A random set of monitoring locations were taken from NOAA data that had both years of interest (1948 and 2018) as well as data for both summary metrics of interest (dx70 and dx90, which are described below).
Format
A data frame with 197 observations on the following 7 variables.
- station
Station ID.
- latitude
Latitude of the station.
- longitude
Longitude of the station.
- dx70_1948
Number of days above 70 degrees in 1948.
- dx70_2018
Number of days above 70 degrees in 2018.
- dx90_1948
Number of days above 90 degrees in 1948.
- dx90_2018
Number of days above 90 degrees in 2018.
Source
https://www.ncdc.noaa.gov/cdo-web, retrieved 2019-04-24.
Details
Please keep in mind that these are two annual snapshots, and a complete analysis would consider much more than two years of data and much additional information for those years.
Examples
# Data sampled are from the US, Europe, and Australia.
# This geographic limitation may be due to the particular
# years considered, since locations without both 1948 and
# 2018 were discarded for this (simple) dataset.
plot(climate70$longitude, climate70$latitude)
plot(climate70$dx70_1948, climate70$dx70_2018)
abline(0, 1, lty = 2)
plot(climate70$dx90_1948, climate70$dx90_2018)
abline(0, 1, lty = 2)
hist(climate70$dx70_2018 - climate70$dx70_1948)
hist(climate70$dx90_2018 - climate70$dx90_1948)
t.test(climate70$dx70_2018 - climate70$dx70_1948)
#>
#> One Sample t-test
#>
#> data: climate70$dx70_2018 - climate70$dx70_1948
#> t = 2.0558, df = 196, p-value = 0.04113
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> 0.1663027 8.0062861
#> sample estimates:
#> mean of x
#> 4.086294
#>
t.test(climate70$dx90_2018 - climate70$dx90_1948)
#>
#> One Sample t-test
#>
#> data: climate70$dx90_2018 - climate70$dx90_1948
#> t = 2.3702, df = 196, p-value = 0.01875
#> alternative hypothesis: true mean is not equal to 0
#> 95 percent confidence interval:
#> 0.4868224 5.3101319
#> sample estimates:
#> mean of x
#> 2.898477
#>