R时间序列多线图

Question

I have a very big dataset that I'd like to illustrate using plotly in R. A sample of my dataset is shown below:

    > new_data_2
# Groups:   newdatum [8]
  date       activity       totaal
  <date>     <fct>          <int>
1 2019-11-21 N11            144
2 2019-09-22 N11            129
3 2019-05-15 N22            117
4 2019-01-23 N22            12
5 2019-07-04 N22            12
6 2019-07-18 N22            12
...

For every activity I want to display the amount (totaal) per date (date) in a time series plot. Somehow I don't get it right in R. Somehow I need to group my activity to display, but I can't figure it out.

new_data_2 %>% 
group_by(activity) %>% 
plot_ly(x=new_data_2$newdatum) %>% 
add_lines(y=~new_data_2$totaal, color = ~factor(newdatum))

It does display an empty plot and not with the 'activity' on the left side.

What i want to achieve is:

Answer 1

You're on the right track, but after the group_by() you need to tell R to do something to the groups.

new_data_2 %>%
  group_by(activity, date) %>%  # use two groupings since you want by activity & date 
  summarise(totaal_2 = sum(totaal))

That should get to the dataframe you're looking for. You can use ggplot & plotly on it from there.

I would recommend reshaping the data first (as above), saving it as a new object, and then graphing it. Doing it this way helps you see each step along the way. Pipes %>% are great, but can make each step difficult to see.

Answer 2

This might not be very obvious at first, but the structure of your data is ideal for plot with multiple time series. You don't even need to worry with the group_by function. Your dataset seems to hava a long format where the dates in the date column and the names in activity column are not unique. But you will have only one variable per activity and date.

Given the correct specifications, plot_ly() will group your data using color=~activity like this: p <- plot_ly(new_data2, x = ~date, y = ~totall, color = ~activity) %>% add_lines() . Since you haven't provided a data sample that is large enough, I'll use the built-in dataset economics_long to show you how you can do this. First of all, notice how the structure of my sampled dataset matches yours:

           date variable value
1    1967-07-01  psavert  12.5
2    1967-08-01  psavert  12.5
3    1967-09-01  psavert  11.7
4    1967-10-01  psavert  12.5
5    1967-11-01  psavert  12.5
6    1967-12-01  psavert  12.1
...

Plot:

Code:

library(plotly)
library(dplyr)

# data
data("economics_long")
df <- data.frame(economics_long)

# keep only some variables that have values on a comparable level
df <- df %>% filter(!(variable %in% c('pop', 'pce', 'unemploy')))

# plotly time series
p <- plot_ly(df, x = ~date, y = ~value, color = ~variable) %>%
  add_lines()

# show plot
p