gsod: Utilities for exploring NOAA GSOD weather data

gsod, the weather package of DataBrew, is an effort to streamline the way that researchers and data scientists interact with and utilize weather data.

Objectives

The objectives of gsod package are simple:

Make accessing historical weather data easy.
Make accessing historical weather data fast.

Installation

To use the gsod package, you’ll first need to install it. With a good internet connection, run the following from within R.

if(!require(devtools)) install.packages("devtools")
install_github('databrew/gsod')

Once you’ve installed the package, you can use its functionality in any R script by simply including the following line:

library(gsod)

Basic use

Let’s settle the debate once and for all: which has better weather, the Dutch capital of Amsterdam or the Catalan capital of Barcelona.

# Get weather for 2012-2016
library(gsod)
weather <- bind_rows(gsod2012,
                      gsod2013,
                      gsod2014,
                      gsod2015,
                      gsod2016)

Now we’ll find the nearest weather stations to our locations of interest through the gsod package’s find_nearest_station function:

# Get the station ids
ids <- bind_rows(find_nearest_station('Amsterdam, Netherlands'),
                 find_nearest_station('Barcelona, Catalonia'))
ids

         stnid   lon    lat       km  stn_name
1 062400-99999 4.764 52.309 11.65975  SCHIPHOL
2 081810-99999 2.078 41.297 13.80614 BARCELONA

Next, we’ll plot our weather stations on a world map:

library(rworldmap)
library(ggplot2)
library(ggrepel)
library(databrew)
world <- map_data(map="world")

g <- 
  ggplot() + 
  theme_databrew() +
  geom_map(data=world, 
           map=world,
           aes(map_id=region, x=long, y=lat),
           fill = 'lightblue',
           alpha = 0.8) +
  geom_point(data = ids,
             aes(x = lon,
                 y = lat),
             alpha = 0.6,
             size = 3,
             color = 'red') +
  labs(x = 'Longitude',
       y = 'Latitude') +
  theme(legend.position = 'bottom') +
  geom_label_repel(data = ids,
             aes(x = lon,
                 y = lat,
                 label = stn_name),
             alpha = 0.7,
             size = 2)
g

To start, we’ll have a look at the daily high and low temperatures.

# Get weather for only our two stations
weather <- weather %>%
  filter(stnid %in% ids$stnid)
# Make long tidy data for plotting temperatures
long <- weather %>%
  dplyr::select(date, max, min, stn_name) %>%
  gather(key, value, max:min)
# Compare temperature
g <- ggplot(data = long,
       aes(x = date,
           y = value,
           color = key)) +
  geom_line(alpha = 0.6) +
  facet_wrap(~stn_name) +
  theme(legend.position = 'bottom') +
  scale_color_manual(name = '',
                     values = c('blue', 'red')) +
  theme_databrew() +
  labs(x = 'Date',
       y = 'Degrees (celcius)')
g

For many, rainfall matters more than temperature. Let’s have a look at the percentage of days per month which have any precipitation:

rain <- weather %>%
  mutate(month = format(date, '%m')) %>%
  group_by(month, stn_name) %>%
  summarise(rainy = length(which(prcp > 0)),
            days = n()) %>%
  ungroup %>%
  mutate(p = rainy / days * 100)

ggplot(data = rain,
       aes(x = month,
           y = p,
           color = stn_name,
           group = stn_name)) +
  geom_line() +
  theme_databrew() +
  scale_color_manual(name = '',
                     values = c('blue', 'red')) +
  labs(x = 'Month',
       y = 'Percentage',
       title = 'Percentage of days which are rainy') +
  ylim(0,100)

gsod: Utilities for exploring NOAA GSOD weather data

DataBrew

2017-12-13

Objectives

Installation

Basic use

Conclusion

Details