10 Question Quiz on Data Cleaning in R

Quiz 12

Here is a 10 question quiz on data cleaning in R:

Test Your Data Cleaning Skills in R!

This quiz will cover:

- Identifying missing data
- Handling outliers
- Fixing inconsistencies
- Tidying and reshaping
- Checking data integrity

See if you can:

- Find missing values and impute them
- Detect and address outliers
- Fix formatting errors and typos
- Tidy messy data into a clean form
- Validate data quality checks

Mastering these skills will take your R data wrangling to the next level!

1 / 10

. What is the best way to find missing values in an R dataframe?

which(is_empty(df))

identify_na(df)

is.null(df)

is.na() or sum(is.na(df))

2 / 10

How could you impute missing numeric values with the median in R?

impute(df, median)

fill_missing(df, median)

df[is.na(df)] <- median(df, na.rm=TRUE)

replace(df, median)

3 / 10

What is a good way to detect and handle outliers in R?

Remove observations with outliers

Check distributions and transform or cap outliers

Use robust methods like median

Always keep outliers

4 / 10

How might you fix inconsistently formatted data in R?

Scale the data

Use string manipulation functions like str_replace()

Remove corrupted data

Impute missing values

5 / 10

What package in R helps tidy messy data?

sweep

reshape2

tidyr

ggplot2

6 / 10

What step helps safeguard data quality after cleaning?

Visual inspection

Validation checks

Removing outliers

Imputation

7 / 10

What might cause outliers in real-world data?

Normal variability

Data entry errors

Software bugs

All of the above

8 / 10

In R, what is a common cause of missing data?

Outliers

Random sampling

NA values

Changing variable formats

9 / 10

What method handles both missing values and outliers?

Imputation

Robust models

Removing observations

Data scaling

10 / 10

When checking for data integrity, what might you verify?

Formats, ranges, duplicates

Statistical distributions

Outliers

Transformation need

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10 Question Quiz on Data Cleaning in R

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