Monthly mean for hourly data to check seaonality

[sikhulile]

Date Original_Forecast Residual_Foreca~ Dispatchable_Ge~ Residual_Demand

1 2019-04-01 NA 20127. 21035. 21035.
2 2019-04-01 NA 19822. 20548. 20548.
3 2019-04-01 NA 19726. 20413. 20413.
4 2019-04-01 NA 19939. 20476. 20476.
5 2019-04-01 NA 20794. 21156. 21156.
6 2019-04-01 NA 22703. 23079. 23079.

... with 8 more variables: MLR , Installed , PCLF ,

UCLF , OCLF , UCLF_OCLF , Month , Year

I have the data above from 2019 to 2013, i want to determine the overall monthly mean, to check seasonality. It is load reduction data recorded per hour,

[gsapijaszko]

I would consider dplyr::group_by(Month) and mean. But it definitely would help if you would provide the a bit of data structure you are using and the code you tried so far.

Regards,
Grzegorz

[sikhulile]

monthlymean <- load_2 %>%
group_by(month = lubridate::floor_date(Date, 'month')) %>%
summarize(sum_MLR = mean(MLR))
I used the following code and i got the monthly mean e.g. for April 2019, April 2020 April 2021 etc separately. I want to do a circular plot for MLR. So I need an overall mean for January for all the years Feb, Mar, etc

[sikhulile]

ggplot(data = monthlymean, aes(x = month, y = sum_MLR, fill = month)) +
geom_bar(stat = "identity")+
theme_minimal() +
theme(legend.position = "none")+
coord_polar(start = 0)+
ylim(0,3000)

This is my code for circular plot

[sikhulile]

I would have loved my circular plot to show me the means according to months