To tidy this dataset, we first use pivot_longer() to make the dataset longer. In this section, I’ll provide some standard vocabulary for describing the structure and semantics of a dataset, and then use those definitions to define tidy data. Here’s how it would look like. In this case we want to split after the first character: Storing the values in this form resolves a problem in the original data. A dataset is messy or tidy depending on how rows, columns and tables are matched up with observations, variables and types. A general rule of thumb is that it is easier to describe functional relationships between variables (e.g., z is a linear combination of x and y, density is the ratio of weight to volume) than between rows, and it is easier to make comparisons between groups of observations (e.g., average of group a vs. average of group b) than between groups of columns. #> # wk35 , wk36 , wk37 , wk38 , wk39 , wk40 . #> # … with 8 more rows, and 3 more variables: `$100-150k` , `>150k` , #> religion income frequency, #> , #> 1 Agnostic <$10k 27, #> 2 Agnostic $10-20k 34, #> 3 Agnostic $20-30k 60, #> 4 Agnostic $30-40k 81, #> 5 Agnostic $40-50k 76, #> 6 Agnostic $50-75k 137, #> 7 Agnostic $75-100k 122, #> 8 Agnostic $100-150k 109, #> 9 Agnostic >150k 84, #> 10 Agnostic Don't know/refused 96, #> artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8, #> , #> 1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA, #> 2 2Ge+h… The … 2000-09-02 91 87 92 NA NA NA NA NA, #> 3 3 Doo… Kryp… 2000-04-08 81 70 68 67 66 57 54 53, #> 4 3 Doo… Loser 2000-10-21 76 76 72 69 67 65 55 59, #> 5 504 B… Wobb… 2000-04-15 57 34 25 17 17 31 36 49, #> 6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2, #> 7 A*Tee… Danc… 2000-07-08 97 97 96 95 100 NA NA NA, #> 8 Aaliy… I Do… 2000-01-29 84 62 51 41 38 35 35 38, #> 9 Aaliy… Try … 2000-03-18 59 53 38 28 21 18 16 14, #> 10 Adams… Open… 2000-08-26 76 76 74 69 68 67 61 58. If you consider how many data analysis operations involve all of the values in a variable (every aggregation function), you can see how important it is to extract these values in a simple, standard way. This dataset has three variables, religion, income and frequency. This is a wrapper around expand(), dplyr::left_join() and replace_na() that's useful for completing missing combinations of data. An example of this type of cleaning can be found at https://github.com/hadley/data-baby-names which takes 129 yearly baby name tables provided by the US Social Security Administration and combines them into a single file. You can either pass it a regular expression to split on (the default is to split on non-alphanumeric columns), or a vector of character positions. #> # wk53 , wk54 , wk55 , wk56 , wk57 , wk58 . The code below loads daily weather data from the Global Historical Climatology Network for one weather station (MX17004) in Mexico for five months in 2010. In later stages, you change focus to traits, computed by averaging together multiple questions. Posted on July 22, 2020 by kjytay in R bloggers | 0 Comments. #> # wk41 , wk42 , wk43 , wk44 , wk45 , wk46 . The columns are almost always labeled and the rows are sometimes labeled. Fixing this requires widening the data: pivot_wider() is inverse of pivot_longer(), pivoting element and value back out across multiple columns: This form is tidy: there’s one variable in each column, and each row represents one day. An example of this type of tidying is illustrated in https://github.com/hadley/data-fuel-economy, which shows the tidying of epa fuel economy data for over 50,000 cars from 1978 to 2008. Every value belongs to a variable and an observation. This format is also used to record regularly spaced observations over time. #> religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` `$75-100k`, #> , #> 1 Agnostic 27 34 60 81 76 137 122, #> 2 Atheist 12 27 37 52 35 70 73, #> 3 Buddhist 27 21 30 34 33 58 62, #> 4 Catholic 418 617 732 670 638 1116 949, #> 5 Don’t k… 15 14 15 11 10 35 21, #> 6 Evangel… 575 869 1064 982 881 1486 949, #> 7 Hindu 1 9 7 9 11 34 47, #> 8 Histori… 228 244 236 238 197 223 131, #> 9 Jehovah… 20 27 24 24 21 30 15, #> 10 Jewish 19 19 25 25 30 95 69. #> # wk59 , wk60 , wk61 , wk62 , wk63 , wk64 . Compare the different versions of the classroom data: in the messy version you need to use different strategies to extract different variables. Tidy data is a standard way of mapping the meaning of a dataset to its structure. The following sections illustrate each problem with a real dataset that I have encountered, and show how to tidy them. Suzy failed the first quiz, so she decided to drop the class. Surprisingly, most messy datasets, including types of messiness not explicitly described above, can be tidied with a small set of tools: pivoting (longer and wider) and separating.
