Date does not show up on x-axis in ggplot when ARIMA model is plotted

tidyverse
1

I am still somewhat new to R programming although I have figured out how to get some of my required forecasting models to work. The one problem that I have is that I can't get the Date column from my dataset to print on the x-axis of my final ARIMA ggplot rendering. It just shows an index going in intervals from 0 to 200 to 400 to 600. Everything else works except for the date display. Here is my full code:

sentiments = read.csv('trollscores.csv', stringsAsFactors = FALSE) sentiments$Date <- as.Date(sentiments$Date, format = "%m/%d/%Y")

library(ggplot2)
library(forecast)
library(tseries)
library(timetk)

ggplot(sentiments, aes(Date, sentiments$SentimentLM)) + geom_line() + 
scale_x_date('month')  + ylab("Sentiment Scores") +
xlab("")

count_ts = ts(sentiments[,c('SentimentLM')])
sentiments$clean_cnt = tsclean(count_ts)

ggplot() +
geom_line(data = sentiments, aes(x = Date, y = clean_cnt)) +
ylab('Sentiment Count')

sentiments$cnt_ma = ma(sentiments$clean_cnt, order=7) # using the clean 
count with no outliers
sentiments$cnt_ma30 = ma(sentiments$clean_cnt, order=30)

ggplot() +
geom_line(data = sentiments, aes(x = Date, y = clean_cnt, colour = 
"Counts")) 
+
geom_line(data = sentiments, aes(x = Date, y = cnt_ma,   colour = "Weekly 
Moving Average"))  +
geom_line(data = sentiments, aes(x = Date, y = cnt_ma30, colour = "Monthly 
Moving Average"))  +
ylab('Sentiment Score')

count_ma = ts(na.omit(sentiments$cnt_ma), frequency=30)
decomp = stl(count_ma, s.window="periodic")
deseasonal_cnt <- seasadj(decomp)
plot(decomp)

adf.test(count_ma, alternative = "stationary")

Acf(count_ma, main='')

Pacf(count_ma, main='')

count_d1 = diff(deseasonal_cnt, differences = 1)
plot(count_d1,col="blue")
adf.test(count_d1, alternative = "stationary")

Acf(count_d1, main='ACF for Differenced Series')
Pacf(count_d1, main='PACF for Differenced Series')

auto.arima(deseasonal_cnt, seasonal=FALSE)

fit<-auto.arima(deseasonal_cnt, seasonal=FALSE)
tsdisplay(residuals(fit), lag.max=45, main='(0,1,2) Model Residuals')

fit2 = arima(deseasonal_cnt, order=c(1,1,7))
tsdisplay(residuals(fit2), lag.max=15, main='Seasonal Model Residuals')

fcast <- forecast(fit2, h=30)
plot(fcast)

hold <- window(ts(deseasonal_cnt), start=700)

fit_no_holdout = arima(ts(deseasonal_cnt[-c(700:725)]), order=c(1,1,7))

fcast_no_holdout <- forecast(fit_no_holdout,h=25)
plot(fcast_no_holdout, main=" ")
lines(ts(deseasonal_cnt))

library(sweep)
library(tidyquant)

ne_sweep <- sw_sweep(fcast,timetk_idx = TRUE,rename_index = "date")

# Visualizing the forecast
ne_sweep %>%
ggplot(aes(x = date, y = value, color = key)) +
# Prediction intervals
geom_ribbon(aes(ymin = lo.95, ymax = hi.95), 
          fill = "#D5DBFF", color = NA, size = 0) +
geom_ribbon(aes(ymin = lo.80, ymax = hi.80, fill = key), 
          fill = "#596DD5", color = NA, size = 0, alpha = 0.8) +
# Actual & Forecast
geom_line(size = 1) + 
geom_point(size = 2) +
# Aesthetics
theme_tq(base_size = 16) +
scale_color_tq() +
labs(title = "Sentiment 3-Year Forecast", x = "", y = "Level of sentiment")
2

There's a lot going on in this code, and it's not clear to me which plot is the one you're having trouble with. I think it will be a lot more likely that someone will be able to help you if you can cut this down to a minimal, reproducible example? That means isolating just the ggplot2 code that is causing you trouble and providing enough sample data to demonstrate the issue.

For more on the nitty-gritty of how to do this:

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