## 1. Get Hours into Tibble
## ---------------------
hrsThreshold <- function(input_data, group1, group2, group2_String) {
# print ({{group2}}) # for some ridiculous reason, code doesn't work without this print statement in!
# Hours
hrs <-
# thresholds
input_data %>%
mutate( score = rowSums(select(., starts_with("less_than")), na.rm = TRUE)) %>%
# scores & summary
group_by({{ group1 }}, {{ group2 }}, score) %>%
summarise(n_points = n()) %>%
mutate(percent = n_points / sum(n_points)) %>%
mutate(score = as_factor(score)) %>%
# add percentage labels.
group_by({{ group1 }}, {{ group2 }}) %>%
mutate(ymax = cumsum(percent),
ymin = c(0, head(cumsum(percent), n = -1))) %>%
mutate(label_pos = (ymax+ymin) / 2,
label = ifelse(percent < 0.01, "", paste0(round(percent*100, 0), "%"))) %>%
ungroup()
hrs
# Hours All
hrs_all <-
hrs %>%
drop_na( {{ group2 }} ) %>%
select(-label) %>%
# Sum n_points
group_by({{ group1 }}, score) %>%
summarise_at(
vars(n_points),
.funs = sum) %>%
mutate(percent = n_points / sum(n_points)) %>%
# redo geometry of rectangles.
group_by({{ group1 }}) %>%
mutate(ymax = cumsum(percent),
ymin = c(0, head(cumsum(percent), n = -1))) %>%
mutate(label_pos = (ymax+ymin) / 2,
label = ifelse(percent < 0.01, "", paste0(round(percent*100, 0), "%"))) %>%
# Change room_type = All
mutate({{ group2 }} := "all")
# Join All with Variable
hrs_by_room <- full_join(hrs_all, hrs)
return(hrs_by_room)
}
# Debug Code
analysis_vars <- c("room_type", "tenure")
analysis_index <- 2
# Dynamic Variable
mytempVar <- analysis_vars[analysis_index]
mytempVarTitle <- mytempVar
mytempVarTitle <- str_replace(mytempVarTitle, "[_]", " ")
mytempVarTitle <- str_to_title(mytempVarTitle)
mytempVar_ <- sym(mytempVar)
# run command
hrs_by_room <- hrsThreshold(risk_assigned, hhi, mytempVar_, c({{mytempVar_}}))
The code above only works if the line :
print ({{group2}})
is uncommented!!! This command, on the face of it, does NOTHING. But without it on my machine, the following error comes:
Error: Must group by variables found in `.data`.
* Column `mytempVar_` is not found.
Run `rlang::last_error()` to see where the error occurred.
> rlang::last_error()
<error/rlang_error>
Must group by variables found in `.data`.
* Column `mytempVar_` is not found.
Backtrace:
1. global::hrsThreshold(...)
11. dplyr:::group_by.data.frame(...)
12. dplyr::group_by_prepare(.data, ..., .add = .add, caller_env = caller_env())
Run `rlang::last_trace()` to see the full context.
> rlang::last_trace()
<error/rlang_error>
Must group by variables found in `.data`.
* Column `mytempVar_` is not found.
Backtrace:
x
1. +-global::hrsThreshold(...)
2. | \-`%>%`(...)
3. +-dplyr::ungroup(.)
4. +-dplyr::mutate(...)
5. +-dplyr::mutate(...)
6. +-dplyr::group_by(...)
7. +-dplyr::mutate(., score = as_factor(score))
8. +-dplyr::mutate(., percent = n_points/sum(n_points))
9. +-dplyr::summarise(., n_points = n())
10. +-dplyr::group_by(...)
11. \-dplyr:::group_by.data.frame(...)
12. \-dplyr::group_by_prepare(.data, ..., .add = .add, caller_env = caller_env())
I've confirmed this is the issue that has been crashing my entire report application for days, in multiple functions. I only finally got it workoing by accidentally putting print statments in to debug the problem. Whereupon it mysteriously started to work. What on earth is going on?
Source File
library(tidyverse)
risk_assigned <- read.csv( text="hhi,device_id,rental_type,room,room_type,ts,temp,less_than_21,less_than_18,less_than_16,less_than_12
MyOrg,2C7970,Council Rental,Lounge,living,1598291349,18.3,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598292249,18.3,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598293149,18.3,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598294049,18.3,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598294949,18.2,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598353449,18.5,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598354354,18.5,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598355254,18.5,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598356154,18.5,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598357054,18.5,1,NA,NA,NA
MyOrg,2C7970,Council Rental,Lounge,living,1598357954,18.5,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598358854,18.5,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598359754,18.6,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598360654,18.5,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598361554,18.5,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598362454,18.4,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598363354,18.4,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598364254,18.4,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598365154,18.4,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598366054,18.3,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598366954,18.2,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598367850,18.2,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598368750,18.1,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598369650,18.1,1,NA,NA,NA
MyOrg,2C7971,Private Rental,Lounge,living,1598370550,18,1,NA,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598371450,17.9,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598372350,17.9,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598373250,17.8,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598374150,17.8,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598375050,17.8,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598375950,17.7,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598376850,17.6,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598377750,17.6,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598378650,17.6,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598379553,17.5,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598380453,17.5,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598381353,17.4,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598439851,17.7,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598440751,17.7,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598441651,17.6,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598442551,17.6,1,1,NA,NA
MyOrg,2C7972,Private Rental,Lounge,living,1598443451,17.5,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598444351,17.5,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598445251,17.4,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598446151,17.4,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598447051,17.3,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598447951,17.3,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598448851,17.1,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598449751,17.1,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598450651,17.1,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598451554,17.1,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598452454,17.1,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598453354,17.1,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598454254,17.1,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598455154,17.1,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598456054,17,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598456954,17,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598457854,16.9,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598458754,16.9,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598459654,16.9,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598460554,16.9,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598461454,16.8,1,1,NA,NA
MyOrg,2C7973,Council Rental,Lounge,living,1598462354,16.8,1,1,NA,NA
MyOrg,2C7974,Council Rental,Lounge,living,1598463254,16.7,1,1,NA,NA
MyOrg,2C7974,Council Rental,Lounge,living,1598464154,16.8,1,1,NA,NA
MyOrg,2C7974,Council Rental,Lounge,living,1598465051,16.7,1,1,NA,NA
MyOrg,2C7974,Council Rental,Lounge,living,1598465951,16.8,1,1,NA,NA
MyOrg,2C7974,Council Rental,Lounge,living,1598466851,16.7,1,1,NA,NA")
Here is a minimal example:
var <- rlang::sym("cyl")
fn <- function(var, force) {
if (force) {
force(var)
}
dplyr::group_by(mtcars, {{ var }})
}
The expected behaviour is:
fn(var, force = FALSE)
#> Error: Must group by variables found in `.data`.
#> * Column `var` is not found.
This is unexpected behaviour:
fn(var, force = TRUE)
#> # A tibble: 32 × 11
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> # … with 28 more rows
There are two causes to this unexpected behaviour.
Using {{ out of context is equivalent to forcing an argument. So print({{ var }}) is equivalent to force(var). See https://rlang.r-lib.org/reference/topic-inject-out-of-context.html.
It is not possible to defuse a forced argument. And because the compiler unwraps constants from promises, we can't detect that case to throw an error instead. This is why forced arguments are injected rather than defused. See https://rlang.r-lib.org/reference/topic-embrace-non-args.html.
We can't fix either (1) or (2) because tidy evaluation is not implemented in the core language, it's implemented on top of it.
In your example, it seems that you would like to pass a column name to your function through an argument.
The first thing to note is that since that argument is passed to group_by() with {{, your function inherits all the behaviour of group_by(). It takes unquoted column names and supports all tidy eval features.
When you have a column name in a string, the canonical way to pass it is with the .data pronoun:
var <- "cyl"
fn <- function(var) {
dplyr::group_by(mtcars, {{ var }})
}
fn(var)
#> Error: Must group by variables found in `.data`.
#> * Column `var` is not found.
fn(.data[[var]])
#> # A tibble: 32 × 11
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> # … with 28 more rows