6.1 readr: Import and Export

1. Paths, once more

Note 1.3 is the full story. A short reminder: store course files under a project, use a relative path from that project, and do not hard-code C:/Users/.... getwd() is the console’s working directory. A Quarto file in analysis/ reaches data with "../data/hate_crimes2.csv" or here::here("data", "hate_crimes2.csv").

The Data section of the course page has the hate_crimes*.csv files. Download them into a data/ folder in your project. They are not tied to one week.

2. readr reads flat files into tibbles

readr comes with library(tidyverse). It is faster than read.csv() and it does not turn characters into factors.

A .csv separates columns with commas. A .tsv uses tabs ( in read_lines()). read_csv2() uses semicolons.

library(tidyverse)

read_lines("data/hate_crimes2.csv", n_max = 10)
hate_crimes <- read_csv("data/hate_crimes2.csv")

read_lines() is how you see the delimiter before you choose a reader. A URL works as file if the file is public.

Do not import an Excel workbook if you can avoid it. Export a sheet to CSV, then read the CSV. Watch for empty columns Excel leaves behind.

3. Check the import immediately

readr prints the types it guessed. Then check:

str(hate_crimes)

## Error:
## ! object 'hate_crimes' not found

nrow(hate_crimes)

## Error:
## ! object 'hate_crimes' not found

ncol(hate_crimes)

## Error:
## ! object 'hate_crimes' not found

head(hate_crimes, 10)

## Error:
## ! object 'hate_crimes' not found

tail(hate_crimes, 10)

## Error:
## ! object 'hate_crimes' not found

hate_crimes |>
  summarize(across(everything(), ~ sum(is.na(.x))))

## Error:
## ! object 'hate_crimes' not found

nrow(distinct(hate_crimes))

## Error:
## ! object 'hate_crimes' not found

If a column that should be numeric is character, unique() usually shows why: "missing", ".", "-99".

Tell read_csv() those strings are missing:

read_csv("data/hate_crimes_1a.csv", na = "missing")

Other useful arguments:

• col_types — you set the types (note 6.2)
• skip — drop leading comment lines
• comment = "#" — drop lines that start with #
• locale — decimal mark and other regional defaults (US by default)

4. Write files back out

write_csv(hate_crimes, "data/hate_crimes_clean.csv")

write_tsv() and write_csv2() match the readers. saveRDS(obj, "obj.rds") and readRDS("obj.rds") keep a single R object with its types. That is for your own later session, not for sharing with Excel.

5. Practice

1. read_lines() on each hate_crimes*.csv in the Data section, then load it with the matching read_*(). Check head() and the NA counts.
2. Load hate_crimes_1a.csv once as-is and once with na = "missing".