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r - 通过在 R 中使用 dplyr 计算来融化数据框和分组

问题描述

我的样本数据`

structure(list(state = c("AP", "AP"), district = c("krishna", 
"guntur"), rate = c(170104.5156, 1343.78134), growth_in_2016 = c(0.3844595, 
0.3678), growth_in_2017 = c(0.444595, 0.8445), growth_in_2018 = c(0.323699, 
0.36213), growth_in_2019 = c(0.5777, 0.35256), growth_in_2020 = c(0.2669097, 
0.9097)), class = c("data.table", "data.frame"), row.names = c(NA,-2L), .internal.selfref = <pointer: 0x00000000026c1ef0>)

`

我正在尝试按州和地区分组，然后计算每年的月增长率。

每月计算公式为：(1+rates*growth_in_year)^(1/12) -1 如有错误请指正

`

state     district     date        rates
AP        krishna    2016-12-31       x
AP        krishna    2017-01-31       y
AP        krishna    2017-02-28       z
AP        krishna    2017-03-30       a
AP        krishna    2017-04-31       b
AP        krishna    2017-05-30       c
AP        krishna    2017-06-31       d

其他地区也是如此。每个地区的费率必须每年递增。我想要日期格式而不是年份格式。

标签： rdataframedplyrplyrreshape2

解决方案

我们可以先将gather数据转为长格式，然后group_by state,district和year，找到新的月rate，从列名中提取年份，并创建一个list代表全年月份最后一天的日期，最后计算累积总和rate以获得增量每个月的价值。

library(dplyr)
library(tidyr)

df %>%
  gather(key, value, -(1:3)) %>%
  group_by(state, district, key) %>%
  mutate(rate = (1 + rate * value)^(1/12) - 1, 
         year = sub(".*(\\d{4})", "\\1", key),
        dates = list(seq(as.Date(paste0(year, "-01-01")),
                     as.Date(paste0(year, "-12-01")), by = "month")- 1)) %>%
  unnest() %>% 
  mutate(rate = cumsum(rate)) %>%
  select(-year)


#  state district  rate key            value dates     
#  <chr> <chr>    <dbl> <chr>          <dbl> <date>    
# 1 AP    krishna   1.52 growth_in_2016 0.384 2015-12-31
# 2 AP    krishna   3.04 growth_in_2016 0.384 2016-01-31
# 3 AP    krishna   4.56 growth_in_2016 0.384 2016-02-29
# 4 AP    krishna   6.08 growth_in_2016 0.384 2016-03-31
# 5 AP    krishna   7.60 growth_in_2016 0.384 2016-04-30
# 6 AP    krishna   9.12 growth_in_2016 0.384 2016-05-31
# 7 AP    krishna  10.6  growth_in_2016 0.384 2016-06-30
# 8 AP    krishna  12.2  growth_in_2016 0.384 2016-07-31
# 9 AP    krishna  13.7  growth_in_2016 0.384 2016-08-31
#10 AP    krishna  15.2  growth_in_2016 0.384 2016-09-30
# … with 110 more rows

数据

df <- structure(list(state = c("AP", "AP"), district = c("krishna", 
"guntur"), rate = c(170104.5156, 1343.78134), growth_in_2016 = c(0.3844595, 
0.3678), growth_in_2017 = c(0.444595, 0.8445), growth_in_2018 = c(0.323699, 
0.36213), growth_in_2019 = c(0.5777, 0.35256), growth_in_2020 = c(0.2669097, 
0.9097)), class = c("data.table", "data.frame"), row.names = c(NA, -2L))

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