如何从 csv 预测 R 中的下降趋势

在等待你的数据和更具体的要求后，你可以用tidyverse和ggplot的趋势线绘制线条图.geom_smooth()函数通过lm添加一个线性模型，您可以将se设置为TRUE以确定可信区间.

library(tidyverse)

carbon <- read_csv("https://datahub.io/core/co2-fossil-global/r/global.csv")

carbon %>% 
  ggplot(aes(x=Year, y=Total)) + geom_line() + 
  geom_smooth(method = lm, se = FALSE) +
  labs(title = "Global CO2 Emissions from Fossil Fuels since 1751",
       subtitle = "Source: https://datahub.io/core/co2-fossil-global") + 
  theme_minimal()

您是否计划在回归模型中加入其他内容？

Update个

这是作者的数据.

carbon <- data.frame(fossil_fuel =c(69.50426, 68.33529, 68.08371, 68.65155, 72.15907, 71.78847, 72.04432, 73.28336, 73.99019, 73.55609, 74.91764, 77.81596, 78.85325, 73.80238, 75.30230, 70.61190, 70.01013, 67.43910, 64.03521, 57.17204, 55.26921, 52.13594, 49.62602, 47.03623),
          year <- seq(from = 1996, to = 2019, by = 1))

carbon %>% 
  ggplot(aes(x=year, y=fossil_fuel)) + geom_line() + 
  geom_smooth(method = lm, se = FALSE) +
  labs(x = "Year", y = "Fossil Fuel %") + 
  theme_minimal()

lm(fossil_fuel ~ year, data=carbon)

Coefficients:
(Intercept)         year  
    1693.89        -0.81