dplyr，purrr，动态生成/计算 R 中的新列

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, eg 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.