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<li class="chapter" data-level="3" data-path="character-vector.html"><a href="character-vector.html"><i class="fa fa-check"></i><b>3</b> Character vector</a></li>
<li class="chapter" data-level="4" data-path="data-management-in-r.html"><a href="data-management-in-r.html"><i class="fa fa-check"></i><b>4</b> Data Management in R</a></li>
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<li class="chapter" data-level="5.4" data-path="connecting-to-and-accessing-a-postgresql-database.html"><a href="connecting-to-and-accessing-a-postgresql-database.html#querying-data-from-postgresql"><i class="fa fa-check"></i><b>5.4</b> Querying Data from PostgreSQL</a>
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<li class="chapter" data-level="5.5" data-path="connecting-to-and-accessing-a-postgresql-database.html"><a href="connecting-to-and-accessing-a-postgresql-database.html#handling-errors-and-troubleshooting"><i class="fa fa-check"></i><b>5.5</b> Handling Errors and Troubleshooting</a></li>
<li class="chapter" data-level="5.6" data-path="connecting-to-and-accessing-a-postgresql-database.html"><a href="connecting-to-and-accessing-a-postgresql-database.html#conclusion-1"><i class="fa fa-check"></i><b>5.6</b> Conclusion</a></li>
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<li class="chapter" data-level="6" data-path="descriptive-statistics-and-visualisations.html"><a href="descriptive-statistics-and-visualisations.html"><i class="fa fa-check"></i><b>6</b> Descriptive Statistics and Visualisations</a>
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<li class="chapter" data-level="6.1" data-path="descriptive-statistics-and-visualisations.html"><a href="descriptive-statistics-and-visualisations.html#introduction-to-descriptive-statistics"><i class="fa fa-check"></i><b>6.1</b> Introduction to Descriptive Statistics</a>
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<li class="chapter" data-level="6.1.1" data-path="descriptive-statistics-and-visualisations.html"><a href="descriptive-statistics-and-visualisations.html#understanding-descriptive-statistics"><i class="fa fa-check"></i><b>6.1.1</b> Understanding Descriptive Statistics</a></li>
<li class="chapter" data-level="6.1.2" data-path="descriptive-statistics-and-visualisations.html"><a href="descriptive-statistics-and-visualisations.html#basic-descriptive-statistics-in-r"><i class="fa fa-check"></i><b>6.1.2</b> Basic Descriptive Statistics in R</a></li>
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<li class="chapter" data-level="6.2" data-path="descriptive-statistics-and-visualisations.html"><a href="descriptive-statistics-and-visualisations.html#creating-visualisations-with-ggplot2"><i class="fa fa-check"></i><b>6.2</b> Creating Visualisations with ggplot2</a>
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<li class="chapter" data-level="6.2.1" data-path="descriptive-statistics-and-visualisations.html"><a href="descriptive-statistics-and-visualisations.html#introduction-to-ggplot2"><i class="fa fa-check"></i><b>6.2.1</b> Introduction to ggplot2</a></li>
<li class="chapter" data-level="6.2.2" data-path="descriptive-statistics-and-visualisations.html"><a href="descriptive-statistics-and-visualisations.html#creating-basic-plots"><i class="fa fa-check"></i><b>6.2.2</b> Creating Basic Plots</a></li>
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<li class="chapter" data-level="7" data-path="survey-analysis-in-r.html"><a href="survey-analysis-in-r.html"><i class="fa fa-check"></i><b>7</b> Survey Analysis in R</a>
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<li class="chapter" data-level="7.1" data-path="survey-analysis-in-r.html"><a href="survey-analysis-in-r.html#introduction-to-survey-data"><i class="fa fa-check"></i><b>7.1</b> Introduction to Survey Data</a>
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<li class="chapter" data-level="7.1.2" data-path="survey-analysis-in-r.html"><a href="survey-analysis-in-r.html#required-packages"><i class="fa fa-check"></i><b>7.1.2</b> Required Packages</a></li>
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<li class="chapter" data-level="7.2" data-path="survey-analysis-in-r.html"><a href="survey-analysis-in-r.html#importing-and-preparing-survey-data"><i class="fa fa-check"></i><b>7.2</b> Importing and Preparing Survey Data</a>
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<li class="chapter" data-level="7.2.1" data-path="survey-analysis-in-r.html"><a href="survey-analysis-in-r.html#importing-spss-data"><i class="fa fa-check"></i><b>7.2.1</b> Importing SPSS Data</a></li>
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<li class="chapter" data-level="7.3" data-path="survey-analysis-in-r.html"><a href="survey-analysis-in-r.html#descriptive-analysis-of-survey-data"><i class="fa fa-check"></i><b>7.3</b> Descriptive Analysis of Survey Data</a>
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<li class="chapter" data-level="7.3.1" data-path="survey-analysis-in-r.html"><a href="survey-analysis-in-r.html#calculating-weighted-means-and-totals"><i class="fa fa-check"></i><b>7.3.1</b> Calculating Weighted Means and Totals</a></li>
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<li class="chapter" data-level="7.4" data-path="survey-analysis-in-r.html"><a href="survey-analysis-in-r.html#working-with-the-srvyr-package"><i class="fa fa-check"></i><b>7.4</b> Working with the srvyr Package</a>
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<h1><span class="header-section-number">Chapter 3</span> Character vector<a href="character-vector.html#character-vector" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p>names &lt;- c(“Alice”, “Bob”, “Charlie”)
Data Frames: Two-dimensional tables where each column can be of a different type. Data frames are similar to SPSS datasets.</p>
<p>r
Copy code
# Creating a data frame
df &lt;- data.frame(Name = c(“Alice”, “Bob”), Age = c(25, 30))
Lists: Collections of objects that can be of different types, including other lists.</p>
<p>r
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# Creating a list
my_list &lt;- list(Name = “Alice”, Age = 25, Scores = c(90, 85, 88))
Factors: Used for categorical data. They store both the values and the corresponding levels.</p>
<p>r
Copy code
# Creating a factor
gender &lt;- factor(c(“Male”, “Female”, “Female”, “Male”))
(Add screenshot here showing examples of each data type and structure in R)</p>
<p>2.3.2 Comparing R Data Types to SPSS Data Types
In SPSS:</p>
<p>Variables can be numeric, string, or categorical.
Data Structures include datasets, which are similar to R data frames.
In R:</p>
<p>Vectors are like SPSS variables.
Data Frames are akin to SPSS datasets, with columns of different types.
Lists provide more flexibility compared to SPSS’s data structures.
Factors are used for categorical data, similar to SPSS’s categorical variables.
(Add screenshot here comparing SPSS data types and structures to their R equivalents)</p>
<p>2.4 Basic Operations and Functions in R
2.4.1 Arithmetic Operations
R performs basic arithmetic operations similarly to SPSS. Examples include addition, subtraction, multiplication, and division.</p>
<p>r
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# Arithmetic operations
addition &lt;- 5 + 3
subtraction &lt;- 10 - 4
multiplication &lt;- 7 * 2
division &lt;- 8 / 2
exponentiation &lt;- 2^3
Exercise: Execute these operations in R and compare with results from SPSS’s Compute function.
(Add screenshot here showing arithmetic operations in R and their SPSS equivalents)</p>
<p>2.4.2 Logical Operations
Logical operations include comparisons like equality and inequality, as well as logical operators such as AND and OR.</p>
<p>r
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# Logical operations
equal &lt;- (5 == 5) # TRUE
not_equal &lt;- (5 != 3) # TRUE
greater_than &lt;- (5 &gt; 3) # TRUE
and_operation &lt;- (5 &gt; 3 &amp; 4 &lt; 6) # TRUE
or_operation &lt;- (5 &gt; 6 | 4 &lt; 6) # TRUE
Exercise: Test these logical operations in R and compare them to SPSS’s logical operators.
(Add screenshot here showing logical operations in R and their SPSS equivalents)</p>
<p>2.4.3 Basic Functions
R includes many built-in functions for statistical calculations, data manipulation, and more. Common functions include mean(), sd(), sum(), and length().</p>
<p>r
Copy code
# Basic functions
data &lt;- c(1, 2, 3, 4, 5)
mean_value &lt;- mean(data)
sd_value &lt;- sd(data)
total_sum &lt;- sum(data)
data_length &lt;- length(data)
Exercise: Use these functions to perform basic calculations in R and compare with similar functions in SPSS.
(Add screenshot here showing the use of basic functions in R and their SPSS equivalents)</p>
<p>2.5 Conclusion
In this chapter, you’ve learned about the RStudio interface, how to import data from SPSS, coding conventions, and the basics of R data types, structures, and operations. With this foundation, you’re prepared to start working with data in R and leveraging its powerful capabilities for analysis. In the next chapter, we’ll delve into more advanced data manipulation techniques to further enhance your skills.</p>
<p>(Add a final screenshot or image to reinforce the concepts covered, such as a completed script with various operations in R)</p>

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