Weather — Weather • mosaicData

2016-17 weather in several cities

data(Weather)

Format

A data frame with weather-related variables for several world cities.

city: City name.
date: Date.
year: Numeric year.
month: Numeric month.
day: Numeric day.
high_temp, avg_temp, low_temp: High, average, and low temperature for the day in degrees F.
high_dewpt, avg_dewpt, low_dewpt: High, average, and low dew point for the day in degrees F.
high_humidity, avg_humidity, low_humidity: High, average, and low relative humidity.
high_hg, avg_hg, low_hg: High, average, and low sea level pressure in inches of mercury.
high_vis, avg_vis, low_vis: High, average, and low visability for the day in miles.
high_wind, avg_wind, low_wind: High, average, and low wind speed for the day in mph.
precip: Precipitation for the day -- a character vale; T means "trace amount".
events: Character string naming weather events on the day (Rain, Fog, Snow, etc.)

Source

These data were downloaded from WeatherUnderground in January 2018.

Examples

if (require(dplyr)) {
  Weather |>
    group_by(city, year) |>
    summarise(
      min_temp = min(low_temp),
      max_temp = max(high_temp)
      )
}
#> `summarise()` has grouped output by 'city'. You can override using the
#> `.groups` argument.
#> # A tibble: 10 × 4
#> # Groups:   city [5]
#>    city       year min_temp max_temp
#>    <chr>     <dbl>    <dbl>    <dbl>
#>  1 Auckland   2016       35       82
#>  2 Auckland   2017       35       80
#>  3 Beijing    2016        1       98
#>  4 Beijing    2017        5      102
#>  5 Chicago    2016      -13       93
#>  6 Chicago    2017       -4       95
#>  7 Mumbai     2016       54      100
#>  8 Mumbai     2017       53      101
#>  9 San Diego  2016       41      101
#> 10 San Diego  2017       41       98

if (require(ggformula)) {
  Weather |>
    gf_linerange(low_temp + high_temp ~ date | city ~ ., 
    color = ~ (high_temp + low_temp) / 2, show.legend = FALSE) |>
    gf_refine(scale_color_gradientn(colors = rev(rainbow(5))))
}