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R in 3 Months (Fall 2022)

R in 3 Months Fall 2022 Week 8 Project Assignment

Transcript

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For this week's assignment we're asking you to think about how your dataset(s) tidy... and untidy.

There are two sets of two functions for tidying data from {tidyr}

  • pivot_longer() and pivot_wider() for transforming between wide and long datasets (and back again).

  • separate() and separate_rows() for dealing with columns that contain multiple observations. This is extremely common in survey datasets.

We'd like to see you experiment with using one or more of these functions for tidying your data.

In my example video below I used the United States", "US", "Americas", "United Kingdom", "GB", "Europe", "France", "FR", "Europe", "Germany", "DE", "Europe", "Canada", "CA", "Americas", "Saudi Arabai", "SA", "Asia", "Egypt", "EG", "Africa", "Estonia", "EE", "Europe", "New Zealand", "ZA", "Oceania", "Australia", "AU", "Oceania", "China", "CN", "Asia", "Signapore", "SG", "Asia", "Spain", "ES", "Europe", "Japan", "JO", "Asia" ) wdi_indicators - c("percent_pop_using_internet" = "IT.NET.USER.ZS", "gdp_per_cap" = "NY.GDP.PCAP.KD") WDI(countries_df$country.code, indicator = wdi_indicators) %% as_tibble() ">code you can find in this gist.

Have any questions? Put them below and we will help you out!

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Course Content

142 Lessons

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R in 3 Months Fall 2022 Week 10 Drop-in Session