I am trying to add new (future) data (not in the nested "data" column) to the add_predictions function in modelr. For example:
EuStockMarkets %>%
as.tibble() %>%
mutate(bus_day = date_decimal(as.numeric(time(EuStockMarkets)))) %>%
gather(key = "stk_name", value = "price", -bus_day) %>%
group_by(stk_name) %>%
nest() %>%
mutate(model = map(.x = data, .f = ~ lm(formula = price ~ bus_day, data = .x)),
tidy_model = map(model,tidy),
bus_day = as.list(seq.POSIXt(from = as.POSIXct(x = "2018-01-21 05:00:00", tz = "MST"),
to = as.POSIXct(x = "2018-01-21 05:00:00", tz = "MST"),
by = "day")), # future date
yhat2018 = map2(.x = model, .y = bus_day,
.f = ~ add_predictions(model = .x, data = .y)))
#> Error in EuStockMarkets %>% as.tibble() %>% mutate(bus_day = date_decimal(as.numeric(time(EuStockMarkets)))) %>% : could not find function "%>%"
I get
"Error in mutate_impl(.data, dots) :
Does anyone have some suggestions on how to get this to work?
1 Like
davis
January 18, 2018, 1:46pm
2
The data argument to add_predictions() should be a data.frame containing all the columns (named correctly) necessary to make the prediction. Meaning it needs to be a data.frame with a column named bus_day. What you are doing in the map2 is just passing the bus_day vector of dates to add_predictions() not a data.frame.
The quick and dirty fix is (look at the last line):
library(tidyverse)
library(modelr)
library(lubridate)
library(broom)
EuStockMarkets %>%
as.tibble() %>%
mutate(bus_day = date_decimal(as.numeric(time(EuStockMarkets)))) %>%
gather(key = "stk_name", value = "price", -bus_day) %>%
group_by(stk_name) %>%
nest() %>%
mutate(model = map(.x = data, .f = ~ lm(formula = price ~ bus_day, data = .x)),
tidy_model = map(model, tidy),
bus_day = as.list(seq.POSIXt(from = as.POSIXct(x = "2018-01-21 05:00:00", tz = "MST"),
to = as.POSIXct(x = "2018-01-21 05:00:00", tz = "MST"),
by = "day")),
yhat2018 = map2(.x = model, .y = bus_day,
.f = ~ add_predictions(model = .x, data = data.frame(bus_day = .y))))
Created on 2018-01-18 by the reprex package (v0.1.1.9000).
2 Likes
Hey @davis (or anyone else if Davis is busy),
I have another (dumb) followup question:
EuStockMarkets %>%
as.tibble() %>%
mutate(bus_day = lubridate::date_decimal(as.numeric(time(EuStockMarkets)))) %>%
gather(key = "stk_name", value = "price", -bus_day) %>%
group_by(stk_name) %>%
nest() %>%
mutate(model = map(.x = data, .f = ~ lm(formula = price ~ bus_day, data = .x)),
tidy_model = map(model, tidy),
bus_day = seq.POSIXt(from = as.POSIXct(x = "2018-01-01 05:00:00", tz = "MST"),
to = as.POSIXct(x = "2018-01-01 05:00:00", tz = "MST"),
by = "day"),
yhat2018 = map2(.x = model, .y = bus_day, .f = ~ add_predictions(model = .x, data = data.frame(bus_day = .y)))) %>%
select(yhat2018) %>%
unnest()
But not for multiple days
EuStockMarkets %>%
as.tibble() %>%
mutate(bus_day = lubridate::date_decimal(as.numeric(time(EuStockMarkets)))) %>%
gather(key = "stk_name", value = "price", -bus_day) %>%
group_by(stk_name) %>%
nest() %>%
mutate(model = map(.x = data, .f = ~ lm(formula = price ~ bus_day, data = .x)),
tidy_model = map(model, tidy),
bus_day = seq.POSIXt(from = as.POSIXct(x = "2018-01-01 05:00:00", tz = "MST"),
to = as.POSIXct(x = "2018-01-05 05:00:00", tz = "MST"),
by = "day"),
yhat2018 = map2(.x = model, .y = bus_day, .f = ~ add_predictions(model = .x, data = data.frame(bus_day = .y)))) %>%
select(yhat2018) %>%
unnest()
Any suggestions?
davis
January 19, 2018, 9:48pm
4
The problem here is with the bus_day column. It used to be 1 date that mutate() knew how to repeat 4 times to stick in that column. Now its a vector of 5 dates and mutate() doesn't know what to do. If it were me, I'd break this up a bit.
library(tidyverse)
library(broom)
library(modelr)
nested_stock <- EuStockMarkets %>%
as.tibble() %>%
mutate(bus_day = lubridate::date_decimal(as.numeric(time(EuStockMarkets)))) %>%
gather(key = "stk_name", value = "price", -bus_day) %>%
group_by(stk_name) %>%
nest()
forecast_dates <- tibble(
bus_day = seq.POSIXt(
from = as.POSIXct(x = "2018-01-01 05:00:00", tz = "MST"),
to = as.POSIXct(x = "2018-01-05 05:00:00", tz = "MST"),
by = "day")
)
nested_stock_models <- nested_stock %>%
mutate(
forecast_dates = list(forecast_dates),
model = map(.x = data, .f = ~ lm(formula = price ~ bus_day, data = .x)),
tidy_model = map(model, tidy),
yhat2018 = map2(.x = model, .y = forecast_dates, .f = ~ add_predictions(model = .x, data = .y))
)
nested_stock_models
#> # A tibble: 4 x 6
#> stk_name data forecast_dates model tidy_model yhat2018
#> <chr> <list> <list> <lis> <list> <list>
#> 1 DAX <tibble [1,860 × 2]> <tibble [5 × 1]> <S3:… <data.fra… <tibble…
#> 2 SMI <tibble [1,860 × 2]> <tibble [5 × 1]> <S3:… <data.fra… <tibble…
#> 3 CAC <tibble [1,860 × 2]> <tibble [5 × 1]> <S3:… <data.fra… <tibble…
#> 4 FTSE <tibble [1,860 × 2]> <tibble [5 × 1]> <S3:… <data.fra… <tibble…
nested_stock_models %>%
select(stk_name, yhat2018) %>%
unnest()
#> # A tibble: 20 x 3
#> stk_name bus_day pred
#> <chr> <dttm> <dbl>
#> 1 DAX 2018-01-01 05:00:00 12841
#> 2 DAX 2018-01-02 05:00:00 12842
#> 3 DAX 2018-01-03 05:00:00 12843
#> 4 DAX 2018-01-04 05:00:00 12844
#> 5 DAX 2018-01-05 05:00:00 12846
#> 6 SMI 2018-01-01 05:00:00 19829
#> 7 SMI 2018-01-02 05:00:00 19831
#> 8 SMI 2018-01-03 05:00:00 19833
#> 9 SMI 2018-01-04 05:00:00 19835
#> 10 SMI 2018-01-05 05:00:00 19837
#> 11 CAC 2018-01-01 05:00:00 6919
#> 12 CAC 2018-01-02 05:00:00 6919
#> 13 CAC 2018-01-03 05:00:00 6920
#> 14 CAC 2018-01-04 05:00:00 6920
#> 15 CAC 2018-01-05 05:00:00 6921
#> 16 FTSE 2018-01-01 05:00:00 13550
#> 17 FTSE 2018-01-02 05:00:00 13552
#> 18 FTSE 2018-01-03 05:00:00 13553
#> 19 FTSE 2018-01-04 05:00:00 13554
#> 20 FTSE 2018-01-05 05:00:00 13555
Created on 2018-01-19 by the reprex package (v0.1.1.9000).
2 Likes