I have an annual mean timeseries dataset for 15 years, and I am trying to find the average change/increase/decrease in this timeseries.
The timeseries I have is spatial (average values for each grid-cell/pixel, years repeat).
How can I do this in R via dplyr?
Sample data
year = c(2005, 2005, 2005, 2005, 2006, 2006, 2006, 2006, 2007, 2007, 2007, 2007, 2008, 2008, 2008, 2008)
Tmean = c(24, 24.5, 25.8,25, 24.8, 25, 23.5, 23.8, 24.8, 25, 25.2, 25.8, 25.3, 25.6, 25.2, 25)
Code
library(tidyverse)
df = data.frame(year, Tmean)
change = df$year %>%
# Sort by year
arrange(year) %>%
mutate(Diff_change = Tmean - lag(Tmean), # Difference in Tmean between years
Rate_percent = (Diff_change / year)/Tmean * 100) # Percent change # **returns inf values**
Average_change = mean(change$Rate_percent, na.rm = TRUE)
>Solution :
We can put those vectors into a data frame to use with dplyr (…if that’s how you want to do it; this is straightforward with base R as well).
library(dplyr)
df <- data.frame(year, Tmean)
change <- df %>%
arrange(year) %>%
mutate(Diff_change = Tmean - lag(Tmean), # Difference in Tmean between years
Diff_time = year - lag(year),
Rate_percent = (Diff_change/Diff_time)/lag(Tmean) * 100) # Percent change
Average_change = mean(change$Rate_percent, na.rm = TRUE)
Results (with updated question data)
> change
year Tmean Diff_change Rate_percent
1 2005 24.0 NA NA
2 2005 24.5 0.5 2.0833333
3 2005 25.8 1.3 5.3061224
4 2005 25.0 -0.8 -3.1007752
5 2006 24.8 -0.2 -0.8000000
6 2006 25.0 0.2 0.8064516
7 2006 23.5 -1.5 -6.0000000
8 2006 23.8 0.3 1.2765957
9 2007 24.8 1.0 4.2016807
10 2007 25.0 0.2 0.8064516
11 2007 25.2 0.2 0.8000000
12 2007 25.8 0.6 2.3809524
13 2008 25.3 -0.5 -1.9379845
14 2008 25.6 0.3 1.1857708
15 2008 25.2 -0.4 -1.5625000
16 2008 25.0 -0.2 -0.7936508
> Average_change
[1] 0.3101632