10.2 Wrangling Data Review

It is always useful to remind ourselves of the dataset with a random sample:

ds  %>% sample_frac() %>% select(date, location, sample(3:length(vars), 5))
## # A tibble: 176,747 x 7
##    date       location    humidity_9am cloud_9am max_temp temp_9am rain_tomorrow
##    <date>     <chr>              <int>     <int>    <dbl>    <dbl> <fct>        
##  1 2013-07-10 Walpole               97        NA     17       11   No           
##  2 2010-01-25 SydneyAirp…           80         8     26.9     22.3 No           
##  3 2009-12-18 Sale                  64         2     22.4     14.9 No           
##  4 2019-08-11 PerthAirpo…           62         0     22.4     13.9 No           
##  5 2018-05-11 Albury                91         8      9.9      5.7 Yes          
##  6 2013-04-22 Penrith               90        NA     24.4     16   No           
##  7 2013-06-16 Dartmoor             100        NA     13.2      5.1 Yes          
##  8 2017-08-11 CoffsHarbo…           29        NA     27.7     21.9 No           
##  9 2019-10-26 Perth                 56         0     28.9     21.3 No           
## 10 2013-10-25 WaggaWagga            60         0     19.3      9.6 No           
## # … with 176,737 more rows
glimpse(ds)
## Rows: 176,747
## Columns: 24
## $ date            <date> 2008-12-01, 2008-12-02, 2008-12-03, 2008-12-04, 2008-…
## $ location        <chr> "Albury", "Albury", "Albury", "Albury", "Albury", "Alb…
## $ min_temp        <dbl> 13.4, 7.4, 12.9, 9.2, 17.5, 14.6, 14.3, 7.7, 9.7, 13.1…
## $ max_temp        <dbl> 22.9, 25.1, 25.7, 28.0, 32.3, 29.7, 25.0, 26.7, 31.9, …
## $ rainfall        <dbl> 0.6, 0.0, 0.0, 0.0, 1.0, 0.2, 0.0, 0.0, 0.0, 1.4, 0.0,…
## $ evaporation     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ sunshine        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wind_gust_dir   <ord> W, WNW, WSW, NE, W, WNW, W, W, NNW, W, N, NNE, W, SW, …
## $ wind_gust_speed <dbl> 44, 44, 46, 24, 41, 56, 50, 35, 80, 28, 30, 31, 61, 44…
## $ wind_dir_9am    <ord> W, NNW, W, SE, ENE, W, SW, SSE, SE, S, SSE, NE, NNW, W…
## $ wind_dir_3pm    <ord> WNW, WSW, WSW, E, NW, W, W, W, NW, SSE, ESE, ENE, NNW,…
## $ wind_speed_9am  <dbl> 20, 4, 19, 11, 7, 19, 20, 6, 7, 15, 17, 15, 28, 24, 4,…
## $ wind_speed_3pm  <dbl> 24, 22, 26, 9, 20, 24, 24, 17, 28, 11, 6, 13, 28, 20, …
## $ humidity_9am    <int> 71, 44, 38, 45, 82, 55, 49, 48, 42, 58, 48, 89, 76, 65…
## $ humidity_3pm    <int> 22, 25, 30, 16, 33, 23, 19, 19, 9, 27, 22, 91, 93, 43,…
## $ pressure_9am    <dbl> 1007.7, 1010.6, 1007.6, 1017.6, 1010.8, 1009.2, 1009.6…
## $ pressure_3pm    <dbl> 1007.1, 1007.8, 1008.7, 1012.8, 1006.0, 1005.4, 1008.2…
## $ cloud_9am       <int> 8, NA, NA, NA, 7, NA, 1, NA, NA, NA, NA, 8, 8, NA, NA,…
## $ cloud_3pm       <int> NA, NA, 2, NA, 8, NA, NA, NA, NA, NA, NA, 8, 8, 7, NA,…
## $ temp_9am        <dbl> 16.9, 17.2, 21.0, 18.1, 17.8, 20.6, 18.1, 16.3, 18.3, …
## $ temp_3pm        <dbl> 21.8, 24.3, 23.2, 26.5, 29.7, 28.9, 24.6, 25.5, 30.2, …
## $ rain_today      <fct> No, No, No, No, No, No, No, No, No, Yes, No, Yes, Yes,…
## $ risk_mm         <dbl> 0.0, 0.0, 0.0, 1.0, 0.2, 0.0, 0.0, 0.0, 1.4, 0.0, 2.2,…
## $ rain_tomorrow   <fct> No, No, No, No, No, No, No, No, Yes, No, Yes, Yes, Yes…


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