使用 facet 绘制线性时间趋势

Question

我想使用 facet wrap:

在数据点上叠加线性趋势回归模型

我要找的线性回归是

value ~ time 其中时间是 seq(1:length(dates))

在下面的示例中，A 组有 3 个观察值，B 组有 4 个观察值

我的密码是

dates = as.Date(c("2017-01-01", "2017-02-01", "2017-03-01", "2017-01-01", "2017-02-01", 
    "2017-03-01", "2017-04-01"))
group = c("A", "A", "A", "B", "B", "B", "B")
value = c(2, 3, 1, 1, 3, 2, 5)
data = data.frame(dates = dates, group = group, value = value)


ggplot(data = data, aes(x = factor(dates), y = value, group = 1)) + 
    geom_point() + 
    geom_line() + 
    geom_smooth(method = "lm", formula = value ~ seq(1:length(dates))) + 
    facet_wrap(~group, ncol = 1, scales = "free_y")

我还想在图表上打印回归的斜率系数

有什么想法吗？

Answer 1

你可以试试这个：

data$id <- ave(data$value, data$group, FUN=seq_along)

ggplot(data = data, aes(x = factor(dates), y = value, group = group)) + 
  geom_point() + 
  geom_line() + 
  geom_smooth(method = "lm", aes(x=id, y=value)) + 
  facet_wrap(~group, ncol = 1, scales = "free_y")

Answer 2

借助一点 tidyverse 魔法，您可以将模型保存在 data.frame 中，这样您就可以随心所欲地绘制：

library(tidyverse)

data %>% nest(-group) %>% 
    mutate(model = map(data, ~lm(value ~ dates, data = .x)), 
           predictions = map(model, predict), 
           slope = map_dbl(model, ~coef(.x)[2])) %>% 
    unnest(data, predictions) %>% 
    ggplot(aes(dates, value)) + 
    geom_line(color = 'gray50') + 
    geom_point() + 
    geom_line(aes(y = predictions), color = 'blue', size = .75) + 
    geom_text(aes(label = paste('beta==', round(slope, 5)), 
                  x = min(dates) + 1, 
                  y = max(value)), 
              hjust = 0, parse = TRUE) + 
    facet_wrap(~group, ncol = 1, scales = 'free_y')

如果您愿意，也可以手动绘制置信区间图，或者像往常一样使用 geom_smooth：

data %>% nest(-group) %>% 
    mutate(model = map(data, ~lm(value ~ dates, data = .x)), 
           slope = map_dbl(model, ~coef(.x)[2])) %>% 
    unnest(data) %>% 
    ggplot(aes(dates, value)) + 
    geom_line(color = 'gray50') + 
    geom_point() + 
    geom_smooth(method = 'lm') +
    geom_text(aes(label = paste('beta==', round(slope, 5)), 
                  x = min(dates) + 1, 
                  y = max(value)), 
              hjust = 0, parse = TRUE) + 
    facet_wrap(~group, ncol = 1, scales = 'free_y')

请注意，此方法的计算量更大，因为 geom_smooth 会重新拟合模型。如果您愿意，broom 和 modelr 也可用于修改模型。

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