Key Concepts

Review core concepts you need to learn to master this subject

Tidy Dataset

In a tidy dataset each variable is represented by a column, and each row is a separate observation. Tidy datasets are the best way to conduct data analysis on specific data. By adhering to the standard of a tidy dataset, it is easier for an analyst to extract from. Datasets that are not tidy present some issues in their structure such as one column storing multiple variables, the same information of a variable is spread out in multiple columns, or the variables can be stored in both rows and columns.

distinct() dplyr

The distinct() function from dplyr package is used to keep only unique rows on a data frame. If there are duplicate rows, the function will preserve only the first row. The function can be used to remove equal rows of a dataframe, and to remove rows in a data frame based on unique column values or unique combination of columns values.

str_sub() function

The str_sub() function from the stringr package can split a string by index position separating combined data values into their individual components. The function uses the start= and end= arguments to perform the split operation. This function can be used with mutate() from dplyr in order to generate multiple new columns on a data frame based on split string values of a particular column.

separate() Function

The separate() function from the tidyr package, is used to separate a single character column of a data frame into multiple columns. Arguments of this function are, in order, a dataframe, the column used to create the new columns(column name or column position in the data frame), the new column names that will be used, and the separator argument. The default seperator will match any non-alphanumeric sequence, such as a space or semicolon.

str() Function

The str() function display the internal structure of an R object that is passed as parameter of the function. The function outputs the data structure of the object as well as the elements of the object. When the object is a dataframe, the function returns the data type of each column in the data frame, the number of observations and the number and variables.

gsub() R Function

The base R gsub() function searches for a regular expression in a string and replaces it. The function recieve a string or character to replace, a replacement value, and the object that contains the regular expression. We can use it to replace substrings within a single string or in each string in a vector.

When combined with dplyr’s mutate() function, a column of a data frame can be cleaned to enable analysis.

R as.numeric() Function

The base R as.numeric() function can coerce character string objects into numeric types.

This function is useful because often numbers are stored as characters which do not allow operations or analysis. The function receives the object to be transformed as a parameter and transforms it to numeric.

When this function is combined with the mutate() function from dplyr, new columns of a dataframe can be created with the numeric data type.

gather() tidyr

The gather() function from tidyr package is useful to gather columns over a data frame into key-value pairs, changing the shape of a data frame from wide to long. The original data frame has multiple columns that can be gathered, in a unique structure of key-value pair with all values in one column and the column names in another column.

Data Cleaning in R
Lesson 1 of 1
  1. 1
    A huge part of data science involves acquiring raw data and getting it into a form ready for analysis. Some have estimated that data scientists spend 80% of their time cleaning and manipulating dat…
  2. 2
    We often describe data that is easy to analyze and visualize as “tidy data”. What does it mean to have tidy data? For data to be tidy, it must have: - Each variable as a separate column - Each row…
  3. 3
    Often, you have the same data separated out into multiple files. Let’s say that you have a ton of files following the filename structure: ‘file_1.csv’, ‘file_2.csv’, ‘file_3.csv’, and so on. The p…
  4. 4
    Since we want - Each variable as a separate column - Each row as a separate observation We would want to reshape a table like: |Account|Checking|Savings| |-|-|-| |”12456543”|8500|8900| |…
  5. 5
    Often we see duplicated rows of data in the data frames we are working with. This could happen due to errors in data collection or in saving and loading the data. To check for duplicates, we can u…
  6. 6
    In trying to get clean data, we want to make sure each column represents one type of measurement. Often, multiple measurements are recorded in the same column, and we want to separate these out so …
  7. 7
    Let’s say we have a column called “type” with data entries in the format “admin_US” or “user_Kenya”, as shown in the table below. |id|type| |-|-| |1011|”user_Kenya”| |1112|”admin_US”| |1113…
  8. 8
    Each column of a data frame can hold items of the same data type. The data types that R uses are: character, numeric (real or decimal), integer, logical, or complex. Often, we want to convert bet…
  9. 9
    Sometimes we need to modify strings in our data frames to help us transform them into more meaningful metrics. For example, in our fruits table from before: |item|price|calories| |-|-|-|…
  10. 10
    Great! We have looked at a number of different methods we may use to get data into the format we want for analysis. Specifically, we have covered: diagnosing the “tidiness” of data combining …

What you'll create

Portfolio projects that showcase your new skills

Pro Logo

How you'll master it

Stress-test your knowledge with quizzes that help commit syntax to memory

Pro Logo