I have data in lazy_tbl and create columns which I would to use in future to calculate something else, but I'm missing something as getting errors. Here is example.
library(dtplyr)
library(dplyr)
library(implied)
#helper
possibly_mean <- possibly(mean, otherwise = NA)
ready_to_calculate %>%
lazy_dt() %>%
mutate(avgH = map_dbl(odds_1x2, ~possibly_mean(.x$home_odds, na.rm = T)),
avgD = map_dbl(odds_1x2, ~possibly_mean(.x$draw_odds, na.rm = T)),
avgA = map_dbl(odds_1x2, ~possibly_mean(.x$away_odds, na.rm = T)),
prob1 = pmap_dbl(list(avgH, avgD, avgA), ~implied::implied_probabilities(c(..1, ..2, ..3), method = "wpo")$probabilities[1,1]))
This will give me following error:
Error in `[.data.table`(copy(`_DT20`), , `:=`(c("avgH", "avgD", "avgA", :
Variable '1' is not found in calling scope. Looking in calling scope because this symbol was prefixed with .. in the j= parameter.
If I use as_tibble() before mutating last row, I will get answer I'm looking for. So what am I missing here and how can I use just created columns in formulas in future?
ready_to_calculate %>%
lazy_dt() %>%
mutate(avgH = map_dbl(odds_1x2, ~possibly_mean(.x$home_odds, na.rm = T)),
avgD = map_dbl(odds_1x2, ~possibly_mean(.x$draw_odds, na.rm = T)),
avgA = map_dbl(odds_1x2, ~possibly_mean(.x$away_odds, na.rm = T))) %>%
as_tibble() %>%
mutate(prob1 = pmap_dbl(list(avgH, avgD, avgA), ~implied::implied_probabilities(c(..1, ..2, ..3), method = "wpo")$probabilities[1,1]))
# A tibble: 2 × 7
home_team away_team odds_1x2 avgH avgD avgA prob1
<chr> <chr> <list> <dbl> <dbl> <dbl> <dbl>
1 Arsenal Aston Villa <df [18 × 4]> 1.96 3.64 3.92 0.496
2 Leeds Wolves <lgl [1]> NA NA NA NA
DATA:
dput(ready_to_calculate)
structure(list(home_team = c("Arsenal", "Leeds"), away_team = c("Aston Villa",
"Wolves"), odds_1x2 = list(structure(list(bookmaker = c("10Bet",
"188BET", "1xBet", "888sport", "bet-at-home", "bet365", "Betfair",
"Betsafe", "Betsson", "BetVictor", "Betway", "bwin", "ComeOn",
"Interwetten", "Pinnacle", "Unibet", "William Hill", "Betfair Exchange"
), home_odds = c(2.05, 2.02, 1.84, 1.93, 2.02, 1.85, 1.91, 2.05,
2.05, 1.95, 1.91, 1.9, 2.05, 1.95, 2.04, 1.96, 1.91, 1.97), draw_odds = c(3.6,
3.6, 3.85, 3.6, 3.6, 3.75, 3.7, 3.65, 3.65, 3.6, 3.6, 3.5, 3.65,
3.6, 3.65, 3.7, 3.6, 3.61), away_odds = c(3.65, 3.8, 4.41, 4.1,
3.65, 4, 4.2, 3.7, 3.7, 4, 4, 4, 3.75, 3.85, 3.87, 4.1, 3.9,
3.95)), class = "data.frame", row.names = c(NA, 18L)), NA)), class = "data.frame", row.names = c(NA,
-2L))
I think the anonymous lambda function combined with accessing the elements with ..1 etc. is the problem. We can put this in a custom function and just call it as bare function name inside pmap_dbl:
library(dtplyr)
library(dplyr)
library(purrr)
library(implied)
#helper
possibly_mean <- possibly(mean, otherwise = NA)
calc_implied_probalities <- function(x,y,z) {
out <- implied_probabilities(
c(x, y, z),
method = "wpo")$probabilities[1,1]
}
ready_to_calculate %>%
lazy_dt() %>%
mutate(avgH = map_dbl(odds_1x2, ~possibly_mean(.x$home_odds, na.rm = T)),
avgD = map_dbl(odds_1x2, ~possibly_mean(.x$draw_odds, na.rm = T)),
avgA = map_dbl(odds_1x2, ~possibly_mean(.x$away_odds, na.rm = T)),
prob1 = pmap_dbl(list(avgH, avgD, avgA), calc_implied_probalities)
)
#> Source: local data table [2 x 7]
#> Call: copy(`_DT1`)[, `:=`(c("avgH", "avgD", "avgA", "prob1"), {
#> avgH <- map_dbl(odds_1x2, ~possibly_mean(.x$home_odds, na.rm = TRUE))
#> avgD <- map_dbl(odds_1x2, ~possibly_mean(.x$draw_odds, na.rm = TRUE))
#> avgA <- map_dbl(odds_1x2, ~possibly_mean(.x$away_odds, na.rm = TRUE))
#> prob1 <- pmap_dbl(list(avgH, avgD, avgA), ..calc_implied_probalities)
#> .(avgH, avgD, avgA, prob1)
#> })]
#>
#> home_team away_team odds_1x2 avgH avgD avgA prob1
#> <chr> <chr> <list> <dbl> <dbl> <dbl> <dbl>
#> 1 Arsenal Aston Villa <df [18 × 4]> 1.96 3.64 3.92 0.496
#> 2 Leeds Wolves <lgl [1]> NA NA NA NA
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
Data from OP:
ready_to_calculate <- structure(list(home_team = c("Arsenal", "Leeds"), away_team = c("Aston Villa",
"Wolves"), odds_1x2 = list(structure(list(bookmaker = c("10Bet",
"188BET", "1xBet", "888sport", "bet-at-home", "bet365", "Betfair",
"Betsafe", "Betsson", "BetVictor", "Betway", "bwin", "ComeOn",
"Interwetten", "Pinnacle", "Unibet", "William Hill", "Betfair Exchange"
), home_odds = c(2.05, 2.02, 1.84, 1.93, 2.02, 1.85, 1.91, 2.05,
2.05, 1.95, 1.91, 1.9, 2.05, 1.95, 2.04, 1.96, 1.91, 1.97), draw_odds = c(3.6,
3.6, 3.85, 3.6, 3.6, 3.75, 3.7, 3.65, 3.65, 3.6, 3.6, 3.5, 3.65,
3.6, 3.65, 3.7, 3.6, 3.61), away_odds = c(3.65, 3.8, 4.41, 4.1,
3.65, 4, 4.2, 3.7, 3.7, 4, 4, 4, 3.75, 3.85, 3.87, 4.1, 3.9,
3.95)), class = "data.frame", row.names = c(NA, 18L)), NA)), class = "data.frame", row.names = c(NA,
-2L))
Created on 2023-03-14 with reprex v2.0.2