How do I read a CSV in R with base R?

df <- read.csv('file.csv', stringsAsFactors=FALSE). Base R read.csv() is available without any packages. Add check.names=FALSE to preserve column names with spaces or special characters.

How do I load a CSV with tidyverse in R?

library(tidyverse); df <- read_csv('file.csv'). read_csv() from the readr package handles UTF-8 encoding, shows a column spec summary, and returns a tibble — the tidyverse alternative to a data frame.

Free R code generator — tidyverse · ggplot2 — dtype inference — no upload

Convert CSV to R code
tidyverse, dplyr
and ggplot2 ready.

Q: How do I read a CSV in R?

library(readr); df <- read_csv('file.csv'). The readr package (part of tidyverse) is faster than base R read.csv() and automatically infers column types. Install with: install.packages('tidyverse').

Q: How do I read a CSV in R with base R?

df <- read.csv('file.csv', stringsAsFactors=FALSE). Base R read.csv() is available without any packages. Add check.names=FALSE to preserve column names with spaces or special characters.

Upload your CSV and get a ready-to-run R script. Column types inferred automatically — integer, numeric, logical, Date, character. Choose basic, tidyverse analysis or ggplot2 chart mode. Download as .R or copy to clipboard. Your data never leaves your browser.

Your data never leaves your browser

Column types inferred

tidyverse + ggplot2

Download as .R

Always free

Need the reverse? R to CSV

CSV to R Code Generator 100% client-side

Drop your CSV file here

.csv, .txt or .tsv — column types inferred from your data

or paste CSV directly

Script mode

Basic

read_csv, glimpse, summary and head. The fastest way to explore a new dataset.

readr

tidyverse

dplyr filter, group_by + summarise, correlation matrix and a ggplot2 histogram.

dplyrggplot2

ggplot2

Scatter plot or bar chart starter — uses your actual column names. Save to PNG.

ggplot2tidyverse

R script ready

How the R code is generated

CSVShift reads your column names and samples up to 200 rows to infer R types before writing a line of code.

Column type inference

Each column is sampled. All whole numbers → integer. Decimals → numeric. TRUE/FALSE/YES/NO → logical. YYYY-MM-DD → Date. Everything else → character.

Script generation

The inferred types are used to write type-aware code — as.Date() for date columns, across() for numeric summaries, column names inserted directly into aes(x=, y=) in ggplot2.

Run in RStudio or Posit Cloud

Download the .R file and open it in RStudio or Posit Cloud. Replace the filename at the top if needed. The script runs without modification for most CSVs with standard column names.

R CSV quick reference

The most useful commands for working with CSV data in R.

Essential readr + dplyr commands

library(tidyverse) df <- read_csv("file.csv") # Load (readr) glimpse(df) # Column types + preview summary(df) # Stats for all columns df %>% filter(col > 100) # Filter rows df %>% select(col1, col2) # Select columns df %>% arrange(desc(col)) # Sort descending df %>% group_by(cat) %>% summarise(mean = mean(num, na.rm = TRUE)) df %>% mutate(new = col1 / col2) # Add column write_csv(df, "output.csv") # Save

Requires: install.packages("tidyverse"). The tidyverse loads readr, dplyr, ggplot2 and tibble in one call.

Base R — no packages needed

df <- read.csv("file.csv", stringsAsFactors = FALSE) nrow(df); ncol(df) # Dimensions str(df) # Column types head(df, 10) # First 10 rows df[df$col > 100, ] # Filter rows df[, c("col1", "col2")] # Select columns df[order(-df$col), ] # Sort descending aggregate(num ~ cat, df, mean) # Group + mean write.csv(df, "output.csv", row.names = FALSE)

Base R read.csv() is available without installing packages. stringsAsFactors=FALSE prevents text columns being treated as factor levels.

Key R packages for CSV data

readr

Fast CSV reading with automatic type inference and UTF-8 handling. Returns a tibble with a column spec summary. Part of tidyverse.

install.packages("readr")

dplyr

Grammar of data manipulation: filter, select, mutate, group_by, summarise, arrange. The standard for tidyverse data wrangling.

install.packages("dplyr")

ggplot2

Grammar of graphics — build plots by adding layers: data, aesthetics, geom, scales, theme. The R visualisation standard.

install.packages("ggplot2")

data.table

High-performance CSV reading and in-memory data manipulation. fread() is 2-10× faster than read_csv() for large files (100MB+).

install.packages("data.table")

When do you need CSV to R?

Statistical analysis

R is the standard language for statistical analysis in academia and research. CSV exports from data collection tools, surveys and experiments need to be read into R for regression, ANOVA, time series and survival analysis workflows.

Academic research

Journals increasingly require reproducible analysis in R or Python. Converting a CSV dataset to a tidyverse R script produces code that can be included as supplementary material or run by reviewers.

Data visualisation

ggplot2 produces publication-quality charts. The ggplot2 mode generates a scatter or bar chart starter that uses your actual column names — fewer blank canvas moments.

Business reporting

CSV exports from CRM, ERP and analytics platforms can be loaded into R for automated reporting with knitr and RMarkdown, producing reproducible HTML or PDF reports on a schedule.

Related CSV tools

All data science and bi tools

Code written from
your columns, not a template. No guessing.

CSVShift reads your column names and infers types from actual data — not guesswork. The generated script uses your real column names in aes(x=col1, y=col2), wraps date columns in as.Date(), and selects numeric columns for cor(). No placeholder strings to replace.

Type-aware code

Integer, numeric, logical, Date and character types inferred from your data — not assumed.

Three script modes

Basic explore, full tidyverse analysis or ggplot2 chart starter — choose what your workflow needs.

Real column names

Your actual column names appear in every aes(), across() and select() call — no placeholder editing.

Free, no conditions

No row limit, no watermark, no account. Funded by display advertising only.

Frequently asked questions

How do I read a CSV in R?

With tidyverse: library(tidyverse); df <- read_csv('file.csv'). With base R: df <- read.csv('file.csv', stringsAsFactors=FALSE). The tidyverse read_csv() is faster, handles UTF-8 by default and returns a tibble with a column spec summary.

What types does CSVShift infer for R?

CSVShift samples up to 200 rows per column. All whole-number values → integer. Decimals → numeric. TRUE/FALSE/YES/NO → logical. YYYY-MM-DD dates → Date. Everything else → character. Mixed columns default to character.

How do I install tidyverse in R?

Run once in the R console: install.packages("tidyverse"). This installs readr, dplyr, ggplot2, tidyr, purrr and tibble. Then load in each script with library(tidyverse).

Is my data safe when generating R code online?

Yes. CSVShift reads the CSV and generates the R script entirely in your browser. Your data is never uploaded to any server. Open the Network inspector during generation — zero outbound requests carry your data.

Convert CSV to R codetidyverse, dplyrand ggplot2 ready.