10 Essential R Commands for Data Analysis - You Need to Know

Unlock the potential of R for data analysis: dplyr, ggplot2, lm(), summary(), and key commands for robust data insights. Level up your R skills!

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15. Dec 2023
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10 Essential R Commands for Data Analysis - You Need to Know















R is one of the most popular choices among the computer languages used for data analysis and statistical computing. Whether you're new to R or want to improve your data analysis skills, it's critical to understand the basic commands. Here are 10 essential commands that are necessary to do reliable and meaningful data analysis in the R environment.

1. read.csv()

One important function in R for quickly importing tabular data from CSV files—a common format for datasets—is read.csv(). This function imports data into R in an efficient manner, making analysis inside the environment easier.

2. dplyr Package

The R package dplyr has a flexible set of functions designed for efficient data manipulation. Select() and filter() are two commands that help with specific column selection and focused row filtering, respectively. Furthermore, mutate() allows users to easily define new variables, which together improve and streamline the complex data wrangling process.

3. ggplot2 Package

As a powerful package for data visualisation in R, ggplot2 is the best. Its ggplot() tool is essential for creating a variety of plots, and geom_bar() makes it easier to create visually striking bar charts. When combined, these commands provide users the ability to produce engaging and educational visual representations of their data.

4. summary()

One useful tool for producing summary statistics on numerical data is R's summary() function. It provides vital statistics like mean, median, and quartiles, illuminating the distribution of data and highlighting any missing values. In short, it offers a thorough overview that is critical for preliminary data analysis and comprehension.

5. lm() - Linear Regression

An essential tool for performing linear regression analysis in R is the lm() function. It allows for in-depth investigation and analysis of the connections between variables, offering important insights into their interdependencies within datasets, by fitting a linear model to the data.

6. subset()

The subset() function in R is an essential tool for extracting data according to predefined conditions. It makes it easier to filter out particular rows or columns from a dataset, which makes focused analysis and efficient data processing possible.

7. table()

R's table() function, which creates frequency tables, is essential to data analysis. It provides important insights into the distribution and occurrences within the dataset's categorical variables by concisely showcasing the counts of unique categorical variable values.

8. merge()

The merge() function in R is a valuable tool for integrating datasets based on common columns. This feature makes it easier to integrate different datasets and enables thorough analysis by merging pertinent data from many sources in R settings.

9. aggregate()

R's aggregate() function is a useful tool for applying functions to subsets of data, allowing for thorough summary according to predefined groupings or categories. This feature makes it easier to derive informative summaries for particular data segments or categories.

10. ifelse() Function

The ifelse() function in R uses conditional logic to allocate values based on predefined criteria. This adaptability enhances the flexibility of data manipulation in R by effectively recoding values depending on user-defined criteria, which is useful for creating new variables or changing current data.

Proficiency in these R commands empowers data analysts to adeptly navigate data tasks—exploring, cleansing, analyzing, and visualizing information. These skills are essential for providing thorough insights, encouraging knowledgeable decision-making across many areas and sectors, and successfully optimising plans and results.

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