naniar (version 0.5.2)

oceanbuoys: West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997.

Description

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 (http://www.pmel.noaa.gov/tao/index.shtml).

Usage

data(oceanbuoys)

Format

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

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.

See Also

library(MissingDataGUI) (data named "tao")

Examples

# NOT RUN {
# explore the missingness with vis_miss
library(naniar)

vis_miss(oceanbuoys)

# Look at the missingness in the variables
miss_var_summary(oceanbuoys)

# 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

 # what if we explore the value of air temperature and humidity based on
 # the missingness of each

 oceanbuoys %>%
   bind_shadow() %>%
   ggplot(aes(x = air_temp_c,
              fill = humidity_NA)) +
       geom_histogram()

 oceanbuoys %>%
   bind_shadow() %>%
   ggplot(aes(x = humidity,
              fill = air_temp_c_NA)) +
       geom_histogram()

# }