Read txt files with Tidyverse
Step 1: Import the readr package
First off, make sure that the Tidyverse readr package is installed in your RStudio (or Jupyter) dev environment , and install it if required:
if (!require(readr)) {
install.packages("readr")
}
Next import the readr library for using t in your development environment:
library ("readr")Step 2: Define path to text / csv file
Next define a path to the file you would like to import. The path could be either in your local file system or a remote server location.
In our case we will import some simple course related information from the local directory.
txt_path = "C:\\Temp\\Courses_Data.csv" Step 3: Import text file as DataFrame
We can now import the text file as a DataFrame:
courses_data <- read_csv(txt_path, col_names = TRUE)Note: the col_names attribute signals that the file has column headers. If that’s not the case, make sure to set col_names=FALSE.
Note: You can browse your DataFrame content from the Environment tab in RStudio.
Import text and csv files with R base
Use the following procedure for reading the contents of a text file line by line into a DataFrame:
Step 1: Define path to csv/txt file
As shown above, the first step is to define the file that we would like to import into our R development environment.
txt_path = "C:\\Temp\\Courses_Data.csv" Step 2: Read file as DataFrame
Next step is to invoke the R base read.csv function to get the text file contents into a DataFrame.
courses_data <- read.csv (txt_path)We can easily verify that courses_data is a DataFrame object by using the following function:
is.data.frame(courses_data)This will return the value TRUE in the console.
Get text with read.table
An alternative option to get rectangular data stored as txt or csv into R is using the read.table method:
courses_data <- read.table (txt_path, header = TRUE, sep =",", dec=".")Note that in this case, you need to specify the delimiting character (sep) that is used in your file as well the decimal separator (dec)