Data Science Desktop Survival Guide by Graham Williams
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A Glimpse of the Dataset

20180721 A useful alternative to gain some insight into the dataset is through dplyr::glimpse().

# A quick view of the contents of the dataset.

glimpse(weather)

## Rows: 366
## Columns: 24
## $ Date          <date> 2007-11-01, 2007-11-02, 2007-11-03, 2007-11-04, 2...
## $ Location      <chr> "Canberra", "Canberra", "Canberra", "Canberra", "C...
## $ MinTemp       <dbl> 8.0, 14.0, 13.7, 13.3, 7.6, 6.2, 6.1, 8.3, 8.8, 8....
## $ MaxTemp       <dbl> 24.3, 26.9, 23.4, 15.5, 16.1, 16.9, 18.2, 17.0, 19...
## $ Rainfall      <dbl> 0.0, 3.6, 3.6, 39.8, 2.8, 0.0, 0.2, 0.0, 0.0, 16.2...
## $ Evaporation   <dbl> 3.4, 4.4, 5.8, 7.2, 5.6, 5.8, 4.2, 5.6, 4.0, 5.4, ...
## $ Sunshine      <dbl> 6.3, 9.7, 3.3, 9.1, 10.6, 8.2, 8.4, 4.6, 4.1, 7.7,...
## $ WindGustDir   <ord> NW, ENE, NW, NW, SSE, SE, SE, E, S, E, N, E, WNW, ...
## $ WindGustSpeed <dbl> 30, 39, 85, 54, 50, 44, 43, 41, 48, 31, 30, 41, 30...
## $ WindDir9am    <ord> SW, E, N, WNW, SSE, SE, SE, SE, E, S, SE, E, S, WN...
## $ WindDir3pm    <ord> NW, W, NNE, W, ESE, E, ESE, E, ENE, ESE, NW, NW, N...
## $ WindSpeed9am  <dbl> 6, 4, 6, 30, 20, 20, 19, 11, 19, 7, 6, 2, 6, 7, 6,...
## $ WindSpeed3pm  <dbl> 20, 17, 6, 24, 28, 24, 26, 24, 17, 6, 9, 15, 7, 20...
## $ Humidity9am   <int> 68, 80, 82, 62, 68, 70, 63, 65, 70, 82, 74, 54, 62...
## $ Humidity3pm   <int> 29, 36, 69, 56, 49, 57, 47, 57, 48, 32, 34, 35, 29...
## $ Pressure9am   <dbl> 1019.7, 1012.4, 1009.5, 1005.5, 1018.3, 1023.8, 10...
## $ Pressure3pm   <dbl> 1015.0, 1008.4, 1007.2, 1007.0, 1018.5, 1021.7, 10...
## $ Cloud9am      <int> 7, 5, 8, 2, 7, 7, 4, 6, 7, 7, 1, 0, 0, 1, 0, 0, 7,...
## $ Cloud3pm      <int> 7, 3, 7, 7, 7, 5, 6, 7, 7, 1, 2, 3, 1, 4, 1, 3, 6,...
## $ Temp9am       <dbl> 14.4, 17.5, 15.4, 13.5, 11.1, 10.9, 12.4, 12.1, 14...
## $ Temp3pm       <dbl> 23.6, 25.7, 20.2, 14.1, 15.4, 14.8, 17.3, 15.5, 18...
## $ RainToday     <fct> No, Yes, Yes, Yes, Yes, No, No, No, No, Yes, No, N...
## $ RISK_MM       <dbl> 3.6, 3.6, 39.8, 2.8, 0.0, 0.2, 0.0, 0.0, 16.2, 0.0...
## $ RainTomorrow  <fct> Yes, Yes, Yes, Yes, No, No, No, No, Yes, No, No, N...

Again we receive a printed summary of the dataset, reporting on the number of observations and variables, but now the table is effectively rotated so that all variables can be listed along with their data type and a selection of their values for the first few observations.