West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997.

Real-time data from moored ocean buoys for improved detection, understanding and prediction of El Ni'o and La Ni'a. The data is collected by the Tropical Atmosphere Ocean project (https://www.pmel.noaa.gov/gtmba/pmel-theme/pacific-ocean-tao).

Usage

data(oceanbuoys)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 736 rows and 8 columns.

Source

https://www.pmel.noaa.gov/tao/drupal/disdel/

Details

Format: a data frame with 736 observations on the following 8 variables.

year: A numeric with levels 1993 1997.
latitude: A numeric with levels -5 -2 0.
longitude: A numeric with levels -110 -95.
sea_temp_c: Sea surface temperature(degree Celsius), measured by the TAO buoys at one meter below the surface.
air_temp_c: Air temperature(degree Celsius), measured by the TAO buoys three meters above the sea surface.
humidity: Relative humidity(%), measured by the TAO buoys 3 meters above the sea surface.
wind_ew: The East-West wind vector components(M/s). TAO buoys measure the wind speed and direction four meters above the sea surface. If it is positive, the East-West component of the wind is blowing towards the East. If it is negative, this component is blowing towards the West.
wind_ns: The North-South wind vector components(M/s). TAO buoys measure the wind speed and direction four meters above the sea surface. If it is positive, the North-South component of the wind is blowing towards the North. If it is negative, this component is blowing towards the South.

Examples


vis_miss(oceanbuoys)


# Look at the missingness in the variables
miss_var_summary(oceanbuoys)
#> # A tibble: 8 × 3
#>   variable   n_miss pct_miss
#>   <chr>       <int>    <num>
#> 1 humidity       93   12.6  
#> 2 air_temp_c     81   11.0  
#> 3 sea_temp_c      3    0.408
#> 4 year            0    0    
#> 5 latitude        0    0    
#> 6 longitude       0    0    
#> 7 wind_ew         0    0    
#> 8 wind_ns         0    0    
if (FALSE) {
# Look at the missingness in air temperature and humidity
library(ggplot2)
p <-
ggplot(oceanbuoys,
       aes(x = air_temp_c,
           y = humidity)) +
     geom_miss_point()

 p

 # for each year?
 p + facet_wrap(~year)

 # this shows that there are more missing values in humidity in 1993, and
 # more air temperature missing values in 1997

 # see more examples in the vignette, "getting started with naniar".
}