Looking to create a DataFrame in R?

If so, I’ll show you the steps to create a DataFrame in R using a simple example.

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

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 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 |

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") age <- c(23, 41, 32) df <- data.frame(name, age) print (df)

Notice that it’s necessary to wrap *text* with quotes (as in the case for the values under the *name* column), but it’s not required to use quotes for *numeric *values (as in the case for the values under the *age* column).

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

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"), age = c(23, 41, 32) ) print (df)

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

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.

In my case, I stored the CSV file on my desktop, under the following path:

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

- The file name (as highlighted in blue) is: ‘MyData’

You may pick a different file name based on your needs - While the file extension (as highlighted in green) is: ‘.csv’

You have to add the ‘.csv’ extension when importing csv files into R - Finally, use double backslash (‘\\’) within the path name to avoid any errors in R

Putting everything together, this how the code would look like in R (you’ll need to change the path name to the location where the CSV file is stored on *your* computer):

mydata <- read.csv("C:\\Users\\Ron\\Desktop\\MyData.csv", header = TRUE) 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 next, and final section, I’ll show you how to apply some basic stats in R.

### Applying Basic Stats in R

Once you created the DataFrame, you can apply different computations and statistical analysis to your data.

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

name <- c("Jon", "Bill", "Maria") age <- c(23, 41, 32) df <- data.frame(name, age) print (max(df$age))

If your run the code in R, you’ll get the maximum age of 41.

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

name <- c("Jon", "Bill", "Maria") age <- c(23, 41, 32) df <- data.frame(name, age) print (mean(df$age))

And once you run the code, you’ll get the mean age of 32.

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

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