() ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.4.0 ✔ purrr 1.0.0 ✔ tibble 3.1.8 ✔ dplyr 1.0.10 ✔ tidyr 1.2.1 ✔ stringr 1.5.0 ✔ readr 2.1.3 ✔ forcats 0.5.2 ...
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Now, let’s widen the data (and remove some useless columns with dplyr::select()):climate_tidy <- climate_long %>% select(-`Series name`, -SCALE, -Decimals) %>% pivot_wider(names_from = `Series code`, values_from = value)
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dplyr: A grammar of data manipulation. Contribute to tidyverse/dplyr development by creating an account on GitHub.
nor additional prompts or offers for further assistance.\n\nYou have at your disposal a DuckDB database containing this schema:\n\nTable: tips\nColumns:\n- total_bill (FLOAT)\n- tip (FLOAT)\n- sex (TEXT)\n Categorical values: 'Female', 'Male'\n- smoker (TEXT)\n Categorical values...