10.35 The Shape of the Dataset
20180721 Once the dataset is loaded we want to get a basic idea of what it looks like—its shape. Being an extended data frame (what we call a tibble), we can display the data as a tibble simply by printing the data referred to by the variable name.
## # A tibble: 366 × 24
## Date Location MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustDir
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <ord>
## 1 2023-07-01 Canberra 4.6 13.9 0 NA NA NNW
## 2 2023-07-02 Canberra -4.1 15.7 0 NA NA SSW
## 3 2023-07-03 Canberra -3.3 13.4 0.2 NA NA SE
## 4 2023-07-04 Canberra 3 12 0 NA NA N
## 5 2023-07-05 Canberra 7.4 15 2.8 NA NA NW
## 6 2023-07-06 Canberra -0.6 12.4 0.2 NA NA N
## 7 2023-07-07 Canberra 2.5 11.8 0.2 NA NA NW
## 8 2023-07-08 Canberra 5.6 9.9 0 NA NA NW
## 9 2023-07-09 Canberra 6.2 11.8 4.4 NA NA NW
## 10 2023-07-10 Canberra 6.5 12.2 0 NA NA WNW
## # ℹ 356 more rows
## # ℹ 16 more variables: WindGustSpeed <dbl>, WindDir9am <ord>, WindDir3pm <ord>,
## # WindSpeed9am <dbl>, WindSpeed3pm <dbl>, Humidity9am <int>,
## # Humidity3pm <int>, Pressure9am <dbl>, Pressure3pm <dbl>, Cloud9am <int>,
## # Cloud3pm <int>, Temp9am <dbl>, Temp3pm <dbl>, RainToday <fct>,
## # RISK_MM <dbl>, RainTomorrow <fct>We observe that dataset consists of 366 observations of 24 variables. The enhanced nature of the data frame that representing it as a tibble brings to us is that the printout is more informative. The first few observations are shown with a subset of the variables followed by a list of all of the other variables.
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