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Questions and Answers
The primary goal of Exploratory Data Analysis (EDA) is to confirm the accuracy of collected data.
False
Data frames are the fundamental data structure used in R for organizing observations where each column represents a variable.
True
The dplyr package was developed by Hadley Wickham and is optimized for handling lists of objects in R.
False
The filter() function in dplyr is used to select columns from a data frame.
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The mutate() function is useful for creating new variables in a data frame or transforming existing ones.
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To exclude specific variables from a data frame, the select() function can be used with a negative sign in dplyr.
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The %>% operator is commonly used in dplyr to chain together multiple functions in a sequence.
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group_by() and summarize() are functions in dplyr that are often used together for calculating summary statistics across specific groups.
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EDA primarily focuses on testing hypotheses and confirming expected relationships in data.
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An iterative cycle in EDA involves generating questions, visualizing data, and refining questions based on insights gained.
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Study Notes
Exploratory Data Analysis (EDA)
- The primary goal of EDA is to understand and explore data, rather than confirming accuracy.
- EDA involves generating questions, visualizing data, and refining questions based on insights gained.
- Data frames are the fundamental data structure in R for organizing data.
- Each column in a data frame represents a variable.
dplyr Package
- Developed by Hadley Wickham, dplyr is optimized for handling data frames.
- filter() selects rows based on conditions.
- mutate() creates new variables or transforms existing ones.
- select() is used to choose specific columns. Adding a negative sign before a variable name excludes it.
- The %>% operator (pipe) chains functions together sequentially.
- group_by() and summarize() are used together to calculate summary statistics across groups.
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Description
This quiz covers the fundamental concepts of Exploratory Data Analysis (EDA) and emphasizes its importance in verifying the accuracy of data collected. Test your knowledge on EDA techniques and their applications in data science.
