Summary

You can now inspect a text file, import it with explicit settings, validate its contents, and choose an appropriate export format.

What you learned

  1. Inspect before importing. Identify the source, reporting period, units, metadata lines, header, delimiter, decimal mark, encoding, and missing-value symbols. A .csv extension alone does not specify these settings.
  2. Make the import reproducible. Keep the raw download and use code that records the settings. read.table() and its CSV variants read delimited text; an Excel workbook requires a suitable reader or a documented export to text first.
  3. Validate the result. Use head(), str(), and dim(). Check column names and types, row counts, missing values, plausible ranges, and the meaning of identifiers.
  4. Clean with a reason. Preserve identifiers as text when leading zeros matter. Distinguish missing values from zero and identify regional levels and overlapping categories before calculating totals.
  5. Export and check again. Reimport an exported file to confirm that values and important identifiers survived.

A short recap in R

Use the quakes.csv download from the import lesson, saved as Data/quakes.csv inside your RStudio project. Start from the project root. This particular teaching CSV has four metadata lines, a header, semicolon separators, and decimal commas.

quakes_data <- read.table("Data/quakes.csv",
                          skip = 4, header = TRUE, sep = ";", dec = ",")
dim(quakes_data)
head(quakes_data)
str(quakes_data)

The result should have 1,000 rows and five numeric columns: latitude (lat), longitude (long), depth in kilometers (depth), magnitude (mag), and number of reporting stations (stations). These are checks of this file, not defaults for another dataset.

Settings and formats to remember

Setting or format Why it matters
header, skip, sep, dec Identify the table and correctly separate names and values.
fileEncoding, na.strings Decode text correctly and recognize documented missing-value markers.
colClasses Specify column types, for example character for region identifiers.
CSV with write.table() or write.csv() Exchanges tabular text; choose separators/decimal marks and usually row.names = FALSE. R-specific classes are not automatically preserved.
RDS with saveRDS() and readRDS() Saves and restores one R object, including its structure and classes.

The Destatis example demonstrates why an official source still needs careful preprocessing: a German statistical export includes metadata, coded names, decimal commas, blanks, and several geographic levels. The dictionary and source notes are part of understanding the data, not optional decoration. Do not add Germany to its own states or sum parent categories together with their subcategories.

For batch processing, first test the complete import and validation on one file. Then identify files explicitly with list.files() and build paths with file.path(). The following unit explains the loops and functions needed to repeat those steps reliably.

Check your understanding

  1. A CSV imports as one column. Which setting would you inspect first, and how would you check the raw file?
  2. Why might a region code such as "01" require character storage, while an area in hectares requires numeric storage?
  3. Why can an import finish without errors and still give an unsuitable dataset for analysis?

Revisit import and export and batch processing if needed.

Test what you’ve learned so far!

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