Hi,
I'm trying to create a factorial for 3 conditions out of 8, columns G to N (e.g. 8!/3!(8-3)!). Generate all combinations using a loop with the R combn() function, loop over each combination, then calculate the prevalence for each according to age_group. Is this possible and what ways does conditions_df need to be changed to achieve this?
https://docs.google.com/spreadsheets/d/1LWpVqR2yHLQeZH6pHVbrCl-rHIw6PwSGgahDVzmkzEs/edit#gid=0
Thanks
google sheets are generally a poor way to share content to the forum.
Please review the recommended ways:
Hi, thanks for the advice. Is this what is meant by a reprex?
Thanks
conditions_df(
~ID, ~Age, ~Sex, ~CarsQuintile, ~age_group, ~CarsQuintile_group, ~Diabetes, ~Asthma, ~Stroke, ~Heart.attack, ~COPD, ~Hypertension, ~Eczema, ~Depression,
1L, 18L, 1L, 2L, "18 - 24", 3L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L,
2L, 77L, 1L, 1L, "65 - 74", 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L,
3L, 25L, 1L, 3L, "25 - 34", 4L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
4L, 30L, 1L, 1L, "25 - 34", 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
5L, 54L, 1L, 1L, "55 - 64", 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
6L, 78L, 1L, 5L, "75 - 84", 5L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L,
7L, 69L, 1L, 1L, "65 - 74", 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L,
8L, 62L, 1L, 1L, "55 - 64", 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
9L, 68L, 1L, 5L, "55 - 64", 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L,
10L, 63L, 1L, 1L, "55 - 64", 3L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L
)
How to get the combinations depends on what is to be done with it next. The simplest approach:
dat <- data.frame(
ID =
c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
Age =
c(18, 77, 25, 30, 54, 78, 69, 62, 68, 63),
Sex =
c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
CarsQuintie =
c(2, 1, 3, 1, 1, 5, 1, 1, 5, 1),
age_group =
c("18 - 24", "65 - 74", "25 - 34", "25 - 34", "55 - 64", "75 - 84", "65 - 74", "55 - 64", "55 - 64", "55 - 64"),
CarsQuintie_group =
c(3, 1, 4, 3, 1, 5, 1, 2, 1, 3),
Diabetes =
c(1, 0, 0, 0, 0, 1, 1, 0, 1, 1),
Asthma =
c(1, 1, 0, 0, 0, 1, 1, 0, 1, 0),
Stroke =
c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0),
Heart.attack =
c(1, 1, 0, 0, 0, 1, 1, 0, 1, 1),
COPD =
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
Hypertension =
c(0, 0, 1, 0, 1, 0, 1, 0, 0, 0),
Eczema =
c(0, 1, 0, 0, 1, 0, 0, 0, 1, 0),
Depression =
c(0, 0, 0, 1, 0, 0, 0, 1, 0, 0))
# Sex and COPD are constants
dat <- dat[,-c(3,11)]
# rearrange so that age_group comes first
table(dat)
#> , , CarsQuintie = 1, age_group = 18 - 24, CarsQuintie_group = 1, Diabetes = 0, Asthma = 0, Stroke = 0, Heart.attack = 0, Hypertension = 0, Eczema = 0, Depression = 0
#>
#> Age
#> ID 18 25 30 54 62 63 68 69 77 78
#> 1 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0
#>
#> , , CarsQuintie = 2, age_group = 18 - 24, CarsQuintie_group = 1, Diabetes = 0, Asthma = 0, Stroke = 0, Heart.attack = 0, Hypertension = 0, Eczema = 0, Depression = 0
#>
#> Age
#> ID 18 25 30 54 62 63 68 69 77 78
#> 1 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0
#>
#> , , CarsQuintie = 3, age_group = 18 - 24, CarsQuintie_group = 1, Diabetes = 0, Asthma = 0, Stroke = 0, Heart.attack = 0, Hypertension = 0, Eczema = 0, Depression = 0
#>
#> Age
#> ID 18 25 30 54 62 63 68 69 77 78
#> 1 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0
#>
#> , , CarsQuintie = 5, age_group = 18 - 24, CarsQuintie_group = 1, Diabetes = 0, Asthma = 0, Stroke = 0, Heart.attack = 0, Hypertension = 0, Eczema = 0, Depression = 0
#>
#> Age
#> ID 18 25 30 54 62 63 68 69 77 78
#> 1 0 0 0 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0 0 0 0
#> 8 0 0 0 0 0 0 0 0 0 0
#> 9 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 0 0 0 0 0 0 0 0
#> ... MUCH more
Thanks, how did you make this transformation?
Cut and pasted to text file, did some light editing to convert to csv, imported with readr::read_csv, cut and pasted the output of dput on the imported object, changed the first function from structure to data.frame and deleted all the trailing metadata.
Thanks very much.
Each row is a person (actual df has 1.3million) where 1/0 tells us if the person has/doesn't have single conditions. I want to examine measures of "mulitmorbidity", which is where a person has multiple conditions (a binary outcome, for example someone who has asthma, stroke, and COPD meets the criteria if the definition is having any 3 conditions).
There are different ways of defining multimorbidity, so useful to see how prevalence changes depending on how many diseases are required to meet the definition. If we define presence of multimorbidity as having 3 or more conditions out of the 8 (columns 7:13), calculate prevalence of people who meet this criteria for all the different combinations for all people within each age_group bin (then plot the distribution as a mean and SD). Repeat if the definition is 4 or more conditions. Is that possible?
Thanks, I'll try and share in this format from now on, appreciate the time you spent on this
To make it simpler, I have removed variables not needed at this stage.
Thanks
Every R problem can be thought of with advantage as the interaction of three objects— an existing object, x , a desired object,y , and a function, f, that will return a value of y given x as an argument. In other words, school algebra— f(x) = y. Any of the objects can be composites.
The object x is the data frame; it's composite, consisting of vectors (columns). y is an indicator of comorbidity, which exists if three or more columns have a value of 3 or greater. A composite function f will get us there. They are the subset operator [, the which function that performs logical tests on vectors, and the rowSum function that adds matrix elements by rows, which are vectors. (Just make sure the vector is all numeric.)
dat <- data.frame(
ID =
c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
Age =
c(18, 77, 25, 30, 54, 78, 69, 62, 68, 63),
Sex =
c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
CarsQuintie =
c(2, 1, 3, 1, 1, 5, 1, 1, 5, 1),
age_group =
c("18 - 24", "65 - 74", "25 - 34", "25 - 34", "55 - 64", "75 - 84", "65 - 74", "55 - 64", "55 - 64", "55 - 64"),
CarsQuintie_group =
c(3, 1, 4, 3, 1, 5, 1, 2, 1, 3),
Diabetes =
c(1, 0, 0, 0, 0, 1, 1, 0, 1, 1),
Asthma =
c(1, 1, 0, 0, 0, 1, 1, 0, 1, 0),
Stroke =
c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0),
Heart.attack =
c(1, 1, 0, 0, 0, 1, 1, 0, 1, 1),
COPD =
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
Hypertension =
c(0, 0, 1, 0, 1, 0, 1, 0, 0, 0),
Eczema =
c(0, 1, 0, 0, 1, 0, 0, 0, 1, 0),
Depression =
c(0, 0, 0, 1, 0, 0, 0, 1, 0, 0))
# Sex and COPD are constants
dat <- dat[,-c(3,11)]
# create a placeholder variable for result
dat$comorbid <- FALSE
# Exclude non-morbidities from subset
# find rows in which the sub of diseases greater than 2
comorbids <- dat[which(rowSums(dat[,6:12]) > 2),1]
dat[comorbids,"comorbid"] <- TRUE
pander::pander(dat)
| ID | Age | CarsQuintie | age_group | CarsQuintie_group | Diabetes | Asthma |
|---|---|---|---|---|---|---|
| 1 | 18 | 2 | 18 - 24 | 3 | 1 | 1 |
| 2 | 77 | 1 | 65 - 74 | 1 | 0 | 1 |
| 3 | 25 | 3 | 25 - 34 | 4 | 0 | 0 |
| 4 | 30 | 1 | 25 - 34 | 3 | 0 | 0 |
| 5 | 54 | 1 | 55 - 64 | 1 | 0 | 0 |
| 6 | 78 | 5 | 75 - 84 | 5 | 1 | 1 |
| 7 | 69 | 1 | 65 - 74 | 1 | 1 | 1 |
| 8 | 62 | 1 | 55 - 64 | 2 | 0 | 0 |
| 9 | 68 | 5 | 55 - 64 | 1 | 1 | 1 |
| 10 | 63 | 1 | 55 - 64 | 3 | 1 | 0 |
Table continues below
| Stroke | Heart.attack | Hypertension | Eczema | Depression | comorbid |
|---|---|---|---|---|---|
| 0 | 1 | 0 | 0 | 0 | TRUE |
| 1 | 1 | 0 | 1 | 0 | TRUE |
| 0 | 0 | 1 | 0 | 0 | FALSE |
| 0 | 0 | 0 | 0 | 1 | FALSE |
| 0 | 0 | 1 | 1 | 0 | FALSE |
| 0 | 1 | 0 | 0 | 0 | TRUE |
| 0 | 1 | 1 | 0 | 0 | TRUE |
| 0 | 0 | 0 | 0 | 1 | FALSE |
| 0 | 1 | 0 | 1 | 0 | TRUE |
| 0 | 1 | 0 | 0 | 0 | FALSE |
Thanks for this very helpful explanation and code.
I would like to work out the prevalence of each specific combination of 3 diseases, for example:
Calculate prevalence in each each age_bin for:
Combination 1: Diabetes, Asthma, Stroke
Combination 2: Diabetes, Asthma, Heart.attack
Combination 3: Diabetes, Asthma, COPD
Combination 4: Diabetes, Asthma, Hypertension
Combination 5: Diabetes, Asthma, Eczema
... and so on (I think there might be total of 56 potential combinations, but might be wrong)
Combine prevalence for each combination to create a distribution with mean and SD, stratified according to age_group bin and then plot?
See if this gets you to the next step.
dat <- data.frame(
ID =
c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
Age =
c(18, 77, 25, 30, 54, 78, 69, 62, 68, 63),
Sex =
c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
CarsQuintie =
c(2, 1, 3, 1, 1, 5, 1, 1, 5, 1),
age_group =
c("18 - 24", "65 - 74", "25 - 34", "25 - 34", "55 - 64", "75 - 84", "65 - 74", "55 - 64", "55 - 64", "55 - 64"),
CarsQuintie_group =
c(3, 1, 4, 3, 1, 5, 1, 2, 1, 3),
Diabetes =
c(1, 0, 0, 0, 0, 1, 1, 0, 1, 1),
Asthma =
c(1, 1, 0, 0, 0, 1, 1, 0, 1, 0),
Stroke =
c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0),
Heart.attack =
c(1, 1, 0, 0, 0, 1, 1, 0, 1, 1),
COPD =
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
Hypertension =
c(0, 0, 1, 0, 1, 0, 1, 0, 0, 0),
Eczema =
c(0, 1, 0, 0, 1, 0, 0, 0, 1, 0),
Depression =
c(0, 0, 0, 1, 0, 0, 0, 1, 0, 0))
# Sex and COPD are constants
dat <- dat[,-c(3,11)]
# create a placeholder variable for result
dat$comorbid <- FALSE
# Exclude non-morbidities from subset
# find rows in which the sub of diseases greater than 2
comorbids <- dat[which(rowSums(dat[,6:12]) > 2),1]
dat[comorbids,"comorbid"] <- TRUE
# find the combinations of 7 items (corresponding to the column positions
# of the data frame that indicate a disease type presence)
cases <- combn(6:12,3)
# example, the combination of Diabetes, Asthma and Stroke
dat[,cases[,1]]
#> Diabetes Asthma Stroke
#> 1 1 1 0
#> 2 0 1 1
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> 6 1 1 0
#> 7 1 1 0
#> 8 0 0 0
#> 9 1 1 0
#> 10 1 0 0
# function to find if conditions > 2 for any given combination
make_comb <- function(x) dat[which(rowSums(dat[,cases[,x]]) > 2),1]
# function to create a subset by combination with rows that satisfy > 2
show_result <- function(x) dat[dat[make_comb(x)][which(rowSums(dat[,cases[,x]]) > 2),1],]
# example for first combination, which has no more than 2 per row
show_result(1)
#> [1] ID Age CarsQuintie age_group
#> [5] CarsQuintie_group Diabetes Asthma Stroke
#> [9] Heart.attack Hypertension Eczema Depression
#> [13] comorbid
#> <0 rows> (or 0-length row.names)
# example for second combination, which has rows with more than 2 per
show_result(2)
#> ID Age CarsQuintie age_group CarsQuintie_group Diabetes Asthma Stroke
#> 1 1 18 2 18 - 24 3 1 1 0
#> 6 6 78 5 75 - 84 5 1 1 0
#> 7 7 69 1 65 - 74 1 1 1 0
#> 9 9 68 5 55 - 64 1 1 1 0
#> Heart.attack Hypertension Eczema Depression comorbid
#> 1 1 0 0 0 TRUE
#> 6 1 0 0 0 TRUE
#> 7 1 1 0 0 TRUE
#> 9 1 0 1 0 TRUE
# do all at one pass
apply(cases, 2, show_result)
#> [[1]]
#> ID Age CarsQuintie age_group CarsQuintie_group Diabetes Asthma Stroke
#> 1 1 18 2 18 - 24 3 1 1 0
#> 2 2 77 1 65 - 74 1 0 1 1
#> 6 6 78 5 75 - 84 5 1 1 0
#> 7 7 69 1 65 - 74 1 1 1 0
#> 9 9 68 5 55 - 64 1 1 1 0
#> 10 10 63 1 55 - 64 3 1 0 0
#> Heart.attack Hypertension Eczema Depression comorbid
#> 1 1 0 0 0 TRUE
#> 2 1 0 1 0 TRUE
#> 6 1 0 0 0 TRUE
#> 7 1 1 0 0 TRUE
#> 9 1 0 1 0 TRUE
#> 10 1 0 0 0 FALSE
#>
#> [[2]]
#> ID Age CarsQuintie age_group CarsQuintie_group Diabetes Asthma Stroke
#> 1 1 18 2 18 - 24 3 1 1 0
#> 2 2 77 1 65 - 74 1 0 1 1
#> 6 6 78 5 75 - 84 5 1 1 0
#> 7 7 69 1 65 - 74 1 1 1 0
#> 9 9 68 5 55 - 64 1 1 1 0
#> 10 10 63 1 55 - 64 3 1 0 0
#> Heart.attack Hypertension Eczema Depression comorbid
#> 1 1 0 0 0 TRUE
#> 2 1 0 1 0 TRUE
#> 6 1 0 0 0 TRUE
#> 7 1 1 0 0 TRUE
#> 9 1 0 1 0 TRUE
#> 10 1 0 0 0 FALSE
#>
#> [[3]]
#> ID Age CarsQuintie age_group CarsQuintie_group Diabetes Asthma Stroke
#> 1 1 18 2 18 - 24 3 1 1 0
#> 2 2 77 1 65 - 74 1 0 1 1
#> 6 6 78 5 75 - 84 5 1 1 0
#> 7 7 69 1 65 - 74 1 1 1 0
#> 9 9 68 5 55 - 64 1 1 1 0
#> 10 10 63 1 55 - 64 3 1 0 0
#> Heart.attack Hypertension Eczema Depression comorbid
#> 1 1 0 0 0 TRUE
#> 2 1 0 1 0 TRUE
#> 6 1 0 0 0 TRUE
#> 7 1 1 0 0 TRUE
#> 9 1 0 1 0 TRUE
#> 10 1 0 0 0 FALSE
#>
#### remaining output omitted
#> [[4]]
#> ID Age CarsQuintie age_group CarsQuintie_group Diabetes Asthma Stroke
#> 1 1 18 2 18 - 24 3 1 1 0
#> 2 2 77 1 65 - 74 1 0 1 1
#> 6 6 78 5 75 - 84 5 1 1 0
#> 7 7 69 1 65 - 74 1 1 1 0
#> 9 9 68 5 55 - 64 1 1 1 0
#> 10 10 63 1 55 - 64 3 1 0 0
#> Heart.attack Hypertension Eczema Depression comorbid
#> 1 1 0 0 0 TRUE
#> 2 1 0 1 0 TRUE
#> 6 1 0 0 0 TRUE
#> 7 1 1 0 0 TRUE
#> 9 1 0 1 0 TRUE
#> 10 1 0 0 0 FALSE
#>
#> [[5]]
#> ID Age CarsQuintie age_group CarsQuintie_group Diabetes Asthma Stroke
#> 1 1 18 2 18 - 24 3 1 1 0
#> 2 2 77 1 65 - 74 1 0 1 1
#> 6 6 78 5 75 - 84 5 1 1 0
#> 7 7 69 1 65 - 74 1 1 1 0
#> 9 9 68 5 55 - 64 1 1 1 0
#> 10 10 63 1 55 - 64 3 1 0 0
#### remaining output omitted
Hi, thank you so much for this. This is exactly what I am looking for. Sorry late reply, I have spent the last few days trying working on this (very new to R!) on the real csv file. When I run this on the reprex it works, but when running it on the real csv with same structure but longer file I get the following error messages
Error: Must subset columns with a valid subscript vector.
x Subscript cases[, x] must be a simple vector, not a matrix.
Run rlang::last_error() to see where the error occurred.
do all at one pass
apply(cases, 2, show_result)
Error: Must subset columns with a valid subscript vector.
x Subscriptcases[, x]must be a simple vector, not a matrix.
Runrlang::last_error()to see where the error occur when it gets to these commands. Do you know why this might be happening with my csv file and not the reprex?
show_result(1)
show_result(2)
apply(cases, 2, show_result)
Error: Must subset columns with a valid subscript vector.
x Subscript cases[, x] must be a simple vector, not a matrix.
Run rlang::last_error() to see where the error occurred.
do all at one pass
apply(cases, 2, show_result)
Error: Must subset columns with a valid subscript vector.
x Subscriptcases[, x]must be a simple vector, not a matrix.
Runrlang::last_error()to see where the error occurred.
Sorry meant to say it runs as in the reprex until the following commands:
show_result(1)
show_result(2)
apply(cases, 2, show_result)
when I get the following error messages:
Error: Must subset columns with a valid subscript vector.
x Subscript cases[, x] must be a simple vector, not a matrix.
Run rlang::last_error() to see where the error occurred.
Error: Must subset columns with a valid subscript vector.
x Subscript cases[, x] must be a simple vector, not a matrix.
Run rlang::last_error() to see where the error occurred.
Things to debug:
class(cases) cases[,3] # or some other columnrlang::last_error() immediately after running apply(cases, 2, show_result)show_results function is using cases to subset the data, likeThanks very much. Not sure I understand the first three changes, but I made the final one at the show_result function. Code and errors below. Thank you again.
dat <- dat[,-c(3,45)]
dat$comorbid <- FALSE
comorbids <- dat[which(rowSums(dat[,7:20]) > 2),1]
dat[comorbids,"comorbid"] <- TRUE
cases <- combn(7:20,3)
dat[,cases[,1]]
make_comb <- function(x) dat[which(rowSums(dat[,cases[,x]]) > 2),1]
show_result <- function(x) dat[dat[make_comb(x)][which(rowSums(dat[,cases[,1]]) > 2),1],]
show_result(1)
show_result(2)
apply(cases, 2, show_result)
Shows this in console:
dat <- dat[,-c(3,45)]
dat$comorbid <- FALSE
comorbids <- dat[which(rowSums(dat[,7:20]) > 2),1]
dat[comorbids,"comorbid"] <- TRUE
Error: Must assign to rows with a valid subscript vector.
x Subscriptcomorbidshas the wrong typetbl_df<UniquePatientID:double>.
It must be logical, numeric, or character.
Runrlang::last_error()to see where the error occurred.cases <- combn(7:20,3)
dat[,cases[,1]]
Depression PainfulCondition ActiveAsthma
1 0 0 0
2 0 0 1
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 0 1 0
10 0 0 0
make_comb <- function(x) dat[which(rowSums(dat[,cases[,x]]) > 2),1]
show_result <- function(x) dat[dat[make_comb(x)][which(rowSums(dat[,cases[,1]]) > 2),1],]
show_result(1)
Error: Must subset columns with a valid subscript vector.
x Subscriptmake_comb(x)has the wrong typetbl_df<UniquePatientID:double>.
Runrlang::last_error()to see where the error occurred. >
show_result(2)
Error: Must subset columns with a valid subscript vector.
x Subscriptmake_comb(x)has the wrong typetbl_df<UniquePatientID:double>.
Runrlang::last_error()to see where the error occurred. >
apply(cases, 2, show_result)
Error: Must subset columns with a valid subscript vector.
x Subscriptcases[, x]must be a simple vector, not a matrix.
Runrlang::last_error()to see where the error occurred.
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