Generally speaking, you may use the following template in order to create a DataFrame in R:

first_column <- c("value_1", "value_2", ...) second_column <- c("value_1", "value_2", ...) df <- data.frame(first_column, second_column)

Alternatively, you may apply this syntax to get the same DataFrame:

df <- data.frame (first_column = c("value_1", "value_2", ...), second_column = c("value_1", "value_2", ...) )

Next, you’ll see how to apply each of the above templates in practice.

## Create a DataFrame in R

Let’s start with a simple example, where the dataset is:

Name |
Age |

Jon | 23 |

Bill | 41 |

Maria | 32 |

Ben | 58 |

Tina | 26 |

The goal is to capture that data in R using a DataFrame.

Using the first template that you saw at the beginning of this guide, the DataFrame would look like this:

Name <- c("Jon", "Bill", "Maria", "Ben", "Tina") Age <- c(23, 41, 32, 58, 26) df <- data.frame(Name, Age) print(df)

Note that it’s necessary to place quotes around *text* (for the values under the *Name* column), but it’s not required to use quotes around *numeric *values (for the values under the *Age* column).

Once you run the above code in R, you’ll get this simple DataFrame:

```
Name Age
1 Jon 23
2 Bill 41
3 Maria 32
4 Ben 58
5 Tina 26
```

The values in R match with those in our dataset.

You can achieve the same outcome by using the second template (don’t forget to place a closing bracket at the end of your DataFrame – as captured in the third line of the code below):

df <- data.frame(Name = c("Jon", "Bill", "Maria", "Ben", "Tina"), Age = c(23, 41, 32, 58, 26) ) print(df)

Run the above code in R, and you’ll get the same results:

```
Name Age
1 Jon 23
2 Bill 41
3 Maria 32
4 Ben 58
5 Tina 26
```

Note, that you can also create a DataFrame by importing the data into R.

For example, if you stored the original data in a CSV file, you can simply import that data into R, and then assign it to a DataFrame.

For demonstration purposes, let’s assume that a CSV file is stored under the following path:

C:\\Users\\Ron\\Desktop\\Test\\MyData.csv

Where:

- The file name (as highlighted in blue) is:
**MyData** - The file extension (as highlighted in green) is:
**.csv**. You have to add the ‘.csv’ extension when importing csv files into R - Double backslash (‘\\’) is used within the path to avoid any errors in R

This how the complete code would look like in R (you’ll need to change the path name to reflect the location where the CSV file is stored on *your* computer):

mydata <- read.csv("C:\\Users\\Ron\\Desktop\\Test\\MyData.csv") df <- data.frame(mydata) print(df)

After you created the DataFrame in R, using either of the above methods, you can then apply some statistical analysis.

In the final section below, you’ll see how to apply some basic stats in R.

### Applying Basic Stats in R

Once you created the DataFrame, you may apply different computations and statistical analysis.

For instance, to find the maximum age in our data, simply apply the following code in R:

Name <- c("Jon", "Bill", "Maria", "Ben", "Tina") Age <- c(23, 41, 32, 58, 26) df <- data.frame(Name, Age) print(max(df$Age))

If your run the code in R, you’ll get the maximum age of *58*.

Similarly, you can easily compute the mean age by applying:

Name <- c("Jon", "Bill", "Maria", "Ben", "Tina") Age <- c(23, 41, 32, 58, 26) df <- data.frame(Name, Age) print(mean(df$Age))

And once you run the code, you’ll get the mean age of *36*.

Those are just 2 examples, but once you created a DataFrame in R, you may apply an assortment of computations and statistical analysis.

You can find additional info about creating a DataFrame in R by reviewing the R documentation.