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.

# Print the dataset in a human useful way.

weather
## # A tibble: 366 × 24
##    Date       Location MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustDir
##    <date>     <chr>      <dbl>   <dbl>    <dbl>       <dbl>    <dbl> <ord>      
##  1 2007-11-01 Canberra     8      24.3      0           3.4      6.3 NW         
##  2 2007-11-02 Canberra    14      26.9      3.6         4.4      9.7 ENE        
##  3 2007-11-03 Canberra    13.7    23.4      3.6         5.8      3.3 NW         
##  4 2007-11-04 Canberra    13.3    15.5     39.8         7.2      9.1 NW         
##  5 2007-11-05 Canberra     7.6    16.1      2.8         5.6     10.6 SSE        
##  6 2007-11-06 Canberra     6.2    16.9      0           5.8      8.2 SE         
##  7 2007-11-07 Canberra     6.1    18.2      0.2         4.2      8.4 SE         
##  8 2007-11-08 Canberra     8.3    17        0           5.6      4.6 E          
##  9 2007-11-09 Canberra     8.8    19.5      0           4        4.1 S          
## 10 2007-11-10 Canberra     8.4    22.8     16.2         5.4      7.7 E          
## # 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.



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