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Exploring Data with dplyr
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When you work with data frames in R, the dplyr package gives you a powerful set of tools for exploring and manipulating your data. The most important dplyr verbs are select, filter, arrange, mutate, and summarize. Each verb performs a specific type of operation:
select: choose specific columns from your data;filter: keep only rows that meet certain conditions;arrange: reorder rows based on column values;mutate: add new columns or transform existing ones;summarize: reduce your data to summary statistics.
These verbs allow you to quickly inspect and explore your data frames, making it easier to focus on the information that matters most.
12345678910111213141516library(dplyr) options(crayon.enabled = FALSE) # Creating a sample tibble df <- tibble::tibble( name = c("Alice", "Bob", "Carol", "David"), age = c(25, 30, 22, 35), score = c(88, 92, 95, 85) ) # Using select and filter to subset the tibble result <- df %>% select(name, score) %>% filter(score > 90) print(result)
A key feature of dplyr is the pipe operator %>%, which lets you chain together multiple operations in a clear, readable sequence. Instead of nesting functions inside each other, you pass the result of one operation directly into the next. This approach makes your code easier to read and understand, especially as your data wrangling tasks become more complex.
Svep för att börja koda
Use dplyr to create a new tibble called high_scores that only contains the name and score columns for rows where the score is greater than 80.
- Use the
selectfunction to choose only thenameandscorecolumns. - Use the
filterfunction to keep only rows where thescorecolumn is greater than 80. - Assign the result to a new variable named
high_scores.
Lösning
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