covid19_ua
Аналіз "диванного експерта" відкритих даних по захворюваності на COVID19 в Україні
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Побудова прогнозів із використанням Facebook Prophet
Це лише заготовка, сторінка роботі.
library(tidyverse)
library(prophet)
## Loading required package: Rcpp
## Loading required package: rlang
##
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
##
## %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
## flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
## splice
Prophet
https://facebook.github.io/prophet/docs/quick_start.html#r-api
First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns ds and y, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyton Manning’s Wikipedia page, available here.
df <- read_csv('../covid19_by_area_type_hosp_dynamics.csv')
## Parsed with column specification:
## cols(
## zvit_date = col_date(format = ""),
## registration_area = col_character(),
## priority_hosp_area = col_character(),
## edrpou_hosp = col_character(),
## legal_entity_name_hosp = col_character(),
## legal_entity_lat = col_number(),
## legal_entity_lng = col_number(),
## person_gender = col_character(),
## person_age_group = col_character(),
## add_conditions = col_character(),
## is_medical_worker = col_character(),
## new_susp = col_double(),
## new_confirm = col_double(),
## active_confirm = col_double(),
## new_death = col_double(),
## new_recover = col_double()
## )
df <- df %>%
select(zvit_date, new_confirm) %>%
group_by(zvit_date) %>%
summarise(new_confirm = sum(new_confirm)) %>%
filter(zvit_date > as.Date("2020-04-01"))
df <- df %>% rename(ds = zvit_date, y = new_confirm)
head(df)
## # A tibble: 6 x 2
## ds y
## <date> <dbl>
## 1 2020-04-02 129
## 2 2020-04-03 148
## 3 2020-04-04 166
## 4 2020-04-05 102
## 5 2020-04-06 189
## 6 2020-04-07 158
plot(df)
We call the prophet function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data and are described in later pages of this documentation.
m <- prophet(df)
## Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.
## Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.
Predictions are made on a dataframe with a column ds containing the dates for which predictions are to be made. The make_future_dataframe function takes the model object and a number of periods to forecast and produces a suitable dataframe. By default it will also include the historical dates so we can evaluate in-sample fit.
future <- make_future_dataframe(m, periods = 14)
tail(future)
## ds
## 90 2020-06-30
## 91 2020-07-01
## 92 2020-07-02
## 93 2020-07-03
## 94 2020-07-04
## 95 2020-07-05
As with most modeling procedures in R, we use the generic predict function to get our forecast. The forecast object is a dataframe with a column yhat containing the forecast. It has additional columns for uncertainty intervals and seasonal components.
forecast <- predict(m, future)
tail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')])
## ds yhat yhat_lower yhat_upper
## 90 2020-06-30 690.3806 562.6570 815.8319
## 91 2020-07-01 759.9309 632.1227 885.1850
## 92 2020-07-02 731.7619 605.0651 852.5193
## 93 2020-07-03 741.4327 615.5433 866.4643
## 94 2020-07-04 686.1864 565.6935 820.4180
## 95 2020-07-05 557.6894 429.1464 679.2928
You can use the generic plot function to plot the forecast, by passing in the model and the forecast dataframe.
plot(m, forecast)
You can use the prophet_plot_components function to see the forecast broken down into trend, weekly seasonality, and yearly seasonality.
prophet_plot_components(m, forecast)
More details about the options available for each method are available in the docstrings, for example, via ?prophet or ?fit.prophet. This documentation is also available in the reference manual on CRAN.