Reshaping Data with Pivot Functions
As you prepare data for analysis or modeling, you often encounter datasets that are not structured in the most useful way. Sometimes, data is in a wide format, where each variable gets its own column, but your analysis or model expects a long format, where observations are stacked in rows. Other times, you may need to go from long to wide for reporting or visualization. Pivoting data allows you to reshape datasets between these formats, making them easier to work with for different analytical tasks. This flexibility is essential when preparing features for machine learning, aggregating results, or visualizing trends over time.
12345678910111213141516171819202122232425262728library(tidyr) library(dplyr) # Sample data in wide format scores <- data.frame( student = c("Alice", "Bob", "Carol"), math = c(90, 85, 88), english = c(95, 80, 92) ) print(scores) # Pivot from wide to long format scores_long <- pivot_longer( scores, cols = c(math, english), names_to = "subject", values_to = "score" ) print(as.data.frame(scores_long)) # Pivot back from long to wide format scores_wide <- pivot_wider( scores_long, id_cols = student, names_from = subject, values_from = score ) print(as.data.frame(scores_wide))
When you use pivot_longer(), the cols argument specifies which columns to reshape into longer format. The names_to argument tells R what to call the new column that will contain the names of the original columns (like "subject" in the example). The values_to argument sets the name for the new column that will store the values from those columns (like "score"). For pivot_wider(), the id_cols argument identifies the columns that should remain as identifiers (such as "student"), while names_from and values_from decide which columns create new headers and which supply their values.
Be careful when pivoting data — if you have duplicate combinations of identifier columns and pivoted columns, you might lose data or get unexpected results. Also, duplicate column names can cause errors or overwrite data during the pivot process. Always check your data for unique identifiers before reshaping.
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As you prepare data for analysis or modeling, you often encounter datasets that are not structured in the most useful way. Sometimes, data is in a wide format, where each variable gets its own column, but your analysis or model expects a long format, where observations are stacked in rows. Other times, you may need to go from long to wide for reporting or visualization. Pivoting data allows you to reshape datasets between these formats, making them easier to work with for different analytical tasks. This flexibility is essential when preparing features for machine learning, aggregating results, or visualizing trends over time.
12345678910111213141516171819202122232425262728library(tidyr) library(dplyr) # Sample data in wide format scores <- data.frame( student = c("Alice", "Bob", "Carol"), math = c(90, 85, 88), english = c(95, 80, 92) ) print(scores) # Pivot from wide to long format scores_long <- pivot_longer( scores, cols = c(math, english), names_to = "subject", values_to = "score" ) print(as.data.frame(scores_long)) # Pivot back from long to wide format scores_wide <- pivot_wider( scores_long, id_cols = student, names_from = subject, values_from = score ) print(as.data.frame(scores_wide))
When you use pivot_longer(), the cols argument specifies which columns to reshape into longer format. The names_to argument tells R what to call the new column that will contain the names of the original columns (like "subject" in the example). The values_to argument sets the name for the new column that will store the values from those columns (like "score"). For pivot_wider(), the id_cols argument identifies the columns that should remain as identifiers (such as "student"), while names_from and values_from decide which columns create new headers and which supply their values.
Be careful when pivoting data — if you have duplicate combinations of identifier columns and pivoted columns, you might lose data or get unexpected results. Also, duplicate column names can cause errors or overwrite data during the pivot process. Always check your data for unique identifiers before reshaping.
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