Output separate dataframes in a list using purrr::map_dfr() - r

I'm looking to sequentially read in data and the transform it in two disparate scripts then combine the results into a list of dataframes:
library(tidyverse)
dat_list <- list(as_tibble(mtcars),as_tibble(mtcars),as_tibble(mtcars))
test_func <- function(x) {
dat <- x
gear_avg <- dat %>%
group_by(gear) %>%
summarize(value=mean(mpg))
carb_avg <- dat %>%
group_by(carb) %>%
summarize(value=mean(mpg))
df_list <- list(as_tibble(gear_avg),as_tibble(carb_avg))
return(df_list)
}
test_dat <- map_dfr(dat_list, test_func)
desired_output <-
list(
test_dat %>% filter(!is.na(gear)) %>% select(-carb),
test_dat %>% filter(!is.na(carb)) %>% select(-gear)
)
This is what I would expect to work but it just outputs a single dataframe.

Try using purrr::transpose:
map(transpose(test_dat), bind_rows)
From the purrr cheatsheet here is a little visual aid to understand what that function does:
Also, test_func does not return anything. So, in your reprex you should add the following as the last line: return(df_list)
Output
[[1]]
# A tibble: 9 x 2
gear value
<dbl> <dbl>
1 3 16.1
2 4 24.5
3 5 21.4
4 3 16.1
5 4 24.5
6 5 21.4
7 3 16.1
8 4 24.5
9 5 21.4
[[2]]
# A tibble: 18 x 2
carb value
<dbl> <dbl>
1 1 25.3
2 2 22.4
3 3 16.3
4 4 15.8
5 6 19.7
6 8 15
7 1 25.3
8 2 22.4
9 3 16.3
10 4 15.8
11 6 19.7
12 8 15
13 1 25.3
14 2 22.4
15 3 16.3
16 4 15.8
17 6 19.7
18 8 15

Related

pass a function a vector or undefined number of arguments

I want to be able to pass a function an undefined number of arguments via ... but also to be able to pass it a vector. Here is a silly example:
library(tidyverse)
df <- data.frame(gear = as.character(unique(mtcars$gear)),
id = 1:3)
myfun <- function(...) {
ids_lst <- lst(...)
df2 <- bind_rows(map(ids_lst, function(x)
mtcars %>%
filter(gear == x) %>%
select(mpg)), .id = "gear") %>%
left_join(df)
df2
}
#these all work:
myfun(3)
myfun(3, 4)
myfun(3, 4, 5)
Passing it a vector doesn't work though:
myvector <- unique(mtcars$gear)
myfun(myvector)
The problem is because of the way the function collects the arguments and how it returns them:
myfun_lst <- function(...) {
ids_lst <- lst(...)
ids_lst
}
myfun_lst(3, 4, 5)
# $`3`
# [1] 3
# $`4`
# [1] 4
# $`5`
# [1] 5
myfun_lst(myvector)
# $myvector
# [1] 4 3 5
I thought a fix would be to test if the input is a vector, something like:
myfun_final <- function(...) {
if(is.vector(...) & !is.list(...)) {
ids_lst <- as.list(...)
names(ids_lst) <- (...)
} else {
ids_lst <- lst(...)
}
df2 <- bind_rows(map(ids_lst, function(x)
mtcars %>%
filter(gear == x) %>%
select(mpg)), .id = "gear") %>%
left_join(df)
df2
}
Now, passing the function a vector works but collecting the arguments doesn't:
myfun_final(3, 4, 5)
myfun_final(myvector)
What is a good way to solve this?
Thanks
Of course you can change your function so that it will work with both, regular arguments myfun(3, 4, 5) and a vector myfun(myvector), as shown in the answer above.
Another option is that you make use of argument splicing by unquoting with the bang bang bang operator !!!. This operator is only supported in certain {rlang} and {tidyverse} functions. In your example you evaluate the dots ... inside purrr::map which supports argument splicing. Therefore there might not be the need to rewrite your function:
library(tidyverse)
# your original function:
myfun <- function(...) {
ids_lst <- lst(...)
df2 <- bind_rows(map(ids_lst, function(x)
mtcars %>%
filter(gear == x) %>%
select(mpg)), .id = "gear") %>%
left_join(df)
df2
}
myvector <- unique(mtcars$gear)
myfun(!!! myvector) # works
#> Joining, by = "gear"
#> gear mpg id
#> 1 4 21.0 1
#> 2 4 21.0 1
#> 3 4 22.8 1
#> 4 4 24.4 1
#> 5 4 22.8 1
#> 6 4 19.2 1
#> 7 4 17.8 1
#> 8 4 32.4 1
#> 9 4 30.4 1
#> 10 4 33.9 1
#> ...
myfun(3, 4, 5) # works
#> Joining, by = "gear"
#> gear mpg id
#> 1 3 21.4 2
#> 2 3 18.7 2
#> 3 3 18.1 2
#> 4 3 14.3 2
#> 5 3 16.4 2
#> 6 3 17.3 2
#> 7 3 15.2 2
#> 8 3 10.4 2
#> 9 3 10.4 2
#> 10 3 14.7 2
#> ...
Created on 2021-12-30 by the reprex package (v0.3.0)
You can read more about unquoting with the bang bang bang operator here.
Finally, you should think about the users of your function. If you are the only user then choose whatever suits you. In case there are other users you should think about how they expect the function to work. Probably users don't expect a function to work with several arguments and at the same time, alternatively, by providing those arguments in a vector. In the tidyverse argument splicing with !!! is a well established concept. In base R we would usually use do.call("myfun", as.list(myvector)) to achieve something similar.
To add another option:
The purrr package has a family of lift functions which can be used to alter the kind of arguments a function takes. The most prominent is lift_dl which transforms a function that takes dots as argument to a function that takes a list or vector as argument. This could also be used to solve the problem without the need to rewrite the function:
lift_dl(myfun)(myvector)
#> Joining, by = "gear"
#> gear mpg id
#> 1 4 21.0 1
#> 2 4 21.0 1
#> 3 4 22.8 1
#> 4 4 24.4 1
#> 5 4 22.8 1
#> 6 4 19.2 1
#> 7 4 17.8 1
#> 8 4 32.4 1
#> 9 4 30.4 1
#> 10 4 33.9 1
#> ...
Created on 2022-01-01 by the reprex package (v0.3.0)
How about testing if ... is of length 1 and if the only argument passed through is a vector? If not so, then consider ... a list of scalers and capture them with lst(...).
myfun_final <- function(...) {
if (...length() == 1L && is.vector(..1))
ids_lst <- `names<-`(..1, ..1)
else
ids_lst <- lst(...)
df2 <- bind_rows(map(ids_lst, function(x)
mtcars %>%
filter(gear == x) %>%
select(mpg)), .id = "gear") %>%
left_join(df)
df2
}
Test
> myfun_final(3)
Joining, by = "gear"
gear mpg id
1 3 21.4 2
2 3 18.7 2
3 3 18.1 2
4 3 14.3 2
5 3 16.4 2
6 3 17.3 2
7 3 15.2 2
8 3 10.4 2
9 3 10.4 2
10 3 14.7 2
11 3 21.5 2
12 3 15.5 2
13 3 15.2 2
14 3 13.3 2
15 3 19.2 2
> myfun_final(3,4,5)
Joining, by = "gear"
gear mpg id
1 3 21.4 2
2 3 18.7 2
3 3 18.1 2
4 3 14.3 2
5 3 16.4 2
6 3 17.3 2
7 3 15.2 2
8 3 10.4 2
9 3 10.4 2
10 3 14.7 2
11 3 21.5 2
12 3 15.5 2
13 3 15.2 2
14 3 13.3 2
15 3 19.2 2
16 4 21.0 1
17 4 21.0 1
18 4 22.8 1
19 4 24.4 1
20 4 22.8 1
21 4 19.2 1
22 4 17.8 1
23 4 32.4 1
24 4 30.4 1
25 4 33.9 1
26 4 27.3 1
27 4 21.4 1
28 5 26.0 3
29 5 30.4 3
30 5 15.8 3
31 5 19.7 3
32 5 15.0 3
> myfun_final(c(3,4,5))
Joining, by = "gear"
gear mpg id
1 3 21.4 2
2 3 18.7 2
3 3 18.1 2
4 3 14.3 2
5 3 16.4 2
6 3 17.3 2
7 3 15.2 2
8 3 10.4 2
9 3 10.4 2
10 3 14.7 2
11 3 21.5 2
12 3 15.5 2
13 3 15.2 2
14 3 13.3 2
15 3 19.2 2
16 4 21.0 1
17 4 21.0 1
18 4 22.8 1
19 4 24.4 1
20 4 22.8 1
21 4 19.2 1
22 4 17.8 1
23 4 32.4 1
24 4 30.4 1
25 4 33.9 1
26 4 27.3 1
27 4 21.4 1
28 5 26.0 3
29 5 30.4 3
30 5 15.8 3
31 5 19.7 3
32 5 15.0 3

How do I build a dplyr summarize statement programmatically?

I'm trying to do some dplyr programming and having trouble. I'd like to group_by an arbitrary number of variables (thus, across), and then summarize based on arbitrary length (but all the same length) vectors of:
The column to apply the function to
The function to apply
The name of the new column
So, like in a map or apply statement, I want to execute code that ends up looking like:
data %>%
group_by(group_column) %>%
summarize(new_name_1 = function_1(column_1),
summarize(new_name_2 = function_2(column_2))
Here's an example of what I want and my best shot so far. I know I can use the names argument to clean those up if I use across, but I'm not confident that across is the correct way. Finally, I'll be applying this to fairly large dataframes, so I'd rather not calculate the extra columns.
Desired result
mtcars %>%
group_by(across(c("cyl", "carb"))) %>%
summarise(across(c("disp", "hp"), list(mean = mean, sd = sd))) %>%
select(cyl, carb, disp_mean, hp_sd)
#> `summarise()` regrouping output by 'cyl' (override with `.groups` argument)
#> # A tibble: 9 x 4
#> # Groups: cyl [3]
#> cyl carb disp_mean hp_sd
#> <dbl> <dbl> <dbl> <dbl>
#> 1 4 1 91.4 16.1
#> 2 4 2 117. 24.9
#> 3 6 1 242. 3.54
#> 4 6 4 164. 7.51
#> 5 6 6 145 NA
#> 6 8 2 346. 14.4
#> 7 8 3 276. 0
#> 8 8 4 406. 21.7
#> 9 8 8 301 NA
What I get
mtcars %>%
group_by(across(c("cyl", "carb"))) %>%
summarise(across(c("disp", "hp"), list(mean = mean, sd = sd)))
#> `summarise()` regrouping output by 'cyl' (override with `.groups` argument)
#> # A tibble: 9 x 6
#> # Groups: cyl [3]
#> cyl carb disp_mean disp_sd hp_mean hp_sd
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 1 91.4 21.4 77.4 16.1
#> 2 4 2 117. 27.1 87 24.9
#> 3 6 1 242. 23.3 108. 3.54
#> 4 6 4 164. 4.39 116. 7.51
#> 5 6 6 145 NA 175 NA
#> 6 8 2 346. 43.4 162. 14.4
#> 7 8 3 276. 0 180 0
#> 8 8 4 406. 57.8 234 21.7
#> 9 8 8 301 NA 335 NA
With different functions on different columns, an option is to use collap from collapse
library(collapse)
collap(mtcars, ~ cyl + carb, custom = list(fmean = 4, fsd = 5))
-output
cyl disp hp carb
1 4 91.38 16.133815 1
2 4 116.60 24.859606 2
3 6 241.50 3.535534 1
4 6 163.80 7.505553 4
5 6 145.00 NA 6
6 8 345.50 14.433757 2
7 8 275.80 0.000000 3
8 8 405.50 21.725561 4
9 8 301.00 NA 8
Or the index can be dynamically generated with match
collap(mtcars, ~ cyl + carb, custom = list(fmean =
match('disp', names(mtcars)), fsd = match('hp', names(mtcars))))
With tidyverse, an option is to loop over the column names of interest and the functions in map2 and do a join later
library(dplyr)
library(purrr)
library(stringr)
map2(c("disp", "hp"), c("mean", "sd"), ~
mtcars %>%
group_by(across(c('cyl', 'carb'))) %>%
summarise(across(all_of(.x), match.fun(.y),
.names = str_c("{.col}_", .y)), .groups = 'drop')) %>%
reduce(inner_join)
-output
# A tibble: 9 x 4
cyl carb disp_mean hp_sd
<dbl> <dbl> <dbl> <dbl>
1 4 1 91.4 16.1
2 4 2 117. 24.9
3 6 1 242. 3.54
4 6 4 164. 7.51
5 6 6 145 NA
6 8 2 346. 14.4
7 8 3 276. 0
8 8 4 406. 21.7
9 8 8 301 NA
I have a package on github {dplyover}
which can help with this kind of tasks. In this case we could use over2 to
loop over two character vectors simultaniously. The first vector contains the
variable names as string, which is why we have to wrap .x in sym() when
applying a function to it. The second vector contains the function names,
which we use as .y in a do.call. over2 creates the desired names automatically.
library(dplyr)
library(dplyover) # https://github.com/TimTeaFan/dplyover
mtcars %>%
group_by(across(c("cyl", "carb"))) %>%
summarise(over2(c("disp", "hp"),
c("mean", "sd"),
~ do.call(.y, list(sym(.x)))
))
#> `summarise()` has grouped output by 'cyl'. You can override using the `.groups` argument.
#> # A tibble: 9 x 4
#> # Groups: cyl [3]
#> cyl carb disp_mean hp_sd
#> <dbl> <dbl> <dbl> <dbl>
#> 1 4 1 91.4 16.1
#> 2 4 2 117. 24.9
#> 3 6 1 242. 3.54
#> 4 6 4 164. 7.51
#> 5 6 6 145 NA
#> 6 8 2 346. 14.4
#> 7 8 3 276. 0
#> 8 8 4 406. 21.7
#> 9 8 8 301 NA
An alternative way building on the same logic is to use purrr::map2. However,
here we have to put some effort into creating vectors with the desired names.
library(purrr)
# setup vectors and names
myfuns <- c("mean", "sd")
myvars <- c("disp", "hp") %>%
set_names(., paste(., myfuns, sep = "_"))
mtcars %>%
group_by(across(c("cyl", "carb"))) %>%
summarise(map2(myvars,
myfuns,
~ do.call(.y, list(sym(.x)))
) %>% bind_cols()
)
#> `summarise()` has grouped output by 'cyl'. You can override using the `.groups` argument.
#> # A tibble: 9 x 4
#> # Groups: cyl [3]
#> cyl carb disp_mean hp_sd
#> <dbl> <dbl> <dbl> <dbl>
#> 1 4 1 91.4 16.1
#> 2 4 2 117. 24.9
#> 3 6 1 242. 3.54
#> 4 6 4 164. 7.51
#> 5 6 6 145 NA
#> 6 8 2 346. 14.4
#> 7 8 3 276. 0
#> 8 8 4 406. 21.7
#> 9 8 8 301 NA
Created on 2021-08-20 by the reprex package (v2.0.1)

Compare means of 2 levels of group_by in one data set in R

Take for example the mtcars data set.
I would like to compare the ratio of mpg of cars that are grouped by cyl only , to the cars that are grouped by both cyl and carb.
Problem is that grouping the dataset using dplyr creates one level of granularity which makes it impossible to compare to a different level of grouping.
So what I did was create 2 new data sets, with each grouping, and then joined them together to compare the 2 means with a mutated column, as below.
This worked, but it just seems like a a roundabout inefficient way to code . What is the proper way to do this?
my code:
cyl_only <- mtcars %>%
group_by(cyl) %>%
summarise(cyl_only_mean= mean(mpg))
cyl_carb <- mtcars %>%
group_by(cyl,carb) %>%
summarise(cyl_carb_mean= mean(mpg))
cyl_carb_join <- cyl_only %>%
left_join(cyl_carb,by="cyl")
mtcars_result <- mutate(cyl_carb_join,ratio= cyl_only_mean/cyl_carb_mean)
You can accomplish this without doing the joining by bringing along the summary information needed to calculate a mean.
mtcars %>%
group_by(cyl, carb) %>%
summarise(sum_mpg = sum(mpg),
count = n(),
cyl_carb_mean = mean(mpg)) %>%
group_by(cyl) %>%
mutate(cyl_only_mean = sum(sum_mpg) / sum(count),
ratio = cyl_only_mean/cyl_carb_mean)
cyl carb sum_mpg count cyl_carb_mean cyl_only_mean ratio
<dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 4 1 138. 5 27.6 26.7 0.967
2 4 2 155. 6 25.9 26.7 1.03
3 6 1 39.5 2 19.8 19.7 1.00
4 6 4 79 4 19.8 19.7 1.00
5 6 6 19.7 1 19.7 19.7 1.00
6 8 2 68.6 4 17.2 15.1 0.880
7 8 3 48.9 3 16.3 15.1 0.926
8 8 4 78.9 6 13.2 15.1 1.15
9 8 8 15 1 15 15.1 1.01

custom grouped dplyr function (sample_n)

I am trying to apply a sampling function in a grouped fashion to a data frame, where it should sample n samples from each group, or all group members if the group size is smaller than n.
Using dplyr, I first tried
library(dplyr)
mtcars %>% group_by(cyl) %>% sample_n(2)
This works when n is smaller than all the group sizes but does not take the full group when I choose n larger than the group size (note that there are 7 cars in one of the cyl groups):
mtcars %>% group_by(cyl) %>% sample_n(8)
Error: `size` must be less or equal than 7 (size of data),
set `replace` = TRUE to use sampling with replacement
I tried to solve this by creating an adapted group_n function like so:
sample_n_or_all <- function(tbl, n) {
if (nrow(tbl) < n)return(tbl)
sample_n(tbl, n)
}
but using my custom function (mtcars %>% group_by(cyl) %>% sample_n_or_all(8)) generates the same error.
Any suggestions how I can adapt my function so I can apply it to each of the groups? Or another solution to the problem?
We could check the number of rows in the group and pass the value to sample_n accordingly.
library(dplyr)
n <- 8
temp <- mtcars %>% group_by(cyl) %>% sample_n(if(n() < n) n() else n)
temp
# mpg cyl disp hp drat wt qsec vs am gear carb
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
# 2 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
# 3 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
# 4 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
# 5 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
# 6 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
# 7 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
# 8 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
# 9 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#10 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
# … with 13 more rows
We can check number of rows in each group after that.
table(temp$cyl)
#4 6 8
#8 7 8
table(mtcars$cyl)
# 4 6 8
#11 7 14
We can do this without using a logical condition with pmin
library(dplyr)
tmp <- mtcars %>%
group_by(cyl) %>%
sample_n(pmin(n(), n))
# A tibble: 23 x 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 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
# 2 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
# 3 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
# 4 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
# 5 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
# 6 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
# 7 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
# 8 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
# 9 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
#10 21 6 160 110 3.9 2.62 16.5 0 1 4 4
# … with 13 more rows
-checking
table(tmp$cyl)
# 4 6 8
# 8 7 8

Add baseline/grand total with group_by() in dplyr

When I've grouped my data by certain attributes, I want to add a "grand total" line that gives a baseline of comparison. Let's group mtcars by cylinders and carburetors, for example:
by_cyl_carb <- mtcars %>%
group_by(cyl, carb) %>%
summarize(median_mpg = median(mpg),
avg_mpg = mean(mpg),
count = n())
...yields these results:
> by_cyl_carb
# A tibble: 9 x 5
# Groups: cyl [?]
cyl carb median_mpg avg_mpg count
<dbl> <dbl> <dbl> <dbl> <int>
1 4 1 27.3 27.6 5
2 4 2 25.2 25.9 6
3 6 1 19.8 19.8 2
4 6 4 20.1 19.8 4
5 6 6 19.7 19.7 1
6 8 2 17.1 17.2 4
7 8 3 16.4 16.3 3
8 8 4 13.8 13.2 6
9 8 8 15 15 1
What is the code I need to make it provide a baseline or grand total that would sum (or mean or median) over all of the data? The desired data would be something like this:
cyl carb median_mpg avg_mpg count
<chr> <chr> <dbl> <dbl> <int>
1 4 1 27.3 27.6 5
2 4 2 25.2 25.9 6
3 6 1 19.8 19.8 2
4 6 4 20.1 19.8 4
5 6 6 19.7 19.7 1
6 8 2 17.1 17.2 4
7 8 3 16.4 16.3 3
8 8 4 13.8 13.2 6
9 8 8 15 15 1
10 ttl ttl 19.2 20.1 32
A twist on this would be able to manipulate the output so that the sub-grouped data would be rolled up. For example:
11 ttl 1 13.8 13.2 6
12 ttl 2 15 15 1
13 ttl 3 19.3 20.4 32
14 ... etc ...
The real-life example I am using this for is median sale price of homes by geography by year. Hence I want to report out the median sale price for each geography-year I'm interested in, but I want a baseline comparison for each year regardless of geography.
Edit: Solved with two solutions
#camille referenced this link, which solved the problem, as well as #MKR offering a solution. Here is one code that might work:
by_cyl_carb <- mtcars %>%
mutate_at(vars(c(cyl,carb)), funs(as.character(.))) %>%
bind_rows(mutate(., cyl = "All cylinders")) %>%
bind_rows(mutate(., carb = "All carburetors")) %>%
group_by(cyl, carb) %>%
summarize(median_mpg = median(mpg),
avg_mpg = mean(mpg),
count = n())
> by_cyl_carb
# A tibble: 19 x 5
# Groups: cyl [?]
cyl carb median_mpg avg_mpg count
<chr> <chr> <dbl> <dbl> <int>
1 4 1 27.3 27.6 5
2 4 2 25.2 25.9 6
3 4 All carburetors 26 26.7 11
4 6 1 19.8 19.8 2
5 6 4 20.1 19.8 4
6 6 6 19.7 19.7 1
7 6 All carburetors 19.7 19.7 7
8 8 2 17.1 17.2 4
9 8 3 16.4 16.3 3
10 8 4 13.8 13.2 6
11 8 8 15 15 1
12 8 All carburetors 15.2 15.1 14
13 All cylinders 1 22.8 25.3 7
14 All cylinders 2 22.1 22.4 10
15 All cylinders 3 16.4 16.3 3
16 All cylinders 4 15.2 15.8 10
17 All cylinders 6 19.7 19.7 1
18 All cylinders 8 15 15 1
19 All cylinders All carburetors 19.2 20.1 32
A solution using dplyr::bind_rows and mutate_at can be achieved as:
library(tidyverse)
mtcars %>%
group_by(cyl, carb) %>%
summarize(median_mpg = median(mpg),
avg_mpg = mean(mpg),
count = n()) %>%
ungroup() %>%
mutate_at(vars(cyl:carb), funs(as.character(.))) %>%
bind_rows(summarise(cyl = "ttl", carb = "ttl", mtcars, median_mpg = median(mpg),
avg_mpg = mean(mpg),
count = n()))
# # A tibble: 10 x 5
# cyl carb median_mpg avg_mpg count
# <chr> <chr> <dbl> <dbl> <int>
# 1 4 1 27.3 27.6 5
# 2 4 2 25.2 25.9 6
# 3 6 1 19.8 19.8 2
# 4 6 4 20.1 19.8 4
# 5 6 6 19.7 19.7 1
# 6 8 2 17.1 17.2 4
# 7 8 3 16.4 16.3 3
# 8 8 4 13.8 13.2 6
# 9 8 8 15.0 15.0 1
#10 ttl ttl 19.2 20.1 32

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