atet / csv

This reference will show R and Python code to read, view, manipulate, transform, and save out comma-separated value (CSV) files. Though actions are largely similar, details and caveats regarding more advanced tabular file formats such as Excel Workbooks will not be covered here.

---

Table of Contents

• 0. Requirements
• 1. Source Data
• 2. Read and Write File
• 3. Examine Contents
• 4. Subsetting
• 5. Transforming
• 6. Merging
• 7. Special

Supplemental

• Other Resources
• Troubleshooting
• References

---

0. Requirements

Only R and Python will be used in this reference. To quickly deploy either (or both, if you're adventurous!) of these programming languages and their development environments, I would suggest leveraging Docker.

Instructions to install Docker for Windows 10/11 can be found here: https://github.com/atet/wsl

After Docker is installed on your computer, click to expand the steps below to easily download and create the development environments:

R and RStudio

In your WSL terminal, download the rocker/rstudio¹ Docker image (~700 MB) and start the container:

$ docker pull rocker/rstudio:4.4.2
$ docker run -dit -p 8787:8787 --name rstudio -e PASSWORD=rstudio rocker/rstudio:4.4.2

In a web browser, visit http://localhost:8787 and log into RStudio using the username rstudio and password rstudio.

Python and Jupyter Notebook

In your WSL terminal, download the jupyter/minimal-notebook² Docker image (~450 MB) and start the container:

$ docker pull jupyter/minimal-notebook:x86_64-ubuntu-22.04
$ docker run -dit -p 8888:8888 --name jupyter -e JUPYTER_TOKEN=jupyter jupyter/minimal-notebook:x86_64-ubuntu-22.04

In a web browser, visit http://localhost:8888 and log into Jupyter using the token jupyter.

Back to Top

---

1. Source Data

We will be working with a simple comma-separated (CSV) file that could be downloaded directly from this GitHub repository into your programming environment (here) or simply copy/pasted into a new file called office.csv.

This file contains three rows (a.k.a. records) and three columns (a.k.a. fields) for a total of nine cells (not including column headers):

ID,FIRST NAME,QUOTE
2,Pam,"Just go after what you want and act fast, because life just isn't that long."
1,Pan,"There's a lot of beauty in ordinary things. Isn't that kind of the point?"
3,Jan,"There are always a million reasons not to do something."

Troubleshooting:

• Ensure that there are no spaces between delimiting commas as any "initial spaces" will cause issues with Microsoft Excel and Python Pandas reading the comma-separated value correctly.

Back to Top

---

2. Read and Write File

R

Python

# Read office.csv from GitHub as R data.frame object df = read.csv( "https://raw.githubusercontent.com/atet/csv/refs/heads/main/dat/office.csv", check.names = FALSE, stringsAsFactors = FALSE ) # View structure of the object str(df) # Save out object as office2.csv file to working directory write.csv(df, "office2.csv", row.names = FALSE) # View working directory where file was saved getwd()

# Install Pandas package and import packages import os os.system("pip install pandas") import pandas as pd # Read office.csv from GitHub as Pandas DataFrame object df = pd.read_csv( "https://raw.githubusercontent.com/atet/csv/refs/heads/main/dat/office.csv" ) # View structure of the object df.dtypes # Save out object as office2.csv file to working directory df.to_csv("office2.csv", index=False) # View working directory where file was saved os.getcwd()

Back to Top

---

3. Examine Contents

R	Python
# See the first two rows of the data head(df, n = 2) # See the last two rows of the data tail(df, n = 2) # Prints truncated output up to max.print = 1,000 default print(df) # Increase max.print number to display more options(max.print = 9999) print(df)	# See the first two rows of the data df.head(2) # See the last two rows of the data df.tail(2) # Prints truncated output if data exceeds display.min_rows = 10 default # and up to display.max_rows = 60 default print(df) # Increase display.min/max_rows numbers to display more pd.set_option('display.max_rows', 9999) pd.set_option('display.max_rows', 100) print(df)

Back to Top

---

4. Subsetting

• Indices
  • By row(s)
  • By column(s)
  • By row(s) and Column(s)
• Matching (R infix)

R	Python

Back to Top

---

5. Transforming

• Summarize/pivot
• Split-apply-combine
• NOTE: R vectorization only applies to R numeric matrices

R	Python

Back to Top

---

6. Merging

• Joins (left, inner, right, outer)

R	Python

Back to Top

---

7. Special

• Dates

R	Python

Back to Top

---

Other Resources

Description	URL Link
null	null

Back to Top

---

Troubleshooting

Issue	Solution
"It's not working!"	This concise tutorial has distilled hours of sweat, tears, and troubleshooting; it can't not work

Back to Top

---

References

Citation	Description	Docker Hub Link
1	R and RStudio Server Docker image from Rocker Project	https://hub.docker.com/r/rocker/rstudio
2	Python and Jupyter Labs Docker image from Jupyter Project	https://hub.docker.com/r/jupyter/minimal-notebook

Back to Top

---

Copyright © 2025-∞ Athit Kao, Terms and Conditions