In stevepowell99/CausalMapFunctions: Functions for Causal Mapping
# devtools::load_all();devtools::document();pkgdown::build_site(lazy=F)
options(dplyr.summarise.inform = FALSE)
library(CausalMapFunctions)
library(knitr)
library(htmltools)
library(configr)
library(dbplyr)
library(DBI)
library(kableExtra)
library(scales)
library(tictoc)
knitr::opts_chunk$set(echo = T,warning=F,message=F)
pp=function(x,N=99){
print(x,n=N)
}
replace_null <- function(x,replacement=0){
if(is.null(x)) replacement else x
}
append_rows <- function(val,name){
val <- val %>% mutate_all(clean_send)
dbWriteTable(con=conn,name = local(name),value=local(val) ,append=TRUE,row.names=FALSE)
}
print_inner_filter <- function(xx){
# browser()
xx$graf <- NULL
fil <- xx[[1]] %>% unlist %>% str_remove_all("^pipe_") %>% str_replace("_"," ")
xx <- xx[-1]
nams=names(xx)
tibble(names=nams,vals=unlist(xx)) %>%
unite(filter,sep="=") %>%
pull(filter) %>%
unlist %>%
paste0(.,collapse=" ") %>%
paste0(fil," ",.)
}
print_filter <- function(graf){
if(!exists("xtitle"))xtitle <- ""
if(!exists("xdescription"))xdescription <- ""
if(is.null(attr(graf,"info")))return()
# browser()
story <- attr(graf,"info")[[1]] %>% keep(~str_detect(.,"^load_mapfile")) %>% unlist %>% str_remove("load_mapfile *path=")
input_ace <- attr(graf,"info") %>% list_modify("load_mapfile" = NULL) %>% unlist %>% keep(.!=".") %>% map(~str_remove_all(.,"^pipe_") %>% str_replace("_"," ")) %>% paste0(collapse = "\n")
filters <- paste0("&filters=",URLencode(input_ace %>% replace_null(""),reserved=T))
message("appending")
append_rows(
tibble(likes=1,recommended=T,filter=input_ace %>% replace_null(""),hidden=F,title=xtitle %>% replace_null(""),description=xdescription %>% replace_null(""),date=time_stamp(),project=story)
,"shortlinks")
attr(graf,"info") %>%
map(~print_inner_filter(.) %>%
str_trim %>%
div(style=";")
) %>% tagList %>% div(style="padding:10px;background-color:aliceblue;") %>% div(
if(xtitle!="")h5(xtitle),
if(xdescription!="")p(xdescription),
.,
a("Link",href=paste0("https://causalmap.shinyapps.io/CausalMap2?story=",story,filters)),style="padding:10px;background-color:aliceblue;")
}
# make_interactive_map <- function(graf){
# tagList(print_filter(graf),CausalMapFunctions::make_interactive_map(graf))
# }
# make_print_map <- function(graf){
# tagList(print_filter(graf),CausalMapFunctions::make_print_map(graf))
# }
clean_send <- function(vec){
if(vec %>% unique %>% `%in%`(c(F,T)) %>% all) vec <- as.numeric(vec)
if(F & is.character(vec)) {
# Encoding(vec)="UTF-8"
vec <- stri_enc_toascii(vec)
}
vec
}
recodes <- tibble(old="Damage to Property",new="changed")
recodes <- tibble(old="Damage to Property",new="Damage to Businesses")
pipe_recode_factors(example2,recodes=recodes) %>% make_interactive_map()
example2$settings <- list(recodes=recodes)
pipe_recode_factors(example2) %>% make_interactive_map()
oi <- load_mapfile("C:/Users/steve/Downloads/2oirefugeetestimonies-2023-01-16.xlsx")
sh %>%
pipe_find_links(field = "source_frequency",value = 7,operator="greater") %>%
pipe_find_factors(field = "label",value = "no change",operator="notcontains") %>%
pipe_bundle_links() %>%
make_interactive_map()
sh %>%
pipe_find_links(field = "source_frequency",value = 7,operator="greater") %>%
pipe_find_factors(field = "label",value = "no change",operator="notcontains") %>%
pipe_bundle_links() %>%
make_print_map()
message(1)
pipe_update_mapfile() # creates an empty map
pipe_update_mapfile() %>%
make_interactive_map()
load_mapfile() %>% # creates an empty map
make_interactive_map()
example2
example2 %>% pipe_coerce_mapfile %>% pipe_update_mapfile() %>% make_interactive_map()
example2 %>% pipe_coerce_mapfile %>% pipe_update_mapfile(factors=example2$factors) %>% make_interactive_map()
ee <- example2
path <- "C:/Users/steve/Downloads/kantar-swan-short-2022-09-06.xlsx"
example_file <- load_mapfile("example-file")
test <- load_mapfile("resignations2")
test %>%
make_interactive_map()
swan <- load_mapfile(path=path)
# example_file <- load_mapfile(path="data/example-file.xlsx")
example_file <- load_mapfile(path="C:/Users/steve/Dropbox/Projects/CausalMapFunctions/data/example-file.xlsx")
tic();swan %>% pipe_coerce_mapfile() %>% make_interactive_map();toc()
tic();swan %>% pipe_coerce_mapfile();toc()
example2 %>% pipe_coerce_mapfile() %>% make_interactive_map()
ee %>%
pipe_label_links("link_id",fun = "unique") %>%
make_interactive_map
ee %>%
pipe_label_links("link_id",fun = "unique") %>%
make_print_map()
ee %>%
pipe_set_print(grv_layout="circo") %>%
make_print_map()
example_file %>%
make_interactive_map()
tic()
example_file %>%
pipe_select_factors(10) %>%
pipe_bundle_links() %>%
pipe_scale_links() %>%
make_print_map()
toc()
example_file %>%
pipe_find_factors(value="Nutrition") %>%
pipe_bundle_links() %>%
pipe_scale_links() %>%
make_print_map()
example_file %>%
pipe_select_factors() %>%
pipe_bundle_links() %>%
pipe_scale_links() %>%
pipe_color_factors() %>%
make_print_map()
tf <- load_mapfile("tearfund-all")
tf %>%
parse_commands("zoom factors level=1
find links field=source_frequency value=7 operator=greater
trace paths length=3 remove_links=FALSE threads_direction=down from=CCM to=Good family/ personal relationships/ support
find links field=hashtags value=#plain_coding operator=notcontains
bundle links field=simple_bundle
label factors field=thread_count add_field_name=FALSE clear_previous=FALSE
label links value=count: downstream_threads add_field_name=FALSE clear_previous=TRUE
scale links value=count: downstream_threads
color factors field=found_type
color links field=threads fun=count") %>%
make_print_map()
tic()
tf %>% pipe_zoom_factors() # 6 times as long
toc()
tic()
example_file %>% pipe_zoom_factors()
toc()
tic();example_file %>% pipe_trace_paths(from="method/practice",to="Income",length = 3,remove_links = F);toc()
# increases from .3 to 3 seconds
tic();example_file %>% pipe_trace_paths(from="method/practice",to="Income",length = 3,threads_direction = "down",remove_links = F);toc()
# length doesnt make a difference
tic();example_file %>% pipe_trace_paths(from="method/practice",to="Income",length = 6,remove_links = F);toc()
# increases from .3 to 3 seconds
tic();example_file %>% pipe_trace_paths(from="method/practice",to="Income",length = 6,threads_direction = "down",remove_links = F);toc()
tic();tf %>% pipe_trace_paths(from="Christian faith",to="Increased material",length = 3,remove_links = F);toc()
tic();tf %>% pipe_trace_paths(from="Christian faith",to="Increased material",length = 3,remove_links = F) %>% make_interactive_map();toc()
tic();tf %>% pipe_zoom_factors() %>% pipe_trace_paths(from="Christian faith",to="Increased material",length = 3,remove_links = F) %>% pipe_cluster_factors("e") %>% pipe_remove_brackets() %>% pipe_color_factors()%>% pipe_color_links();toc()
# oh my gosh, this is the one
tic();tf %>% pipe_trace_paths(from="Christian faith",to="Increased material",length = 3,remove_links = F,threads_direction = "down");toc()
tf$links %>% nrow
example_file$links %>% nrow
tf
example_file %>%
pipe_find_links("simple_frequency",value = 15,"greater") %>%
make_interactive_map
example_file %>%
pipe_find_links("simple_frequency",value = 1500,"greater") %>%
make_interactive_map
example_file %>%
pipe_find_links("simple_frequency",value = 1500,"greater") %>%
make_print_map
example_file %>%
pipe_select_links(5) %>%
make_interactive_map
example_file %>%
pipe_find_statements(field="statement_id",value=5,operator="equals") %>%
make_interactive_map
example_file %>%
pipe_find_factors(value="health") %>%
make_interactive_map
Zero up and down
# shows nothing because isolated are removed
ee %>%
pipe_find_factors(value="rainfall",up=0,down=0,remove_isolated = T) %>%
make_interactive_map
ee %>%
pipe_find_factors(value="rainfall",up=0,down=0,remove_isolated = F) %>%
make_interactive_map
# shows nothing because isolated are removed
ee %>%
pipe_find_factors(value="Damage",up=0,down=0,remove_isolated = T) %>%
make_interactive_map
ee %>%
pipe_find_factors(value="Damage",up=1,down=1,remove_isolated = F) %>%
make_interactive_map
ee %>%
pipe_find_factors(value="Damage",up=1,down=1,remove_isolated = F) %>%
make_interactive_map
Selecting and finding
If you filter the factors of a mapfile, e.g. show only factors with labels beginning xyz,
- also the links are filtered (removing links to removed factors)
- the statements are not touched
If you filter the links of a mapfile, e.g. show only links with hashtags containing xyz,
- the factors are not filtered (but using a different command you can remove any factors which no longer have any links)
- the statements are not touched
If you filter the statements of a mapfile, e.g. show only statements with texts containing xyz,
- also the links are filtered (removing links to removed statements)
- the factors are filtered
graf <- ee
tic();graf <- pipe_update_mapfile(graf,factors=graf$factors);graf;toc()
ee %>%
pipe_select_factors(3) %>%
make_interactive_map
Simple frequency
ee %>%
pipe_find_factors("frequency",value = 1,"greater") %>%
make_interactive_map
ee %>%
pipe_find_factors("frequency",value = 1,"greater") %>%
make_print_map
ee %>%
pipe_find_factors("frequency",value = 1,"greater",up=0,down=0) %>%
make_interactive_map
Showing interlinks or not
If you don't want to show interlinks between downstream factors, use find links instead.
example_file %>%
pipe_find_factors(value="health",up=0,down=1,remove_isolated = T) %>%
pipe_bundle_links() %>%
make_print_map
example_file %>%
pipe_find_links(value="health",field="from_label",operator="contains") %>%
pipe_bundle_links() %>%
make_print_map
Case insensitive
ee %>%
pipe_find_factors(value="business") %>%
make_interactive_map
notcontains
ee %>%
pipe_find_factors(value="sea",operator="notcontains") %>%
make_interactive_map
ee %>% pipe_find_statements(value="1",operator="notcontains",field="statement_id")%>%
make_interactive_map
ee %>% pipe_find_statements(value="1",operator="notcontains",field="statement_id")%>%
make_interactive_map
notequals
ee %>% pipe_find_statements(value="1",operator="notequals",field="statement_id")%>%
make_interactive_map
ee %>%
pipe_find_factors(value="Coastal erosion",operator="notequals") %>%
make_interactive_map
OR
Does not work in functions, but works when parsed:
ee %>%
pipe_find_factors(value="business OR property") %>%
make_interactive_map
ee %>%
parse_commands("find factors field=label operator=contains value=Business OR Property") %>%
make_interactive_map
Order matters
example_file %>%
pipe_find_factors(value="health") %>%
pipe_select_factors(5) %>%
make_interactive_map
No result
example_file %>%
pipe_find_factors(value="asdfasdfasdf") %>%
make_interactive_map
example_file %>%
pipe_find_links(field="from_label",value="health",operator="contains") %>%
make_interactive_map
Numerical comparison
example_file %>%
pipe_find_statements(field="statement_id",value=20,operator="less") %>%
make_interactive_map
No result
example_file %>%
pipe_find_statements(field="statement_id",value=20000000,operator="greater") %>%
make_interactive_map
Highlight only
ee %>%
pipe_find_factors(value="Damage",highlight_only=T) %>% pipe_color_factors(field="found") %>%
make_interactive_map()
ee %>%
pipe_find_links(field="to_label",value="Damage",highlight_only=T) %>%
pipe_color_links(field="found",fun="literal") %>%
make_interactive_map()
Clustering sources
pipe_cluster_sources(example_file) %>%
.$sources %>%
select(contains("cluster"))
example_file %>%
parse_commands("cluster sources n_clusters=all") %>%
.$sources %>%
select(contains("cluster"))
example_file %>%
parse_commands("cluster sources n_clusters=2") %>%
.$sources %>%
select(contains("cluster"))
example_file %>%
parse_commands("cluster sources") %>%
.$sources %>%
select(contains("cluster"))
Calculated fields
example_file %>%
pipe_find_factors(value="health",up = 1,down=1) %>%
make_mentions_tabl()
example_file %>%
pipe_find_factors(value="health",up = 1,down=1) %>%
.$factors %>% select(label,source_count)
Conditional formatting
example_file %>%
pipe_select_factors(5) %>%
pipe_scale_factors(field="frequency") %>%
pipe_bundle_links %>%
make_interactive_map
example_file %>%
pipe_select_factors(5) %>%
pipe_color_text(field="frequency") %>%
pipe_bundle_links %>%
make_interactive_map
example_file %>%
pipe_select_factors(5) %>%
pipe_scale_factors(field="frequency") %>%
make_print_map
example_file %>%
pipe_select_factors(5) %>%
pipe_color_factors(field="frequency") %>%
pipe_color_borders(field="betweenness") %>%
pipe_wrap_factors(5) %>%
pipe_bundle_links %>%
make_interactive_map
Wrap factors
example_file %>%
pipe_select_factors(5) %>%
pipe_color_links(value="count: link_id") %>%
pipe_wrap_factors(5) %>%
pipe_bundle_links %>%
make_interactive_map
example_file %>%
pipe_select_factors(5) %>%
pipe_color_links(value="count: link_id") %>%
pipe_wrap_factors(5) %>%
pipe_bundle_links %>%
make_print_map
Wrap links
ee %>%
pipe_label_links("from_label",fun = "unique") %>%
pipe_wrap_links(6) %>%
pipe_bundle_links %>%
make_interactive_map
ee %>%
pipe_label_links("from_label",fun = "unique") %>%
pipe_wrap_links(6) %>%
make_print_map
ee %>%
pipe_label_links(value="unique: from_label") %>%
pipe_wrap_links(6) %>%
make_interactive_map
Remove brackets
example_file$factors$label[32] <- "Income [E]"
example_file %>%
pipe_select_factors(5) %>%
make_interactive_map
example_file %>%
pipe_select_factors(5) %>%
pipe_remove_brackets() %>%
make_interactive_map
example_file$factors$label[32] <- "Income"
Bundle factors
example_file %>%
pipe_bundle_factors(value = "health") %>%
pipe_select_factors(5) %>%
pipe_bundle_links() %>%
make_interactive_map
Self loops
example_file %>%
pipe_bundle_factors(value = "health") %>%
pipe_select_factors(5) %>%
pipe_bundle_links() %>%
pipe_remove_selfloops() %>%
make_interactive_map
Format links without bundling
ee %>%
pipe_color_links("link_id",fun = "mean") %>%
make_interactive_map
ee %>%
pipe_scale_links("link_id",fun = "mean") %>%
make_interactive_map
Label factors with and without field names
example_file %>%
pipe_find_factors(value="Improved health") %>%
# pipe_zoom_factors(1) %>%
pipe_select_factors(top=10) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="source_id",fun = "count") %>%
pipe_label_factors(field="source_count",add_field_name = F) %>%
pipe_wrap_factors() %>%
make_print_map
example_file %>%
pipe_find_factors(value="Improved health") %>%
# pipe_zoom_factors(1) %>%
pipe_select_factors(top=10) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="source_id",fun = "count") %>%
pipe_label_factors(field="source_count",add_field_name = T) %>%
pipe_wrap_factors() %>%
make_print_map
Label links
example_file %>%
pipe_select_links(10) %>%
pipe_label_links("link_id",fun = "literal") %>%
pipe_label_links("from_label",fun = "literal",add_field_name = T,clear_previous = F) %>%
make_interactive_map
example_file %>%
pipe_select_links(10) %>%
pipe_bundle_links() %>%
pipe_label_links("link_id",fun = "literal",add_field_name = T) %>%
make_interactive_map
example_file %>%
pipe_select_links(10) %>%
pipe_bundle_links() %>%
pipe_label_links("link_id",fun = "literal") %>%
pipe_label_links("from_label",fun = "literal",clear_previous = F) %>%
make_interactive_map
example_file %>%
pipe_select_links(10) %>%
pipe_bundle_links() %>%
parse_commands("label links field=statement_id fun=unique clear_previous=F") %>%
make_interactive_map
Bundling links
Note the defaults for bundle_links and label_links:
oo %>%
pipe_select_factors(5) %>%
pipe_bundle_links(("#Name of province") ) %>%
pipe_label_links(("#Name of province") ) %>%
make_interactive_map
oo %>%
parse_commands("
select links top=15
select factors top=10
bundle links field=#2
label factors field=frequency add_field_name=FALSE clear_previous=FALSE
scale links value=count: link_id
label links field=#2Name of village fun=unique add_field_name=FALSE clear_previous=TRUE
color text field=is_opposable lo=black hi=#f26d04") %>%
make_interactive_map
oo %>%
pipe_select_factors(5) %>%
pipe_bundle_links("village" ) %>%
pipe_label_links("village" ,fun = "unique" ) %>%
make_interactive_map
ll %>%
pipe_find_factors(value="economic") %>%
pipe_select_factors(5) %>%
pipe_bundle_links() %>%
pipe_label_links() %>%
make_interactive_map
ll %>%
pipe_find_factors(value="economic") %>%
pipe_select_factors(5) %>%
pipe_bundle_links() %>%
pipe_label_links() %>%
make_interactive_map
Note the default for bundle_links is equivalent to simple_bundle:
ll %>%
pipe_find_factors(value="economic") %>%
pipe_select_factors(5) %>%
pipe_bundle_links(group="simple_bundle") %>%
pipe_label_links() %>%
make_interactive_map
Group and label by sex and scale by count:
ll %>%
pipe_select_factors(5) %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_scale_links("link_id",fun = "count") %>%
pipe_label_links("1. Sex",fun = "unique") %>%
pipe_color_links("1. Sex",fun = "unique") %>%
make_interactive_map
ll %>%
pipe_select_links(16) %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_label_links("link_id",fun = "count") %>%
make_interactive_map
Group by sex and scale and colour by count:
example_file %>%
pipe_select_factors(5) %>%
pipe_bundle_links(field="Sex of the head of the household") %>%
pipe_color_links("link_id",fun = "count") %>%
pipe_scale_links("link_id",fun = "count") %>%
pipe_label_links("link_id",fun = "count") %>%
make_interactive_map
Proportion
ll %>%
pipe_find_factors(value="sed yield") %>%
pipe_bundle_links(field="District") %>%
pipe_color_links(field="District",fun="unique") %>%
pipe_label_links(value="percent: link_id") %>%
pipe_scale_links(field="link_id",fun="count") %>%
make_print_map
ll %>%
pipe_find_factors(value="sed yield") %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_color_links(field="1. Sex",fun="unique") %>%
pipe_label_links(field="link_id",fun="percent") %>%
pipe_scale_links(field="link_id",fun="count") %>%
make_print_map
ll %>%
pipe_find_factors(value="sed yield") %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_color_links(field="1. Sex",fun="unique") %>%
pipe_label_links(field="source_id",fun="percent") %>%
pipe_scale_links(field="source_id",fun="percent") %>%
make_print_map
ll %>%
pipe_find_factors(value="sed yield",down=0) %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_color_links(field="source_id",fun="percent") %>%
pipe_label_links(field="source_id",fun="percent") %>%
pipe_scale_links(field="source_id",fun="count") %>%
make_print_map
Surprise
ll %>%
pipe_find_factors(value="sed yield",down=0) %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_color_links(field="1. Sex",fun="unique") %>%
pipe_label_links(field="source_id",fun="surprise") %>%
pipe_scale_links(field="source_id",fun="count") %>%
make_print_map
ll %>%
pipe_find_factors(value="sed yield",down=0) %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_label_links(field="1. Sex",fun="unique") %>%
pipe_color_links(field="source_id",fun="surprise") %>%
pipe_scale_links(field="source_id",fun="count") %>%
make_print_map
Using initials to shorten the labels
oo %>%
pipe_find_factors(value="crops",down=0) %>%
pipe_bundle_links(field="#4Sex of the respondent") %>%
pipe_label_links(field="#4Sex of the respondent",fun="initials") %>%
pipe_color_links(field="source_id",fun="surprise") %>%
pipe_scale_links(field="source_id",fun="count") %>%
make_print_map
oh %>%
pipe_select_factors(10) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="from_label",fun="initials") %>%
make_print_map
Recalculating
Shouldn't make any difference as factor and link fields are now recalculated after every transform; but beforeids and afterids are added only on load_mapfile.
ll %>%
pipe_find_factors(value="WASH") %>%
pipe_color_factors(field="betweenness") %>%
make_interactive_map
ll %>%
pipe_find_factors(value="WASH") %>%
pipe_recalculate_factors() %>%
pipe_color_factors(field="betweenness") %>%
make_interactive_map
Mark links
so %>%
pipe_label_links(field = "source_id",fun="literal") %>%
pipe_mark_links(field="source_id") %>%
make_print_map
e3 %>%
pipe_label_links(field = "source_id",fun="literal") %>%
pipe_mark_links(field="source_id") %>%
make_print_map
ee %>%
pipe_label_links(field = "source_id",fun="literal") %>%
pipe_mark_links(field="source_id") %>%
make_print_map
so %>%
pipe_bundle_links(field = "source_id") %>%
pipe_label_links(field = "source_id",fun="literal") %>%
pipe_mark_links(field="source_id") %>%
make_print_map
so %>%
pipe_combine_opposites() %>%
pipe_bundle_links(field = "flipped_bundle") %>%
pipe_label_links(field = "source_id",fun="literal") %>%
pipe_mark_links(field="source_id") %>%
make_print_map
ll %>%
pipe_select_factors(5) %>%
pipe_bundle_links(field = "simple_bundle") %>%
pipe_mark_links(field="source_id") %>%
make_print_map
Show continuity
#original:
so %>%
pipe_label_links(field = "source_id",fun="unique") %>%
make_print_map
so %>%
pipe_label_links(field = "source_id",fun="unique") %>%
pipe_show_continuity(field="source_id",type="label") %>%
make_print_map
so %>%
pipe_label_links(field = "source_id",fun="unique") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
ee %>%
pipe_label_links(field = "source_id",fun="unique") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
example_file %>%
pipe_label_links(field = "source_id",fun="unique") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
example2 %>%
pipe_zoom_factors() %>%
pipe_select_factors() %>%
pipe_show_continuity(field="source_id") %>%
pipe_label_links(field = "source_id",fun="unique") %>%
make_print_map
example2 %>%
pipe_bundle_factors("Flooding") %>%
pipe_show_continuity(field="source_id") %>%
pipe_label_links(field = "source_id",fun="unique") %>%
make_print_map
example2 %>%
pipe_bundle_factors("Flooding") %>%
pipe_bundle_links(field = "source_id") %>%
pipe_label_links(field = "source_id",fun="literal") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
so %>%
pipe_bundle_links() %>%
pipe_label_links(field = "source_id",fun="unique") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
so %>%
pipe_combine_opposites() %>%
pipe_label_links("link_id","literal") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
so %>%
pipe_combine_opposites() %>%
pipe_show_continuity() %>%
make_print_map
so %>%
pipe_combine_opposites() %>%
pipe_bundle_links(field = "flipped_bundle") %>%
pipe_show_continuity(field="source_id") %>%
pipe_label_links(field = "source_id",fun="unique") %>%
make_print_map
# Order should not matter, but you can't put bundle links later
so %>%
pipe_combine_opposites() %>%
pipe_show_continuity(field="source_id") %>%
pipe_bundle_links(field = "flipped_bundle") %>%
pipe_label_links(field = "source_id",fun="unique") %>%
make_print_map
ll %>%
pipe_select_factors(5) %>%
pipe_bundle_links(field = "simple_bundle") %>%
pipe_scale_links(field = "link_id",fun="count") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
hz %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
hz %>%
pipe_zoom_factors(1) %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
Group and label by sex and scale by count:
ll %>%
pipe_select_factors(5) %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_scale_links("link_id",fun = "count") %>%
pipe_label_links("1. Sex",fun = "unique") %>%
pipe_color_links("1. Sex",fun = "unique") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
ll %>%
pipe_select_factors(5) %>%
pipe_find_links(field = "statement_id",value=90,operator="greater") %>%
pipe_find_links(field = "statement_id",value=290,operator="less") %>%
pipe_bundle_links(field="1. Sex") %>%
pipe_scale_links("link_id",fun = "count") %>%
pipe_label_links("source_id",fun = "unique") %>%
pipe_color_links("1. Sex",fun = "unique") %>%
pipe_show_continuity(field="source_id") %>%
make_print_map
Nested maps
tf %>%
pipe_zoom_factors(1) %>%
pipe_select_factors(5) %>%
make_interactive_map
tf %>%
pipe_zoom_factors(1) %>%
pipe_bundle_links() %>%
pipe_label_links() %>%
pipe_scale_links() %>%
make_print_map
Zooming out and showing source count / frequency
example_file %>%
pipe_zoom_factors(level = 1) %>%
pipe_select_factors(10) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="source_id",fun = "count") %>%
pipe_label_factors(field="frequency",add_field_name = F) %>%
pipe_wrap_factors() %>%
make_print_map
example_file %>%
pipe_zoom_factors(level = 2) %>%
pipe_select_factors(10) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="source_id",fun = "count") %>%
pipe_label_factors(field="frequency",add_field_name = F) %>%
pipe_wrap_factors() %>%
make_print_map
example_file %>%
pipe_zoom_factors(1,preserve_frequency = 4,frequency_field = "frequency",frequency_other = "(other)") %>%
pipe_select_factors(10) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_factors(field="frequency",add_field_name = F) %>%
pipe_wrap_factors() %>%
make_print_map
oig %>%
pipe_zoom_factors(1,preserve_frequency = 20,frequency_field = "source_count") %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="source_id",fun = "count") %>%
pipe_label_factors(field="source_count",add_field_name = F) %>%
pipe_color_borders(field="frequency_preserved",lo="white") %>%
pipe_wrap_factors() %>%
make_print_map
tt %>%
pipe_zoom_factors(1,preserve_frequency = 80) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="link_id",fun = "count") %>%
pipe_label_factors(field="frequency",add_field_name = F) %>%
pipe_color_borders(field="frequency_preserved",lo="white") %>%
pipe_wrap_factors() %>%
make_print_map
cc %>%
pipe_find_factors(value="Improved health") %>%
pipe_zoom_factors(1) %>%
pipe_select_factors(top=10) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="source_id",fun = "count") %>%
pipe_label_factors(field="source_count",add_field_name = F) %>%
pipe_label_factors(field="frequency",add_field_name = F) %>%
pipe_wrap_factors() %>%
make_print_map
cc %>%
pipe_find_factors(value="Improved health") %>%
pipe_zoom_factors(1) %>%
pipe_select_factors(top=10) %>%
pipe_bundle_links(field="simple_bundle") %>%
pipe_label_links(field="source_id",fun = "count") %>%
pipe_label_factors(field="source_count",add_field_name = F) %>%
pipe_label_factors(field="frequency",add_field_name = F) %>%
pipe_wrap_factors() %>%
make_print_map
oi %>%
pipe_find_factors(value="Take a loan") %>%
pipe_zoom_factors(1,preserve_frequency = 2,frequency_other = "(other)") %>%
# pipe_label_factors("source_count") %>%
# pipe_label_links(value="unique:source_id") %>%
make_print_map
Combining opposites
example_file %>%
pipe_combine_opposites() %>%
pipe_zoom_factors() %>%
pipe_select_factors() %>%
make_print_map()
example_file %>%
pipe_combine_opposites() %>%
pipe_zoom_factors() %>%
pipe_select_factors() %>%
pipe_color_borders(fixed="white") %>%
make_print_map()
example_file %>%
pipe_combine_opposites() %>%
pipe_zoom_factors() %>%
pipe_select_factors() %>%
pipe_color_borders(field = "frequency") %>%
make_print_map()
hh %>%
make_print_map()
hh %>%
pipe_combine_opposites %>%
pipe_label_links(value="count:link_id") %>%
make_print_map()
# artificial arrowlabels
hh %>%
pipe_combine_opposites %>% pipe_update_mapfile(.,links=.$links %>% mutate(color="limegreen",taillabel=c("","","\u274C","\u274C","","\u274C"),headlabel=c("","","\u274C","\u274C","\u274C","\u274C"))) %>% make_print_map()
hh %>%
pipe_combine_opposites %>% pipe_update_mapfile(.,links=.$links %>% mutate(color="limegreen",taillabel=c("+","+","~","~","+","~"),headlabel=c("+","+","~","~","~","~"))) %>% make_print_map()
hh %>%
pipe_combine_opposites %>% pipe_update_mapfile(.,links=.$links %>% mutate(color="limegreen",arrowtail=c("","","nonetee","nonetee","","nonetee"),arrowhead=c("nonetee","","veenonenonetee","veenonenonetee","veenonenonetee","veenonenonetee"))) %>% make_print_map()
Nested maps with opposites
Note colours in Interactive view
tt %>%
pipe_zoom_factors(1) %>%
pipe_combine_opposites() %>%
pipe_select_links(3) %>%
make_print_map
tt %>%
pipe_zoom_factors(1) %>%
pipe_select_links(15) %>%
pipe_combine_opposites() %>%
pipe_bundle_links("flipped_bundle") %>%
pipe_label_links(value="count:link_id") %>%
make_print_map
tt %>%
pipe_zoom_factors(1) %>%
pipe_combine_opposites() %>%
pipe_select_links(3) %>%
make_interactive_map
Factor quick fields
# factors <- de$factors
de %>%
pipe_color_text(field="time") %>%
make_print_map
Hiding quickfields
de %>%
pipe_hide_quickfields %>%
make_print_map
de %>%
pipe_color_borders(field="happiness") %>%
pipe_label_factors(field = "time") %>%
pipe_hide_quickfields %>%
make_print_map
equivalent:
de %>%
pipe_find_factors(field="time",value="after",up=0,down=0) %>%
make_print_map
de %>%
pipe_find_factors(field="label",value="time:after",up=0,down=0) %>%
make_print_map
zoom
de %>%
pipe_zoom_factors(1) %>%
pipe_color_factors(field="time") %>%
make_print_map
Link quick fields
# factors <- de$factors
de %>%
pipe_recalculate_all() %>%
pipe_color_links(field="plausibility") %>%
make_print_map
de %>%
pipe_color_text(field="happiness") %>%
pipe_hide_quickfields %>%
make_print_map
de %>%
pipe_label_factors(field = "time") %>%
pipe_hide_quickfields %>%
make_print_map
Path tracing
cat("### Single\n")
example_file %>%
pipe_trace_paths(from = "Organisation 1",to="Ability to buy food",length = 3) %>%
pipe_select_factors(15) %>%
make_interactive_map
ma %>%
pipe_trace_paths(from = "Shared roles",to="balanced",length = 1) %>%
pipe_select_factors(15) %>%
make_interactive_map
ma %>%
pipe_trace_paths(from = "Covid-19",to="Business",length = 2) %>%
pipe_select_factors(15) %>%
make_interactive_map
oo %>%
pipe_select_factors(15) %>%
pipe_trace_paths(from = "Learnt about farming",to="",length = 5) %>%
make_interactive_map
ee %>%
pipe_trace_paths(from = "Funds",to="area",length = 5) %>%
make_interactive_map
cat("### Case insensitive\n")
ee %>%
pipe_trace_paths(from = "funds",to="aREa",length = 5) %>%
make_interactive_map
cat("### Failing; no paths at all\n")
ee %>%
pipe_trace_paths(from = "xx",to="yy",length = 5) %>%
make_interactive_map
cat("### Failing; no paths\n")
ee %>%
pipe_trace_paths(from = "Funds",to="yy",length = 5) %>%
make_interactive_map
ee %>%
pipe_trace_paths(from = "xx",to="Property",length = 5) %>%
make_interactive_map
cat("### Implicit multiple\n")
ee %>%
pipe_trace_paths(from = "High",to="Damage",length = 5) %>%
make_interactive_map
cat("### Explicit multiple\n")
ee %>%
pipe_trace_paths(from = "High",to="Property | Business",length = 5) %>%
make_interactive_map
ee %>%
pipe_trace_paths(from = "High",to="Property OR Business",length = 5) %>%
make_interactive_map
cat("Should this be possible?")
ee %>%
pipe_trace_paths(from = "High",to="Property OR Business",length = 5) %>%
make_interactive_map
tt %>%
pipe_trace_paths(from = "Capabilities",to="[OP3]",length = 2) %>%
make_interactive_map
ee %>%
pipe_trace_paths(from = "Funds",to="area",length = 5) %>%
make_print_map()
Threads
example_file %>%
pipe_zoom_factors(1) %>%
pipe_select_factors(10) %>%
pipe_trace_paths(length=4,from="knowledge",threads_direction="down") %>%
pipe_bundle_links() %>%
pipe_scale_links() %>%
pipe_color_links(field="threads") %>%
pipe_label_links(field="threads") %>%
pipe_color_factors(field="thread_count") %>%
pipe_label_factors(field="thread_count") %>%
make_print_map()
example_file %>%
pipe_zoom_factors(1) %>%
pipe_select_factors(6) %>%
pipe_trace_paths(length=4,from="knowledge",threads_direction="down") %>%
pipe_bundle_links() %>%
pipe_scale_links() %>%
# pipe_color_links(field="threads") %>%
pipe_label_links(field="threads",fun = "unique") %>%
pipe_wrap_links(12) %>%
pipe_color_factors(field="thread_count") %>%
pipe_label_factors(field="thread_count") %>%
make_print_map()
Robustness
## request is large but ok
pa %>%
pipe_trace_paths(from = "",to="~Impact on wellbeing",length = 1) %>%
make_print_map()
pa %>%
pipe_trace_paths(from = "Covid",to="",length = 3) %>%
make_print_map()
## request is large
if(F){e3 %>%
pipe_trace_robustness(from = "High",to="Damage",length = 5) %>%
make_print_map()
ee %>%
pipe_trace_robustness(from = "High",to="Damage",length = 5) %>%
get_robustness()
tt %>%
pipe_trace_robustness(from = "Capabilities",to="[OP3]",length = 2) %>%
pipe_wrap_factors() %>%
make_print_map()
tt %>%
pipe_trace_robustness(from = "Capabilities",to="[OP3",length = 2) %>%
pipe_wrap_factors() %>%
make_print_map()
## checking the robustness gets transferred
tt %>%
pipe_trace_robustness(from = "Capabilities",to="[OP3]",length = 2) %>%
pipe_wrap_factors() %>%
pipe_wrap_links() %>%
pipe_select_links(10) %>%
get_robustness()
tt %>%
pipe_trace_robustness(from = "Capabilities",to="[OP3",length = 2) %>%
get_robustness()
tt %>%
pipe_trace_robustness(from = "Capabilities; [P13",to="[OP3]",length = 2) %>%
get_robustness()
e3 %>%
pipe_trace_robustness(from = "High",to="People moving",length = 5) %>%
make_print_map()
e3 %>%
pipe_trace_robustness(from = "High",to="",length = 5) %>%
make_print_map()
e3 %>%
pipe_trace_robustness(from = "High",to="People moving",length = 5) %>%
get_robustness() %>%
kable
e3 %>%
pipe_trace_robustness(from = "High",to="Flooding",length = 5) %>%
get_robustness() %>%
kable
e3 %>%
pipe_trace_robustness(from = "High",to="Damage",length = 5) %>%
get_robustness() %>%
kable
e3 %>%
pipe_trace_robustness(from = "External",to="Damage",length = 5) %>%
get_robustness() %>%
kable
e3 %>%
pipe_trace_robustness(from = "External",to="Outcome",length = 5) %>%
get_robustness()%>%
kable
}
Robustness by field
Just one source:
ee %>%
pipe_trace_robustness(from = "Funds",to="Increased",length = 5,field = "source_id") %>% get_robustness
ll %>%
pipe_trace_robustness(from = "Cash",to="Income",length = 5,field="source_id") %>%
get_robustness()
Check that opposites colouring is always preserved?
if(F){
hh %>%
pipe_trace_robustness(from = "Revision",to="happy",length = 5) %>%
pipe_combine_opposites %>%
make_print_map()
hh %>%
pipe_combine_opposites %>%
pipe_find_factors(value="exam") %>%
make_print_map()
hh %>%
pipe_combine_opposites %>%
pipe_zoom_factors() %>%
pipe_find_factors(value="exam") %>%
pipe_select_factors(2) %>%
pipe_select_links(3) %>%
make_print_map()
}
Colours in interactive map
hh %>%
pipe_combine_opposites %>%
pipe_zoom_factors() %>%
make_interactive_map()
upstream
e3 %>%
pipe_trace_paths(from = "",to="Outcome",length = 4) %>%
pipe_trace_threads(direction="up") %>%
pipe_bundle_links() %>%
pipe_label_links(field = "source_id",fun = "literal") %>%
pipe_label_links(field = "has_upstream_threads",fun = "sum",clear_previous = F) %>%
pipe_color_links(field = "has_upstream_threads",fun = "sum") %>%
pipe_color_borders(field="n_upstream_threads_surviving") %>%
pipe_label_factors(field="n_upstream_threads_surviving") %>%
make_print_map()
oi %>%
pipe_trace_paths(from = "Flee",to="",length = 3) %>%
pipe_trace_threads(direction="down") %>%
pipe_bundle_links() %>%
pipe_label_links(field = "has_downstream_threads",fun = "sum") %>%
pipe_label_links(field = "source_id",fun = "literal",clear_previous = F) %>%
pipe_color_links(field = "has_downstream_threads",fun = "sum") %>%
pipe_color_factors(field="found_type") %>%
make_print_map
oi %>%
pipe_trace_paths(from = "",to="Financial diary",length = 2) %>%
pipe_trace_threads(direction="up") %>%
pipe_bundle_links() %>%
pipe_label_links(field = "has_upstream_threads",fun = "sum") %>%
pipe_label_links(field = "source_id",fun = "literal",clear_previous = F) %>%
pipe_color_links(field = "has_upstream_threads",fun = "sum") %>%
pipe_color_factors(field="found_type") %>%
make_print_map
if(F){
graf <- tt1
graf$factors %>% filter(factor_id %notin% graf$links$from & factor_id %notin% graf$links$to) %>% nrow
graf$links %>% filter(from %notin% graf$factors$factor_id & to %notin% graf$factors$factor_id) %>% nrow
graf$links %>% filter(statement_id %notin% graf$statements$statement_id) %>% nrow
}
Cluster factors
ee %>%
pipe_coerce_mapfile %>%
pipe_cluster_factors("Damage OR Flood") %>%
make_print_map
Pipe-able:
ee %>%
pipe_cluster_factors("Damage OR Flood") %>%
pipe_cluster_factors("Rising") %>%
make_print_map
Large map
cm <-
load_mapfile("cmi-gender")
library("tictoc")
tic()
make_interactive_map(cm)
toc()
Data manipulation, file management etc
Accessing the data
One column in one table
ll %>%
pipe_find_factors(value="economic") %>%
.$factors %>%
.$label %>%
knitr::kable()
Merging two maps
if(T)merge_mapfile(ee,tt %>% pipe_select_factors(top=8)) %>%
pipe_color_factors(field="map_id") %>%
pipe_color_links(field="map_id",fun="unique") %>%
make_interactive_map
Note warning if factor labels are shared
if(T)load_mapfile("example2") %>%
pipe_coerce_mapfile %>%
pipe_merge_mapfile("example2") %>%
pipe_color_factors(field="map_id") %>%
pipe_color_links(field="map_id",fun="unique") %>%
make_interactive_map
Editing maps directly
There is no guarantee that the resulting map is still a standard mapfile.
ee$factors$label[1] <- "Label changed"
ee %>% make_interactive_map
Editing maps with pipe_update_mapfile
There is no guarantee that the resulting map is still a standard mapfile.
ee %>%
pipe_update_mapfile(factors = ee$factors %>% mutate(label="one")) %>%
make_interactive_map
Printing the filters
# example2 %>%
# pipe_coerce_mapfile %>%
# pipe_find_links(field = "link_id",value=1) %>%
# pipe_select_factors(20) %>%
# pipe_remove_isolated() %>%
# pipe_select_links(20) %>%
# pipe_zoom_factors() %>%
# pipe_find_statements(field = "text",value="Flo") %>%
# pipe_find_factors(value="Flo") %>%
# pipe_remove_brackets("[") %>%
# pipe_trace_robustness(from = "Flood",to="Damage") %>%
# pipe_trace_paths(from = "Flood",to="Damage") %>%
# pipe_combine_opposites() %>%
# pipe_bundle_links() %>%
# pipe_label_links() %>%
# attr("info") %>%
# str
#
# example2 %>%
# pipe_coerce_mapfile() %>%
# pipe_bundle_links() %>%
# pipe_scale_links() %>%
# pipe_label_links() %>%
# pipe_color_links() %>%
# pipe_scale_factors(field = "frequency") %>%
# pipe_label_factors() %>%
# pipe_color_factors() %>%
# pipe_color_borders() %>%
# pipe_mark_links() %>%
# pipe_show_continuity() %>%
# pipe_cluster_factors() %>%
# pipe_wrap_factors() %>%
# pipe_wrap_links() %>%
# print_filter()
You can also load up an Excel file:
# system.file("extdata", "quip-lorem", package = "CausalMapFunctions") %>%
# get_mapfile_from_excel()
stevepowell99/CausalMapFunctions documentation built on Oct. 12, 2023, 11:13 a.m.
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# devtools::load_all();devtools::document();pkgdown::build_site(lazy=F) options(dplyr.summarise.inform = FALSE) library(CausalMapFunctions) library(knitr) library(htmltools) library(configr) library(dbplyr) library(DBI) library(kableExtra) library(scales) library(tictoc) knitr::opts_chunk$set(echo = T,warning=F,message=F) pp=function(x,N=99){ print(x,n=N) } replace_null <- function(x,replacement=0){ if(is.null(x)) replacement else x } append_rows <- function(val,name){ val <- val %>% mutate_all(clean_send) dbWriteTable(con=conn,name = local(name),value=local(val) ,append=TRUE,row.names=FALSE) } print_inner_filter <- function(xx){ # browser() xx$graf <- NULL fil <- xx[[1]] %>% unlist %>% str_remove_all("^pipe_") %>% str_replace("_"," ") xx <- xx[-1] nams=names(xx) tibble(names=nams,vals=unlist(xx)) %>% unite(filter,sep="=") %>% pull(filter) %>% unlist %>% paste0(.,collapse=" ") %>% paste0(fil," ",.) } print_filter <- function(graf){ if(!exists("xtitle"))xtitle <- "" if(!exists("xdescription"))xdescription <- "" if(is.null(attr(graf,"info")))return() # browser() story <- attr(graf,"info")[[1]] %>% keep(~str_detect(.,"^load_mapfile")) %>% unlist %>% str_remove("load_mapfile *path=") input_ace <- attr(graf,"info") %>% list_modify("load_mapfile" = NULL) %>% unlist %>% keep(.!=".") %>% map(~str_remove_all(.,"^pipe_") %>% str_replace("_"," ")) %>% paste0(collapse = "\n") filters <- paste0("&filters=",URLencode(input_ace %>% replace_null(""),reserved=T)) message("appending") append_rows( tibble(likes=1,recommended=T,filter=input_ace %>% replace_null(""),hidden=F,title=xtitle %>% replace_null(""),description=xdescription %>% replace_null(""),date=time_stamp(),project=story) ,"shortlinks") attr(graf,"info") %>% map(~print_inner_filter(.) %>% str_trim %>% div(style=";") ) %>% tagList %>% div(style="padding:10px;background-color:aliceblue;") %>% div( if(xtitle!="")h5(xtitle), if(xdescription!="")p(xdescription), ., a("Link",href=paste0("https://causalmap.shinyapps.io/CausalMap2?story=",story,filters)),style="padding:10px;background-color:aliceblue;") } # make_interactive_map <- function(graf){ # tagList(print_filter(graf),CausalMapFunctions::make_interactive_map(graf)) # } # make_print_map <- function(graf){ # tagList(print_filter(graf),CausalMapFunctions::make_print_map(graf)) # } clean_send <- function(vec){ if(vec %>% unique %>% `%in%`(c(F,T)) %>% all) vec <- as.numeric(vec) if(F & is.character(vec)) { # Encoding(vec)="UTF-8" vec <- stri_enc_toascii(vec) } vec }
recodes <- tibble(old="Damage to Property",new="changed") recodes <- tibble(old="Damage to Property",new="Damage to Businesses") pipe_recode_factors(example2,recodes=recodes) %>% make_interactive_map() example2$settings <- list(recodes=recodes) pipe_recode_factors(example2) %>% make_interactive_map() oi <- load_mapfile("C:/Users/steve/Downloads/2oirefugeetestimonies-2023-01-16.xlsx") sh %>% pipe_find_links(field = "source_frequency",value = 7,operator="greater") %>% pipe_find_factors(field = "label",value = "no change",operator="notcontains") %>% pipe_bundle_links() %>% make_interactive_map() sh %>% pipe_find_links(field = "source_frequency",value = 7,operator="greater") %>% pipe_find_factors(field = "label",value = "no change",operator="notcontains") %>% pipe_bundle_links() %>% make_print_map() message(1) pipe_update_mapfile() # creates an empty map pipe_update_mapfile() %>% make_interactive_map() load_mapfile() %>% # creates an empty map make_interactive_map() example2 example2 %>% pipe_coerce_mapfile %>% pipe_update_mapfile() %>% make_interactive_map() example2 %>% pipe_coerce_mapfile %>% pipe_update_mapfile(factors=example2$factors) %>% make_interactive_map() ee <- example2 path <- "C:/Users/steve/Downloads/kantar-swan-short-2022-09-06.xlsx" example_file <- load_mapfile("example-file") test <- load_mapfile("resignations2") test %>% make_interactive_map() swan <- load_mapfile(path=path) # example_file <- load_mapfile(path="data/example-file.xlsx") example_file <- load_mapfile(path="C:/Users/steve/Dropbox/Projects/CausalMapFunctions/data/example-file.xlsx") tic();swan %>% pipe_coerce_mapfile() %>% make_interactive_map();toc() tic();swan %>% pipe_coerce_mapfile();toc() example2 %>% pipe_coerce_mapfile() %>% make_interactive_map() ee %>% pipe_label_links("link_id",fun = "unique") %>% make_interactive_map ee %>% pipe_label_links("link_id",fun = "unique") %>% make_print_map() ee %>% pipe_set_print(grv_layout="circo") %>% make_print_map() example_file %>% make_interactive_map() tic() example_file %>% pipe_select_factors(10) %>% pipe_bundle_links() %>% pipe_scale_links() %>% make_print_map() toc() example_file %>% pipe_find_factors(value="Nutrition") %>% pipe_bundle_links() %>% pipe_scale_links() %>% make_print_map() example_file %>% pipe_select_factors() %>% pipe_bundle_links() %>% pipe_scale_links() %>% pipe_color_factors() %>% make_print_map() tf <- load_mapfile("tearfund-all") tf %>% parse_commands("zoom factors level=1 find links field=source_frequency value=7 operator=greater trace paths length=3 remove_links=FALSE threads_direction=down from=CCM to=Good family/ personal relationships/ support find links field=hashtags value=#plain_coding operator=notcontains bundle links field=simple_bundle label factors field=thread_count add_field_name=FALSE clear_previous=FALSE label links value=count: downstream_threads add_field_name=FALSE clear_previous=TRUE scale links value=count: downstream_threads color factors field=found_type color links field=threads fun=count") %>% make_print_map() tic() tf %>% pipe_zoom_factors() # 6 times as long toc() tic() example_file %>% pipe_zoom_factors() toc() tic();example_file %>% pipe_trace_paths(from="method/practice",to="Income",length = 3,remove_links = F);toc() # increases from .3 to 3 seconds tic();example_file %>% pipe_trace_paths(from="method/practice",to="Income",length = 3,threads_direction = "down",remove_links = F);toc() # length doesnt make a difference tic();example_file %>% pipe_trace_paths(from="method/practice",to="Income",length = 6,remove_links = F);toc() # increases from .3 to 3 seconds tic();example_file %>% pipe_trace_paths(from="method/practice",to="Income",length = 6,threads_direction = "down",remove_links = F);toc() tic();tf %>% pipe_trace_paths(from="Christian faith",to="Increased material",length = 3,remove_links = F);toc() tic();tf %>% pipe_trace_paths(from="Christian faith",to="Increased material",length = 3,remove_links = F) %>% make_interactive_map();toc() tic();tf %>% pipe_zoom_factors() %>% pipe_trace_paths(from="Christian faith",to="Increased material",length = 3,remove_links = F) %>% pipe_cluster_factors("e") %>% pipe_remove_brackets() %>% pipe_color_factors()%>% pipe_color_links();toc() # oh my gosh, this is the one tic();tf %>% pipe_trace_paths(from="Christian faith",to="Increased material",length = 3,remove_links = F,threads_direction = "down");toc() tf$links %>% nrow example_file$links %>% nrow tf
example_file %>% pipe_find_links("simple_frequency",value = 15,"greater") %>% make_interactive_map example_file %>% pipe_find_links("simple_frequency",value = 1500,"greater") %>% make_interactive_map example_file %>% pipe_find_links("simple_frequency",value = 1500,"greater") %>% make_print_map
example_file %>% pipe_select_links(5) %>% make_interactive_map
example_file %>% pipe_find_statements(field="statement_id",value=5,operator="equals") %>% make_interactive_map
example_file %>% pipe_find_factors(value="health") %>% make_interactive_map
Zero up and down
# shows nothing because isolated are removed ee %>% pipe_find_factors(value="rainfall",up=0,down=0,remove_isolated = T) %>% make_interactive_map ee %>% pipe_find_factors(value="rainfall",up=0,down=0,remove_isolated = F) %>% make_interactive_map # shows nothing because isolated are removed ee %>% pipe_find_factors(value="Damage",up=0,down=0,remove_isolated = T) %>% make_interactive_map ee %>% pipe_find_factors(value="Damage",up=1,down=1,remove_isolated = F) %>% make_interactive_map ee %>% pipe_find_factors(value="Damage",up=1,down=1,remove_isolated = F) %>% make_interactive_map
Selecting and finding
If you filter the factors of a mapfile, e.g. show only factors with labels beginning xyz,
- also the links are filtered (removing links to removed factors)
- the statements are not touched
If you filter the links of a mapfile, e.g. show only links with hashtags containing xyz,
- the factors are not filtered (but using a different command you can remove any factors which no longer have any links)
- the statements are not touched
If you filter the statements of a mapfile, e.g. show only statements with texts containing xyz,
- also the links are filtered (removing links to removed statements)
- the factors are filtered
graf <- ee tic();graf <- pipe_update_mapfile(graf,factors=graf$factors);graf;toc() ee %>% pipe_select_factors(3) %>% make_interactive_map
Simple frequency
ee %>% pipe_find_factors("frequency",value = 1,"greater") %>% make_interactive_map ee %>% pipe_find_factors("frequency",value = 1,"greater") %>% make_print_map
ee %>% pipe_find_factors("frequency",value = 1,"greater",up=0,down=0) %>% make_interactive_map
Showing interlinks or not
If you don't want to show interlinks between downstream factors, use find links instead.
example_file %>% pipe_find_factors(value="health",up=0,down=1,remove_isolated = T) %>% pipe_bundle_links() %>% make_print_map example_file %>% pipe_find_links(value="health",field="from_label",operator="contains") %>% pipe_bundle_links() %>% make_print_map
Case insensitive
ee %>% pipe_find_factors(value="business") %>% make_interactive_map
notcontains
ee %>% pipe_find_factors(value="sea",operator="notcontains") %>% make_interactive_map
ee %>% pipe_find_statements(value="1",operator="notcontains",field="statement_id")%>% make_interactive_map
ee %>% pipe_find_statements(value="1",operator="notcontains",field="statement_id")%>% make_interactive_map
notequals
ee %>% pipe_find_statements(value="1",operator="notequals",field="statement_id")%>% make_interactive_map
ee %>% pipe_find_factors(value="Coastal erosion",operator="notequals") %>% make_interactive_map
OR
Does not work in functions, but works when parsed:
ee %>% pipe_find_factors(value="business OR property") %>% make_interactive_map
ee %>% parse_commands("find factors field=label operator=contains value=Business OR Property") %>% make_interactive_map
Order matters
example_file %>% pipe_find_factors(value="health") %>% pipe_select_factors(5) %>% make_interactive_map
No result
example_file %>% pipe_find_factors(value="asdfasdfasdf") %>% make_interactive_map
example_file %>% pipe_find_links(field="from_label",value="health",operator="contains") %>% make_interactive_map
Numerical comparison
example_file %>% pipe_find_statements(field="statement_id",value=20,operator="less") %>% make_interactive_map
No result
example_file %>% pipe_find_statements(field="statement_id",value=20000000,operator="greater") %>% make_interactive_map
Highlight only
ee %>% pipe_find_factors(value="Damage",highlight_only=T) %>% pipe_color_factors(field="found") %>% make_interactive_map()
ee %>% pipe_find_links(field="to_label",value="Damage",highlight_only=T) %>% pipe_color_links(field="found",fun="literal") %>% make_interactive_map()
Clustering sources
pipe_cluster_sources(example_file) %>% .$sources %>% select(contains("cluster")) example_file %>% parse_commands("cluster sources n_clusters=all") %>% .$sources %>% select(contains("cluster")) example_file %>% parse_commands("cluster sources n_clusters=2") %>% .$sources %>% select(contains("cluster")) example_file %>% parse_commands("cluster sources") %>% .$sources %>% select(contains("cluster"))
Calculated fields
example_file %>% pipe_find_factors(value="health",up = 1,down=1) %>% make_mentions_tabl() example_file %>% pipe_find_factors(value="health",up = 1,down=1) %>% .$factors %>% select(label,source_count)
Conditional formatting
example_file %>% pipe_select_factors(5) %>% pipe_scale_factors(field="frequency") %>% pipe_bundle_links %>% make_interactive_map example_file %>% pipe_select_factors(5) %>% pipe_color_text(field="frequency") %>% pipe_bundle_links %>% make_interactive_map example_file %>% pipe_select_factors(5) %>% pipe_scale_factors(field="frequency") %>% make_print_map
example_file %>% pipe_select_factors(5) %>% pipe_color_factors(field="frequency") %>% pipe_color_borders(field="betweenness") %>% pipe_wrap_factors(5) %>% pipe_bundle_links %>% make_interactive_map
Wrap factors
example_file %>% pipe_select_factors(5) %>% pipe_color_links(value="count: link_id") %>% pipe_wrap_factors(5) %>% pipe_bundle_links %>% make_interactive_map example_file %>% pipe_select_factors(5) %>% pipe_color_links(value="count: link_id") %>% pipe_wrap_factors(5) %>% pipe_bundle_links %>% make_print_map
Wrap links
ee %>% pipe_label_links("from_label",fun = "unique") %>% pipe_wrap_links(6) %>% pipe_bundle_links %>% make_interactive_map ee %>% pipe_label_links("from_label",fun = "unique") %>% pipe_wrap_links(6) %>% make_print_map
ee %>% pipe_label_links(value="unique: from_label") %>% pipe_wrap_links(6) %>% make_interactive_map
Remove brackets
example_file$factors$label[32] <- "Income [E]" example_file %>% pipe_select_factors(5) %>% make_interactive_map example_file %>% pipe_select_factors(5) %>% pipe_remove_brackets() %>% make_interactive_map example_file$factors$label[32] <- "Income"
Bundle factors
example_file %>% pipe_bundle_factors(value = "health") %>% pipe_select_factors(5) %>% pipe_bundle_links() %>% make_interactive_map
Self loops
example_file %>% pipe_bundle_factors(value = "health") %>% pipe_select_factors(5) %>% pipe_bundle_links() %>% pipe_remove_selfloops() %>% make_interactive_map
Format links without bundling
ee %>% pipe_color_links("link_id",fun = "mean") %>% make_interactive_map ee %>% pipe_scale_links("link_id",fun = "mean") %>% make_interactive_map
Label factors with and without field names
example_file %>% pipe_find_factors(value="Improved health") %>% # pipe_zoom_factors(1) %>% pipe_select_factors(top=10) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="source_id",fun = "count") %>% pipe_label_factors(field="source_count",add_field_name = F) %>% pipe_wrap_factors() %>% make_print_map example_file %>% pipe_find_factors(value="Improved health") %>% # pipe_zoom_factors(1) %>% pipe_select_factors(top=10) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="source_id",fun = "count") %>% pipe_label_factors(field="source_count",add_field_name = T) %>% pipe_wrap_factors() %>% make_print_map
Label links
example_file %>% pipe_select_links(10) %>% pipe_label_links("link_id",fun = "literal") %>% pipe_label_links("from_label",fun = "literal",add_field_name = T,clear_previous = F) %>% make_interactive_map example_file %>% pipe_select_links(10) %>% pipe_bundle_links() %>% pipe_label_links("link_id",fun = "literal",add_field_name = T) %>% make_interactive_map example_file %>% pipe_select_links(10) %>% pipe_bundle_links() %>% pipe_label_links("link_id",fun = "literal") %>% pipe_label_links("from_label",fun = "literal",clear_previous = F) %>% make_interactive_map example_file %>% pipe_select_links(10) %>% pipe_bundle_links() %>% parse_commands("label links field=statement_id fun=unique clear_previous=F") %>% make_interactive_map
Bundling links
Note the defaults for bundle_links and label_links:
oo %>% pipe_select_factors(5) %>% pipe_bundle_links(("#Name of province") ) %>% pipe_label_links(("#Name of province") ) %>% make_interactive_map oo %>% parse_commands(" select links top=15 select factors top=10 bundle links field=#2 label factors field=frequency add_field_name=FALSE clear_previous=FALSE scale links value=count: link_id label links field=#2Name of village fun=unique add_field_name=FALSE clear_previous=TRUE color text field=is_opposable lo=black hi=#f26d04") %>% make_interactive_map
oo %>% pipe_select_factors(5) %>% pipe_bundle_links("village" ) %>% pipe_label_links("village" ,fun = "unique" ) %>% make_interactive_map
ll %>% pipe_find_factors(value="economic") %>% pipe_select_factors(5) %>% pipe_bundle_links() %>% pipe_label_links() %>% make_interactive_map
ll %>% pipe_find_factors(value="economic") %>% pipe_select_factors(5) %>% pipe_bundle_links() %>% pipe_label_links() %>% make_interactive_map
Note the default for bundle_links is equivalent to simple_bundle:
ll %>% pipe_find_factors(value="economic") %>% pipe_select_factors(5) %>% pipe_bundle_links(group="simple_bundle") %>% pipe_label_links() %>% make_interactive_map
Group and label by sex and scale by count:
ll %>% pipe_select_factors(5) %>% pipe_bundle_links(field="1. Sex") %>% pipe_scale_links("link_id",fun = "count") %>% pipe_label_links("1. Sex",fun = "unique") %>% pipe_color_links("1. Sex",fun = "unique") %>% make_interactive_map
ll %>% pipe_select_links(16) %>% pipe_bundle_links(field="1. Sex") %>% pipe_label_links("link_id",fun = "count") %>% make_interactive_map
Group by sex and scale and colour by count:
example_file %>% pipe_select_factors(5) %>% pipe_bundle_links(field="Sex of the head of the household") %>% pipe_color_links("link_id",fun = "count") %>% pipe_scale_links("link_id",fun = "count") %>% pipe_label_links("link_id",fun = "count") %>% make_interactive_map
Proportion
ll %>% pipe_find_factors(value="sed yield") %>% pipe_bundle_links(field="District") %>% pipe_color_links(field="District",fun="unique") %>% pipe_label_links(value="percent: link_id") %>% pipe_scale_links(field="link_id",fun="count") %>% make_print_map
ll %>% pipe_find_factors(value="sed yield") %>% pipe_bundle_links(field="1. Sex") %>% pipe_color_links(field="1. Sex",fun="unique") %>% pipe_label_links(field="link_id",fun="percent") %>% pipe_scale_links(field="link_id",fun="count") %>% make_print_map
ll %>% pipe_find_factors(value="sed yield") %>% pipe_bundle_links(field="1. Sex") %>% pipe_color_links(field="1. Sex",fun="unique") %>% pipe_label_links(field="source_id",fun="percent") %>% pipe_scale_links(field="source_id",fun="percent") %>% make_print_map
ll %>% pipe_find_factors(value="sed yield",down=0) %>% pipe_bundle_links(field="1. Sex") %>% pipe_color_links(field="source_id",fun="percent") %>% pipe_label_links(field="source_id",fun="percent") %>% pipe_scale_links(field="source_id",fun="count") %>% make_print_map
Surprise
ll %>% pipe_find_factors(value="sed yield",down=0) %>% pipe_bundle_links(field="1. Sex") %>% pipe_color_links(field="1. Sex",fun="unique") %>% pipe_label_links(field="source_id",fun="surprise") %>% pipe_scale_links(field="source_id",fun="count") %>% make_print_map
ll %>% pipe_find_factors(value="sed yield",down=0) %>% pipe_bundle_links(field="1. Sex") %>% pipe_label_links(field="1. Sex",fun="unique") %>% pipe_color_links(field="source_id",fun="surprise") %>% pipe_scale_links(field="source_id",fun="count") %>% make_print_map
Using initials to shorten the labels
oo %>% pipe_find_factors(value="crops",down=0) %>% pipe_bundle_links(field="#4Sex of the respondent") %>% pipe_label_links(field="#4Sex of the respondent",fun="initials") %>% pipe_color_links(field="source_id",fun="surprise") %>% pipe_scale_links(field="source_id",fun="count") %>% make_print_map
oh %>% pipe_select_factors(10) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="from_label",fun="initials") %>% make_print_map
Recalculating
Shouldn't make any difference as factor and link fields are now recalculated after every transform; but beforeids and afterids are added only on load_mapfile.
ll %>% pipe_find_factors(value="WASH") %>% pipe_color_factors(field="betweenness") %>% make_interactive_map ll %>% pipe_find_factors(value="WASH") %>% pipe_recalculate_factors() %>% pipe_color_factors(field="betweenness") %>% make_interactive_map
Mark links
so %>% pipe_label_links(field = "source_id",fun="literal") %>% pipe_mark_links(field="source_id") %>% make_print_map e3 %>% pipe_label_links(field = "source_id",fun="literal") %>% pipe_mark_links(field="source_id") %>% make_print_map ee %>% pipe_label_links(field = "source_id",fun="literal") %>% pipe_mark_links(field="source_id") %>% make_print_map so %>% pipe_bundle_links(field = "source_id") %>% pipe_label_links(field = "source_id",fun="literal") %>% pipe_mark_links(field="source_id") %>% make_print_map so %>% pipe_combine_opposites() %>% pipe_bundle_links(field = "flipped_bundle") %>% pipe_label_links(field = "source_id",fun="literal") %>% pipe_mark_links(field="source_id") %>% make_print_map ll %>% pipe_select_factors(5) %>% pipe_bundle_links(field = "simple_bundle") %>% pipe_mark_links(field="source_id") %>% make_print_map
Show continuity
#original: so %>% pipe_label_links(field = "source_id",fun="unique") %>% make_print_map so %>% pipe_label_links(field = "source_id",fun="unique") %>% pipe_show_continuity(field="source_id",type="label") %>% make_print_map so %>% pipe_label_links(field = "source_id",fun="unique") %>% pipe_show_continuity(field="source_id") %>% make_print_map ee %>% pipe_label_links(field = "source_id",fun="unique") %>% pipe_show_continuity(field="source_id") %>% make_print_map example_file %>% pipe_label_links(field = "source_id",fun="unique") %>% pipe_show_continuity(field="source_id") %>% make_print_map example2 %>% pipe_zoom_factors() %>% pipe_select_factors() %>% pipe_show_continuity(field="source_id") %>% pipe_label_links(field = "source_id",fun="unique") %>% make_print_map example2 %>% pipe_bundle_factors("Flooding") %>% pipe_show_continuity(field="source_id") %>% pipe_label_links(field = "source_id",fun="unique") %>% make_print_map example2 %>% pipe_bundle_factors("Flooding") %>% pipe_bundle_links(field = "source_id") %>% pipe_label_links(field = "source_id",fun="literal") %>% pipe_show_continuity(field="source_id") %>% make_print_map so %>% pipe_bundle_links() %>% pipe_label_links(field = "source_id",fun="unique") %>% pipe_show_continuity(field="source_id") %>% make_print_map so %>% pipe_combine_opposites() %>% pipe_label_links("link_id","literal") %>% pipe_show_continuity(field="source_id") %>% make_print_map so %>% pipe_combine_opposites() %>% pipe_show_continuity() %>% make_print_map so %>% pipe_combine_opposites() %>% pipe_bundle_links(field = "flipped_bundle") %>% pipe_show_continuity(field="source_id") %>% pipe_label_links(field = "source_id",fun="unique") %>% make_print_map # Order should not matter, but you can't put bundle links later so %>% pipe_combine_opposites() %>% pipe_show_continuity(field="source_id") %>% pipe_bundle_links(field = "flipped_bundle") %>% pipe_label_links(field = "source_id",fun="unique") %>% make_print_map
ll %>% pipe_select_factors(5) %>% pipe_bundle_links(field = "simple_bundle") %>% pipe_scale_links(field = "link_id",fun="count") %>% pipe_show_continuity(field="source_id") %>% make_print_map hz %>% pipe_show_continuity(field="source_id") %>% make_print_map hz %>% pipe_zoom_factors(1) %>% pipe_show_continuity(field="source_id") %>% make_print_map
Group and label by sex and scale by count:
ll %>% pipe_select_factors(5) %>% pipe_bundle_links(field="1. Sex") %>% pipe_scale_links("link_id",fun = "count") %>% pipe_label_links("1. Sex",fun = "unique") %>% pipe_color_links("1. Sex",fun = "unique") %>% pipe_show_continuity(field="source_id") %>% make_print_map ll %>% pipe_select_factors(5) %>% pipe_find_links(field = "statement_id",value=90,operator="greater") %>% pipe_find_links(field = "statement_id",value=290,operator="less") %>% pipe_bundle_links(field="1. Sex") %>% pipe_scale_links("link_id",fun = "count") %>% pipe_label_links("source_id",fun = "unique") %>% pipe_color_links("1. Sex",fun = "unique") %>% pipe_show_continuity(field="source_id") %>% make_print_map
Nested maps
tf %>% pipe_zoom_factors(1) %>% pipe_select_factors(5) %>% make_interactive_map
tf %>% pipe_zoom_factors(1) %>% pipe_bundle_links() %>% pipe_label_links() %>% pipe_scale_links() %>% make_print_map
Zooming out and showing source count / frequency
example_file %>% pipe_zoom_factors(level = 1) %>% pipe_select_factors(10) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="source_id",fun = "count") %>% pipe_label_factors(field="frequency",add_field_name = F) %>% pipe_wrap_factors() %>% make_print_map example_file %>% pipe_zoom_factors(level = 2) %>% pipe_select_factors(10) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="source_id",fun = "count") %>% pipe_label_factors(field="frequency",add_field_name = F) %>% pipe_wrap_factors() %>% make_print_map example_file %>% pipe_zoom_factors(1,preserve_frequency = 4,frequency_field = "frequency",frequency_other = "(other)") %>% pipe_select_factors(10) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_factors(field="frequency",add_field_name = F) %>% pipe_wrap_factors() %>% make_print_map oig %>% pipe_zoom_factors(1,preserve_frequency = 20,frequency_field = "source_count") %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="source_id",fun = "count") %>% pipe_label_factors(field="source_count",add_field_name = F) %>% pipe_color_borders(field="frequency_preserved",lo="white") %>% pipe_wrap_factors() %>% make_print_map tt %>% pipe_zoom_factors(1,preserve_frequency = 80) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="link_id",fun = "count") %>% pipe_label_factors(field="frequency",add_field_name = F) %>% pipe_color_borders(field="frequency_preserved",lo="white") %>% pipe_wrap_factors() %>% make_print_map cc %>% pipe_find_factors(value="Improved health") %>% pipe_zoom_factors(1) %>% pipe_select_factors(top=10) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="source_id",fun = "count") %>% pipe_label_factors(field="source_count",add_field_name = F) %>% pipe_label_factors(field="frequency",add_field_name = F) %>% pipe_wrap_factors() %>% make_print_map cc %>% pipe_find_factors(value="Improved health") %>% pipe_zoom_factors(1) %>% pipe_select_factors(top=10) %>% pipe_bundle_links(field="simple_bundle") %>% pipe_label_links(field="source_id",fun = "count") %>% pipe_label_factors(field="source_count",add_field_name = F) %>% pipe_label_factors(field="frequency",add_field_name = F) %>% pipe_wrap_factors() %>% make_print_map oi %>% pipe_find_factors(value="Take a loan") %>% pipe_zoom_factors(1,preserve_frequency = 2,frequency_other = "(other)") %>% # pipe_label_factors("source_count") %>% # pipe_label_links(value="unique:source_id") %>% make_print_map
Combining opposites
example_file %>% pipe_combine_opposites() %>% pipe_zoom_factors() %>% pipe_select_factors() %>% make_print_map() example_file %>% pipe_combine_opposites() %>% pipe_zoom_factors() %>% pipe_select_factors() %>% pipe_color_borders(fixed="white") %>% make_print_map() example_file %>% pipe_combine_opposites() %>% pipe_zoom_factors() %>% pipe_select_factors() %>% pipe_color_borders(field = "frequency") %>% make_print_map() hh %>% make_print_map() hh %>% pipe_combine_opposites %>% pipe_label_links(value="count:link_id") %>% make_print_map() # artificial arrowlabels hh %>% pipe_combine_opposites %>% pipe_update_mapfile(.,links=.$links %>% mutate(color="limegreen",taillabel=c("","","\u274C","\u274C","","\u274C"),headlabel=c("","","\u274C","\u274C","\u274C","\u274C"))) %>% make_print_map() hh %>% pipe_combine_opposites %>% pipe_update_mapfile(.,links=.$links %>% mutate(color="limegreen",taillabel=c("+","+","~","~","+","~"),headlabel=c("+","+","~","~","~","~"))) %>% make_print_map() hh %>% pipe_combine_opposites %>% pipe_update_mapfile(.,links=.$links %>% mutate(color="limegreen",arrowtail=c("","","nonetee","nonetee","","nonetee"),arrowhead=c("nonetee","","veenonenonetee","veenonenonetee","veenonenonetee","veenonenonetee"))) %>% make_print_map()
Nested maps with opposites
Note colours in Interactive view
tt %>% pipe_zoom_factors(1) %>% pipe_combine_opposites() %>% pipe_select_links(3) %>% make_print_map tt %>% pipe_zoom_factors(1) %>% pipe_select_links(15) %>% pipe_combine_opposites() %>% pipe_bundle_links("flipped_bundle") %>% pipe_label_links(value="count:link_id") %>% make_print_map tt %>% pipe_zoom_factors(1) %>% pipe_combine_opposites() %>% pipe_select_links(3) %>% make_interactive_map
Factor quick fields
# factors <- de$factors de %>% pipe_color_text(field="time") %>% make_print_map
Hiding quickfields
de %>% pipe_hide_quickfields %>% make_print_map de %>% pipe_color_borders(field="happiness") %>% pipe_label_factors(field = "time") %>% pipe_hide_quickfields %>% make_print_map
equivalent:
de %>% pipe_find_factors(field="time",value="after",up=0,down=0) %>% make_print_map de %>% pipe_find_factors(field="label",value="time:after",up=0,down=0) %>% make_print_map
zoom
de %>% pipe_zoom_factors(1) %>% pipe_color_factors(field="time") %>% make_print_map
Link quick fields
# factors <- de$factors de %>% pipe_recalculate_all() %>% pipe_color_links(field="plausibility") %>% make_print_map de %>% pipe_color_text(field="happiness") %>% pipe_hide_quickfields %>% make_print_map de %>% pipe_label_factors(field = "time") %>% pipe_hide_quickfields %>% make_print_map
Path tracing
cat("### Single\n") example_file %>% pipe_trace_paths(from = "Organisation 1",to="Ability to buy food",length = 3) %>% pipe_select_factors(15) %>% make_interactive_map ma %>% pipe_trace_paths(from = "Shared roles",to="balanced",length = 1) %>% pipe_select_factors(15) %>% make_interactive_map ma %>% pipe_trace_paths(from = "Covid-19",to="Business",length = 2) %>% pipe_select_factors(15) %>% make_interactive_map oo %>% pipe_select_factors(15) %>% pipe_trace_paths(from = "Learnt about farming",to="",length = 5) %>% make_interactive_map ee %>% pipe_trace_paths(from = "Funds",to="area",length = 5) %>% make_interactive_map cat("### Case insensitive\n") ee %>% pipe_trace_paths(from = "funds",to="aREa",length = 5) %>% make_interactive_map cat("### Failing; no paths at all\n") ee %>% pipe_trace_paths(from = "xx",to="yy",length = 5) %>% make_interactive_map cat("### Failing; no paths\n") ee %>% pipe_trace_paths(from = "Funds",to="yy",length = 5) %>% make_interactive_map ee %>% pipe_trace_paths(from = "xx",to="Property",length = 5) %>% make_interactive_map cat("### Implicit multiple\n") ee %>% pipe_trace_paths(from = "High",to="Damage",length = 5) %>% make_interactive_map cat("### Explicit multiple\n") ee %>% pipe_trace_paths(from = "High",to="Property | Business",length = 5) %>% make_interactive_map ee %>% pipe_trace_paths(from = "High",to="Property OR Business",length = 5) %>% make_interactive_map cat("Should this be possible?") ee %>% pipe_trace_paths(from = "High",to="Property OR Business",length = 5) %>% make_interactive_map tt %>% pipe_trace_paths(from = "Capabilities",to="[OP3]",length = 2) %>% make_interactive_map ee %>% pipe_trace_paths(from = "Funds",to="area",length = 5) %>% make_print_map()
Threads
example_file %>% pipe_zoom_factors(1) %>% pipe_select_factors(10) %>% pipe_trace_paths(length=4,from="knowledge",threads_direction="down") %>% pipe_bundle_links() %>% pipe_scale_links() %>% pipe_color_links(field="threads") %>% pipe_label_links(field="threads") %>% pipe_color_factors(field="thread_count") %>% pipe_label_factors(field="thread_count") %>% make_print_map() example_file %>% pipe_zoom_factors(1) %>% pipe_select_factors(6) %>% pipe_trace_paths(length=4,from="knowledge",threads_direction="down") %>% pipe_bundle_links() %>% pipe_scale_links() %>% # pipe_color_links(field="threads") %>% pipe_label_links(field="threads",fun = "unique") %>% pipe_wrap_links(12) %>% pipe_color_factors(field="thread_count") %>% pipe_label_factors(field="thread_count") %>% make_print_map()
Robustness
## request is large but ok pa %>% pipe_trace_paths(from = "",to="~Impact on wellbeing",length = 1) %>% make_print_map() pa %>% pipe_trace_paths(from = "Covid",to="",length = 3) %>% make_print_map() ## request is large if(F){e3 %>% pipe_trace_robustness(from = "High",to="Damage",length = 5) %>% make_print_map() ee %>% pipe_trace_robustness(from = "High",to="Damage",length = 5) %>% get_robustness() tt %>% pipe_trace_robustness(from = "Capabilities",to="[OP3]",length = 2) %>% pipe_wrap_factors() %>% make_print_map() tt %>% pipe_trace_robustness(from = "Capabilities",to="[OP3",length = 2) %>% pipe_wrap_factors() %>% make_print_map() ## checking the robustness gets transferred tt %>% pipe_trace_robustness(from = "Capabilities",to="[OP3]",length = 2) %>% pipe_wrap_factors() %>% pipe_wrap_links() %>% pipe_select_links(10) %>% get_robustness() tt %>% pipe_trace_robustness(from = "Capabilities",to="[OP3",length = 2) %>% get_robustness() tt %>% pipe_trace_robustness(from = "Capabilities; [P13",to="[OP3]",length = 2) %>% get_robustness() e3 %>% pipe_trace_robustness(from = "High",to="People moving",length = 5) %>% make_print_map() e3 %>% pipe_trace_robustness(from = "High",to="",length = 5) %>% make_print_map() e3 %>% pipe_trace_robustness(from = "High",to="People moving",length = 5) %>% get_robustness() %>% kable e3 %>% pipe_trace_robustness(from = "High",to="Flooding",length = 5) %>% get_robustness() %>% kable e3 %>% pipe_trace_robustness(from = "High",to="Damage",length = 5) %>% get_robustness() %>% kable e3 %>% pipe_trace_robustness(from = "External",to="Damage",length = 5) %>% get_robustness() %>% kable e3 %>% pipe_trace_robustness(from = "External",to="Outcome",length = 5) %>% get_robustness()%>% kable }
Robustness by field
Just one source:
ee %>% pipe_trace_robustness(from = "Funds",to="Increased",length = 5,field = "source_id") %>% get_robustness ll %>% pipe_trace_robustness(from = "Cash",to="Income",length = 5,field="source_id") %>% get_robustness()
Check that opposites colouring is always preserved?
if(F){ hh %>% pipe_trace_robustness(from = "Revision",to="happy",length = 5) %>% pipe_combine_opposites %>% make_print_map() hh %>% pipe_combine_opposites %>% pipe_find_factors(value="exam") %>% make_print_map() hh %>% pipe_combine_opposites %>% pipe_zoom_factors() %>% pipe_find_factors(value="exam") %>% pipe_select_factors(2) %>% pipe_select_links(3) %>% make_print_map() }
Colours in interactive map
hh %>% pipe_combine_opposites %>% pipe_zoom_factors() %>% make_interactive_map()
upstream
e3 %>% pipe_trace_paths(from = "",to="Outcome",length = 4) %>% pipe_trace_threads(direction="up") %>% pipe_bundle_links() %>% pipe_label_links(field = "source_id",fun = "literal") %>% pipe_label_links(field = "has_upstream_threads",fun = "sum",clear_previous = F) %>% pipe_color_links(field = "has_upstream_threads",fun = "sum") %>% pipe_color_borders(field="n_upstream_threads_surviving") %>% pipe_label_factors(field="n_upstream_threads_surviving") %>% make_print_map() oi %>% pipe_trace_paths(from = "Flee",to="",length = 3) %>% pipe_trace_threads(direction="down") %>% pipe_bundle_links() %>% pipe_label_links(field = "has_downstream_threads",fun = "sum") %>% pipe_label_links(field = "source_id",fun = "literal",clear_previous = F) %>% pipe_color_links(field = "has_downstream_threads",fun = "sum") %>% pipe_color_factors(field="found_type") %>% make_print_map oi %>% pipe_trace_paths(from = "",to="Financial diary",length = 2) %>% pipe_trace_threads(direction="up") %>% pipe_bundle_links() %>% pipe_label_links(field = "has_upstream_threads",fun = "sum") %>% pipe_label_links(field = "source_id",fun = "literal",clear_previous = F) %>% pipe_color_links(field = "has_upstream_threads",fun = "sum") %>% pipe_color_factors(field="found_type") %>% make_print_map
if(F){ graf <- tt1 graf$factors %>% filter(factor_id %notin% graf$links$from & factor_id %notin% graf$links$to) %>% nrow graf$links %>% filter(from %notin% graf$factors$factor_id & to %notin% graf$factors$factor_id) %>% nrow graf$links %>% filter(statement_id %notin% graf$statements$statement_id) %>% nrow }
Cluster factors
ee %>% pipe_coerce_mapfile %>% pipe_cluster_factors("Damage OR Flood") %>% make_print_map
Pipe-able:
ee %>% pipe_cluster_factors("Damage OR Flood") %>% pipe_cluster_factors("Rising") %>% make_print_map
Large map
cm <- load_mapfile("cmi-gender") library("tictoc") tic() make_interactive_map(cm) toc()
Data manipulation, file management etc
Accessing the data
One column in one table
ll %>% pipe_find_factors(value="economic") %>% .$factors %>% .$label %>% knitr::kable()
Merging two maps
if(T)merge_mapfile(ee,tt %>% pipe_select_factors(top=8)) %>% pipe_color_factors(field="map_id") %>% pipe_color_links(field="map_id",fun="unique") %>% make_interactive_map
Note warning if factor labels are shared
if(T)load_mapfile("example2") %>% pipe_coerce_mapfile %>% pipe_merge_mapfile("example2") %>% pipe_color_factors(field="map_id") %>% pipe_color_links(field="map_id",fun="unique") %>% make_interactive_map
Editing maps directly
There is no guarantee that the resulting map is still a standard mapfile.
ee$factors$label[1] <- "Label changed" ee %>% make_interactive_map
Editing maps with pipe_update_mapfile
There is no guarantee that the resulting map is still a standard mapfile.
ee %>% pipe_update_mapfile(factors = ee$factors %>% mutate(label="one")) %>% make_interactive_map
Printing the filters
# example2 %>% # pipe_coerce_mapfile %>% # pipe_find_links(field = "link_id",value=1) %>% # pipe_select_factors(20) %>% # pipe_remove_isolated() %>% # pipe_select_links(20) %>% # pipe_zoom_factors() %>% # pipe_find_statements(field = "text",value="Flo") %>% # pipe_find_factors(value="Flo") %>% # pipe_remove_brackets("[") %>% # pipe_trace_robustness(from = "Flood",to="Damage") %>% # pipe_trace_paths(from = "Flood",to="Damage") %>% # pipe_combine_opposites() %>% # pipe_bundle_links() %>% # pipe_label_links() %>% # attr("info") %>% # str # # example2 %>% # pipe_coerce_mapfile() %>% # pipe_bundle_links() %>% # pipe_scale_links() %>% # pipe_label_links() %>% # pipe_color_links() %>% # pipe_scale_factors(field = "frequency") %>% # pipe_label_factors() %>% # pipe_color_factors() %>% # pipe_color_borders() %>% # pipe_mark_links() %>% # pipe_show_continuity() %>% # pipe_cluster_factors() %>% # pipe_wrap_factors() %>% # pipe_wrap_links() %>% # print_filter()
You can also load up an Excel file:
# system.file("extdata", "quip-lorem", package = "CausalMapFunctions") %>% # get_mapfile_from_excel()
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