## 43 setosa
## 44 setosa
## 45 setosa
## 46 setosa
## 47 setosa
## 48 setosa
## 49 setosa
## 50 setosa
## 51 versicolor
## 52 versicolor
## 53 versicolor
## 54 versicolor
## 55 versicolor
## 56 versicolor
## 57 versicolor
## 58 versicolor
## 59 versicolor
## 60 versicolor
## 61 versicolor
## 62 versicolor
## 63 versicolor
## 64 versicolor
## 65 versicolor
## 66 versicolor
## 67 versicolor
## 68 versicolor
## 69 versicolor
## 70 versicolor
## 71 versicolor
## 72 versicolor
## 73 versicolor
## 74 versicolor
## 75 versicolor
## 76 versicolor
## 77 versicolor
## 78 versicolor
## 79 versicolor
## 80 versicolor
## 81 versicolor
## 82 versicolor
## 83 versicolor
## 84 versicolor
## 85 versicolor
## 86 versicolor
## 87 versicolor
## 88 versicolor
## 89 versicolor
## 90 versicolor
## 91 versicolor
## 92 versicolor
## 93 versicolor
## 94 versicolor
## 95 versicolor
## 96 versicolor
## 97 versicolor
## 98 versicolor
## 99 versicolor
## 100 versicolor
## 101 virginica
## 102 virginica
## 103 virginica
## 104 virginica
## 105 virginica
## 106 virginica
## 107 virginica
## 108 virginica
## 109 virginica
## 110 virginica
## 111 virginica
## 112 virginica
## 113 virginica
## 114 virginica
## 115 virginica
## 116 virginica
## 117 virginica
## 118 virginica
## 119 virginica
## 120 virginica
## 121 virginica
## 122 virginica
## 123 virginica
## 124 virginica
## 125 virginica
## 126 virginica
## 127 virginica
## 128 virginica
## 129 virginica
## 130 virginica
## 131 virginica
## 132 virginica
## 133 virginica
## 134 virginica
## 135 virginica
## 136 virginica
## 137 virginica
## 138 virginica
## 139 virginica
## 140 virginica
## 141 virginica
## 142 virginica
## 143 virginica
## 144 virginica
## 145 virginica
## 146 virginica
## 147 virginica
## 148 virginica
## 149 virginica
## 150 virginicaThis aligns with the broader theme of Statistics,
showcasing how dplyr supports efficient data
manipulation.
Extracting Specific Rows
Extracting specific rows from a dataset is a common operation. Let’s
explore this using dplyr and the slice
function.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosaThis aligns with the broader theme of Data
Analysis: Concepts, Techniques, & Real-World Insights,
showcasing how dplyr enables specific row extractions.
Randomly Sampling Rows
Randomly sampling rows is useful for creating representative subsets.
Let’s explore this using dplyr and the
sample_n function.
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.7 1.5 0.4 setosa
## 2 4.4 2.9 1.4 0.2 setosa
## 3 5.2 3.5 1.5 0.2 setosa
## 4 4.6 3.4 1.4 0.3 setosa
## 5 5.2 3.4 1.4 0.2 setosaThis aligns with the broader theme of dplyr Conclusion In conclusion, dplyr emerges as a versatile and efficient package for data manipulation in R. Whether you are a beginner or an experienced data analyst, mastering dplyr opens up a world of possibilities for handling and transforming datasets with ease.
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