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: 198,656 × 7
##    date       location     humidity_3pm cloud_3pm pressure_3pm wind_gust_dir
##    <date>     <chr>               <int>     <int>        <dbl> <ord>        
##  1 2012-02-14 CoffsHarbour           60         2        1018  SSW          
##  2 2019-08-29 Newcastle              NA        NA          NA  <NA>         
##  3 2018-11-11 CoffsHarbour           59        NA        1020. SE           
##  4 2010-12-01 Darwin                 47         7        1003. S            
##  5 2009-05-21 Wollongong             64         5        1024. ESE          
##  6 2020-03-24 Nuriootpa              67        NA        1021. ESE          
##  7 2012-11-26 Woomera                13         7        1007. ENE          
##  8 2019-07-27 Sale                   74        NA          NA  <NA>         
##  9 2014-07-17 Tuggeranong            51        NA        1010. NW           
## 10 2009-08-08 Walpole                64        NA        1012. WNW          
## # … with 198,646 more rows, and 1 more variable: pressure_9am <dbl>
glimpse(ds)
## Rows: 198,656
## 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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