Tidy

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When his over-zealous tidying begins to have a damaging impact on the forest and its inhabitants, Pete realises that he may have gone too far - and sets about trying to put things right. In this first example, we only have one document (the poem), but we will explore examples with multiple documents soon. At the end of the story, the animals have a picnic. What food would these animals eat as part of a picnic? Create some pictures of trees using finger painting. Here are some instructions from the author, Emily Gravett:

Carry out some role-play activities based on the book, e.g. interview the other forest animals to ask them what they think about Pete’s actions.Why do trees have leaves? Can you find out and think of ways to share this information with others? Can you find out about different types of leaves? library ( dplyr ) text_df <- tibble (line = 1 : 4, text = text ) text_df #> # A tibble: 4 × 2 #> line text #> #> 1 1 Because I could not stop for Death - #> 2 2 He kindly stopped for me - #> 3 3 The Carriage held but just Ourselves - #> 4 4 and Immortality text <- c ( "Because I could not stop for Death -", "He kindly stopped for me -", "The Carriage held but just Ourselves -", "and Immortality" ) text #> [1] "Because I could not stop for Death -" #> [2] "He kindly stopped for me -" #> [3] "The Carriage held but just Ourselves -" #> [4] "and Immortality" The next part of the book moves forward with more details on the process of creating an effective model. Chapters 10 through 15 focus on creating good estimates of performance as well as tuning model hyperparameters.

Look at photos of badgers (and other forest animals) and use these as the starting point for your own pictures and paintings. Find out more about badgers. Where do they live? What do they eat? Can you write a report about them?library ( janeaustenr ) library ( dplyr ) library ( stringr ) original_books <- austen_books ( ) %>% group_by ( book ) %>% mutate (linenumber = row_number ( ), chapter = cumsum ( str_detect ( text, regex ( " Find the locations of forests in your local area. Could you visit some of them? Plan the journey there. Finally, the last section of this book, Chapters 16 through 21, covers other important topics for model building. We discuss more advanced feature engineering approaches like dimensionality reduction and encoding high cardinality predictors, as well as how to answer questions about why a model makes certain predictions and when to trust your model predictions. Can you create a book that has a ‘window’ in the front cover? Could you use this window in different creative ways? The two basic arguments to unnest_tokens used here are column names. First we have the output column name that will be created as the text is unnested into it ( word, in this case), and then the input column that the text comes from ( text, in this case). Remember that text_df above has a column called text that contains the data of interest.