How to draw a beautiful forecast plot in R ?

Hi, I'm a new beginner of R. I predict some economic burden by using ARIMA MODEL,and I want to draw a beautiful forecast line ,but I don't know how to do it!
This is my coding:

``````g3_time=ts(G_forecast\$us,start = min(G_forecast\$year),max(G_forecast\$year))
plot(g3_time)
ndiffs(g3_time)
dus <- diff(g3_time,2)
plot(dus)
acf(dus)
pacf(dus)
fit <- arima(g3_time,order=c(0,2,0))
fit
accuracy(fit)
qqnorm(fit\$residuals)
qqline(fit\$residuals)
Box.test(fit\$residuals,type="Ljung-Box")
f_p1 <- forecast(fit,h=11,level=c(95))
plot(f_p1)
lines(f_p1\$fitted,col="green")
lines(g3_time,col="orange")
``````

This is my final plot

HOW CAN I DRAW THIS PLOT BEAUTIFUL?
Thank you so much

My personal suggestions to you are:

1. Use ggplot. ggplot gives beautiful graphs.
2. If you are ready to go a step ahead, use the package `plotly`. It gives you interactivity.
3. Use nice themes (like theme_minimal and hundreds of themes are available in R).
4. Increase the size of lines. (give size 1.5 for example).

Hope it helps,
Regards

Thank you so much for your reply, kuttan.But the problem is I don't know how to put the three different values(existing values&forecast values&fitted values)in one table ,and then I can use ggplot2

It would be useful if you can provide a sample data of yours.

SURE! THIS IS MY EXAMPLE DATA:

``````G_forecast<-structure(list(year = 1990:2019, us = c(25578.2787763689, 25955.303661286,
26607.4952715988, 27284.2726771786, 28470.3454469103, 29723.5508466642,
31386.8965981198, 33484.2558245705, 35484.6077885256, 37888.8342144187,
40746.5610245587, 42754.7873459213, 45094.2076872047, 47974.2296121668,
51638.4571215021, 55204.5341690812, 59170.5868424335, 63426.3357531801,
66401.4002328101, 67191.3108071306, 72055.2297680338, 77662.0388629392,
84823.0535243136, 93065.2340556945, 101378.210402337, 108379.42056402,
114001.553113478, 119217.426273179, 122812.286428169, 124737.879190838
)), class = "data.frame", row.names = c(NA, -30L))
``````

Thank you so much!!!!

Hi,guys. I found a way to show beautiful forecast plot in StackOverflow(This is the link:How to plot a subset of forecast in R? - Stack Overflow), this is the code:

``````   library(lubridate)
library(tidyverse)
library(scales)
library(ggfortify)

# Convert pred from list to data frame object
df1 <- fortify(pred) %>% as_tibble()

# Convert ts decimal time to Date class
df1\$Date <- as.Date(date_decimal(df1\$Index), "%Y-%m-%d")
str(df1)

# Remove Index column and rename other columns
# Select only data pts after 2017
df1 <- df1 %>%
select(-Index) %>%
filter(Date >= as.Date("2017-01-01")) %>%
rename("Low95" = "Lo 95",
"Low80" = "Lo 80",
"High95" = "Hi 95",
"High80" = "Hi 80",
"Forecast" = "Point Forecast")
df1

### Updated: To connect the gap between the Data & Forecast,
# assign the last non-NA row of Data column to the corresponding row of other columns
lastNonNAinData <- max(which(complete.cases(df1\$Data)))
df1[lastNonNAinData, !(colnames(df1) %in% c("Data", "Fitted", "Date"))] <- df1\$Data[lastNonNAinData]

# Or: use [geom_segment](http://ggplot2.tidyverse.org/reference/geom_segment.html)

plt1 <- ggplot(df1, aes(x = Date)) +
ggtitle("Ticket amount") +
xlab("Time frame") + ylab("Quantity") +
geom_ribbon(aes(ymin = Low95, ymax = High95, fill = "95%")) +
geom_ribbon(aes(ymin = Low80, ymax = High80, fill = "80%")) +
geom_point(aes(y = Data, colour = "Data"), size = 4) +
geom_line(aes(y = Data, group = 1, colour = "Data"),
linetype = "dotted", size = 0.75) +
geom_line(aes(y = Fitted, group = 2, colour = "Fitted"), size = 0.75) +
geom_line(aes(y = Forecast, group = 3, colour = "Forecast"), size = 0.75) +
scale_x_date(breaks = scales::pretty_breaks(), date_labels = "%b %y") +
scale_colour_brewer(name = "Legend", type = "qual", palette = "Dark2") +
scale_fill_brewer(name = "Intervals") +
guides(colour = guide_legend(order = 1), fill = guide_legend(order = 2)) +
theme_bw(base_size = 14)
plt1

``````

I tried so many times, but I still can't repeat his results in my data, can you guys help me?
Thank you soooooo much!!!

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