Time Series

Exercise: Dữ liệu chuỗi thời gian Time Series

Mô phỏng dữ liệu chuỗi thời gian

---

library(ggplot2)
library(scales)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

data("economics")
economics %>% head()

## # A tibble: 6 × 6
##   date         pce    pop psavert uempmed unemploy
##   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
## 1 1967-07-01  507. 198712    12.6     4.5     2944
## 2 1967-08-01  510. 198911    12.6     4.7     2945
## 3 1967-09-01  516. 199113    11.9     4.6     2958
## 4 1967-10-01  512. 199311    12.9     4.9     3143
## 5 1967-11-01  517. 199498    12.8     4.7     3066
## 6 1967-12-01  525. 199657    11.8     4.8     3018

ggplot(data= economics, mapping= aes(x= date,y= psavert)) +
geom_line()+
labs(title= "PersonalSavingsRate",
x= "Date",
y= "PersonalSavingsRate")

#Formore attractivegraphs
#https://www.r-bloggers.com/2013/08/date-formats-in-r/
ggplot(data= economics, mapping= aes(x= date,y= psavert)) +
geom_line(color= "indianred3", linewidth= 1) +
geom_smooth()+
scale_x_date(date_breaks = "5 years", date_labels = "%b-%y") +
labs(title= "PersonalSavingsRate",
subtitle= "From1967to2015",
x= "",
y= "PersonalSavingsRate") +
theme_minimal()

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

library(quantmod)

## Loading required package: xts

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## 
## ######################### Warning from 'xts' package ##########################
## #                                                                             #
## # The dplyr lag() function breaks how base R's lag() function is supposed to  #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or       #
## # source() into this session won't work correctly.                            #
## #                                                                             #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop           #
## # dplyr from breaking base R's lag() function.                                #
## #                                                                             #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning.  #
## #                                                                             #
## ###############################################################################

## 
## Attaching package: 'xts'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

## Loading required package: TTR

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(dplyr)
library(ggplot2)

# ======================
# FPT (HOSE)
# ======================
fpt_xts <- getSymbols(
  "FPT.VN",
  auto.assign = FALSE,
  from = "2021-05-05"
)

## Warning: FPT.VN contains missing values. Some functions will not work if
## objects contain missing values in the middle of the series. Consider using
## na.omit(), na.approx(), na.fill(), etc to remove or replace them.

fpt <- data.frame(
  Date = as.Date(index(fpt_xts)),
  Close = as.numeric(Cl(fpt_xts)),
  Company = "FPT"
) %>%
  na.omit()

# ======================
# Vingroup (HOSE)
# ======================
vic_xts <- getSymbols(
  "VIC.VN",
  auto.assign = FALSE,
  from = "2021-05-05"
)

## Warning: VIC.VN contains missing values. Some functions will not work if
## objects contain missing values in the middle of the series. Consider using
## na.omit(), na.approx(), na.fill(), etc to remove or replace them.

vingroup <- data.frame(
  Date = as.Date(index(vic_xts)),
  Close = as.numeric(Cl(vic_xts)),
  Company = "Vingroup"
) %>%
  na.omit()

# ======================
# Combine
# ======================
data_series <- bind_rows(fpt, vingroup)

# ======================
# Plot
# ======================
ggplot(data_series, aes(x = Date, y = Close, color = Company)) +
  geom_line(linewidth = 1) +
  theme_minimal() +
  labs(
    title = "Vietnam Stocks (HOSE)",
    subtitle = "FPT vs Vingroup",
    x = "Date",
    y = "Closing Price (VND)",
    caption = "Source: Yahoo Finance"
  )

library(ggplot2)
library(dplyr)
library(tidyr)
library(gapminder)

data(gapminder)

# Lọc dữ liệu
plotdata_long <- filter(gapminder, continent == "Asia" & year %in% c(1952, 2007)) %>%
  select(country, year, lifeExp)

# Chuyển sang wide format
plotdata_wide <- spread(plotdata_long, year, lifeExp)
names(plotdata_wide) <- c("Country", "year1952", "year2007")

# Dumbbell chart bằng ggplot2 thuần
ggplot(plotdata_wide, aes(y = reorder(Country, year1952))) +
  geom_segment(aes(x = year1952, xend = year2007, yend = Country), color = "grey") +
  geom_point(aes(x = year1952), color = "blue", size = 3) +
  geom_point(aes(x = year2007), color = "red", size = 3) +
  theme_minimal() +
  labs(
    title = "Change in Life Expectancy",
    subtitle = "From 1952 to 2007",
    x = "Life Expectancy (years)",
    y = ""
  )