dplyr, purrr, dynamically generate/calculate new columns in R - r

Question

I have the following problem. I have a data frame/tibble that has (a lot) of columns that represent a value in different years, e.g. the number of inhabitants in a city at different points in time. I want to generate now columns that give me the growth rate (see pictures attached). It should be something like using mutate() while looping over the columns. I think that should be a common task but I can't find any hint how to do it.
Edit:
A minimal example could look like this:
## Minimal example
library(tidyverse)
## Given data frame
df <- tibble(
City = c("Melbourne", "Sydney", "Adelaide"),
year_2000 = c(100, 100, 205),
year_2001 = c(101, 100, 207),
year_2002 = c(102, 100, 209)
)
## Result
df <- df %>%
mutate(
gr_2000_2001 = year_2001/year_2000*100 - 100,
gr_2001_2002 = year_2002/year_2001*100 - 100
)
I want to find a way to automate/do the mutate command in a smart way, as I have to do it for 150 years.
enter image description here
enter image description here

Answer 1

The easiest way in this example would probably be to make your data tidy and then apply whatever formula you are using to calculate growth rates by using dplyr's lag()function to a data frame grouped by City:
## Minimal example
library(tidyverse)
df <- data.frame(City = c("Melbourne", "Sydney"),
year_2000 = c(100, 100),
year_2001 = c(101,100),
year_2002 = c(102, 102))
df %>%
gather(year, value, 2:4) %>%
group_by(City) %>%
mutate(growth = value/dplyr::lag(value,n=1))
The result is this:
# A tibble: 6 x 4
# Groups: City [2]
City year value growth
<fct> <chr> <dbl> <dbl>
1 Melbourne year_2000 100 NA
2 Sydney year_2000 100 NA
3 Melbourne year_2001 101 1.01
4 Sydney year_2001 100 1
5 Melbourne year_2002 102 1.01
6 Sydney year_2002 102 1.02
If you absolutely need the data in the format you provided in the screenshots, you can then apply spread() to reshape it into the original format. This is not generally recommended, however.