60 Commonly Used Words In R Programming Language

R is a popular programming language widely used for data analysis, statistical computing, and graphical representation. It has a comprehensive environment for data manipulation, calculation, and graphical display. This article explores 100 commonly used words in R programming, explaining each term and its significance, along with clickable links for deeper exploration. These terms will help both beginners and experienced users navigate the world of R programming more effectively.

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60 Commonly Used Words in R Programming

1. R Console
   Explanation: The interface where users write and execute R commands. Learn more.
2. RStudio
   Explanation: A popular integrated development environment (IDE) for R. Learn more.
3. Vector
   Explanation: A basic data structure in R that holds elements of the same type. Learn more.
4. List
   Explanation: A data structure in R that can hold elements of different types, including vectors and other lists. Learn more.
5. Data Frame
   Explanation: A table-like structure used for storing data, with rows and columns in R. Learn more.
6. Matrix
   Explanation: A two-dimensional array where each element has the same type. Learn more.
7. Factor
   Explanation: A data type in R used to store categorical data. Learn more.
8. Function
   Explanation: A reusable block of code in R that performs a specific task. Learn more.
9. Library
   Explanation: A collection of functions and datasets in R. Learn more.
10. Package
    Explanation: A collection of R functions, compiled code, and sample data. Learn more.
11. Tidyverse
    Explanation: A collection of R packages for data science tasks. Learn more.
12. ggplot2
    Explanation: A data visualization package in R. Learn more.
13. dplyr
    Explanation: A package used for data manipulation in R. Learn more.
14. read.csv()
    Explanation: A function to import CSV files into R. Learn more.
15. str()
    Explanation: A function used to display the structure of an R object. Learn more.
16. summary()
    Explanation: A function to provide summary statistics of an R object. Learn more.
17. plot()
    Explanation: A basic plotting function in R for visualizing data. Learn more.
18. hist()
    Explanation: A function to create a histogram of data in R. Learn more.
19. lm()
    Explanation: A function for fitting linear models in R. Learn more.
20. predict()
    Explanation: A function to make predictions based on models in R. Learn more.
21. mean()
    Explanation: A function to calculate the average of a numerical dataset. Learn more.
22. median()
    Explanation: A function to calculate the median of a numerical dataset. Learn more.
23. sd()
    Explanation: A function to compute the standard deviation of a dataset. Learn more.
24. var()
    Explanation: A function to compute the variance of a dataset. Learn more.
25. quantile()
    Explanation: A function to compute quantiles of a dataset. Learn more.
26. apply()
    Explanation: A function to apply a function over the margins of an array. Learn more.
27. lapply()
    Explanation: A function to apply a function over a list or vector. Learn more.
28. sapply()
    Explanation: A function similar to lapply(), but simplifies the result. Learn more.
29. tapply()
    Explanation: A function that applies a function over subsets of a vector. Learn more.
30. by()
    Explanation: A function to apply a function to data grouped by factors. Learn more.
31. ifelse()
    Explanation: A function to perform vectorized conditional operations. Learn more.
32. for loop
    Explanation: A loop that repeats a block of code a set number of times. Learn more.
33. while loop
    Explanation: A loop that repeats code while a condition is true. Learn more.
34. break
    Explanation: A command that exits a loop. Learn more.
35. next
    Explanation: A command to skip the current iteration of a loop. Learn more.
36. return()
    Explanation: A function to exit a function and return a value. Learn more.
37. subset()
    Explanation: A function to filter data based on conditions. Learn more.
38. merge()
    Explanation: A function to combine datasets based on common columns. Learn more.
39. cbind()
    Explanation: A function to combine objects by columns. Learn more.
40. rbind()
    Explanation: A function to combine objects by rows. Learn more.
41. dim()
    Explanation: A function to get or set the dimensions of an object. Learn more.
42. head()
    Explanation: A function to display the first few rows of a dataset. Learn more.
43. tail()
    Explanation: A function to display the last few rows of a dataset. Learn more.
44. which()
    Explanation: A function to return indices of elements that are true. Learn more.
45. is.na()
    Explanation: A function to check for missing values in R. Learn more.
46. na.omit()
    Explanation: A function to remove rows with missing values. Learn more.
47. unique()
    Explanation: A function to return unique values from a dataset. Learn more.
48. sort()
    Explanation: A function to sort data in ascending or descending order. Learn more.
49. order()
    Explanation: A function to return the order of elements in a dataset. Learn more.
50. rank()
    Explanation: A function to compute the ranks of values. Learn more.
51. t()
    Explanation: A function to transpose a matrix or data frame. Learn more.
52. rep()
    Explanation: A function to replicate values in a vector. Learn more.
53. seq()
    Explanation: A function to generate sequences of numbers. Learn more.
54. sample()
    Explanation: A function to generate random samples from a dataset. Learn more.
55. set.seed()
    Explanation: A function to set the seed for random number generation. Learn more.
56. par()
    Explanation: A function to set or query graphical parameters in R. Learn more.
57. dev.off()
    Explanation: A function to close a graphical device. Learn more.
58. boxplot()
    Explanation: A function to create a boxplot in R. Learn more.
59. barplot()
    Explanation: A function to create a barplot in R. Learn more.
60. legend()
    Explanation: A function to add a legend to a plot in R. Learn more.

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Conclusion

Understanding these 60 commonly used terms in R programming will help you develop a deeper understanding of the language and its functionalities. Whether you’re performing data analysis, statistical computations, or graphical visualizations, these words will guide you in mastering R. Explore the provided links for detailed insights and further learning.

References

• Wikipedia – R Programming Language
• RDocumentation for specific functions and terms