如何使用 R 进行 Plot 折线图进行时间序列分析

Question

I am trying to plot a line chart using Date-time and no of tweets at that period of date and time in R.

library(ggplot2)
df1 <- structure(list(Date = structure(c(1L, 1L, 2L, 1L, 1L, 1L), .Label = c("2020-03-12", 
            "2020-03-13"), class = "factor"), Time = structure(c(1L, 1L, 2L, 
            3L, 4L, 5L), .Label = c("00:00:00Z", "00:00:01Z", "00:10:04Z", 
            "00:25:12Z", "01:00:02Z"), class = "factor"), Text = structure(c(5L, 
            3L, 6L, 4L, 2L, 1L), .Label = c("The images of demonstrations and gathering", "Premium policy get activate by company abc", 
            "Launches of rocket", "Premium policy get activate by company abc", 
            "Technology makes trend", "The images of demonstrations and gatherings", 
            "Weather forecasting by xyz"), class = "factor")), class = "data.frame", row.names = c(NA, 
            -6L))
ggplot(df1, aes(x = Date, y = text(count)) + geom_line(aes(color = variable), size = 1)

I tried the above code to plot desired result but got an error. Dataset given like that in csv format.

Date         Time                     Text
2020-03-12   00:00:00Z                The images of demonstrations and gatherings
2020-03-12   00:00:00Z                Premium policy get activate by company abc
2020-03-12   00:00:01Z                Weather forecasting by xyz 
2020-03-12   00:10:04Z                Technology makes trend
2020-03-12   00:25:12Z                Launches of rocket 
2020-03-12   01:00:02Z                Government launch new policy to different sector improvement

I have a dataset of nearly 15 days and want to plot the line chart to visualize the number of tweets (given in text column) to see the trend of tweets on different time and date.

Answer 1

df1 <- structure(list(Date = structure(c(1L, 1L, 2L, 1L, 1L, 1L), .Label = c("3/12/2020", 
            "3/13/2020"), class = "factor"), Time = structure(c(1L, 1L, 2L, 
            3L, 4L, 5L), .Label = c("00:00:00Z", "00:00:01Z", "00:10:04Z", 
            "00:25:12Z", "01:00:02Z"), class = "factor"), Text = structure(c(5L, 
            3L, 6L, 4L, 2L, 1L), .Label = c("Government launch new policy to different sector", 
            "Launches of rocket", "Premium policy get activate by company abc", 
            "Technology makes trend", "The images of demonstrations and gatherings", 
            "Weather forecasting by xyz"), class = "factor"), X = structure(c(1L, 
            1L, 1L, 1L, 1L, 2L), .Label = c("", "improvement"), class = "factor")), class = "data.frame", row.names = c(NA, 
            -6L))                                                      

Creating the dataset df1 as above then running this gives you required plot for hour

library(tidyverse)
library(lubridate)

df1 %>% 
  mutate(Time=hms(Time),
         Date=mdy(Date),
    hour=hour(Time)) %>% 
  count(hour) %>% 
  ggplot(aes(hour,n,group=1))+geom_line()+geom_point()

Answer 2

Is this what you are after?

library(dplyr)
library(lubridate)
library(stringr)
library(ggplot2)

Answer with your data

To demonstrate data wrangling.

# your data; 
df1 <- structure(list(Date = structure(c(1L, 1L, 2L, 1L, 1L, 1L), 
                                       .Label = c("2020-03-12","2020-03-13"), 
                                       class = "factor"), 
                      Time = structure(c(1L, 1L, 2L,3L, 4L, 5L), 
                                       .Label = c("00:00:00Z", "00:00:01Z", "00:10:04Z","00:25:12Z", "01:00:02Z"),
                                       class = "factor"),
                      Text = structure(c(5L,3L, 6L, 4L, 2L, 1L),
                                       .Label = c("The images of demonstrations and gathering", "Premium policy get activate by company abc",
                                                  "Launches of rocket", "Premium policy get activate by company abc",
                                                  "Technology makes trend", "The images of demonstrations and gatherings", "Weather forecasting by xyz"), class = "factor")),
                 class = "data.frame", row.names = c(NA,-6L))

# data wrangle
df2 <- 
  df1 %>% 
  # change all variables from factors to character
  mutate_all(as.character) %>%
  mutate(Time = str_remove(Time, "Z$"), #remove the trailing 'Z' from Time values 
         dt = ymd_hms(paste(Date, Time, sep = " ")), # change text into datetime format using lubridtate::ymd_hms
         dt = ceiling_date(dt, unit="hour")) %>% # round to the end of the named hour, separated for clarity
  group_by(dt) %>%  
  summarise(nr_tweets = n())

# plot

p1 <- ggplot(df2, aes(dt, nr_tweets))+
        geom_line()+
        scale_x_datetime(date_breaks = "1 day", date_labels = "%d/%m")+
        ggtitle("Data from question `df1`")

Answer with made up large dataset

tib <- tibble(dt = sample(seq(ISOdate(2020,05,01), ISOdate(2020,05,15), by = "sec"), 10000, replace = TRUE),
             text = sample(c(letters[1:26], LETTERS[1:26]), 10000, replace = TRUE))


tib1 <- 
  tib %>% 
  mutate(dt = round_date(dt, unit="hour"))%>% 
  group_by(dt) %>%  
  summarise(nr_tweets = n())


p2 <- ggplot(tib1, aes(dt, nr_tweets))+
        geom_line()+
        scale_x_datetime(date_breaks = "1 day", date_labels = "%d/%m")+
        ggtitle("Result using `tib` data made up to answer the question")
  

p1/p2

^{Created on 2020-05-13 by the reprex package (v0.3.0)}