R中按组划分的预测图中的相交线

时间：2018-07-16 12:05:27

标签： r ggplot2 dplyr

在我的数据集中，我执行了除以分组（存储）的预测。 在这里

df=structure(list(Store = c(173L, 173L, 173L, 173L, 173L, 173L, 
173L, 173L, 173L, 173L, 173L, 173L, 173L, 173L, 174L, 174L, 174L, 
174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 
173L, 173L, 173L, 173L, 173L, 173L, 173L, 173L, 173L, 173L, 173L, 
173L, 173L, 173L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 
174L, 174L, 174L, 174L, 174L, 174L, 173L, 173L, 173L, 173L, 173L, 
173L, 173L, 173L, 173L, 173L, 173L, 173L, 173L, 173L, 174L, 174L, 
174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 174L, 
174L), DayOfWeek = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), Date = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L), .Label = c("15.07.2015", "16.07.2015", "17.07.2015"
), class = "factor"), Sales = structure(c(27L, 12L, 16L, 18L, 
9L, 4L, 26L, 23L, 10L, 19L, 7L, 20L, 25L, 5L, 17L, 2L, 11L, 8L, 
3L, 22L, 15L, 14L, 28L, 6L, 1L, 24L, 13L, 21L, 27L, 12L, 16L, 
18L, 9L, 4L, 26L, 23L, 10L, 19L, 7L, 20L, 25L, 5L, 17L, 2L, 11L, 
8L, 3L, 22L, 15L, 14L, 28L, 6L, 1L, 24L, 13L, 21L, 27L, 12L, 
16L, 18L, 9L, 4L, 26L, 23L, 10L, 19L, 7L, 20L, 25L, 5L, 17L, 
2L, 11L, 8L, 3L, 22L, 15L, 14L, 28L, 6L, 1L, 24L, 13L, 21L), .Label = c("10318.344", 
"10725.268", "10765.647", "13546.236", "3418.328", "3939.406", 
"4089.442", "4377.643", "5196.012", "5487.437", "5778.296", "6200.403", 
"6216.929", "6331.589", "6404.693", "6472.833", "6693.678", "6751.922", 
"6770.161", "7510.433", "7736.447", "7743.879", "8107.569", "8119.046", 
"9087.104", "9326.839", "9718.452", "9855.327"), class = "factor"), 
    Promo = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), LinearRegressionForecast = structure(c(22L, 
    9L, 15L, 14L, 8L, 1L, 26L, 20L, 6L, 17L, 3L, 18L, 25L, 2L, 
    11L, 27L, 7L, 5L, 24L, 13L, 10L, 12L, 23L, 4L, 28L, 21L, 
    16L, 19L, 22L, 9L, 15L, 14L, 8L, 1L, 26L, 20L, 6L, 17L, 3L, 
    18L, 25L, 2L, 11L, 27L, 7L, 5L, 24L, 13L, 10L, 12L, 23L, 
    4L, 28L, 21L, 16L, 19L, 22L, 9L, 15L, 14L, 8L, 1L, 26L, 20L, 
    6L, 17L, 3L, 18L, 25L, 2L, 11L, 27L, 7L, 5L, 24L, 13L, 10L, 
    12L, 23L, 4L, 28L, 21L, 16L, 19L), .Label = c("10672.724", 
    "2286.724", "2940.339", "3038.273", "3265.624", "3387.729", 
    "3475.001", "3568.385", "4527.949", "5042.683", "5131.816", 
    "5196.835", "5204.855", "5239.113", "5572.545", "5605.564", 
    "5656.971", "6216.276", "6510.814", "6749.251", "6901.256", 
    "7248.194", "7310.538", "7549.539", "7585.489", "7842.506", 
    "8371.118", "8487.823"), class = "factor"), Type = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Forecast", "obser"
    ), class = "factor")), .Names = c("Store", "DayOfWeek", "Date", 
"Sales", "Promo", "LinearRegressionForecast", "Type"), class = "data.frame", row.names = c(NA, 
-84L))

有列类型。它指示什么类型（预测或强制）。 分别对每个商店进行了预测。 我需要为每个商店（分别为173和174）创建一个预测图，在该图上将预测线和观测线相交（针对日期和销售变量） 怎么做？

所需的情节

enter image description here

对不起，我的油漆

1 个答案:

答案 0 :(得分：1)

数据集中有很多事情需要完成：

首先，因子必须是数字，日期必须是日期对象。我已经使用mutate

然后我们可以按Store和Date进行分组，并使用summarise来查找每日总销售额和每日总预测值

然后使用gather使桌子整齐，然后我们得到clean_df

然后只需使用ggplot制作图enter image description here 

clean_df <- df %>% 
  mutate(Date = as.Date(Date, format = "%d.%m.%Y"),
         Sales = as.numeric(Sales),
         LinearRegressionForecast = as.numeric(LinearRegressionForecast)) %>% 
  select(Date,Sales, LinearRegressionForecast,Store) %>% 
  group_by(Store, Date) %>% 
  summarise(total_daily_sales = sum(Sales, na.rm = T),
            total_daily_forecast = sum(LinearRegressionForecast, na.rm = T)) %>% 
  gather(type, value, -Date, -Store)

clean_df %>% 
  ggplot(aes(x = Date, y = value, col = type)) + 
  geom_line() + 
  facet_wrap(~Store)

代码在这里

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