## # A tibble: 96 × 13
##       country `1952` `1957` `1962` `1967` `1972` `1977` `1982` `1987`
##         <chr>  <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>
## 1     Albania     -9     -9     -9     -9     -9     -9     -9     -9
## 2   Argentina     -9     -1     -1     -9     -9     -9     -8      8
## 3     Armenia     -9     -7     -7     -7     -7     -7     -7     -7
## 4   Australia     10     10     10     10     10     10     10     10
## 5     Austria     10     10     10     10     10     10     10     10
## 6  Azerbaijan     -9     -7     -7     -7     -7     -7     -7     -7
## 7     Belarus     -9     -7     -7     -7     -7     -7     -7     -7
## 8     Belgium     10     10     10     10     10     10     10     10
## 9      Bhutan    -10    -10    -10    -10    -10    -10    -10    -10
## 10    Bolivia     -4     -3     -3     -4     -7     -7      8      9
## # ... with 86 more rows, and 4 more variables: `1992` <int>, `1997` <int>,
## #   `2002` <int>, `2007` <int>

Frequently the first thing you should do when given a dataset is to

• check that the data is tidy,
• identify the observational unit,
• specify the variables, and
• give the types of variables you are presented with.

This will help you with

• choosing the appropriate plot,
• summarizing the data, and
• understanding which inferences can be applied.

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

The third point means we don't mix apples and oranges, keep apples together with apples.

1. Each observation forms a row. In other words, each row corresponds to a single observational unit
2. Each variable forms a column:
   • Some of the variables may be used to identify the observational units. For organizational purposes, it's generally better to put these in the left-hand columns
   • Some of the variables may be observed values associated with each observational unit
3. Each type of observational unit forms a table.

This is just a spreadsheet!

But we are very strict about the previous three rules.

• Loaded via library(nycflights13)

• Contains information about all flights that departed from NYC (e.g. EWR, JFK and LGA) in 2013: 336,776 flights in total.

• To help understand what causes delays, it also includes a number of other useful datasets:

  • weather: hourly meterological data for each airport
  • planes: construction information about each plane
  • airports: airport names and locations
  • airlines: translation between two letter carrier codes and names

All of these data frames are available for viewing here.

(Half sheet of paper is fine. Share with your neighbor.)

• What are the three characteristics of tidy data?
• Draw the DATA diagram that was presented on the board in all previous classes.