In ZellW/LearnPlotting:
https://github.com/GregGunn/R-Users-Group-Makrdown-
title: "R Markdown and Interactive Graphic"
output: slidy_presentation
params:
State: "NC"
HM_Measure: "Males"
Sankey_County: "Mecklenburg"
library(xtable)
library(dplyr)
library(ggplot2)
library(scales)
library(tidyr)
setwd("C:/Users/Greg/Documents/R/R Markdown Presentation 3-15-2015")
The Agenda
-
Introductions
i) Name
ii) Where you work
iii) R Coding Experience
iiii) Last thing you did in R or Current Project
-
R Markdown Overview
- Sample Presentation
- Code Review
What is R markdown?
From RStudio
R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports from R. It combines the core syntax of markdown (an easy to write plain text format) with embedded R code chunks that are run so their output can be included in the final document. R Markdown documents are fully reproducible (they can be automatically regenerated whenever underlying R code or data changes).
As an example, this:
*Welcome*
Becomes this:
Welcome
For a lot of examples on what you can do with RMarkdown check out:
Another great resource is RStudios's cheatsheets:
https://www.rstudio.com/resources/cheatsheets/
Why Use R Markdown
This presentation is an example of how to use R Markdown with interactive graphics {.bigger}
Some of the advantages to this approach include:
-
Repeatability - It is easy to re-run code and produce similar results again and again
-
Flexibility - You can esily change the code to include different time periods or measures
-
Interactivity - Because it is HTML you can include interactive html elements
-
Ease of Sharing - Again, because it is HTML all you need to view it is a web browser
It Ain't All Roses Though
There are some disadvantages
-
Time - It generally takes longer to make a Markdown doc than a regular powerpoint
-
Ease of use - Flexibility comes with a price, and RMarkdown can be finicky sometimes
-
Pre-Reqs - To really customize past the general layouts, you would probably need to know html
Let's look at some data!
It is election season, and I won't be another outlet endlessly examining politics. However some of the coverage got me thinking about demographics, so I thought that might be interesting to examine.
The American Community Survey (ACS), which is part of the Census Bureau is a treasure trove of information. Through a variety of methods they estimate many different aspects of American life.
Luckily for us there is a publicly available API. Not only that, Ezra Glenn at MIT has written an R package with helper fuctions to make it easy(ish) to find and pull back data. He has written a very easy to follow paper on the package, which you can read via the link below.
http://eglenn.scripts.mit.edu/citystate/wp-content/uploads/2013/06/wpid-working_with_acs_R3.pdf
Defining the Geography
The Code
The code below creates a geography for all of North Carolina at the county level.
library(acs)
NC_Area = geo.make(state = params$State, county = "*", check = T)
- The state argument is set by a parameter, which I'll show when we go over the markdown code. I could also have set it with a string of "NC"
- The county parameter of "*" just means all counties, I could have specified counties by name or FIPS code
- Check = TRUE is handy because it makes sure that you have a valid geography before you start trying to pull data
There is a handy lookup table function you can leverage as well if you are unsure of spelling or FIPS number
geo.lookup(state = "NC", county = "A")
data = geo.lookup(state = params$State, county = "A")
print(xtable(data),
type = "html",
html.table.attributes = "border=0",
include.rownames = FALSE)
Finding the right data
Unfortunately grabbing data from the API is not intuitive, mainly because there is SO MUCH DATA. If you really get into it there are several different handbooks you can buy to guid you through all the data
Luckily Ezra has provided us with a handy lookup function.
results(acs.lookup(table.name = "College"))
The call above yields 33 rows, and I've shown you the first few below.
Lookup = results(acs.lookup(table.name = "College"))
print(head(xtable(Lookup),10),
type = "html",
html.table.attributes = "border=0",
include.rownames = FALSE)
Grabbing the Data from the Interwebs
Once you have searched, and found some things you are interested in its time to actually grab the data! This involves 2 steps:
- Use the acs.fetch function to grab the data down as an acs object
- Turn the acs object into a data frame using the estimates
library(DT)
#Actually call the API and return the data as an acs object
NC_Data = acs.fetch(geography = NC_Area,
variable = c("B01003_001", "B01001_002", "B01001_026",
"B25026_003", "B25026_004", "B25026_005",
"B25026_006", "B25026_007", "B25026_008"),
col.names = c("Total", "Males", "Females", "Moved in After 2005",
"Between 2000 and 04", "Between 90 and 99",
"Between 80 and 89", "Between 70 and 79",
"Before 69") )
#Turn acs into a data frame for easier manipulation
NC_DF = data.frame(geography(NC_Data), estimate(NC_Data))%>%
left_join(fips.state, by = c("state" = "STATE"))
row.names(NC_DF) = NULL
datatable(NC_DF)
NC_Data = acs.fetch(geography = NC_Area,
variable = c("B01003_001", "B01001_002", "B01001_026", "B25026_003",
"B25026_004", "B25026_005", "B25026_006", "B25026_007", "B25026_008"),
col.names = c("Total", "Males", "Females", "Moved in After 2005", "Between 2000 and 04",
"Between 90 and 99", "Between 80 and 89", "Between 70 and 79",
"Before 69") )
#Turn acs into a data frame for easier manipulation
NC_DF = data.frame(geography(NC_Data), estimate(NC_Data))%>%
left_join(fips.state, by = c("state" = "STATE"))
library(DT)
datatable(NC_DF, options = list(autoWidth = TRUE),
colnames = gsub(".", " ", colnames(NC_DF), fixed = TRUE) )
Most Populated Counties
The pareto below showing the top 20 most populous counties yeilds unsurprising results. Charlotte is in Mecklenburg County, Raliegh is mostly in Wake County, and Greensboro is in Guilford County
NC_DF%>%
top_n(n = 15, wt = Total)%>%
mutate(County = sapply(regmatches(NAME, regexec("^(.*) County,", NAME)), function(x) x[2]))%>%
arrange(desc(Total))%>%
select(County, Total)%>%
mutate(County = factor(County, levels = County, ordered = TRUE))%>%
ggplot()+
geom_bar(aes(x = County, y = Total),stat = "identity", fill = "#D40010")+
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_y_continuous(label = comma)+
ggtitle("Most Populous Couties")+
ylab("Population")
And here is is a heatmap of all the counties:
library("haven")
library("viridisLite") #A good color scheme for heatmaps
library(highcharter) #Wrapper for the Javascript highcharts package
library(stringr)
library(geojsonio)
data("uscountygeojson")
Heatmap_Data <- NC_DF %>%
mutate(CODE = paste("us",
tolower(STUSAB),
str_pad(county, width = 3, pad = "0"),
sep = "-"))
n <- 32
dstops <- data.frame(q = 0:n/n, c = substring(viridis(n + 1, option = "D"), 0, 7))
dstops <- list.parse2(dstops)
highchart() %>%
hc_title(text = params$HM_Measure) %>%
hc_add_series_map(map = geojson_read("us-nc-all.geo.json"), df = Heatmap_Data,
value = params$HM_Measure, joinBy = c("hc-key", "CODE"),
name = params$HM_Measure, borderWidth = 0.1) %>%
hc_colorAxis(stops = dstops, min = 0, max = 800000) %>%
hc_legend(layout = "vertical", reversed = TRUE,
floating = TRUE, align = "right") %>%
hc_tooltip(valueDecimals = 0)
Sankey Network
The ACS also has a lot of split by several factors, which can be hard to visualize. A good tool to put in your toolbox for this Situation is the Sankey Diagram. After a bit of data cleaning and mainipulation, they can be easily created using the networkD3 package with a single call:
sankeyNetwork(Links = Sankey_Data, Nodes = nodes,
Source = "Source_Num", Target = "Targ_Num", Value = "Value", NodeID = "name")
College_geo = geo.make(state = params$State, county = params$Sankey_County, check = T)
College_Data = acs.fetch(geography = College_geo, table.number = "B14004", col.names = "pretty")
College_DF = data.frame(geography(College_Data), estimate(College_Data))
row.names(College_DF) = NULL
CDF2 = gather(College_DF, Variable, Population, -NAME, -state, -county)
CDF2$VariableSimp = sapply(regmatches(CDF2$Variable, regexec(".*Yrs...(.*)", CDF2$Variable)), function(x) x[2])
CDF2$V1 = sapply(regmatches(CDF2$VariableSimp, regexec("(^.*?)\\.\\..*", CDF2$VariableSimp)), function(x) x[2])
CDF2$V2 = sapply(regmatches(CDF2$VariableSimp, regexec("^.*?\\.\\.(.*?)\\.\\.", CDF2$VariableSimp)), function(x) x[2])
CDF2$V3 = sapply(regmatches(CDF2$VariableSimp, regexec("^.*?\\.\\..*?\\.\\.(.*)", CDF2$VariableSimp)), function(x) x[2])
CDF2$V3 = ifelse(CDF2$V3=="", NA, CDF2$V3)
CDF2$Source = NA
CDF2$Target = NA
CDF2$Value = NA
for(i in 1:nrow(CDF2)){
if(is.na(CDF2[i,"V3"]) & !is.na(CDF2[i,"V2"]) ){
CDF2[i, "Source"] = CDF2[i,"V1"]
CDF2[i, "Target"] = CDF2[i,"V2"]
CDF2[i, "Value"] = CDF2[i,"Population"]
}
else if(!is.na(CDF2[i,"V3"]) & !is.na(CDF2[i,"V2"]) ){
CDF2[i, "Source"] = CDF2[i,"V2"]
CDF2[i, "Target"] = CDF2[i,"V3"]
CDF2[i, "Value"] = CDF2[i,"Population"]
}
else if(is.na(CDF2[i,"V3"]) & is.na(CDF2[i,"V2"]) ){
CDF2[i, "Source"] = "Total"
CDF2[i, "Target"] = CDF2[i,"V1"]
CDF2[i, "Value"] = CDF2[i,"Population"]
}
}
library(networkD3)
Sankey_Data = select(CDF2, Source, Target, Value)[-1,]
nodes = as.data.frame(unique(c(Sankey_Data$Source, Sankey_Data$Target)))
names(nodes) = "name"
nodes$nodevalue = as.numeric(row.names(nodes))-1
Sankey_Data = Sankey_Data%>%
mutate(Source_Num = sapply(Source, function(x){ nodes[match(x, nodes$name), "nodevalue"]}),
Targ_Num = sapply(Target, function(x){ nodes[match(x, nodes$name), "nodevalue"]}))
sankeyNetwork(Links = Sankey_Data, Nodes = nodes,
Source = "Source_Num", Target = "Targ_Num", Value = "Value", NodeID = "name")
Owner vs Renter Population
If it feels like everyone just moved to Charlotte, the numbers back that up. Look at this chart created with the highcharter package, which is a great resource for creating interactive charts.
Own_Rent_Data = NC_Data = acs.fetch(geography = geo.make(state = "NC", county = "Mecklenburg"),
variable = c("B25026_003","B25026_004", "B25026_005", "B25026_006", "B25026_007", "B25026_008",
"B25026_010","B25026_011", "B25026_012", "B25026_013", "B25026_014", "B25026_015"),
col.names = c("Owned Moved in After 2005", "Owned Between 2000 and 04",
"Owned Between 90 and 99", "Owned Between 80 and 89", "Owned Between 70 and 79",
"Owned Before 69","Rent Moved in After 2005", "Rent Between 2000 and 04",
"Rent Between 90 and 99", "Rent Between 80 and 89", "Rent Between 70 and 79",
"Rent Before 69") )
Own_Rent_DF = data.frame(geography(Own_Rent_Data), estimate(Own_Rent_Data))%>%
select(-NAME, -state, -county)%>%
gather(key = Variable,value = Population)%>%
mutate(Own_Rent = sapply(regmatches(Variable, regexec("^(.*?)\\.", Variable)), function(x) x[2]),
VAR = sapply(regmatches(Variable, regexec("^.*?\\.(.*)", Variable)), function(x) x[2]) )%>%
select(-Variable)%>%
spread(Own_Rent, Population, drop = TRUE)
Ownership_Bar = highchart()%>%
hc_chart(type = "column")%>%
hc_title(text = "Own vs Rent") %>%
hc_xAxis(categories = gsub(".", replacement = " ", Own_Rent_DF$VAR, fixed = TRUE)) %>%
hc_add_series(data = Own_Rent_DF$Owned,
name = "Ownership Population")%>%
hc_add_series(data = Own_Rent_DF$Rent,
name = "Renter Population")
Ownership_Bar
ZellW/LearnPlotting documentation built on May 10, 2019, 1:57 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
https://github.com/GregGunn/R-Users-Group-Makrdown-
title: "R Markdown and Interactive Graphic" output: slidy_presentation params: State: "NC" HM_Measure: "Males" Sankey_County: "Mecklenburg"
library(xtable) library(dplyr) library(ggplot2) library(scales) library(tidyr) setwd("C:/Users/Greg/Documents/R/R Markdown Presentation 3-15-2015")
The Agenda
-
Introductions i) Name
ii) Where you work iii) R Coding Experience iiii) Last thing you did in R or Current Project -
R Markdown Overview
- Sample Presentation
- Code Review
What is R markdown?
From RStudio
R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports from R. It combines the core syntax of markdown (an easy to write plain text format) with embedded R code chunks that are run so their output can be included in the final document. R Markdown documents are fully reproducible (they can be automatically regenerated whenever underlying R code or data changes).
As an example, this:
*Welcome*
Becomes this:
Welcome
For a lot of examples on what you can do with RMarkdown check out:
Another great resource is RStudios's cheatsheets:
https://www.rstudio.com/resources/cheatsheets/
Why Use R Markdown
This presentation is an example of how to use R Markdown with interactive graphics {.bigger}
Some of the advantages to this approach include:
Repeatability - It is easy to re-run code and produce similar results again and again
Flexibility - You can esily change the code to include different time periods or measures
Interactivity - Because it is HTML you can include interactive html elements
Ease of Sharing - Again, because it is HTML all you need to view it is a web browser
It Ain't All Roses Though
There are some disadvantages
Time - It generally takes longer to make a Markdown doc than a regular powerpoint
Ease of use - Flexibility comes with a price, and RMarkdown can be finicky sometimes
Pre-Reqs - To really customize past the general layouts, you would probably need to know html
Let's look at some data!
It is election season, and I won't be another outlet endlessly examining politics. However some of the coverage got me thinking about demographics, so I thought that might be interesting to examine.
The American Community Survey (ACS), which is part of the Census Bureau is a treasure trove of information. Through a variety of methods they estimate many different aspects of American life.
Luckily for us there is a publicly available API. Not only that, Ezra Glenn at MIT has written an R package with helper fuctions to make it easy(ish) to find and pull back data. He has written a very easy to follow paper on the package, which you can read via the link below.
http://eglenn.scripts.mit.edu/citystate/wp-content/uploads/2013/06/wpid-working_with_acs_R3.pdf
Defining the Geography
The Code
The code below creates a geography for all of North Carolina at the county level.
library(acs) NC_Area = geo.make(state = params$State, county = "*", check = T)
- The state argument is set by a parameter, which I'll show when we go over the markdown code. I could also have set it with a string of "NC"
- The county parameter of "*" just means all counties, I could have specified counties by name or FIPS code
- Check = TRUE is handy because it makes sure that you have a valid geography before you start trying to pull data
There is a handy lookup table function you can leverage as well if you are unsure of spelling or FIPS number
geo.lookup(state = "NC", county = "A")
data = geo.lookup(state = params$State, county = "A") print(xtable(data), type = "html", html.table.attributes = "border=0", include.rownames = FALSE)
Finding the right data
Unfortunately grabbing data from the API is not intuitive, mainly because there is SO MUCH DATA. If you really get into it there are several different handbooks you can buy to guid you through all the data
Luckily Ezra has provided us with a handy lookup function.
results(acs.lookup(table.name = "College"))
The call above yields 33 rows, and I've shown you the first few below.
Lookup = results(acs.lookup(table.name = "College")) print(head(xtable(Lookup),10), type = "html", html.table.attributes = "border=0", include.rownames = FALSE)
Grabbing the Data from the Interwebs
Once you have searched, and found some things you are interested in its time to actually grab the data! This involves 2 steps:
- Use the acs.fetch function to grab the data down as an acs object
- Turn the acs object into a data frame using the estimates
library(DT) #Actually call the API and return the data as an acs object NC_Data = acs.fetch(geography = NC_Area, variable = c("B01003_001", "B01001_002", "B01001_026", "B25026_003", "B25026_004", "B25026_005", "B25026_006", "B25026_007", "B25026_008"), col.names = c("Total", "Males", "Females", "Moved in After 2005", "Between 2000 and 04", "Between 90 and 99", "Between 80 and 89", "Between 70 and 79", "Before 69") ) #Turn acs into a data frame for easier manipulation NC_DF = data.frame(geography(NC_Data), estimate(NC_Data))%>% left_join(fips.state, by = c("state" = "STATE")) row.names(NC_DF) = NULL datatable(NC_DF)
NC_Data = acs.fetch(geography = NC_Area, variable = c("B01003_001", "B01001_002", "B01001_026", "B25026_003", "B25026_004", "B25026_005", "B25026_006", "B25026_007", "B25026_008"), col.names = c("Total", "Males", "Females", "Moved in After 2005", "Between 2000 and 04", "Between 90 and 99", "Between 80 and 89", "Between 70 and 79", "Before 69") ) #Turn acs into a data frame for easier manipulation NC_DF = data.frame(geography(NC_Data), estimate(NC_Data))%>% left_join(fips.state, by = c("state" = "STATE")) library(DT) datatable(NC_DF, options = list(autoWidth = TRUE), colnames = gsub(".", " ", colnames(NC_DF), fixed = TRUE) )
Most Populated Counties
The pareto below showing the top 20 most populous counties yeilds unsurprising results. Charlotte is in Mecklenburg County, Raliegh is mostly in Wake County, and Greensboro is in Guilford County
NC_DF%>% top_n(n = 15, wt = Total)%>% mutate(County = sapply(regmatches(NAME, regexec("^(.*) County,", NAME)), function(x) x[2]))%>% arrange(desc(Total))%>% select(County, Total)%>% mutate(County = factor(County, levels = County, ordered = TRUE))%>% ggplot()+ geom_bar(aes(x = County, y = Total),stat = "identity", fill = "#D40010")+ theme_minimal()+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ scale_y_continuous(label = comma)+ ggtitle("Most Populous Couties")+ ylab("Population")
And here is is a heatmap of all the counties:
library("haven") library("viridisLite") #A good color scheme for heatmaps library(highcharter) #Wrapper for the Javascript highcharts package library(stringr) library(geojsonio) data("uscountygeojson") Heatmap_Data <- NC_DF %>% mutate(CODE = paste("us", tolower(STUSAB), str_pad(county, width = 3, pad = "0"), sep = "-")) n <- 32 dstops <- data.frame(q = 0:n/n, c = substring(viridis(n + 1, option = "D"), 0, 7)) dstops <- list.parse2(dstops) highchart() %>% hc_title(text = params$HM_Measure) %>% hc_add_series_map(map = geojson_read("us-nc-all.geo.json"), df = Heatmap_Data, value = params$HM_Measure, joinBy = c("hc-key", "CODE"), name = params$HM_Measure, borderWidth = 0.1) %>% hc_colorAxis(stops = dstops, min = 0, max = 800000) %>% hc_legend(layout = "vertical", reversed = TRUE, floating = TRUE, align = "right") %>% hc_tooltip(valueDecimals = 0)
Sankey Network
The ACS also has a lot of split by several factors, which can be hard to visualize. A good tool to put in your toolbox for this Situation is the Sankey Diagram. After a bit of data cleaning and mainipulation, they can be easily created using the networkD3 package with a single call:
sankeyNetwork(Links = Sankey_Data, Nodes = nodes, Source = "Source_Num", Target = "Targ_Num", Value = "Value", NodeID = "name")
College_geo = geo.make(state = params$State, county = params$Sankey_County, check = T) College_Data = acs.fetch(geography = College_geo, table.number = "B14004", col.names = "pretty") College_DF = data.frame(geography(College_Data), estimate(College_Data)) row.names(College_DF) = NULL CDF2 = gather(College_DF, Variable, Population, -NAME, -state, -county) CDF2$VariableSimp = sapply(regmatches(CDF2$Variable, regexec(".*Yrs...(.*)", CDF2$Variable)), function(x) x[2]) CDF2$V1 = sapply(regmatches(CDF2$VariableSimp, regexec("(^.*?)\\.\\..*", CDF2$VariableSimp)), function(x) x[2]) CDF2$V2 = sapply(regmatches(CDF2$VariableSimp, regexec("^.*?\\.\\.(.*?)\\.\\.", CDF2$VariableSimp)), function(x) x[2]) CDF2$V3 = sapply(regmatches(CDF2$VariableSimp, regexec("^.*?\\.\\..*?\\.\\.(.*)", CDF2$VariableSimp)), function(x) x[2]) CDF2$V3 = ifelse(CDF2$V3=="", NA, CDF2$V3) CDF2$Source = NA CDF2$Target = NA CDF2$Value = NA for(i in 1:nrow(CDF2)){ if(is.na(CDF2[i,"V3"]) & !is.na(CDF2[i,"V2"]) ){ CDF2[i, "Source"] = CDF2[i,"V1"] CDF2[i, "Target"] = CDF2[i,"V2"] CDF2[i, "Value"] = CDF2[i,"Population"] } else if(!is.na(CDF2[i,"V3"]) & !is.na(CDF2[i,"V2"]) ){ CDF2[i, "Source"] = CDF2[i,"V2"] CDF2[i, "Target"] = CDF2[i,"V3"] CDF2[i, "Value"] = CDF2[i,"Population"] } else if(is.na(CDF2[i,"V3"]) & is.na(CDF2[i,"V2"]) ){ CDF2[i, "Source"] = "Total" CDF2[i, "Target"] = CDF2[i,"V1"] CDF2[i, "Value"] = CDF2[i,"Population"] } } library(networkD3) Sankey_Data = select(CDF2, Source, Target, Value)[-1,] nodes = as.data.frame(unique(c(Sankey_Data$Source, Sankey_Data$Target))) names(nodes) = "name" nodes$nodevalue = as.numeric(row.names(nodes))-1 Sankey_Data = Sankey_Data%>% mutate(Source_Num = sapply(Source, function(x){ nodes[match(x, nodes$name), "nodevalue"]}), Targ_Num = sapply(Target, function(x){ nodes[match(x, nodes$name), "nodevalue"]})) sankeyNetwork(Links = Sankey_Data, Nodes = nodes, Source = "Source_Num", Target = "Targ_Num", Value = "Value", NodeID = "name")
Owner vs Renter Population
If it feels like everyone just moved to Charlotte, the numbers back that up. Look at this chart created with the highcharter package, which is a great resource for creating interactive charts.
Own_Rent_Data = NC_Data = acs.fetch(geography = geo.make(state = "NC", county = "Mecklenburg"), variable = c("B25026_003","B25026_004", "B25026_005", "B25026_006", "B25026_007", "B25026_008", "B25026_010","B25026_011", "B25026_012", "B25026_013", "B25026_014", "B25026_015"), col.names = c("Owned Moved in After 2005", "Owned Between 2000 and 04", "Owned Between 90 and 99", "Owned Between 80 and 89", "Owned Between 70 and 79", "Owned Before 69","Rent Moved in After 2005", "Rent Between 2000 and 04", "Rent Between 90 and 99", "Rent Between 80 and 89", "Rent Between 70 and 79", "Rent Before 69") ) Own_Rent_DF = data.frame(geography(Own_Rent_Data), estimate(Own_Rent_Data))%>% select(-NAME, -state, -county)%>% gather(key = Variable,value = Population)%>% mutate(Own_Rent = sapply(regmatches(Variable, regexec("^(.*?)\\.", Variable)), function(x) x[2]), VAR = sapply(regmatches(Variable, regexec("^.*?\\.(.*)", Variable)), function(x) x[2]) )%>% select(-Variable)%>% spread(Own_Rent, Population, drop = TRUE) Ownership_Bar = highchart()%>% hc_chart(type = "column")%>% hc_title(text = "Own vs Rent") %>% hc_xAxis(categories = gsub(".", replacement = " ", Own_Rent_DF$VAR, fixed = TRUE)) %>% hc_add_series(data = Own_Rent_DF$Owned, name = "Ownership Population")%>% hc_add_series(data = Own_Rent_DF$Rent, name = "Renter Population") Ownership_Bar
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