Data Cleansing Using R MCQs Solution | TCS Fresco Play | Fresco Play

Data Cleansing Using R MCQs Solution | TCS Fresco Play | Fresco Play

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1. Ignoring missing values from your dataset is an easier and correct approach than updating the dataset with mean / median values
May be correct...

2. Data munging is
A Process to clean messy data

3. Can a technically correct dataset still be incorrect for data analysis?

4. Binning is a method to manage data
noisy data

5. Data cleaning is the most time consuming process in data analysis

6. tail() function shows ___ by default
6 rows

7. print() is the recommended function to view the dataset

8. ____ can be used to view data distribution of a single variable AND ____ can be used to view relation between 2 variables

9. Consider cars built-in R dataset and find out what is the median of dist variable

10. Using head function, identify the 8th row of mtcars built-in dataset
10 26

11. Identify the function which is part of dplyr package that helps in previewing the data.

12. In a tidy data set ___ forms a row and ____ forms a column

13. A dataset with columns (country, disease, #ofdeaths) has values Row1 - (CONGO, TB, 28) Row2 - (SPAIN, TB, 2) Row3 - (EGYPT, TB, 0). Is this is a tidy or messy dataset.?
Tidy Data

14. filter() is for selecting columns and select() is for selecting rows

15. ___ allows to make new variables

16. Which function(s) of dplyr would you use to first subset the columns and then sort them on a particular column?

17. What is the class of and sys.time()

18. Can a variable of factor type be converted to a date type

19. If value of time is system time which is 2016-12-21 18:33:31 UTC. What is the output for time+60

20. What are the possible outlier treatment
all the options

21. Identify the correct ones
separate() makes

22. ____ is similar to separate() function

23. Which one is NOT a special value in R
None of the options

24. ____ can be used to identify the existence of a matching pattern in a string


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