Let’ say I have the following dataset.
How can I compute the average increase in times (I don’t know a proper word for this)?
Meaning you know like I want to tell the audience, that the average temperature in 2021 increased this many times compared to 1980.
Sample data (AvgTMean):
structure(list(year = 1980:2021, AvgTMean = c(24.2700686838937,
23.8852956598276, 25.094446596092, 24.1561175050287, 24.157183605977,
24.3047482638362, 24.7899738481466, 24.5756232655603, 24.5833086228592,
24.7344695534483, 25.3094451071121, 25.2100615173707, 24.3651692293534,
24.5423890611494, 25.2492166633908, 24.7005097837931, 24.2491591827443,
25.0912281781322, 25.0779264303305, 24.403294248319, 24.4983991453592,
24.4292324356466, 24.8179824927011, 24.7243948463075, 24.5086534543966,
24.2818632071983, 24.4567195220259, 24.8402224356034, 24.6574465515086,
24.5440715673563, 23.482670620977, 24.9979594684914, 24.5452453980747,
24.9271462811494, 24.7443215819253, 25.8929839790805, 25.1801908261063,
25.2079308058908, 25.0722425561207, 25.4554644289799, 25.4548979078736,
25.0756772250287)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-42L))
Code:
library(tidyverse)
AvgTMean %>% # Stuck...?
>Solution :
You can use the slope of the regression line to get the increase per year based on fitting a linear model to the data.
fm <- lm(AvgTMean ~ year, DF); fm
##
## Call:
## lm(formula = AvgTMean ~ year, data = DF)
##
## Coefficients:
## (Intercept) year
## -6.93064 0.01582 <-- 0.01582 degree increase per yr
plot(DF)
abline(fm)
