The Annual Data Challenge Expo is jointly sponsored by three American Statistical Association (ASA) Sections – Statistical Computing, Statistical Graphics, and Government Statistics.
The atmos
data set resides in the
nasaweather
package of the R programming language.
It contains a collection of atmospheric variables measured between 1995
and 2000 on a grid of 576 coordinates in the western hemisphere. The
data set comes from the 2025 ASA Data
Expo.
Some of the variables in the atmos data set are:
You can convert the temperature unit from Kelvin to Celsius with the formula
\[Celsius = kelvin – 273.15\]
And you can convert the result to Fahrenheit with the formula
\[ fahrenheit = celsius \times \frac{9}{5} + 32 \]
To analyze this data, we will use the following R packages: library(nasaweather) and library(tidyverse)
library(nasaweather)
library(tidyverse)
For the remainder of the report, we will look only at data from the year 1995 . We aggregate our data by location, using the R code below.
means <- atmos %>%
filter(year == year) %>%
group_by(long, lat) %>%
summarize(temp = mean(temp, na.rm = TRUE),
pressure = mean(pressure, na.rm = TRUE),
ozone = mean(ozone, na.rm = TRUE),
cloudlow = mean(cloudlow, na.rm = TRUE),
cloudmid = mean(cloudmid, na.rm = TRUE),
cloudhigh = mean(cloudhigh, na.rm = TRUE)) %>%
ungroup()
Is the relationship between ozone and temperature useful for understanding fluctuations in ozone? A scatterplot of the variables shows a strong, but unusual relationship.
We suspect that group level effects are caused by environmental conditions that vary by locale. To test this idea, we sort each data point into one of four geographic regions:
means$locale <- "north america"
means$locale[means$lat < 10] <- "south pacific"
means$locale[means$long > -80 & means$lat < 10] <- "south america"
means$locale[means$long > -80 & means$lat > 10] <- "north atlantic"
We suggest that ozone is highly correlated with temperature, but that a different relationship exists for each geographic region.