Summary

You can now reorder observations, combine tables using an appropriate rule, work with text, and summarize values by group.

What you learned

Task	Tool	Main distinction
Sort values	sort(x)	Returns sorted values. Factor order depends on the levels.
Reorder a table	df[order(df$column), ]	order() supplies positions; reorder whole rows to preserve relationships between columns.
Append observations	rbind()	Stacks compatible tables; check names, types, and units.
Put columns side by side	cbind()	Aligns by position, not by a shared identifier; check row correspondence first.
Match tables by identifiers	merge(..., by = "site")	Matches key values even when row orders differ.
Work with text	nchar(), paste(), sprintf(), grep(), sub(), strsplit()	Inspect, construct, find, replace, or split strings.
Summarize by group	aggregate()	Reduces observations to group summaries.
Change table layout	Wide/long reshaping	Changes how measurements are arranged in rows and columns.

For merge(), specify the key explicitly. By default, all common column names are used as keys and only matching combinations are retained. all.x = TRUE keeps every row from the first table, adding missing values where there is no match; all = TRUE keeps unmatched rows from both. Duplicate keys can create multiple matches and increase the number of rows, so check keys and the result.

A short recap in R

These illustrative observations arrive in a different site order from the lookup table. The site identifier, not row position, determines the match.

sites <- data.frame(
  site = c("A", "B", "C"),
  habitat = c("forest", "grassland", "forest")
)
measurements <- data.frame(
  site = c("C", "A", "B"),
  height_cm = c(18, 12, 9)
)
combined <- merge(measurements, sites, by = "site")
combined <- combined[order(combined$height_cm), ]
combined
aggregate(height_cm ~ habitat, data = combined, FUN = mean)

The sorted table contains sites B, A, and C with heights 9, 12, and 18 cm. The mean height is 15 cm for forest and 9 cm for grassland. Aggregation produces one row per habitat; it does not retain each original observation.

Text and table layout

Check spelling, capitalization, and whitespace before matching text keys, and document any changes. Regular expressions describe patterns: characters such as . or * have special meanings. Use literal matching, such as fixed = TRUE where supported, when you mean the exact characters. grep() returns matching positions by default; grepl() returns a logical vector. sub() replaces the first match in each string; gsub() replaces all matches.

In a wide table, repeated measurements can occupy separate columns; in a long table, a variable identifies the measurement and another column holds its value. The lesson demonstrates melt() and dcast() from the additional package reshape2. Reshaping and aggregation serve different purposes, although casting may also aggregate when several observations belong to one output cell. Check the identifiers and intended unit of observation before either operation.

Check your understanding

1. Why would cbind(measurements, sites) give incorrect site/habitat combinations in this example?
2. What happens to an unmatched measurement with the default merge(), and how could you retain it?
3. How do sorting, aggregation, and wide-to-long reshaping each change a table?

Revisit merging, strings, and aggregation and reshaping if needed.