CODEWITHSUNDEEP
r
Xav64
Xav64

Reputation: 149

Calculating a rate of change between min and max years per subgroup

I am relatively new to R and sorry if the question was already asked but I obviously either can't understand the answers or can't find the right key words!

Here is my problem : I have a dataset that looks like that:

   Name           Year  Corg
 1 Bois 17        2001   1.7
 2 Bois 17        2007   2.1
 3 Bois 17        2014   1.9
 4 8-Toume        2000   1.7
 5 8-Toume        2015   1.4
 6 7-Richelien 2  2004   1.1
 7 7-Richelien 2  2017   1.5
 8 7-Richelien 2  2019   1.2
 9  Communaux     2003   1.4
 10 Communaux     2016   3.8
 11 Communaux     2019   2.4
 12 Cocandes      2000   1.7
 13 Cocandes      2014   2.1

As you can see, I sometimes have two or three rows of results per Name (theoretically I could even have 4, 5 or more rows per Name).

For each name, I would like to calculate the annual Corg rate of change between the highest year and lowest year.

More specificaly, I would like to do:

(Corg_of_highest_year/Corg_of_lowest_year)^(1/(lowest_year-highest_year))-1

Could you explain me how you would obtain a summarizing dataset that would look like that:

Name      Length_in_years   Corg_rate
Bois 17   13                0.9%
8-Toume   15                -1.3%
etc.

Upvotes: 2

Views: 85

Answers (4)

JDG
JDG

Reputation: 1364

Here is a solution using data.table:

df = data.table(df)

mat = df[, .(
   Rate = 100*((Corg[which.max(Year)] / Corg[which.min(Year)])^(1/diff(range(Year))) - 1)
), by = Name]

> mat
           Name       Rate
1:       Bois17  0.8592524
2:      8-Toume -1.2860324
3: 7-Richelien2  0.5817615
4:    Communaux  3.4261123
5:     Cocandes  1.5207989

Upvotes: 0

Ronak Shah
Ronak Shah

Reputation: 388962

We can do the calculation using group_by in dplyr

library(dplyr)

df %>%
  group_by(Name) %>%
  summarise(Length = diff(range(Year)), 
        Corg_rate = ((Corg[which.max(Year)]/Corg[which.min(Year)]) ^ 
                      (1/Length) - 1) * 100)

# A tibble: 5 x 3
#  Name         Length Corg_rate
#  <fct>         <int>     <dbl>
#1 7-Richelien2     15     0.582
#2 8-Toume          15    -1.29 
#3 Bois17           13     0.859
#4 Cocandes         14     1.52 
#5 Communaux        16     3.43

To perform the analysis with most recent year and the year with minimum 5 years of difference

df %>%
  group_by(Name) %>%  
  summarise(Length = max(Year) - max(Year[Year <= max(Year) - 5]),
            Corg_rate = (Corg[which.max(Year)]/Corg[Year == max(Year[Year <= (max(Year) - 5)])]) ^ (1/Length) - 1, 
            Corg_rate = Corg_rate * 100)



# Name         Length Corg_rate
#  <fct>         <int>     <dbl>
#1 7-Richelien2     15     0.582
#2 8-Toume          15    -1.29 
#3 Bois17            7    -1.42  
#4 Cocandes         14     1.52 
#5 Communaux        16     3.43

data

df <- structure(list(Name = structure(c(3L, 3L, 3L, 2L, 2L, 1L, 1L, 
1L, 5L, 5L, 5L, 4L, 4L), .Label = c("7-Richelien2", "8-Toume", 
"Bois17", "Cocandes", "Communaux"), class = "factor"), Year = c(2001L, 
2007L, 2014L, 2000L, 2015L, 2004L, 2017L, 2019L, 2003L, 2016L, 
2019L, 2000L, 2014L), Corg = c(1.7, 2.1, 1.9, 1.7, 1.4, 1.1, 
1.5, 1.2, 1.4, 3.8, 2.4, 1.7, 2.1)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"))

Upvotes: 2

R 2 minutes tutorials
R 2 minutes tutorials

Reputation: 171

By first creating an indicator of when the year is max and min in group Name and then spreading the Corg column into MAX_Corg (Corg of the max year) and MIN_corg we can later easily calculate the rate of change.

my_df %>% 
  group_by(Name) %>%
  mutate( #new column denoting the max and min
    year_max_min = ifelse(Year == max(Year), "MAX_corg",
                          ifelse(Year == min(Year), "MIN_corg",
                                 NA
                                 ) 
                          ) 
  ) %>% 
  filter(!(is.na(year_max_min))) %>% # removing NA
  group_by(Name,  year_max_min) %>%  #grouping by Name and max_min indicator
  summarise(Corg= Corg) %>% #summarising
  spread(year_max_min, Corg) %>% #spread the indicator into two column; MAX_corg and MIN_corg
  mutate(
    rate_of_change = (MAX_corg / MIN_corg)^(1/(MIN_corg - MAX_corg)) - 1 # calculates rate of change
    )

Upvotes: 1

Fnguyen
Fnguyen

Reputation: 1177

Use dplyr group_by(name) and then calculate your value. Here is an example

library(dplyr)

data %>%
group_by(name) %>%
summarise(Length = max(Year)-min(Year), Corg_End = sum(Corg[Year==max(Year), Corg_Start = sum(Corg[Year==min(Year)]))

This shows you the logic of grouping, i.e. after group_by(name) max(Year) will give out the highest year per name instead of overall. Using this logic calculating the change rate should be easy but I won't attempt to try for lack of reproducible data.

Upvotes: 0

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