Data Science Desktop Survival Guide by Graham Williams
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Wind Directions

20180723 The three wind direction variables (wind_gust_dir, wind_dir_9am, wind_dir_3pm) are also identified as character. We review the distribution of values here with dplyr::select() identifying any variable that tidyselect::contains() the string _dir and then build a base::table() over those variables.

# Review the distribution of observations across levels.

ds %>%
  select(contains("_dir")) %>%
  sapply(table)

##     wind_gust_dir wind_dir_9am wind_dir_3pm
## N           10989        13978        10475
## NNE          7937         9782         8002
## NE           8715         9335        10092
## ENE          9965         9592         9605
## E           11071        11237        10123
## ESE          9055         9536        10290
## SE          11331        11398        12919
## SSE         10946        10954        11089
## S           11043        10519        11788
## SSW         10809         9272         9902
## SW          10793        10135        11166
## WSW         11136         8392        11700
## W           12122        10183        12411
## WNW         10045         9067        10846
## NW           9705        10488        10315
## NNW          7954         9468         9358

Observe all 16 compass directions are represented and it would make sense to convert this into a factor. Notice that the directions are in alphabetic order and conversion to factor will retain that. Instead we can construct an ordered factor to capture the compass order (from N, NNE, to NW and NNW). We note the ordering of the directions here.

# Levels of wind direction are ordered compas directions.

compass <- c("N", "NNE", "NE", "ENE",
             "E", "ESE", "SE", "SSE",
             "S", "SSW", "SW", "WSW",
             "W", "WNW", "NW", "NNW")