Error: Evaluation error: no applicable method

[thomas123]

Shanghai_zoo %>%
  mutate(Week = ceiling(day / 7),
         Day_str = factor(weekdays(day, abbreviate = TRUE),
                          levels = c('1', '2', '3', '4', '5', '6', '7'))) %>%
  group_by(year, season, month, Day_str) %>%
  summarize(DayMax = max(Value)) %>%
  ggplot(aes(Week, Day_str, fill = DayMax)) + 
  geom_tile(colour = "white") + 
  facet_grid(Year ~ Month) + 
  scale_fill_gradient2(high = "black", mid ="red", low = "blue") +
  labs(x="Week of Month",
       y="",
       title = "Time-Series Calendar Heatmap", 
       subtitle="Chengdu - PM2.5 level", 
       fill = "PM2.5 level")

It appears error following:

Error: Evaluation error: no applicable method for 'weekdays' applied to an object of class "c('integer', 'numeric')".

So should i paste my dataset here?

[mara]

From the weekdays() documentation, it sounds like the first argument (in your case, day, needs to be "inheriting from class POSIXt or Date."

Arguments

x an object inheriting from class "POSIXt" or "Date" .

abbreviate logical vector (possibly recycled). Should the names be abbreviated?

origin an length-one object inheriting from class "POSIXt" or "Date" .

… arguments for other methods

Could you turn this into a self-contained reprex (short for reproducible example)? It will help us help you if we can be sure we're all working with/looking at the same stuff.

install.packages("reprex")

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There's also a nice FAQ on how to do a minimal reprex for beginners, below:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

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