Need to aggregate value by month in R script forecast report

Hi All,

I am very new to R script, i have a r script where i need to edit as per requirement, below is forecast chart where it shows the actuals and forecast values by days, i want to show the summarize value by month. And second image is the requirement.

1) Forecast2) Required chart

Here is my R code-using two columns from dataset for dates and values
### @date 2017-07-28
### R Script used for displaying a graph containing 3-month forecast
### aggregated by day.

Sys.setenv(TZ='UTC')

# Libraries
library('ggplot2')
library('forecast')
library('tseries')
library('lubridate')

# Determine which column is the date column
firstCol = dataset[1,1]
secondCol = dataset[1,2]
isFirstDate <- grepl("[0-9]+-[0-9]+-[0-9]+", toString(firstCol))
isSecondDate <- grepl("[0-9]+-[0-9]+-[0-9]+", toString(secondCol))
dateColumn <- 1
valuesColumn <- 2
if(!isFirstDate){
dateColumn <- 2
valuesColumn <- 1
}
theDates <- data.frame(dataset[dateColumn])
names(theDates) <- c("date")
theValues <- data.frame(dataset[valuesColumn])
names(theValues) <- c("values")
dataset <- cbind(theDates, theValues)

# Get information on dataset, create date sequence
count <- nrow(dataset)
firstdate = dataset$date[1]
firstyear = as.double(substring(dataset$date[1], 1, 4))
lastdate = dataset$date[count]
lastyear = as.double(substring(dataset$date[count], 1, 4))
dataset$date <- sprintf("%s%s", dataset$date, " 00:00:00 UTC")
dataset <- dplyr::arrange(dataset, date)

# Fill in the empty dates with zeros
# Get base values
i <- 2
x <- data.frame(dataset$values[1])
d <- data.frame(dataset$date[1])
names(x) <- c("values")
names(d) <- c("date")

# Loop through each value
while(i <= count){

# Get current day, month values
thisMonth <- substring(dataset$date[i],6,7)
lastMonth <- substring(dataset$date[i-1],6,7)
today <- substring(dataset$date[i],9,10)
yesterday <- substring(dataset$date[i-1],9,10)

# Add missing dates if applicable

# Add as many days as differenced from last date entry
if( ((as.numeric(yesterday) + 1) != as.numeric(today)) & (thisMonth == lastMonth)){

# How many days do we need to fill in?
daysToFill <- as.numeric(today)-as.numeric(yesterday)-1

# Fill in the days
while(daysToFill > 0){
# Add to new dataset
zeroValue <- data.frame(0)
names(zeroValue) <- c("values")
x <- rbind(x, zeroValue)
dateValue <- as.Date(sprintf("%s-%s-%s",substring(dataset$date[i],1,4), thisMonth, toString(as.numeric(substring(dataset$date[i],9,10)) - daysToFill)))
dateValue <- data.frame(sprintf("%s%s", as.Date(dateValue), " 00:00:00 UTC"))
names(dateValue) <- c("date")
d <- rbind(d, dateValue)
# decrement the daysToFill counter
daysToFill <- daysToFill - 1
}

# Case when the first days of month are skipped
} else if(thisMonth != lastMonth){
# Get amount of days to fill
daysToFill <- as.numeric(today)-1

# Add each day missing
while (daysToFill > 0){
# Append dataset
zeroValue <- data.frame(0)
names(zeroValue) <- c("values")
x <- rbind(x, zeroValue)
dateValue <- sprintf("%s-%s-%s",substring(dataset$date[i],1,4), thisMonth, as.character(daysToFill))
dateValue <- data.frame(sprintf("%s%s", as.Date(dateValue), " 00:00:00 UTC"))
names(dateValue) <- c("date")
d <- rbind(d, dateValue)

daysToFill <- daysToFill - 1
}
}

# If no missing values exist, then continue and then add to original dataset
i <- i + 1
}

# Append the dataset, then sort by date
dataset <- rbind(dataset, cbind(d,x))
# update after transforming the dataset
count <- nrow(dataset)
dataset <- dplyr::arrange(dataset, date)

#### Aggregate the Data (by day) ####
# Get base values
i <- 2
x <- data.frame(dataset$values[1])
d <- data.frame(dataset$date[1])
names(x) <- c("values")
names(d) <- c("date")
ptr <- 1

# Loop through each value
while(i <= count){

# Get current and previous months and years
thisMonth <- substring(dataset$date[i],6,7)
lastMonth <- substring(dataset$date[i-1],6,7)
today <- substring(dataset$date[i],9,10)
yesterday <- substring(dataset$date[i-1],9,10)

# Compare month values, either greater or lower
# Also compare year
if( (today > yesterday) || (thisMonth != lastMonth)){

# Increment pointer
ptr <- ptr + 1

# Add new row to Date representing new day
de <- data.frame(dataset$date[i])
names(de) <- c("date")
d <- rbind(d, de)

# Add new row to Values representing new aggregate
dx <- data.frame(dataset$values[i])
names(dx) <- c("values")
x <- rbind(x, dx)

# Otherwise, aggregate data
}
else if( today == yesterday){

x$values[ptr] <- x$values[ptr] + dataset$values[i]

}

# Finish loop, increment i
i <- i + 1
}

dataset <- cbind(d,x)

# update after transforming the dataset
count <- nrow(dataset)

# Create time series object and forecasted ARIMA fit model
y = ts(dataset$values, start=c(firstyear, yday(firstdate)), frequency=365)
y <- tsclean(y)
fit = forecast(auto.arima(y), 95)

# Plot everything and format axis
plot(fit, xaxt="n",main="Forecasted Headcount Requirements (per Day)", ylim=c(0,max(y)),cex.main=1.5)
a = seq(as.Date(firstdate, tz = "UTC"), by="month", length=95 + count)
axis(1, at = decimal_date(a), labels = format(a, "%Y %b %d"), cex.axis=1.0, ylab = 'value',FUN = sum)
abline(v = decimal_date(a), col='grey', lwd=0.5)