readr in R

read_csv is faster than read.csv, and it tells you what it guessed.

library(readr)
library(dplyr)

text <- "day,lang,tpm,note\n2026-07-29,r,96,\n2026-07-30,python,104,fast\n"

runs <- read_csv(text,
                 col_types = cols(
                   day = col_date(format = "%Y-%m-%d"),
                   lang = col_character(),
                   tpm = col_integer(),
                   note = col_character()
                 ),
                 na = c("", "NA"))
print(runs)
print(sapply(runs, class))

path <- tempfile(fileext = ".csv")
write_csv(runs, path)
print(nrow(read_csv(path, show_col_types = FALSE)))

How it works

1. col_types stops the guessing and documents the schema.
2. na says which strings mean missing.
3. problems() reports the rows that did not fit.

Keywords and builtins used here

• c
• col_character
• col_date
• col_integer
• cols
• library
• nrow
• print
• read_csv
• sapply
• tempfile
• write_csv

The run, in numbers

Lines

19

Characters to type

423

Tokens

104

Three-star pace

100 tpm

At the three-star pace of 100 tokens a minute, this run takes about 62 seconds.

Type this snippet

Step 1 of 3 in Reading & selecting, step 9 of 12 in Reshaping & reading.

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