10.36 A Glimpse of the Dataset
20180721 A useful alternative to gain some insight into the dataset is through dplyr::glimpse().
## Rows: 366
## Columns: 24
## $ Date <date> 2007-11-01, 2007-11-02, 2007-11-03, 2007-11-04, 2007-11…
## $ Location <chr> "Canberra", "Canberra", "Canberra", "Canberra", "Canberr…
## $ MinTemp <dbl> 8.0, 14.0, 13.7, 13.3, 7.6, 6.2, 6.1, 8.3, 8.8, 8.4, 9.1…
## $ MaxTemp <dbl> 24.3, 26.9, 23.4, 15.5, 16.1, 16.9, 18.2, 17.0, 19.5, 22…
## $ Rainfall <dbl> 0.0, 3.6, 3.6, 39.8, 2.8, 0.0, 0.2, 0.0, 0.0, 16.2, 0.0,…
## $ Evaporation <dbl> 3.4, 4.4, 5.8, 7.2, 5.6, 5.8, 4.2, 5.6, 4.0, 5.4, 4.2, 7…
## $ Sunshine <dbl> 6.3, 9.7, 3.3, 9.1, 10.6, 8.2, 8.4, 4.6, 4.1, 7.7, 11.9,…
## $ WindGustDir <ord> NW, ENE, NW, NW, SSE, SE, SE, E, S, E, N, E, WNW, NW, NW…
## $ WindGustSpeed <dbl> 30, 39, 85, 54, 50, 44, 43, 41, 48, 31, 30, 41, 30, 44, …
## $ WindDir9am <ord> SW, E, N, WNW, SSE, SE, SE, SE, E, S, SE, E, S, WNW, S, …
## $ WindDir3pm <ord> NW, W, NNE, W, ESE, E, ESE, E, ENE, ESE, NW, NW, NW, W, …
## $ WindSpeed9am <dbl> 6, 4, 6, 30, 20, 20, 19, 11, 19, 7, 6, 2, 6, 7, 6, 7, 6,…
## $ WindSpeed3pm <dbl> 20, 17, 6, 24, 28, 24, 26, 24, 17, 6, 9, 15, 7, 20, 20, …
## $ Humidity9am <int> 68, 80, 82, 62, 68, 70, 63, 65, 70, 82, 74, 54, 62, 67, …
## $ Humidity3pm <int> 29, 36, 69, 56, 49, 57, 47, 57, 48, 32, 34, 35, 29, 20, …
## $ Pressure9am <dbl> 1019.7, 1012.4, 1009.5, 1005.5, 1018.3, 1023.8, 1024.6, …
## $ Pressure3pm <dbl> 1015.0, 1008.4, 1007.2, 1007.0, 1018.5, 1021.7, 1022.2, …
## $ Cloud9am <int> 7, 5, 8, 2, 7, 7, 4, 6, 7, 7, 1, 0, 0, 1, 0, 0, 7, 1, 1,…
## $ Cloud3pm <int> 7, 3, 7, 7, 7, 5, 6, 7, 7, 1, 2, 3, 1, 4, 1, 3, 6, 5, 3,…
## $ Temp9am <dbl> 14.4, 17.5, 15.4, 13.5, 11.1, 10.9, 12.4, 12.1, 14.1, 13…
## $ Temp3pm <dbl> 23.6, 25.7, 20.2, 14.1, 15.4, 14.8, 17.3, 15.5, 18.9, 21…
## $ RainToday <fct> No, Yes, Yes, Yes, Yes, No, No, No, No, Yes, No, No, No,…
## $ RISK_MM <dbl> 3.6, 3.6, 39.8, 2.8, 0.0, 0.2, 0.0, 0.0, 16.2, 0.0, 0.2,…
## $ RainTomorrow <fct> Yes, Yes, Yes, Yes, No, No, No, No, Yes, No, No, No, No,…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.
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
