Plotly Time series forecasting in R - modify default x axis and y axis range

I have a query on Time series forecasting with plotly. I have built a forecasting chart however few things needs to be changed but its not working for me. Please find the attachment for details and kindly let me know the correction or suggestion for R code.

Query:

currently X Axis is showing 2,016.5, 2017 , 2017.5 , etc however I want it to show yearmonth like 2016.04,2014,05,etc.
Please be informed yearmonth is a field in data, please refer attached data

currently Y Axis is showing lables with the difference of 50 K , however I want it to show as 5 K, 10K,15K, etc.

Below is R code used :

library(forecast)
library(plotly)
ord <- order(ds$`Calendar Year-DISPLAY_KEY`,ds$`Calendar Month-DISPLAY_KEY`)
sds <- ds[ord,]
firstRec <- sds[1,]
mn <- as.numeric(firstRec$'Calendar Month-DISPLAY_KEY')
yr <- as.numeric(as.character(firstRec$'Calendar Year-DISPLAY_KEY'))
tm <- ts(data = sds$Calc_Best_DSO , start= c(yr,mn) ,frequency = 12)
plot(tm)
tm[is.na(tm)] <-0

fit <- ets(tm)
fore <- forecast(fit, h = 3, level = c(80, 95))

plot_ly() %>%
add_lines(x = time(tm), y = tm,hoverinfo = "text",
color = I("black"), name = "observed",text= paste("Month: ",sds$`Calendar Month-DISPLAY_KEY`,
"<br>","Year: ",sds$`Calendar Year-DISPLAY_KEY`,
"<br>","DSO: ",sds$Calc_Best_DSO)) %>%
add_ribbons(x = time(fore$mean), ymin = fore$lower[, 2], ymax = fore$upper[, 2],
color = I("gray95"), name = "95% confidence") %>%
add_ribbons(x = time(fore$mean), ymin = fore$lower[, 1], ymax = fore$upper[, 1],
color = I("gray80"), name = "80% confidence") %>%
add_lines(x = time(fore$mean), y = fore$mean, color = I("blue"), name = "prediction")


Below is sample data:

Month,Year,YearMonth,Population
1,2017,201701,100
1,2018,201801,300
2,2018,201802,310
3,2018,201803,320
4,2018,201804,330
2,2017,201702,200
3,2017,201703,300
4,2017,201704,400
5,2017,201705,500
6,2017,201706,600
7,2017,201707,700
8,2017,201708,800
9,2017,201709,900
10,2017,201710,1000
11,2017,201711,1100
12,2017,201712,1200


2 Answers
2

The best way to customize your axis in plotly is to set a variable with the options you want.

For the x-axis, it would look similar to the code below.

a <- list(
autotick = FALSE,
tick0 = 0,
dtick = 1)


This will only show ticks on the x-axis every interval of 1. I realize this was not what you were asking. Using dtick = .01, will make the x-axis hard to read.

For the y-axis, it would look similar to the code below.

b <- list(
autotick = FALSE,
tick0 = 0,
dtick = 5000) #for 5k intervals


Now you must simply input these into your plotly code. Here is an example.

plot_ly()%>%
add_lines(x = time(tm) y = tm, hoverinfo = text, color = I("black"), name = "Observed") %>%
layout(xaxis = a, yaxis = b)


This should work. All you will need to do is define a and b, then simply add the layout to your current plotly code.

Hope this was helpful.

You want to format your dates as R dates. That would help with formatting.

R

date

library(plotly)
x <- read.table(
text = 'Month,Year,YearMonth,Population
1,2017,201701,100
1,2018,201801,300
2,2018,201802,310
3,2018,201803,320
4,2018,201804,330
2,2017,201702,200
3,2017,201703,300
4,2017,201704,400
5,2017,201705,500
6,2017,201706,600
7,2017,201707,700
8,2017,201708,800
9,2017,201709,900
10,2017,201710,1000
11,2017,201711,1100
12,2017,201712,1200',
header = TRUE, sep = ','
)
x$YearMonth <- as.Date(paste0(x$YearMonth, '01'), format = '%Y%m%d') # Formatting as dates
x$Population <- x$Population * 100 # Scaling population to show large numbers
x <- x[with(x, order(YearMonth)), ] # Sorting by date
p <- plot_ly(
data = x, x = ~YearMonth, y = ~Population, type = 'scatter', mode = 'lines'
) %>% layout(
xaxis = list(tickformat = '%Y.%m'), # This formatting option should help with your desired format
yaxis = list(tick0 = min(x$Population), dtick = 5000, tickformat = '.2s') # Be sure to include min for tick0
)
p


This results in the following graph -

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