blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2 values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46 values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M โ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14 values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 โ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 โ | gha_language stringclasses 60 values | src_encoding stringclasses 24 values | language stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 7 9.18M | extension stringclasses 20 values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bae2438133a3963a633456eff031eaf35284c829 | c55c02f27dc68f5a912a0cb7edf232ddc7197f7b | /exercises/test_02_05.R | 98f20234d7e0cd28f90b3e2275a4d94338e90c16 | [
"MIT",
"CC-BY-4.0"
] | permissive | benmarwick/gams-in-r-course | 3e631518be8ab89c9e08d83743aa15053a8bc9d1 | ed45f12a183d1ba023ee43e8b2fa557773c9b5ef | refs/heads/master | 2020-05-27T19:02:16.685461 | 2019-05-27T02:01:13 | 2019-05-27T02:01:13 | 188,754,422 | 0 | 0 | null | 2019-05-27T02:00:18 | 2019-05-27T02:00:18 | null | UTF-8 | R | false | false | 186 | r | test_02_05.R | test <- function() {success("Looking good! Plotting residuals helps you understand the quality of your model fit. Now let's try selecting different parts of your model to visualize.")}
|
11ae0bda64e67d9366969100f5ce6402b046f9ff | 830e99285dbf49c89fb429147e3986bfd242a759 | /R/module_desctools.R | 235ca3e824973386fca6fb0a52464b342e568fc8 | [] | no_license | DaniloCVieira/iMESc | 41501e9f39b79753e2ced4b2dc20f1118eba28d7 | e31e1c6fb13a685873bc875a77ef7c914d7171f6 | refs/heads/main | 2023-09-04T22:00:41.355446 | 2023-08-16T09:56:41 | 2023-08-16T09:56:41 | 412,212,574 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 138,274 | r | module_desctools.R |
#button(
#input(
#link(
##buttons(
#' @export
module_ui_desctools<-function(id){
ns<-NS(id)
tagList(
column(12,style="background: white",
#inline( actionButton(ns("teste_comb"),"SAVE")),
#uiOutput(ns("bug")),
uiOutput(ns("upload_selection")),
uiOutput(ns("panel_main"))
)
)
}
# Server
#' @export
module_server_desctools<-function (input,output,session,vals,df_colors,newcolhabs,df_symbol ){
ns<-session$ns
pw_icon <- base64enc::dataURI(file = "inst/app/www/pwrda_icon.png", mime = "image/png")
smw_icon <- base64enc::dataURI(file = "inst/app/www/smw_icon.png", mime = "image/png")
observeEvent(ignoreInit = T,input$teste_comb,{
savereac()
})
symbols<-c("pch1","pch2","pch3","pch4",'pch5','pch6','pch7',"pch8")
df_symbol <- data.frame(
val = c(16,15,17,18,8,1,5,3)
)
for(i in 1:length(symbols))
{
symbol1<-base64enc::dataURI(file = paste0('inst/app/www/pch',i,".png"), mime = "image/png")
df_symbol$img[i]<- sprintf(paste0(img(src = symbol1, width = '10')))}
box_y_cur<-reactiveValues(df=1)
filter_box2_cur<-reactiveValues(df=1)
filter_box1_cur<-reactiveValues(df=1)
boxplot_X_cur<-reactiveValues(df=1)
bag_smw<-reactiveValues(df=F)
aggreg_reac<-reactiveValues(df=0)
updp<-reactiveValues(df=F)
updp0<-reactiveValues(df=F)
getsolid_col<-reactive({
res<-lapply(vals$newcolhabs, function(x) x(2))
res1<-unlist(lapply(res, function(x) x[1]==x[2]))
solid<-names(res1[res1==T])
pic<-which(vals$colors_img$val%in%solid)
pic
})
observeEvent(ignoreInit = T,input$desc_options,{
if(input$desc_options%in%c('tab_scatter','tab_segrda','tab_rda',"tab_omi")){
shinyjs::hide('data_descX')
}
if(!input$desc_options%in%c('tab_scatter','tab_segrda','tab_rda',"tab_omi")){
shinyjs::show('data_descX')
}
})
output$upload_selection<-renderUI({
validate(need(length(vals$saved_data)>0,"No Datalist found"))
column(12,
strong("Datalist:"),
inline(pickerInput(ns("data_descX"),NULL,choices=names(vals$saved_data), selected=vals$cur_data, options=list(container="body","style-base" = "form-control", style = ""))))
})
observeEvent(ignoreInit = T,input$desc_options,{
vals$cur_desc_options<-input$desc_options
})
observe({
req(is.null(vals$cur_desc_options))
vals$cur_desc_options<-'tab1'
})
observe({
req(input$desc_options)
req(!is.null(vals$cur_desc_options))
req(!vals$cur_desc_options%in%input$desc_options)
vals$cur_desc_options<-'tab1'
})
output$dtab_boxplot<-renderUI({
column(12,uiOutput(ns("stats_cbox")),
div(
uiOutput(ns('boxplot_out'))
))
})
output$dtab_corr<-renderUI({
div(sidebarLayout(sidebarPanel(fluidRow(class="map_control_style",style="color: #05668D",uiOutput(ns('corr_side')))),mainPanel(uiOutput(ns("corr_plot")))))
})
output$dtab_mds<-renderUI({
div(
sidebarLayout(
sidebarPanel(
fluidRow(
class="map_control_style",
style="color: #05668D",
uiOutput(ns('omds_dist')),
uiOutput(ns('mds_options'))
)
),
mainPanel(
uiOutput(ns("stats_cmds")),
uiOutput(ns("stats_pmds"))
)
)
)
})
output$dtab_pca<-renderUI({
div(
sidebarLayout(
sidebarPanel(
fluidRow(
class="map_control_style",
style="color: #05668D",
uiOutput(ns('opca_biplot')),
uiOutput(ns('pca_options_plot')),
uiOutput(ns('pca_summary'))
)
),
mainPanel(
uiOutput(ns("stats_cpca")),
tabsetPanel(id=ns('pca_options'),selected=vals$pca_options,
tabPanel("Plot",
value="pca_plot",
uiOutput(ns("stats_ppca"))),
tabPanel("Summary",
value="pca_summary",
inline(DT::dataTableOutput(ns("summary_pca"))))
)
)
)
)
})
output$dtab_rda<-renderUI({
div(
div(uiOutput(ns("stats_crda"))),
sidebarLayout(
sidebarPanel(
fluidRow(
class="map_control_style",
style="color: #05668D",
uiOutput(ns('orda_options')),
uiOutput(ns('rda_options'))
)
),
mainPanel(
uiOutput(ns("stats_rda"))
)
)
)
})
output$dtab_segrda<-renderUI({
div(
div(
style="background: white",
p(strong("Segmented Redundancy Analysis")),
span(
inline(
span(style="width: 150px",
inline( pickerInput(ns("segrda_X"),span("Y Data", tiphelp("Predictors")), choices=names(vals$saved_data), selected=vals$cur_segrda_X))
)
),
inline( pickerInput(ns("segrda_Y"),span("~ X Data", tiphelp("Response data")), choices=names(vals$saved_data), selected=vals$cur_segrda_Y))
)
),
uiOutput(ns('segrda_panels'))
)
})
output$panel_main<-renderUI({
validate(need(length(vals$saved_data)>0,"No Datalist found"))
req(input$data_descX)
column(12,
tabsetPanel(
id=ns('desc_options'),selected = vals$cur_desc_options,
tabPanel('1. Summaries',
value="tab1",
uiOutput(ns('dtab_summaries'))),
tabPanel('2. Boxplot',
value="tab_box",
uiOutput(ns("dtab_boxplot"))),
tabPanel('3. Ridges',
value="tab2",
uiOutput(ns('dtab_rid'))),
# tabPanel('Scatter',value="tab_scatter",uiOutput(ns('dtab_scatter'))),
tabPanel(
'4. Pair plot',
value="tab_ggpair",
uiOutput(ns('gg_pairs_panel'))
),
# tabPanel('Histogram',value="tab_histo",uiOutput(ns("dtab_histogram"))),
tabPanel('5. Correlation plot',
value="tab_corr",
uiOutput(ns("dtab_corr"))),
tabPanel('6. MDS',
value="tab_mds",
uiOutput(ns("dtab_mds"))),
tabPanel('7. PCA',
value="tab_pca",
uiOutput(ns("dtab_pca"))),
tabPanel('8. RDA',
value="tab_rda",
uiOutput(ns("dtab_rda"))),
tabPanel('9. segRDA',
value="tab_segrda",
uiOutput(ns("dtab_segrda")))
)
)
})
####
observeEvent(getdata_descX(),{
data=getdata_descX()
vals$ggpair.variables<-colnames(data)[1:3]
})
observeEvent(ignoreInit = T,input$gg_run,{
vals$ggpair.variables<-input$ggpair.variables
})
output$gg_pairs_panel<-renderUI({
req(input$data_descX)
data=getdata_descX()
req(length(data)>0)
if(is.null(vals$ggpair.variables)){
vals$ggpair.variables<-colnames(data)[1:3]
}
column(12,
column(3,class="well3",
div(class="map_control_style2",style="color: #05668D",
tags$div(id="ggpicker1",
pickerInput(ns("ggpair.variables"),span("+ Variables:",class='text_alert'),colnames(data), multiple = T,options=list(`actions-box` = TRUE), selected=vals$ggpair.variables)
),
uiOutput(ns("side_msp")),
uiOutput(ns("side_msp_pairs"))
)),
column(9,
withSpinner(uiOutput(ns("msp_pairs")),8)
))
})
output$side_msp<-renderUI({
div(
pickerInput(inputId = ns("fm_palette"),
label = '+ Palette',
choices = vals$colors_img$val,
choicesOpt = list(content =vals$colors_img$img),
selected=vals$cm_palette,
options=list(container="body")),
numericInput(ns("msp_plot_width"), "+ Plot width",550),
numericInput(ns("msp_plot_height"), "+ Plot height",400),
numericInput(ns("msp_plot_base_size"),"+ Base size",12),
)
})
output$side_msp_pairs <- renderUI({
req(input$msp_plot_base_size)
if (is.null(vals$ggpair.box.include)) {
vals$ggpair.box.include <- FALSE
}
div(
div(
div(strong("+ Panels:")),
div(style = "margin-left: 20px; border-bottom: 1px solid; margin-bottom: 5px",
uiOutput(ns("output_ggpair_upper")),
uiOutput(ns("output_ggpair_lower")),
uiOutput(ns("output_ggpair_diag")),
div("+", inline(checkboxInput(ns("ggpair.box.include"), "Y boxplot", vals$ggpair.box.include)))
)
),
div(
uiOutput(ns("ggpair.y.variable"))
),
uiOutput(ns("output_ggpair_method")),
uiOutput(ns("output_ggpair_round")),
uiOutput(ns("output_ggpair_switch")),
uiOutput(ns("output_ggpair_varnames_size")),
uiOutput(ns("output_ggpair_cor_size")),
uiOutput(ns("output_ggpair_pch")),
uiOutput(ns("output_ggpair_points_size")),
uiOutput(ns("output_ggpair_legend_text_size")),
uiOutput(ns("output_ggpair_legend_title_size")),
uiOutput(ns("output_ggpair_alpha_curve")),
uiOutput(ns("output_ggpair_title")),
uiOutput(ns("output_ggpair_plot_title_size")),
uiOutput(ns("output_ggpair_xlab")),
uiOutput(ns("output_ggpair_ylab")),
uiOutput(ns("output_ggpair_axis_text_size")),
uiOutput(ns("output_ggpair_axis_title_size")),
div(inline(uiOutput(ns("output_ggpair_title_corr"))), style = "width: 100%"),
actionLink(
ns("fm_downplot4"), span("+ Download plot 1", icon("fas fa-download"), icon("fas fa-image")), style = "button_active"
)
)
})
output$output_ggpair_upper <- renderUI({
pickerInput(ns("ggpair.upper"), "+ Upper",
choices = list(
"Correlation" = "corr",
"Corr + group" = "corr+group",
"none" = "blank"
),
selected = vals$ggpair.upper)
})
output$output_ggpair_lower <- renderUI({
pickerInput(ns("ggpair.lower"), "+ Lower",
choices = list(
"Points" = "points",
"Points + group" = "points+group",
"none" = "blank"
),
selected = vals$ggpair.lower)
})
output$output_ggpair_diag <- renderUI({
pickerInput(ns("ggpair.diag"), "+ Diagonal",
choices = list(
"Density" = "density",
"Density + group" = "density+group",
"Hist" = "hist",
"Hist+group" = "hist+group",
"none" = "blank"
),
selected = vals$ggpair.diag)
})
output$output_ggpair_method <- renderUI({
pickerInput(ns("ggpair.method"), "+ Correlation method:", c("pearson", "kendall", "spearman", "none"))
})
output$output_ggpair_round <- renderUI({
numericInput(ns("ggpair.round"), "+ Digits:", 3)
})
output$output_ggpair_switch <- renderUI({
pickerInput(ns("ggpair.switch"), "+ Switch:", list("default" = NULL, "x" = "x", "y" = "y", "both" = "both"))
})
output$output_ggpair_varnames_size <- renderUI({
req(input$msp_plot_base_size)
bs<-round(input$msp_plot_base_size/12, 2)
numericInput(ns("ggpair.varnames.size"), "+ Variable name size:", bs*1.4)
})
output$output_ggpair_cor_size <- renderUI({
req(input$msp_plot_base_size)
numericInput(ns("ggpair.cor.size"), "+ Corr size:", 2)
})
output$output_ggpair_pch <- renderUI({
pickerInput(inputId = ns("ggpair.pch"),
label = "+ Point shape",
choices = df_symbol$val,
choicesOpt = list(content = df_symbol$img),
options = list(container = "body"),
width = "100px",
selected = vals$xyf_symbol)
})
output$output_ggpair_points_size <- renderUI({
req(input$msp_plot_base_size)
bs<-round(input$msp_plot_base_size/12, 2)
numericInput(ns("ggpair.points.size"), "+ Points size", bs)
})
output$output_ggpair_legend_text_size <- renderUI({
req(input$msp_plot_base_size)
bs<-round(input$msp_plot_base_size/12, 2)
numericInput(ns("ggpair.legend.text.size"), "+ legend.text.size:", bs)
})
output$output_ggpair_legend_title_size <- renderUI({
req(input$msp_plot_base_size)
bs<-round(input$msp_plot_base_size/12, 2)
numericInput(ns("ggpair.legend.title.size"), "+ legend.title.size:", bs)
})
output$output_ggpair_alpha_curve <- renderUI({
numericInput(ns("ggpair.alpha.curve"), "+ Curve transparency:", 0.8)
})
output$output_ggpair_title <- renderUI({
textInput(ns("ggpair.title"), "+ Title:", "")
})
output$output_ggpair_plot_title_size <- renderUI({
req(input$msp_plot_base_size)
bs<-round(input$msp_plot_base_size/12, 2)
numericInput(ns("ggpair.plot.title.size"), "+ Title size:", bs)
})
output$output_ggpair_xlab <- renderUI({
textInput(ns("ggpair.xlab"), "+ xlab:", "")
})
output$output_ggpair_ylab <- renderUI({
textInput(ns("ggpair.ylab"), "+ ylab:", "")
})
output$output_ggpair_axis_text_size <- renderUI({
req(input$msp_plot_base_size)
bs<-round(input$msp_plot_base_size/12, 2)
numericInput(ns("ggpair.axis.text.size"), "+ Axis tick size:", bs)
})
output$output_ggpair_axis_title_size <- renderUI({
bs<-round(input$msp_plot_base_size/12, 2)
numericInput(ns("ggpair.axis.title.size"), "+ Axis label size:", bs)
})
output$output_ggpair_title_corr <- renderUI({
div(inline(textInput(ns("ggpair.title_corr"), "+ Title corr:", "")), style = "width: 100%")
})
output$ggpair.y.variable<-renderUI({
req(isTRUE(yclude_y()))
req(input$data_descX)
data0<-getdata_descX()
req(length(data0)>0)
data<-vals$saved_data[[input$data_descX]]
req(length(data)>0)
factors<-attr(data,"factors")
div(
style="border-bottom: 1px solid; margin-bottom: 5px; margin-left: 20px",
pickerInput(ns("ggpair.y.variable"),strong("+ Y Variable:", class='text_alert'),colnames(factors), selected=vals$ggpair.y.variable, width="220px")
)
})
observeEvent(input$ggpair.y.variable,{
vals$ggpair.y.variable<-input$ggpair.y.variable
})
output$ggpair.title_corr<-renderUI({
req(input$ggpair.method)
textInput(ns("ggpair.title_corr"),"+ title_corr:",paste(input$ggpair.method,"corr"))
})
observeEvent(ignoreInit = T,input$fm_downplot4,{
vals$hand_plot<-"Pairs-plot"
module_ui_figs("downfigs")
mod_downcenter <- callModule(module_server_figs, "downfigs", vals=vals)
})
output$gg_run_btn<-renderUI({
req(is.null(vals$gg_run))
div(class="save_changes",
actionButton(ns('gg_run'), 'RUN', icon=icon("fas fa-sync"))
)
})
observeEvent(ignoreInit = T,input$gg_run,{
vals$desc_pairplot<-get_ggpair()
vals$gg_run<-F
})
observeEvent(get_ggpair_args(),{
vals$gg_run<-NULL
})
observeEvent(get_ggpair_args(),{
args<-get_ggpair_args()
req( class(args)=="iggpair")
vals$gg_run<-F
vals$desc_pairplot<-get_ggpair()
}, once=T)
yclude_y<-reactive({
req(input$ggpair.lower)
req(input$ggpair.upper)
req(input$ggpair.diag)
req(length(input$ggpair.box.include)>0)
input$ggpair.lower=='points+group'|input$ggpair.upper=='corr+group'|input$ggpair.diag%in%c("density+group","hist+group")| isTRUE( input$ggpair.box.include)
})
get_ggpair_args<-reactive({
args<-try(silent = T,{
req(input$data_descX)
#input<-readRDS("input.rds")
#vals<-readRDS("savepoint.rds")
req(input$ggpair.variables)
req(input$fm_palette)
newdata<-getdata_descX()
req(length(newdata)>0)
req(input$ggpair.variables%in%colnames(newdata))
data<-newdata[,input$ggpair.variables]
pred<-y<-NULL
df=data
cols<-vals$newcolhabs[[input$fm_palette]](1)
my_cols<-cols
if(yclude_y()){
req(input$data_descX)
req(input$ggpair.y.variable)
req(input$data_descX %in% names(vals$saved_data))
factors<-attr(vals$saved_data[[input$data_descX]],"factors")
req(input$ggpair.y.variable %in% colnames(factors))
y<-pred<-factors[rownames(data),input$ggpair.y.variable, drop=F]
#validate(need(nlevels(pred[,1])<=50))
df=cbind(data,pred)
cols<-vals$newcolhabs[[input$fm_palette]](nlevels(pred[,1]))
my_cols<-cols[pred[,1]]
}
include.y<-input$ggpair.box.include
# library(GGally)
size=input$msp_plot_base_size*.09
req(input$ggpair.method)
args<-list(x=data,y=y,
cols=cols,
method=input$ggpair.method,
round=input$ggpair.round,
switch=input$ggpair.switch,
plot.title.size=input$ggpair.plot.title.size,
axis.text.size=input$ggpair.axis.text.size,
axis.title.size=input$ggpair.axis.title.size,
cor.size=input$ggpair.cor.size,
varnames.size=input$ggpair.varnames.size,
points.size=input$ggpair.points.size,
legend.text.size=input$ggpair.legend.text.size,
legend.title.size=input$ggpair.legend.title.size,
alpha.curve=input$ggpair.alpha.curve,
title=input$ggpair.title,
xlab=input$ggpair.xlab,
ylab=input$ggpair.ylab,
title_corr=input$ggpair.title_corr,
include.y=include.y,
pch=as.numeric(input$ggpair.pch),
upper=input$ggpair.upper,
lower=input$ggpair.lower,
diag=input$ggpair.diag
)
# saveRDS(args,"args.rds")
req( !any(sapply(args[-2],length)<1))
# attach(args)
class(args)<-'iggpair'
args
})
req(class(args)=="iggpair")
args
})
get_ggpair<-reactive({
args<-get_ggpair_args()
class(args)=="iggpair"
p<-do.call(gg_pairplot2,args)
p
})
output$msp_pairs<-renderUI({
res<-div(
uiOutput(ns("gg_run_btn")),
renderPlot(vals$desc_pairplot, width=input$msp_plot_width,height=input$msp_plot_height),
em(attr(vals$desc_pairplot,"row1"), style="color: gray")
)
vals$show_ggrun<-F
res
})
###
output$corr_cutoff<-renderUI({
req(input$cutoff_hl!="all")
if(is.null(vals$cor_cutoff)){vals$cor_cutoff<-0.9}
numericInput(ns("cor_cutoff"),NULL,value = vals$cor_cutoff,min = 0.1,max = 1,step = .1, width='100px')
})
observeEvent(ignoreInit = T,input$cor_cutoff,{
vals$cor_cutoff<-input$cor_cutoff
})
output$corr_side<-renderUI({
cor_method = c("pearson", "kendall", "spearman")
cor_use=c( "complete.obs","everything", "all.obs", "na.or.complete", "pairwise.complete.obs")
cor_dendogram = c("both","row","column","none")
cor_scale = c("none","row", "column")
cor_Rowv = c('TRUE','FALSE')
cor_Colv=c('Rowv',T,F)
cor_revC=c('TRUE','FALSE')
cor_na.rm=c('TRUE','FALSE')
cor_labRow=c('TRUE','FALSE')
cor_labCol=c('TRUE','FALSE')
cor_cellnote=c('TRUE','FALSE')
cor_density.info=c("histogram","density","none")
div(
div(pickerInput(ns("cor_method"), span("+ Corr method",tiphelp("correlation coefficient to be computed")),
choices=cor_method)),
div(pickerInput(ns("cor_use"), span("+ Use",tiphelp("method for computing covariances in the presence of missing values")),
choices=cor_use)),
div(
span(strong("+ Filter correlations:"),tiphelp("ifThe pair-wise absolute correlation cutoff "),inline(pickerInput(ns('cutoff_hl'), NULL, choices=list(
"All"="all",
"lower than"="lower",
"higher than"="higher"
), width="100px")),
inline(uiOutput(ns("corr_cutoff"))))
),
div(pickerInput(ns("cor_dendogram"), span("+ Dendogram",tiphelp("indicating whether to draw 'none', 'row', 'column' or 'both' dendrograms")),
choices=cor_dendogram)),
div(pickerInput(ns("cor_scale"), span("+ Scale",tiphelp("indicating if the values should be centered and scaled in either the row direction or the column direction, or none. ")),
choices=cor_scale)),
div(pickerInput(ns("cor_Rowv"), span("+ Rowv",tiphelp("If is TRUE, which implies dendrogram is computed and reordered based on row means")),
choices=cor_Rowv)),
div(pickerInput(ns("cor_Colv"), span("+ Colv",tiphelp(" Colv='Rowv' means that columns should be treated identically to the rows.If is TRUE, which implies dendrogram is computed and reordered based on cols means")),
choices=cor_Colv)),
div(pickerInput(ns("cor_revC"), span("+ revC",tiphelp("Indicating if the column order should be reversed for plotting")),
choices=cor_revC)),
div(pickerInput(ns("cor_na.rm"), span("+ na.rm",tiphelp("indicating whether NAs should be removed")),
choices=cor_na.rm)),
div(pickerInput(ns("cor_labRow"), span("+ labRow",tiphelp("show observation labels")),
choices=cor_labRow)),
div(pickerInput(ns("cor_labCol"), span("+ labCol",tiphelp("show variable labels")),
choices=cor_labCol)),
div(pickerInput(ns("cor_density.info"), span("+ density.info",tiphelp("indicating whether to superimpose a 'histogram', a 'density' plot, or no plot ('none') on the color-key.")),
choices=cor_density.info)),
div(class="palette",span("+ Palette:",inline(
pickerInput(inputId=ns("cor_palette"),
label = NULL,
choices = vals$colors_img$val,
choicesOpt = list(content = vals$colors_img$img), width="120px", selected=vals$cor_palette)
))),
div(class="palette",span(span("+ NA color:",tiphelp("Color to use for missing value")),inline(
pickerInput(inputId=ns("cor_na.color"),
label = NULL,
choices = vals$colors_img$val[getsolid_col()],
choicesOpt = list(content = vals$colors_img$img[getsolid_col()]),
selected="gray",
width="75px")
))),
div(
span(span("+ X margin"),
inline(numericInput(ns("cor_mar_row"),NULL,value = 5,min = 0,step = 1, width='100px')
))
),
div(
span(span("+ Y margin"),
inline(numericInput(ns("cor_mar_col"),NULL,value = 5,min = 0,step = 1, width='100px')
))
),
div(
span(span("+ sep row width:",tiphelp("space between rows")),
inline(numericInput(ns("cor_sepwidth_a"),NULL,value = 0.05,min = 0.1,max = 1,step = .01, width='100px')
))
),
div(
span(span("+ sep col width:",tiphelp("space between columns")),
inline(numericInput(ns("cor_sepwidth_b"),NULL,value = 0.05,min = 0.1,max = 1,step = .01, width='100px')
))
),
div(class="palette",span(span("+ Sep color:",tiphelp("color between rows and coluns")),inline(
pickerInput(inputId=ns("cor_sepcolor"),
label = NULL,
choices = vals$colors_img$val[getsolid_col()],
choicesOpt = list(content = vals$colors_img$img[getsolid_col()]),
selected="white",
width="75px")
))),
hr(),
pickerInput(ns("cor_cellnote"), span("+ Cell note",tiphelp("Show correlation value")),
choices=cor_cellnote),
div(class="palette",
span(span("+ Note color:",tiphelp("Color of the correlation value")),inline(
pickerInput(inputId=ns("cor_noteco"),
label = NULL,
choices = vals$colors_img$val[getsolid_col()],
choicesOpt = list(content = vals$colors_img$img[getsolid_col()]),
selected="black",
width="75px")
))),
div(
span(span("+ Note size:", tiphelp("Size of the correlation value")),
inline(numericInput(ns("cor_notecex"),NULL,value = 1,step=0.1, width='100px')
))
),
div(
actionLink(ns('corr_downp'),"+ Download plot", style="button_active")
),
div(
actionLink(ns('corr_down_results'),span("+ Download Results",icon("fas fa-table")), style="button_active")
)
)
})
observeEvent(ignoreInit = T,input$corr_down_results,{
vals$hand_down<-"Corr result"
module_ui_downcenter("downcenter")
mod_downcenter <- callModule(module_server_downcenter, "downcenter", vals=vals)
})
get_corrdata<-reactive({
req(input$cor_method)
args<-list(data=getdata_descX(),cor_method=input$cor_method,cor_cutoff=input$cor_cutoff,cor_use=input$cor_use,ret=input$cutoff_hl)
# saveRDS(args,"args.rds")
#args<-readRDS("args.rds")
# attach(args)
cordata<-do.call(cordata_filter,args)
cordata
})
output$corr_plot<-renderUI({
cordata=get_corrdata()
vals$corr_results<-cordata
args<-list(cordata=cordata,
newcolhabs=vals$newcolhabs,
cor_palette=input$cor_palette,
cor_sepwidth_a=input$cor_sepwidth_a,
cor_sepwidth_b=input$cor_sepwidth_b,
cor_notecex=input$cor_notecex,
cor_noteco=input$cor_noteco,
cor_na.color=input$cor_na.color,
cor_sepcolor=input$cor_sepcolor,
cor_dendogram=input$cor_dendogram,
cor_scale=input$cor_scale,
cor_Rowv=input$cor_Rowv,
cor_Colv=input$cor_Colv,
cor_revC=input$cor_revC,
cor_na.rm=input$cor_na.rm,
cor_labRow=input$cor_labRow,
cor_labCol=input$cor_labCol,
cor_cellnote=input$cor_cellnote,
cor_density.info=input$cor_density.info,
margins = c(input$cor_mar_row, input$cor_mar_col))
req(!any(unlist(lapply(args,is.null))))
# saveRDS(args,"arg_heat.rds")
#args<-readRDS("args.rds")
renderPlot({
do.call(i_corplot,args)
vals$plot_correlation<-recordPlot()
})
})
observeEvent(ignoreInit = T,input$corr_downp,{
vals$hand_plot<-"Correlation Plot"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
getmissing<-reactive({
vals<-readRDS("savepoint.rds")
data<-vals$saved_data$zeu
req(is.data.frame(vals$saved_data[[input$data_descX]]))
data=vals$saved_data[[input$data_descX]]
image(as.matrix(data))
res0<-res<-which(is.na(data), arr.ind=TRUE)
if(length(res0)>0){
for(i in 1:nrow(res)){
res0[i,1]<-rownames(data)[res[i,1]]
res0[i,2]<-colnames(data)[res[i,2]]
}
colnames(res0)<-c("ID","Variable")
rownames(res0)<-NULL
res<-data.frame( table(res0[,2]))
colnames(res)<-c("Variable","Missing")
rownames(res)<-res[,1]
pic<-colnames(vals$saved_data[[input$data_descX]])[which(colnames(vals$saved_data[[input$data_descX]])%in%res[,1])]
res[,1]<-NULL
if(length(pic)>0)
res[pic,, drop=F]
}
})
get_dataord<-reactive({
req(input$missing_reorder!="N missing")
data=vals$saved_data[[input$data_descX]]
dataord<-if(input$missing_reorder=="Factor"){
attr(data,"factors") } else{data}
dataord
})
observeEvent(ignoreInit = T,input$missing_id1,{
vals$missing_id1<-input$missing_id1
})
observeEvent(ignoreInit = T,input$missing_id2,{
vals$missing_id2<-input$missing_id2
})
observeEvent(ignoreInit = T,input$missing_var1,{
vals$missing_var1<-input$missing_var1
})
observeEvent(ignoreInit = T,input$missing_var2,{
vals$missing_var2<-input$missing_var2
})
observeEvent(ignoreInit = T,input$missing_reorder,{
vals$missing_reorder<-input$missing_reorder
})
observeEvent(ignoreInit = T,input$missing_ord,{
vals$missing_ord<-input$missing_ord
})
output$missing_data<-renderUI({
sidebarLayout(
sidebarPanel(uiOutput(ns('missing_side'))),
mainPanel(uiOutput(ns('missing_plot')))
)
})
output$missing_side<-renderUI({
data=vals$saved_data[[input$data_descX]]
if(is.null(vals$missing_id1)){
ob1<-rownames(data)[c(1,nrow(data))]
va1<-colnames(data)[c(1,ncol(data))]
vals$missing_id1<-ob1[1]
vals$missing_id2<-ob1[2]
vals$missing_var1<-va1[1]
vals$missing_var2<-va1[2]
}
div(class="map_control_style",style="color: #05668D",
div("Row:",
inline(pickerInput(ns("missing_id1"), NULL,rownames(data), selected=vals$missing_id1, width="100px")), strong("to"),inline(
pickerInput(ns("missing_id2"), NULL,rownames(data), selected=vals$missing_id2, width="100px")
)
),
div("Col:",
inline(pickerInput(ns("missing_var1"), NULL,colnames(data), selected=vals$missing_var1, width="100px")), strong("to"),inline(
pickerInput(ns("missing_var2"), NULL,colnames(data), selected=vals$missing_var2, width="100px")
)
),
div("Reorder",
inline(pickerInput(ns("missing_reorder"), NULL,c(
"N missing","Variable","Factor"
), selected=vals$missing_reorder, width="100px")),
inline(uiOutput(ns("missing_ord")))
),
div("+ Palette",
pickerInput(inputId=ns("missing_palette"),label = NULL,choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),options=list(container="body"),selected=vals$colors_img$val[1], width='120px')
),
div(
actionLink(ns('split_missing'),"+ Split into missing and non-missing", style="button_active")
),
div(
actionLink(ns('missing_downp'),"+ Download plot", style="button_active")
),
actionButton(ns("save_teste"),"SAVE")
)
})
observeEvent(ignoreInit = T,input$split_missing,{
#vals<-readRDS("savepoint.rds")
#input<-readRDS('input.rds')
data=vals$saved_data[[input$data_descX]]
req(any(is.na(data)))
factors<-attr(data,"factors")
coords<-attr(data,"coords")
colmiss<-getcol_missing(data)[,1]
comi<-which(colnames(data)%in%as.character(colmiss))
romi<-which(rownames(data)%in%as.character(getrow_missing(data)[,1]))
ylist<-list()
for(i in 1:length(comi)){
romipic<-which(is.na(data[,comi[i]]))
X=data[-romipic,-comi, drop=F]
attr(X,"factors")<-factors[rownames(X),]
attr(X,"coords")<-coords[rownames(X),]
Y=data[-romipic,comi[i], drop=F]
fac<-factors[rownames(Y),]
n_sample<-round(nrow(Y)*20/100)
part<-sample(1:nrow(Y),n_sample)
name0<-paste0("Partition_",colnames(Y))
name1<-make.unique(c(colnames(factors),name0), sep="_")
name_part<-name1[ncol(factors)+1]
fac[name_part]<-NA
fac[rownames(Y)[as.vector(part)] ,name_part]<-"test"
fac[rownames(Y)[-as.vector(part)] ,name_part]<-"training"
fac[name_part]<-factor(fac[,name_part])
attr(Y,"factors")<-fac
attr(Y,"coords")<-coords[rownames(Y),]
ylist[[i]]<-Y
newdata=data[romipic,-comi, drop=F]
attr(newdata,"factors")<-factors[rownames(newdata),]
attr(newdata,"coords")<-coords[rownames(newdata),]
name0<-paste0(input$data_descX,"_COMP_X_to_", colnames(Y))
name1<-make.unique(c(names(vals$saved_data),name0), sep="_")
namemissing<-name1[length(vals$saved_data)+1]
vals$saved_data[[namemissing]]<-X
#name0<-paste0(input$data_descX,"_COMP_Y_", colnames(Y))
# name1<-make.unique(c(names(vals$saved_data),name0), sep="_")
#namemissing<-name1[length(vals$saved_data)+1]
#vals$saved_data[[namemissing]]<-Y
name0<-paste0(input$data_descX,"_MISS_newX_to_",colnames(Y))
name1<-make.unique(c(names(vals$saved_data),name0), sep="_")
namemissing<-name1[length(vals$saved_data)+1]
vals$saved_data[[namemissing]]<-newdata
}
datY<-mergedatacol(ylist)
name0<-paste0(input$data_descX,"_COMP_Y_")
name1<-make.unique(c(names(vals$saved_data),name0), sep="_")
namemissing<-name1[length(vals$saved_data)+1]
vals$saved_data[[namemissing]]<-datY
})
observeEvent(ignoreInit = T,input$missing_downp,{
vals$hand_plot<-"Missing plot"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
output$missing_ord<-renderUI({
choices<-colnames(get_dataord())
pickerInput(ns("missing_ord"), NULL,choices, selected=vals$missing_ord, width="100px")
})
output$missing_plot<-renderUI({
req(input$missing_reorder)
data=vals$saved_data[[input$data_descX]]
ob1<-c(input$missing_id1,input$missing_id2)
obs<-which(rownames(data)%in%ob1)
va1<-c(input$missing_var1,input$missing_var2)
var<-which(colnames(data)%in%va1)
pic_var<-seq(var[1],var[2])
pic_obs<-seq(obs[1],obs[2])
data<-data[pic_obs,pic_var]
renderPlot({
df<-data.frame(data)
df$nmissing<-apply(data,1,function(x) sum(is.na(x)))
if(input$missing_reorder=='N missing'){
a<-reshape2::melt(data.frame(id=rownames(df),df), c("id","nmissing"))
p<-ggplot(a,aes(reorder(variable,nmissing),reorder(id,nmissing)))+ geom_tile(aes(fill=value), color="black")+scale_fill_gradientn(colours= vals$newcolhabs[[input$missing_palette]](100),na.value="black")
} else {
df<-data.frame(data)
df$nmissing<-apply(data,1,function(x) sum(is.na(x)))
req(input$missing_reorder)
dataord<-get_dataord()
ordvar<-dataord[,input$missing_ord]
df$ordvar<-ordvar
a<-reshape2::melt(data.frame(id=rownames(df),df), c("id","nmissing","ordvar"))
p<-ggplot(a,aes(reorder(variable,ordvar),reorder(id,ordvar)))+ geom_tile(aes(fill=value), color="black")+scale_fill_gradientn(colours= vals$newcolhabs[[input$missing_palette]](100),na.value="black")
}
p<-p+theme(axis.text.x = element_text(angle = 45, hjust = 1))+xlab("Variables")+ylab("Observations")
vals$missing_plot<-p
vals$missing_plot
})
})
observeEvent(ignoreInit = T,input$save_teste,{
saveRDS(reactiveValuesToList(vals),"savepoint.rds")
saveRDS(reactiveValuesToList(input),"input.rds")
beep()
#vals<-readRDS("vals.rds")
#input<-readRDS('input.rds')
})
output$cor_downplot<-renderUI({
req(!is.null(vals$corplot))
div(
actionLink(ns('cordown'),"+ Download plot", style="button_active")
)
})
observeEvent(ignoreInit = T,input$data_descX,{
vals$corplot<-NULL
})
observeEvent(ignoreInit = T,input$cordown,{
vals$hand_plot<-"Corr plot"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
pic_pca_results<-reactive({
req(input$show_pca_results)
switch (input$show_pca_results,
'Standard deviations' = 'sdev',
'Rotation'='rotation',
'Centering'='center',
'Scaling'='scale',
'Scores'='x',
'Importance'='importance',
)
})
output$pca_summary<-renderUI({
req(input$pca_options=="pca_summary")
div(
tags$div(
pickerInput(ns("show_pca_results"),"Show result:", c('Importance','Scores',"Standard deviations","Rotation","Centering","Scaling")),style="width: var(--parentHeight);"
),
actionLink(
ns('down_pca_results'),span("+ Download",icon("fas fa-table")))
)
})
observeEvent(ignoreInit = T,input$down_pca_results,{
vals$hand_down<-"PCA result"
module_ui_downcenter("downcenter")
mod_downcenter <- callModule(module_server_downcenter, "downcenter", vals=vals)
})
observeEvent(ignoreInit = T,input$pca_options,{
vals$pca_options<-input$pca_options
})
observeEvent(ignoreInit = T,input$scatter_y_datalist,{
vals$scatter_y_datalist<-input$scatter_y_datalist
})
output$scatter_y_datalist<-renderUI({
pickerInput(ns("scatter_y_datalist"),'Datalist Y', choices=names(vals$saved_data), selected=vals$scatter_y_datalist)
})
observeEvent(ignoreInit = T,input$scatter_x_datalist,{
vals$scatter_x_datalist<-input$scatter_x_datalist
})
output$scatter_x_datalist<-renderUI({
pickerInput(ns("scatter_x_datalist"),'Datalist X', choices=names(vals$saved_data), selected=vals$scatter_x_datalist)
})
output$dtab_scatter<-renderUI({
div(style="background: white",
p(strong("Scatter plot")),
sidebarLayout(
sidebarPanel(
div( class="map_control_style",
style="color: #05668D",
div(inline(uiOutput(ns("scatter_x_datalist"))),
inline(uiOutput(ns("scatter_x")))),
div(
inline(uiOutput(ns("scatter_y_datalist"))),
inline(uiOutput(ns("scatter_y_input")))
),
uiOutput(ns('scatter_side'))
)
),
mainPanel(uiOutput(ns("scatter_plot"))))
)
})
output$scatter_side<-renderUI({
req(input$scatter_x)
req(input$scatter_y)
div(
div(span("+ Shape:",
inline(pickerInput(inputId=ns("scatter_symbol"),
label = NULL,
choices = df_symbol$val,
options=list(container="body"),
choicesOpt = list(content = df_symbol$img), width='75px')))),
div(
span("+ Size:",
inline(numericInput(ns("scatter_cexpoint"),NULL,value = 1,min = 0.1,max = 3,step = .1, width='75px')
))
),
div(span('+ X label:',
inline(
textInput(ns("scatter_xlab"),NULL , value=input$scatter_x, width="120px")
)
)),
div(span('+ Y label:',
inline(
textInput(ns("scatter_ylab"),NULL , value=input$scatter_y, width="120px"))
)),
div(
actionLink(ns('scatter_downplot'),"+ Download plot", style="button_active")
)
)
})
observeEvent(ignoreInit = T,input$scatter_downplot,{
vals$hand_plot<-"Scatter plot"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
#vals<-readRDS("savepoint.rds")
# vals$saved_data$ID_tempo_GF2$Ano==
#vals$saved_data$ID_Variรกveis_GF2$Ano
output$scatter_plot<-renderUI({
datax<-vals$saved_data[[input$scatter_x_datalist]]
datay<-vals$saved_data[[input$scatter_y_datalist]]
dataxy<-data.frame(datax[input$scatter_x],datay[input$scatter_y])
renderPlot({
plot(dataxy, pch=as.numeric(input$scatter_symbol), cex=input$scatter_cexpoint, xlab=input$scatter_xlab, ylab=input$scatter_ylab)
vals$scatter_plot<-recordPlot()
})
})
output$scatter_y_input<-renderUI({
req(input$scatter_y_datalist)
data<-vals$saved_data[[input$scatter_y_datalist]]
div(
pickerInput( ns("scatter_y"),'Y',choices =colnames(data),selected= vals$scatter_y, width="200px"
))
})
output$scatter_x<-renderUI({
req(input$scatter_x_datalist)
data<-vals$saved_data[[input$scatter_x_datalist]]
div(
pickerInput( ns("scatter_x"),"X",choices = colnames(data),selected= vals$scatter_x, width="200px"
))
})
observeEvent(ignoreInit = T,input$scatter_x,{
vals$scatter_x<-input$scatter_x
})
observeEvent(ignoreInit = T,input$scatter_y,{
vals$scatter_y<-input$scatter_y
})
output$bug<-renderUI({
renderPrint({
input$box_linecol
})
})
observeEvent(input$box_reset,{
shinyjs::reset("side_boxplot")
})
output$side_boxplot<-renderUI({
div(class="map_control_style2",style="color: #05668D",
uiOutput(ns('sidebox_horiz')),
uiOutput(ns('sidebox_theme')),
uiOutput(ns('sidebox_palette')),
uiOutput(ns('sidebox_alpha')),
uiOutput(ns('sidebox_linecol')),
uiOutput(ns('sidebox_linewidth')),
uiOutput(ns('sidebox_base_size')),
uiOutput(ns('sidebox_cex.main')),
uiOutput(ns('sidebox_cex.label_panel')),
uiOutput(ns('sidebox_cex.lab')),
uiOutput(ns('sidebox_cex.axes')),
uiOutput(ns('sidebox_title')),
uiOutput(ns('sidebox_xlab')),
uiOutput(ns('sidebox_xlab_rotate')),
uiOutput(ns('sidebox_ylab')),
uiOutput(ns('sidebox_ylab_rotate')),
uiOutput(ns('sidebox_grid')),
uiOutput(ns('sidebox_varwidth')),
uiOutput(ns('sidebox_violin')),
uiOutput(ns('sidebox_width')),
uiOutput(ns('sidebox_height'))
)
})
output$sidebox_width<-renderUI({
numericInput(ns('box_width'),"+ Plot widht:",550, step=50)
})
output$sidebox_height<-renderUI({
numericInput(ns('box_heigth'),"+ Plot heigth:",400, step=50)
})
output$sidebox_xlab_rotate<-renderUI({
numericInput(ns('box_xlab_rotate'),"+ x text angle:", 0,step=5)
})
output$sidebox_ylab_rotate<-renderUI({
numericInput(ns('box_ylab_rotate'),"+ y text angle:", 0,step=5)
})
output$sidebox_theme<-renderUI({
pickerInput(ns("box_theme"),"+ Theme:",c('theme_bw','theme_grey','theme_linedraw','theme_light','theme_minimal','theme_classic'))
})
output$sidebox_horiz<-renderUI({
checkboxInput(ns('box_horiz'),"+ Horizontal:",value=F)
})
output$sidebox_palette<-renderUI({
pickerInput(ns("box_palette"),
label = "+ Palette:",
choices = vals$colors_img$val,
choicesOpt = list(content = vals$colors_img$img))
})
output$sidebox_alpha<-renderUI({
numericInput(ns('box_alpha'),"+ Lighten:", .3, step=0.05)
})
output$sidebox_linecol<-renderUI({
div(
colourpicker::colourInput(ns("box_linecol"),
label = "+ Line color:",
value ="black",showColour="background")
)
})
output$sidebox_linewidth<-renderUI({
numericInput(ns('box_linewidth'),"+ Line width:", .5,step=.1)
})
output$sidebox_base_size<-renderUI({
numericInput(ns('box_base_size'),"+ Base size:", 12,step=1)
})
output$sidebox_cex.axes<-renderUI({
numericInput(ns('box_cex.axes'),"+ Axis size:", 1.5,step=.1)
})
output$sidebox_cex.lab<-renderUI({
numericInput(ns('box_cex.lab'),"+ Label size:", 1.5,step=.1)
})
output$sidebox_cex.main<-renderUI({
numericInput(ns('box_cex.main'),"+ Title size:", 1.5,step=.1)
})
output$sidebox_cex.label_panel<-renderUI({
req(input$box_y)
req(length(input$box_y)>1)
numericInput(ns('box_cex.label_panel'),"+ Panel Title Size:", 1.5,step=.1)
})
output$sidebox_title<-renderUI({
req(input$boxplot_X)
req(input$box_y)
value=ifelse(length(input$box_y)>1,"",paste(input$box_y,"~",input$boxplot_X))
textInput(ns('box_title'),"+ Title:",value)
})
output$sidebox_xlab<-renderUI({
req(input$boxplot_X)
req(input$box_y)
value=ifelse(length(input$box_y)>1,"",input$boxplot_X)
textInput(ns('box_xlab'),"+ x label:",value)
})
output$sidebox_ylab<-renderUI({
req(input$box_y)
value=ifelse(length(input$box_y)>1,"Value",input$box_y)
textInput(ns('box_ylab'),"+ y label:",value)
})
output$sidebox_grid<-renderUI({
checkboxInput(ns('box_grid'),"+ Grid lines:",value=T)
})
output$sidebox_violin<-renderUI({
checkboxInput(ns('box_violin'),"+ Violin:",value=F)
})
output$sidebox_varwidth<-renderUI({
checkboxInput(ns("box_varwidth"),span("+ Varwidth:",tiphelp("Drawn boxes with widths proportional to the square-roots of the number of observations in the groups","right")),F, width="95px")
})
output$stats_pbox<-renderUI({
req(input$filter_box1)
if(input$filter_box1 != "none"){
req(input$filter_box2)
}
req(input$box_width)
req(input$box_width>10)
req(input$box_heigth)
req(input$box_heigth>10)
div(
column(12,renderPlot({
res<-getbox()
req(length(res)>0)
violin<-input$box_violin
horiz=input$box_horiz
base_size=input$box_base_size
cex.axes=input$box_cex.axes*base_size
cex.lab=input$box_cex.lab*base_size
cex.main=input$box_cex.main*base_size
pal<-input$box_palette
box_alpha=input$box_alpha
main=input$box_title
xlab<-input$box_xlab
ylab<-input$box_ylab
box_linecol=input$box_linecol
varwidth=input$box_varwidth
if(length(input$box_cex.label_panel)>0){
cex.label_panel=input$box_cex.label_panel*base_size
} else{
cex.label_panel=1
}
linewidth=input$box_linewidth
theme=input$box_theme
grid=input$box_grid
xlab_rotate=input$box_xlab_rotate
ylab_rotate=input$box_ylab_rotate
vals$pbox_plot<-ggbox(
res, pal,violin,horiz,base_size,cex.axes,cex.lab,cex.main, xlab, ylab,main,box_linecol ,box_alpha,vals$newcolhabs,cex.label_panel,varwidth=varwidth,linewidth=linewidth,theme=theme, grid=grid,xlab_rotate=xlab_rotate,ylab_rotate=ylab_rotate
)
vals$pbox_plot
},
width =input$box_width,
height =input$box_heigth
))
)
})
output$editbox<-renderUI({
fluidRow(class="map_control_style",style="color: #05668D",
column(12,style="border-top: 1px solid #05668D;",
fluidRow(
actionLink(
ns("downp_box"),span("+ Download",icon("fas fa-download")), style="button_active"
)
))
)
})
output$omds_dist<-renderUI({
if(is.null(vals$cur_dist_mds)){vals$cur_dist_mds="Choose one"}
div(
span("+ Distance:",
inline(
pickerInput(ns("distance"),NULL,choices = c("Choose one" = "", c('bray', "euclidean", 'jaccard')), selected=vals$cur_dist_mds, width="125px")
)
)
)
})
observeEvent(ignoreInit = T,input$distance,{
vals$cur_dist_mds<-input$distance
})
output$opca_biplot<-renderUI({
req(input$pca_options=="pca_plot")
div(
span("+",
inline(
checkboxInput(ns("biplot"), span("Biplot", pophelp(NULL,"show biplot arrows")), T, width="75px")
)
)
)
})
output$mds_options<-renderUI({
req(input$distance %in%c("bray","euclidean","jaccard"))
div(
div(
span("+",
inline(checkboxInput(ns("mds_show_symbols"),"Symbol" ,T, width='75px')))),
uiOutput(ns("mds_show_symbols_out")),
div(span("+",
inline(checkboxInput(ns("mds_show_labels"),"Labels",F)
))),
uiOutput(ns("mds_show_labels_out")),
div(
actionLink(
ns('mds_downp'),span("+ Download",icon("fas fa-download")), style="button_active"
)
)
)
})
output$mds_show_labels_out<-renderUI({
req(isTRUE(input$mds_show_labels ))
div(style="margin-left: 5px",
div(span("+ Factor:",
inline(tipify(pickerInput(ns("mds_labfactor"),NULL,choices = colnames(attr(getdata_descX(),"factors")), width="125px"), "label classification factor")
))),
div(span("+ Lab Color:",
inline(tipify(
pickerInput(
inputId=ns("mds_labcolor"),
label = NULL,
selected= vals$colors_img$val[12],choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),width="75px",
options=list(container="body")
), "label classification factor"
)
))),
div(span("+ Lab adj:",
inline(
tipify(pickerInput(ns("mds_labadj"),NULL,choices=c(1:4), width="75px", options=list(containder="body")), "a position specifier for the text. If specified this overrides any adj value given. Values of 1, 2, 3 and 4, respectively indicate positions below, to the left of, above and to the right of the specified (x,y) coordinates.", placement = "right")
))),
div(span("+ Lab offset:",
inline(
tipify(numericInput(ns("mds_offset"),NULL,value = 0,step = .1, width="75px"), "this value controls the distance ('offset') of the text label from the specified coordinate in fractions of a character width.")
))),
div(span("+ Size:",
numericInput(ns("mds_cextext"),NULL,value = 1,min = 0.1,max = 3,step = .1)))
)
})
output$mds_show_symbols_out<-renderUI({
req(isTRUE(input$mds_show_symbols))
div(style="margin-left: 5px",
div(
span("+ Shape:",
inline(pickerInput(inputId=ns("mds_symbol"),
label = NULL,
choices = df_symbol$val,
options=list(container="body"),
choicesOpt = list(content = df_symbol$img), width='75px')))),
div(
span("+ Size:",
inline(numericInput(ns("mds_cexpoint"),NULL,value = 1,min = 0.1,max = 3,step = .1, width='75px')
))
),
div(class="palette",
span("+ Color:",
inline(
tipify(
pickerInput(inputId=ns("mds_colpalette"),label = NULL,choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),options=list(container="body"),selected=vals$colors_img$val[1], width='120px'), "Symbol palette. Choose a gradient to color observations by a factor")))),
uiOutput(ns("mds_fac_palette"))
)
})
output$pca_show_labels_out<-renderUI({
req(isTRUE(input$pca_show_labels))
div(style="margin-left: 5px",
div(span("+ Factor:",
inline(tipify(pickerInput(ns("pca_labfactor"),NULL,choices = colnames(attr(getdata_descX(),"factors")), width="125px"), "label classification factor")
))),
div(span("+ Lab Color:",
inline(tipify(
pickerInput(
inputId=ns("pca_labcolor"),
label = NULL,
selected= vals$colors_img$val[12],choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),width="75px",
options=list(container="body")
), "label classification factor"
)
))),
div(span("+ Lab adj:",
inline(
tipify(pickerInput(ns("pca_labadj"),NULL,choices=c(1:4), width="75px", options=list(containder="body")), "a position specifier for the text. If specified this overrides any adj value given. Values of 1, 2, 3 and 4, respectively indicate positions below, to the left of, above and to the right of the specified (x,y) coordinates.", placement = "right")
))),
div(span("+ Lab offset:",
inline(
tipify(numericInput(ns("pca_offset"),NULL,value = 0,step = .1, width="75px"), "this value controls the distance ('offset') of the text label from the specified coordinate in fractions of a character width.")
))),
div(span("+ Size:",
inline(
tipify(numericInput(ns("pca_cextext"),NULL,value = 1,min = 0.1,max = 3,step = .1), "label text size")
)))
)
})
output$mds_fac_palette<-renderUI({
col<-getcolhabs(vals$newcolhabs,input$mds_colpalette,2)
req(col[1]!=col[2])
div(
span("+ Factor:",
inline(tipify(pickerInput(ns("mds_symbol_factor"),NULL,choices = rev(colnames(attr(getdata_descX(),"factors"))), width='125px'), "symbol classification factor"))))
})
output$pca_options_plot<-renderUI({
req(input$pca_options=="pca_plot")
div(
div(
span("+",
inline(checkboxInput(ns("pca_show_symbols"),"Symbol" ,T, width='75px')))),
uiOutput(ns("pca_show_symbols_out")),
div(span("+",
inline(checkboxInput(ns("pca_show_labels"),"Labels",F)
))),
uiOutput(ns("pca_show_labels_out")),
div(
actionLink(
ns('pca_downp'),span("+ Download",icon("fas fa-download")), style="button_active"
)
)
)
})
output$pca_show_symbols_out<-renderUI({
req(isTRUE(input$pca_show_symbols))
div(style="margin-left: 5px",
div(
span("+ Shape:",
inline(pickerInput(inputId=ns("pca_symbol"),
label = NULL,
choices = df_symbol$val,
options=list(container="body"),
choicesOpt = list(content = df_symbol$img), width='75px')))),
div(
span("+ Size:",
inline(numericInput(ns("pca_cexpoint"),NULL,value = 1,min = 0.1,max = 3,step = .1, width='75px')
))
),
div(class="palette",
span("+ Color:",
inline(
tipify(
pickerInput(inputId=ns("pca_colpalette"),label = NULL,choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),options=list(container="body"),selected=vals$colors_img$val[1], width='120px'), "Symbol palette. Choose a gradient to color observations by a factor")))),
uiOutput(ns("pca_fac_palette"))
)
})
output$pca_fac_palette<-renderUI({
col<-getcolhabs(vals$newcolhabs,input$pca_colpalette,2)
req(col[1]!=col[2])
div(
span("+ Factor:",
inline(tipify(pickerInput(ns("pca_symbol_factor"),NULL,choices = rev(colnames(attr(getdata_descX(),"factors"))), width='125px'), "symbol classification factor"))))
})
observeEvent(ignoreInit = T,input$downp_summ_num,{
vals$hand_plot<-"variable summary"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observeEvent(ignoreInit = T,input$downp_stats_fac,{
vals$hand_plot<-"factor summary"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observeEvent(ignoreInit = T,input$downp_hist,{
vals$hand_plot<-"histogram"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observeEvent(ignoreInit = T,input$mds_downp,{
vals$hand_plot<-"mds"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observeEvent(ignoreInit = T,input$pca_downp,{
vals$hand_plot<-"pca"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
output$dtab_summaries<-renderUI({
if(is.null(vals$curview_summ_options)){vals$curview_summ_options<-'Data'}
column(class="side_results",
12, offset = 0,
navlistPanel(
widths = c(2, 10),
id=ns("summ_options"),selected=vals$summ_options,
tabPanel(
value="Datalist",
title = "Datalist",
datalist_render(getdata_descX())),
tabPanel(
value="Data",
title = "Data",
uiOutput(ns("stats_data"))
),
tabPanel(
value="Variables",
title = "Variables",
uiOutput(ns("stats_var"))
),
tabPanel(
value="Factors",
title = "Factors",
uiOutput(ns("stats_fac"))
)
)
)
})
observeEvent(input$summ_options,{
vals$summ_options<-input$summ_options
})
output$dtab_histogram<-renderUI({
fluidRow(
column(12,actionButton(ns("downp_hist"),icon("fas fa-image"),icon("fas fa-download"), style="button_active")),
column(12,renderPlot({
vals$phist<-phist(getdata_descX())
vals$phist
}))
)
})
observeEvent(ignoreInit = T,input$cextext,{
vals$cextext<-input$cextext
})
output$varhisto_metric<-renderUI({
div(class="cogs_in",style="margin-bottom: 10px; padding:5px",
div(class="merge_datalist",
div(actionLink(ns("show_metrics_act"),"+ Metrics"),
uiOutput(ns('show_metrics'))
)
)
)
})
output$varhisto_out<-renderUI({
div(class="cogs_in",style="margin-bottom: 10px; padding:5px;",
actionLink(ns("show_histovar"),"+ Select the Variables"),
DT::dataTableOutput(ns('histo_var_x'))
#uiOutput(ns('varhisto_out3'))
)
})
observeEvent(ignoreInit = T,input$show_metrics_act,{
shinyjs::toggle("show_metrics")
})
observeEvent(ignoreInit = T,input$show_histovar,{
shinyjs::toggle("histo_var_x")
})
observeEvent(ignoreInit = T,input$varhisto_w3,{
shinyjs::hide('show_histo_color')
},once=T)
observeEvent(ignoreInit = T,input$histo_var_x_rows_selected,{
shinyjs::hide('show_metrics')
},once=T)
observeEvent(input$data_descX,{
data<-getdata_descX()
vals$desc_maxhistvar<-ifelse(ncol(data)>10,10,ncol(data))
})
output$histo_var_x = DT::renderDataTable(
{
req(is.numeric(vals$desc_maxhistvar))
data<-vals$saved_data[[input$data_descX]]
table=data.frame(Variables=colnames(data))
DT::datatable(table, options=list(
dom="t",
lengthMenu = list(c(-1), c("All")),
scrollX = TRUE,
scrollY = "200px",
autoWidth=F
), class ='compact cell-border',rownames=F, colnames="",
selection = list(mode = 'multiple', selected = c(1:vals$desc_maxhistvar)))
})
observe({
req(is.null(vals$varhisto_ps_round))
vals$varhisto_ps_round<-2
})
observeEvent(ignoreInit = T,input$varhisto_ps_round,{
vals$varhisto_ps_round<-input$varhisto_ps_round
})
output$show_metrics<-renderUI({
div(
checkboxGroupInput(ns("varhisto_metric"),NULL,c(
'Min.' ,'1st Qu.',"Mean",'Median','3rd Qu.','Max.'
), selected=vals$varhisto_metric),
div("+ Round",
numericInput(ns("varhisto_ps_round"),NULL, value=vals$varhisto_ps_round, step=010, width="75px")
)
)
})
observe({
req(is.null(vals$varhisto_metric))
vals$varhisto_metric<-c('Min.' ,"Mean",'Max.')
})
observeEvent(ignoreInit = T,input$varhisto_metric,{
vals$varhisto_metric<-input$varhisto_metric
})
output$show_histo_color<-renderUI({
if(is.null(vals$cur_varhisto_palette)){
vals$cur_varhisto_palette<-"black"
}
div(
style="margin-left: 10px",
div(
div(
class="palette",
span("background:",
inline(pickerInput(inputId=ns("varhisto_palette"),label = NULL, choices = vals$colors_img$val[getsolid_col()][c(2,1,3,4,5,6)],choicesOpt = list(content =vals$colors_img$img[getsolid_col()])[c(2,1,3,4,5,6)],selected=vals$cur_varhisto_palette, width='75px')
))
),
div(
class="palette",
span("border",
inline(
pickerInput(inputId=ns("varhisto_border_col"),label = NULL, choices = vals$colors_img$val[getsolid_col()],choicesOpt = list(content =vals$colors_img$img[getsolid_col()]),selected=vals$varhisto_border_col, width='75px'
)
)
)
)),
uiOutput(ns("varhisto_cex")),
uiOutput(ns("varhisto_w1")),
uiOutput(ns("varhisto_w2")),
uiOutput(ns("varhisto_w3")),
)
})
output$varhisto_colors<-renderUI({
div(class="cogs_in",style="margin-bottom: 10px; padding:5px",
actionLink(ns("show_histo_colors_act"),'+ Plot params'),
uiOutput(ns('show_histo_color'))
)
})
output$varhisto_sizeplot<-renderUI({
div(class="cogs_in",style="margin-bottom: 10px; padding:5px",
actionLink(ns("show_varhisto_sizeplot_act"),'+ Plot size'),
uiOutput(ns('show_varhisto_sizeplot'))
)
})
output$show_varhisto_sizeplot<-renderUI({
req(!is.null(vals$varhisto_ps_height))
div(
div("+ Width",
numericInput(ns("varhisto_ps_width"),NULL, value= vals$varhisto_ps_width, step=010, width="75px")
),
div("+ Height",
numericInput(ns("varhisto_ps_height"),NULL, value= vals$varhisto_ps_height, step=10, width="75px")
)
)
})
observe({
req(is.null(vals$varhisto_ps_width))
vals$varhisto_ps_width<-550
})
observeEvent(ignoreInit = T,input$varhisto_ps_height,{
vals$varhisto_ps_height<-input$varhisto_ps_height
})
observeEvent(ignoreInit = T,input$varhisto_ps_width,{
vals$varhisto_ps_width<-input$varhisto_ps_width
})
observeEvent(ignoreInit = T,input$show_varhisto_sizeplot_act,{
shinyjs::toggle("show_varhisto_sizeplot")
})
observeEvent(ignoreInit = T,input$varhisto_ps_height,{
shinyjs::hide('show_varhisto_sizeplot')
},once=T)
observeEvent(ignoreInit = T,input$show_histo_colors_act,{
shinyjs::toggle("show_histo_color")
})
output$stats_var<-renderUI({
column(12, sidebarLayout(
sidebarPanel(
width=4,
div(
class="map_control_style",
style="color: #05668D",
uiOutput(ns("varhisto_out")),
uiOutput(ns("varhisto_metric")),
uiOutput(ns("varhisto_colors")),
uiOutput(ns("varhisto_sizeplot")),
div(
actionLink(ns('downp_summ_num'),tipify(span("+ Download",icon("fas fa-download")), "Download Plot"), style="button_active")
)
)
),
mainPanel(uiOutput(ns("summ_num")))
))
})
output$varhisto_cex<-renderUI({
req(length(input$histo_var_x_rows_selected)>0)
div("+ Text size:",
numericInput(ns("cextext"),NULL, value= 2, step=1, width="100px")
)
})
output$varhisto_w1<-renderUI({
div("+ Var width",
numericInput(ns("varhisto_w1"),NULL, value= vals$varhisto_w1, step=0.05, width="75px")
)
})
output$varhisto_w2<-renderUI({
div("+ Metric width",
numericInput(ns("varhisto_w2"),NULL, value= vals$varhisto_w2, step=0.05, width="75px")
)
})
output$varhisto_w3<-renderUI({
div("+ Histo width",
numericInput(ns("varhisto_w3"),NULL, value= vals$varhisto_w3, step=0.05, width="75px")
)
})
observe({
if(is.null(vals$varhisto_w1)){
vals$varhisto_w1<-0.2
vals$varhisto_w2<-vals$varhisto_w3<-0.35
}
})
observeEvent(ignoreInit = T,input$varhisto_w1,{
vals$varhisto_w1<-input$varhisto_w1
})
observeEvent(ignoreInit = T,input$varhisto_w2,{
vals$varhisto_w2<-input$varhisto_w2
})
observeEvent(ignoreInit = T,input$varhisto_w3,{
vals$varhisto_w3<-input$varhisto_w3
})
output$summ_num_plot<-renderPlot({
req(input$varhisto_palette)
req(input$varhisto_border_col)
req(input$varhisto_metric)
req(input$varhisto_w1)
req(input$varhisto_w2)
req(input$varhisto_w3)
req(input$varhisto_w3)
req(input$cextext)
data=getdata_descX()
req(length(input$histo_var_x_rows_selected)>0)
col<-getcolhabs(vals$newcolhabs,input$varhisto_palette,1)
col_border<-getcolhabs(vals$newcolhabs,input$varhisto_border_col,1)
selected<-colnames(data)[input$histo_var_x_rows_selected]
data<-data[,selected, drop=F]
str_numerics(data, cextext=input$cextext, col=col, border=col_border, show=input$varhisto_metric,width_varname=input$varhisto_w1, width_metrics=input$varhisto_w2, width_histo=input$varhisto_w3, round=input$varhisto_ps_round)
vals$varplot<-recordPlot()
})
output$summ_num<-renderUI({
req(input$varhisto_ps_height)
req(input$varhisto_ps_width)
req(input$varhisto_ps_height>10)
req(input$varhisto_ps_width>10)
plotOutput(ns('summ_num_plot'), height=paste0(input$varhisto_ps_height,"px"), width=paste0(input$varhisto_ps_width,"px"))
})
observeEvent(ignoreInit = T,input$varhisto_border_col,{
vals$varhisto_border_col<-input$varhisto_border_col
})
observeEvent(ignoreInit = T,input$varhisto_palette,{
vals$cur_varhisto_palette<-input$varhisto_palette
})
output$stats_fac<-renderUI({
column(12,
column(12,
h5(strong("Factors:")),
h5(strong("Structure:")),
verbatimTextOutput(ns('strlabels')),
),
column(12,
column(12,actionButton(ns("downp_stats_fac"),icon("fas fa-image"),icon("fas fa-download"), style="button_active")),
column(12,plotOutput(ns("factorsplot")))))
})
output$psummary<-renderPrint({
data=getdata_descX()
withProgress(message = "Calculating Numeric-Attribute summary ... Please, wait!",
min = 1,
max = 13,
{
nas=sum(is.na(unlist(data)))
incProgress(1)
n=data.frame(rbind(Param=paste('Missing values:', nas)))
incProgress(1)
a<-data.frame(rbind(Param=paste('nrow:', nrow(data)),paste('ncol:', ncol(data))))
incProgress(1)
ppsummary("-------------------")
incProgress(1)
ppsummary(n)
ppsummary("-------------------")
incProgress(1)
ppsummary(a)
ppsummary("-------------------")
incProgress(1)
})
})
output$pca_fiz<-renderUI({
renderPlot({
pca_symbol_factor<-pca_symbol_factor()
pca<-prcomp(getdata_descX())
{
col_pts=getcolhabs(vals$newcolhabs,input$pca_colpalette,nlevels(pca_symbol_factor))
gg<-fviz_pca_biplot(pca,geom="points", label="var",habillage=pca_symbol_factor,col.var ='red')
data2<-data.frame(id=rownames(pca$x),factor=pca_symbol_factor,pca$x[,1:2])
if(isTRUE(input$pca_show_symbols )){
gg<-gg+geom_point(data=data2,aes(x = PC1, y = PC2, col=factor), pch=as.numeric(input$pca_symbol), size=input$pca_cexpoint)}
if(isTRUE(input$pca_show_labels )){
gg<-gg+geom_text(data=data2,aes(x = PC1, y = PC2, label = factor, col=factor))
}
gg
}
colorFAC<- data.frame(prev_fac=levels(pca_symbol_factor),col_pts, levels=1:nlevels(pca_symbol_factor))
gg<-gg+scale_color_manual(name=input$pca_labfactor,labels = colorFAC$prev_fac,values = colorFAC$col_pts,drop=F )
if(isFALSE(input$biplot)){
gg$layers<-c(gg$layers[-3])
} else{
gg$layers<-c(gg$layers[-3],gg$layers[3])
}
gg
})
})
output$summary_pca<-DT::renderDataTable({
req(!is.null(vals$pca))
res<- summary(vals$pca)
center<-res$center
scale<-res$scale
sdev<-res$sdev
res$center<-data.frame(center)
res$scale<-data.frame(scale)
res$sdev<-data.frame(sdev)
res<-lapply(res, data.frame)
vals$pca_out<-res[[pic_pca_results()]]
vals$pca_out
},options = list(pageLength = 20, info = FALSE,lengthMenu = list(c(20, -1), c( "20","All")), autoWidth=T,dom = 'lt'), rownames = T,class ='cell-border compact stripe')
observe({
req(input$desc_options=='tab_pca')
req(input$data_descX)
req(input$desc_options)
req(input$data_descX)
validate(need(!anyNA(getdata_descX()), "This functionality does not support missing values; Please use the transformation tool to the handle missing values."))
data<- vals$saved_data[[input$data_descX]]
req(is.data.frame(data))
X = as.matrix(data)
vals$pca<-prcomp(X)
})
output$mdscustom<-renderPlot({plot_mds()})
output$stats_ppca<-renderUI({
validate(need(!anyNA(getdata_descX()), "This functionality does not support missing values; Please use the transformation tool to the handle missing values."))
column( 12,
#uiOutput(ns('pca_fiz')),
renderPlot({
req(!is.null(vals$pca))
suppressWarnings({
args<-list(
pca=vals$pca,
key = pca_symbol_factor(),
points = input$pca_show_symbols,
text = input$pca_show_labels,
palette = input$pca_colpalette,
cex.points = input$pca_cexpoint,
cex.text = input$pca_cextext,
pch=pca_symbol(),
keytext=pca_text_factor(),
biplot=input$biplot,
newcolhabs=vals$newcolhabs,
textcolor=input$pca_labcolor,
pos=input$pca_labadj,
offset=input$pca_offset
)
do.call(ppca,args)
})
vals$ppca_plot<-recordPlot()
vals$ppca_plot
})
)
})
output$stats_pmds<-renderUI({
validate(need(!anyNA(getdata_descX()), "This functionality does not support missing values; Please use the transformation tool to the handle missing values."))
res<-list(
column(12,
column(12,plotOutput(ns("mdscustom"))))
)
res
})
output$factorsplot<-renderPlot({
vals$factorsplot<-pfac(res_pfac())
vals$factorsplot
})
output$stats_data<-renderUI({
column(
12, style = "background: white;",
fluidRow(
column(12,
h5(strong(
"numeric variables:"
))),
column(6, verbatimTextOutput(ns("psummary"))),
column(12,uiOutput(ns("Locate_NA")))
)
)
})
output$Locate_NA<-renderUI({
req(anyNA(unlist(getdata_descX())))
div(
column(12,strong("Missing Values:")),
column(12,
div( tags$style('#missing_values td {padding: 3px;
text-align: left;
font-size:12px}'),
tags$style('#missing_values th {padding: 3px;
text-align: left;
font-size:12px}'),
inline(
DT::dataTableOutput(ns("missing_values"))
)
))
)
})
output$missing_values<-DT::renderDataTable({
data=getdata_descX()
res0<-res<-which(is.na(data), arr.ind=TRUE)
req(nrow(res)>0)
for(i in 1:nrow(res)){
res0[i,1]<-rownames(data)[res[i,1]]
res0[i,2]<-colnames(data)[res[i,2]]
}
colnames(res0)<-c("ID","Variable")
rownames(res0)<-NULL
res0
},options = list(pageLength = 20, info = FALSE,lengthMenu = list(c(20, -1), c( "20","All")), autoWidth=T,dom = 'lt'), rownames = F,class ='cell-border compact stripe')
output$strlabels<-renderPrint({
ppsummary("----------------")
ppsummary(paste("Missing values:",sum(is.na(attr(getdata_descX(),"factors")))))
ppsummary("----------------")
str(attr(getdata_descX(),"factors")[rownames(getdata_descX()),,drop=F])
})
##
getsolid_col<-reactive({
res<-lapply(vals$newcolhabs, function(x) x(2))
res1<-unlist(lapply(res, function(x) x[1]==x[2]))
solid<-names(res1[res1==T])
pic<-which(vals$colors_img$val%in%solid)
pic
})
getgrad_col<-reactive({
res<-lapply(vals$newcolhabs, function(x) x(2))
res1<-unlist(lapply(res, function(x) x[1]==x[2]))
grad<-names(res1[res1==F])
pic<-which(vals$colors_img$val%in%grad)
pic
})
output$save_breakpoints<-renderUI({
if(is.null(vals$bag_smw)){
class="novo"
} else{ class="save_changes"}
if(!isFALSE(vals$splitBP)){
if(!any(unlist(lapply(attr(vals$saved_data[[input$segrda_X]],"factors"), function (x) identical(x,as.vector(vals$splitBP)))))){
popify(
div(class=class,id=ns("save_changes_bp"),
bsButton(ns('tools_saveBP'),icon("fas fa-save"),style='save_button')
),"Save breakpoints from DP",
"this action divides the observations according to the breakpoints and assigns a factor to each split"
)
}
}
})
output$dtab_rid<-renderUI({
sidebarLayout(
sidebarPanel(
div(
class="map_control_style2",
style="color: #05668D",
uiOutput(ns('rid_side'))
)),
mainPanel(
uiOutput(ns('runrid_btn')),
# uiOutput(ns('rid_out'))
)
)
})
output$runrid_btn<-renderUser({
div(
column(12,align="left",
div(
div(id=ns("runrid_btn"),
actionButton(ns('runrid'), 'RUN', icon=icon("fas fa-sync")),
)
)),
uiOutput(ns('rid_out'))
)
})
output$rid_side<-renderUI({
##here
req(input$data_descX)
data<-vals$saved_data[[input$data_descX]]
factors<-attr(data,"factors")
div(
pickerInput(inputId=ns("rid_y"),label = "+ X (factor)",
choices =rev(colnames(factors)),selected=vals$rid_y),
actionLink(ns("show_obs_selection"),"+ Y (numeric)"),
uiOutput(ns('rid_x_variables')),
uiOutput(ns("ridplot_options"))
)
})
output$ridplot_options<-renderUI({
div(
pickerInput(inputId=ns("rid_col"),label = "+ Palette:",choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),options=list(container="body"),selected=vals$colors_img$val[1]),
textInput(ns('rid_tittle'),"+ Title",""),
numericInput(ns('rid_base_size'),"+ Base size",11),
numericInput(ns('rid_ncol'),"+ Nยบ columns",3),
numericInput(ns('rid_width'),"+ Plot widht",700),
numericInput(ns('rid_heigth'),"+ Plot heigth",300),
div(
actionLink(ns("rid_downp"),"Download plot", style="button_active")
)
)
})
output$rid_x_variables<-renderUI({
div(class="cogs_in_div",style="margin-bottom: 10px; padding:5px",
fluidPage(
DT::dataTableOutput(ns('rid_x'))
)
)
})
observeEvent(ignoreInit = T,input$show_obs_selection,{
shinyjs::toggle("rid_x")
})
output$rid_x = DT::renderDataTable(
{ data<-vals$saved_data[[input$data_descX]]
table=data.frame(Variables=colnames(data))
DT::datatable(table, options=list(
dom="t",
lengthMenu = list(c(-1), c("All")),
scrollX = TRUE,
scrollY = "200px",
autoWidth=F
), class ='compact cell-border',rownames=F, colnames="",
selection = list(mode = 'multiple', selected = c(1:3)))
})
plot_rid<-reactive({
req(input$rid_y)
req(input$data_descX)
data=vals$saved_data[[input$data_descX]]
factors<-attr(data,"factors")
req(length(input$rid_x_rows_selected)>0)
data<-data[,input$rid_x_rows_selected, drop=F]
fac<-factors[,input$rid_y]
#savereac()
data$class<-fac
args<-list(data=data,
fac=input$rid_y,
palette=input$rid_col,
newcolhabs=vals$newcolhabs,
ncol=input$rid_ncol,
title=input$rid_tittle,
base_size=input$rid_base_size)
# saveRDS(args,'argsp.rds')
# args<-readRDS('argsp.rds')
# attach(args)
p<- do.call(plot_ridges,args)
shinyjs::removeClass("runrid_btn","save_changes")
p
})
output$rid_out<-renderUI({
req(!is.null(vals$rid_plot))
width=paste0(input$rid_width,"px")
height=paste0(input$rid_heigth,"px")
renderPlot(vals$rid_plot, width=input$rid_width,height=input$rid_heigth)
})
observeEvent(input$rid_x_rows_selected,{
req(input$data_descX)
req(input$rid_x_rows_selected)
vals$rid_plot<- plot_rid()
shinyjs::removeClass("runrid_btn","save_changes")
}, once = T)
observeEvent(input$runrid,{
vals$rid_plot<- plot_rid()
shinyjs::removeClass("runrid_btn","save_changes")
})
observeEvent( vals$rid_plot,{
shinyjs::removeClass("runrid_btn","save_changes")
})
observeEvent(list(input$rid_x_rows_selected,
input$data_descX,
input$rid_col,
input$rid_y,
input$rid_col,
input$rid_ncol
) ,ignoreInit = T,{
req(length(input$rid_x_rows_selected)>0)
req(!is.null(vals$rid_plot))
shinyjs::addClass("runrid_btn","save_changes")
})
output$boxplot_out<-renderUI({
sidebarLayout(
sidebarPanel(
column(12,style="margin: 0px; margin-bottom: -20px",align="right",actionLink(ns("box_reset"),"+ reset")),
uiOutput(ns('side_boxplot')),
uiOutput(ns("editbox"))),
mainPanel(uiOutput(ns("stats_pbox")))
)
})
output$stats_cbox<-renderUI({
div(style="background: white",
p(strong("Box plot")),
inline(uiOutput(ns("box_y_input"))),
inline(uiOutput(ns("boxplot_X"))),
inline(uiOutput(ns("filter_box1"))),
inline(uiOutput(ns("filter_box2")))
)
})
output$box_y_input<-renderUI({
data<-getdata_descX()
div(
div(tipify(
strong("Y ~"),
" y is the data values to be split into groups according to the grouping variable", options = list(container="body")
)),
pickerInput( ns("box_y"),NULL,choices = colnames(data),selected= colnames(data)[1], multiple=T))
})
output$boxplot_X<-renderUI({
div(
div(strong("Factor:")),
pickerInput(ns("boxplot_X"),NULL,
choices =rev(colnames(attr(vals$saved_data[[input$data_descX]],
"factors"))),
selected=vals$boxplot_X, width="200px")
)
})
observeEvent(ignoreInit = T,input$boxplot_X,{vals$box_y<-input$box_y})
observeEvent(ignoreInit = T,input$boxplot_X,{vals$boxplot_X<-input$boxplot_X})
output$filter_box1<-renderUI({
div(
div(strong("Filter:")),
pickerInput(ns("filter_box1"),NULL,choices = c("none", colnames(attr(getdata_descX(),"factors"))),selected=filter_box1_cur$df, width="200px"))
})
output$filter_box2<-renderUI({
req(input$filter_box1)
if (input$filter_box1 != "none") {
data = getdata_descX()
labels<-attr(data,"factors")[rownames(data), input$filter_box1]
div(
div(strong("Class:")),
pickerInput(ns("filter_box2"),
NULL,
choices = c(levels(as.factor(labels))),
selected=filter_box2_cur$df, width="200px")
)
}
})
pca_symbol<-reactive({
if(isFALSE(input$pca_show_symbols)){NA}else{as.numeric(input$pca_symbol)}
})
getdata_descX<-reactive({
req(input$data_descX)
vals$saved_data[[input$data_descX]]})
getbox<-reactive({
req(input$boxplot_X)
req(input$box_y)
req(input$filter_box1)
data=getdata_descX()
labels<-attr(data,"factors")
pic<-1:nrow(data)
req(any(input$boxplot_X%in%colnames(labels)))
x<-labels[input$boxplot_X]
req(any(input$box_y%in%colnames(data)))
y<-data[input$box_y]
if (input$filter_box1 != "none") {
filtro<-as.character(input$filter_box1)
filtro2<-as.character(input$filter_box2)
pic<-which(as.character(labels[, filtro]) == filtro2)
}
res = data.frame(x,y)[pic,]
res[,1]<-res[,1]
res
# saveRDS(res,"res.rds")
#re<-readRDS('res.rds')
# levels(re$Consenso)
#vals<-readRDS("savepoint.rds")
# levels(attr(vals$saved_data[["SANTOS_C1"]],"factors")$Consenso)
})
mds_symbol_factor<-reactive({
req(input$mds_symbol_factor)
col<-getcolhabs(vals$newcolhabs,input$mds_colpalette,2)
if(col[1]!=col[2]){
data = getdata_descX()
attr(data,"factors")[rownames(data), input$mds_symbol_factor]
}else{NULL}
})
mds_text_factor<-reactive({
if(isFALSE(input$mds_show_labels)){NULL} else{
data = getdata_descX()
attr(data,"factors")[rownames(data), input$mds_labfactor]}
})
mds_symbol<-reactive({
if(isFALSE(input$mds_show_symbols)){NA}else{as.numeric(input$mds_symbol)}
})
pca_symbol_factor<-reactive({
req(input$pca_symbol_factor)
col<-getcolhabs(vals$newcolhabs,input$pca_colpalette,2)
if(col[1]!=col[2]){
data = getdata_descX()
attr(data,"factors")[rownames(data), input$pca_symbol_factor]
}else{NULL}
})
pca_text_factor<-reactive({
if(isFALSE(input$pca_show_labels)){NULL} else{
data = getdata_descX()
attr(data,"factors")[rownames(data), input$pca_labfactor]}
})
plot_mds<-reactive({
validate(need(input$distance!='', "Select a distance measure for the mds"))
mds_data = vals$mds
if (exists("mds_data")) {
pmds(
mds_data = mds_data,
key = mds_symbol_factor(),
points =input$mds_show_symbols,
text = input$mds_show_labels,
palette = input$mds_colpalette,
cex.points = input$mds_cexpoint,
cex.text = input$mds_cextext,
pch=mds_symbol(),
keytext=mds_text_factor(),
newcolhabs=vals$newcolhabs,
textcolor=input$mds_labcolor,
pos=input$mds_labadj,
offset=input$mds_offset
)
}
vals$pmds_plot<-recordPlot()
res
})
res_pfac<-reactive({
attr(getdata_descX(),"factors")[rownames(getdata_descX()),,drop=F]
})
observeEvent(ignoreInit = T,input$data_descX,{
req(length(vals$saved_data)>0)
vals$cur_data<-input$data_descX
})
observeEvent(ignoreInit = T,input$rid_downp,{
vals$hand_plot<-"Ridge plot"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs,"downfigs", vals=vals)})
observeEvent(ignoreInit = T,input$rid_y,
vals$rid_y<-input$rid_y)
observeEvent(ignoreInit = T,input$box_y,{
box_y_cur$df<-input$box_y
})
observeEvent(ignoreInit = T,input$filter_box2,{
filter_box2_cur$df<-input$filter_box2
})
observeEvent(ignoreInit = T,input$filter_box1,{
filter_box1_cur$df<-input$filter_box1
})
observeEvent(ignoreInit = T,input$boxplot_X,{
boxplot_X_cur$df<-input$boxplot_X
})
observeEvent(ignoreInit = T,input$downp_box,{
vals$hand_plot<-'boxplot'
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observeEvent(list(input$distance,getdata_descX()),{
req (input$distance %in%c("bray","euclidean","jaccard"))
vals$mds<-metaMDS1(getdata_descX(), distance = input$distance)
})
observeEvent(ignoreInit = T,input$downcenter_rda,{
vals$hand_down<-"rda"
module_ui_downcenter("downcenter")
mod_downcenter <- callModule(module_server_downcenter, "downcenter", vals=vals)
})
##################
### SEGRDA ###
### ###############
output$segrda_header<-renderUI({
div()
})
output$segrda_panels<-renderUI({
req(input$segrda_X)
req(input$segrda_Y)
column(12,
tabsetPanel(id=ns("segrda_panels"),
selected=vals$segrda_panels,
tabPanel("SMW",
uiOutput(ns("segrda_smw"))
),
tabPanel("DP",
uiOutput(ns("segrda_dp"))
),
tabPanel("pwRDA",
p(strong("Piecewise RDA")),
uiOutput(ns("pw_out")))))
})
observeEvent(ignoreInit = T,input$segrda_Y,{
vals$cur_segrda_Y<-input$segrda_Y})
observeEvent(ignoreInit = T,input$segrda_X,{
vals$cur_segrda_X<-input$segrda_X})
output$databank_storage<-renderUI({
div(
column(12,
div(strong("action:"),em("*",vals$hand_save,style="color: SeaGreen")),
div(vals$hand_save2,style="color: gray"),
div(vals$hand_save3))
)
})
output$save_confirm<-renderUI({
actionButton(ns("data_confirm"),strong("confirm"))
})
output$ord_side<-renderUI({
fluidRow(class="map_control_style",style="color: #05668D",
div(
span("+",
checkboxInput(ns("segrda_scale"),span("Scale variables",tiphelp("Scale variables to unit variance (like correlations)")), value=T, width = "100px")
)
),
div(
span("+",
inline(
checkboxInput(ns("segrda_ord"),strong("Axis ordination",pophelp(NULL,"Both the SMW and pwRDA analyses depend on ordered datasets. Defaults to ordering both response and explanatory matrices using one of the axes of the RDA model. If unchecked,please make sure all your inputs are already ordered.")), value=T)
),
inline(uiOutput(ns("ord_check")))
)
),
uiOutput(ns("ord_sure"))
)
})
output$ord_check<-renderUI({
req(isTRUE(input$segrda_ord))
inline(numericInput(ns("axis_ord_segrda"),NULL, value=1, step=1, width="75px"))
})
output$ord_sure<-renderUI({
req(isFALSE(input$segrda_ord))
div(style='white-space: normal;',
strong("Wargning:", style="color: SeaGreen"),"Make sure both X and Y are previously ordered")
})
output$ordplot_matrix<-renderUI({
req(isTRUE(input$segrda_ord))
#mybreaks<-vals$window_pool
fluidRow(
renderPlot({
sim1o<-getord()
#sim1o<-readRDS('sim1o.rds')
mybreaks<-vals$window_pool
#mybreaks<-c(2,50,141)
xo<-sim1o$xo ## ordered explanatory matrix.
yo<-sim1o$yo ## ordered community matrix (untransformed).
x<-sim1o$y
par(mfrow = c(1, 2), mgp = c(1, 1, 0), cex = 0.9)
image(x, main = "Original response data", col = topo.colors(100), axes = F,
xlab = "Observations", ylab = "Variable values")
# abline(v=scales::rescale(mybreaks,c(0,1)), col="red")
image(yo, main = "Ordered response data", col = topo.colors(100), axes = F,
xlab = "Observations", ylab = "Variable values")
#abline(v=scales::rescale(mybreaks,c(0,1)), col="red")
})
)
})
output$segrda_smw<-renderUI({
div(
sidebarLayout(
sidebarPanel(uiOutput(ns('side_smw')),
br(),
div(style="margin-left: 20px",
actionButton(ns("go_smw"),strong( img(src=smw_icon,height='20',width='20'),"run SMW"), style="button_active")
)
),
mainPanel(
tabsetPanel(id=ns("smw_panels"),
tabPanel("Data ordination",
value="swm_1",
uiOutput(ns("ordplot_matrix"))),
tabPanel("SMW results",
value="swm_2",
uiOutput(ns("go_smw")))
)
)
)
)
})
output$ord_windows<-renderUI({
div(
tipify(icon("fas fa-question-circle",style="color: gray"),"Enter a vector of breakpoints (comma delimited, within the data range)"),
"+ Windows",
textInput(ns('custom_windows'), NULL, paste0(get_windows(),collapse=", "), width="200px"),
)
})
get_windows<-reactive({
req(input$segrda_X)
data<-vals$saved_data[[input$segrda_X]]
req(nrow(data)>0)
w<-1:(nrow(data)/2)
w<- w[which(( w %% 2) == 0)]
w<-round(seq(10,w[length(w)], length.out=5))
w[which(( w %% 2) != 0)]<-w[which(( w %% 2) != 0)]+1
w
})
is.even<-function(x){ x %% 2 == 0}
getpool<-reactive({
mybreaks<-NULL
req(input$segrda_X)
req(length(input$custom_windows)>0)
req(!is.na(input$custom_windows))
mybreaks<-as.numeric(unlist(strsplit(input$custom_windows,",")))
data<-vals$saved_data[[input$segrda_X]]
cond0<-length(mybreaks)>0
validate(need(cond0,"The windows vector is empty"))
cond1<-sum(sapply(mybreaks,is.even))==length(mybreaks)
cond2<-min(mybreaks)>=2
cond2_res<-paste("The maximum window size cannot exceed the number of observations:", nrow(data))
validate(need(cond1,"Window sizes must be even"))
validate(need(cond2,"The minimum allowed size of the windows is 2"))
validate(need(max(mybreaks)<=nrow(data),cond2_res))
mybreaks
})
output$smw_tuning<-renderUI({
div(
div(
span(span(tipify(icon("fas fa-question-circle", style="color: gray"),"Dissimilarity index"),"+ Distance:"),
inline(
pickerInput(ns("smw_dist"),NULL, choices=c("bray","euclidean","manhattan","jaccard"), width="100px")
)
)
),
div(
span(span(tipify(actionLink(ns("smw_rand_help"),icon("fas fa-question-circle")),"The type of randomization for significance computation. Click for details"),"+ Randomization:"),
inline(
pickerInput(ns("smw_rand"),NULL, choices=c("shift","plot"), width="70px")
)
)
),
div(
span(span(tipify(icon("fas fa-question-circle", style="color: gray"),"The number of randomizations"),"+ n.rand:"),
inline(
numericInput(ns("smw_nrand"),NULL, value=10, width="100px")
)
)
),
uiOutput(ns("down_we_out"))
)
})
output$down_we_out<-renderUI({
req(length(vals$smw_dp)>0)
tipify(
actionLink(
ns('downp_we'),span("+ Download",icon("fas fa-download")), style="button_active"
),
"Download plot"
)
})
output$side_smw<-renderUI({
fluidRow( class="map_control_style",style="color: #05668D",
uiOutput(ns("ord_side")),
uiOutput(ns("ord_windows")),
uiOutput(ns("smw_tuning"))
)
})
output$go_smw<-renderUI({
getpool()
validate(need(!is.null(vals$window_pool),"The window pool is empty. Use the arrow button to include the window sizes"))
validate(need(length(vals$smw_dp)>0,"Please click 'run SMW' button"))
fluidRow(
column(12,
renderPlot({
if(length(vals$window_pool)>1){w.effect=TRUE
main="Window size effect"} else{w.effect=F
main="Dissimilary Profile"}
suppressWarnings(plot( vals$smw_dp, w.effect =w.effect , main=main))
vals$plot_we<-recordPlot()
})
)
)
})
observeEvent(vals$max_seq,{
req(!is.null(vals$max_seq))
vals$dp_seq.sig<-attr(max_seq(),"min")
})
output$plot_dp<-renderPlot({
req(input$dp_view=='Plot')
req(input$dp_seq.sig)
req(input$dp_cex)
req(input$dp_BPs)
max_seq<-max_seq()
validate(need(input$dp_seq.sig<=max_seq,paste(
"DP shows '", sum(DP_smw()[,5]!="ns"),"' significant dissimilarity values but no breakpoint could be determined for seq.sig='",input$dp_seq.sig,"The maximum value for this input must be","max_seq"
)))
smw<- vals$smw_dp
getDP()
dp_BPs<-if(input$dp_BPs==""){NULL} else{input$dp_BPs}
dp_w<-if(is.na(input$dp_w)){NULL} else{input$dp_w}
par(cex=input$dp_cex)
suppressWarnings(
suppressMessages(
plot(smw,w=dp_w, sig=input$dp_sig, z=input$dp_z, BPs=dp_BPs,
seq.sig=input$dp_seq.sig, bg= getcolhabs(vals$newcolhabs,input$dp_palette,nlevels(as.factor(vals$splitBP[,1]))),bg_alpha=input$dp_bg,cols=c(getcolhabs(vals$newcolhabs,input$dp_dcol,1),getcolhabs(vals$newcolhabs,input$dp_scol,1),getcolhabs(vals$newcolhabs,input$dp_bcol,1)))
)
)
if(isTRUE(updp$df)){
updateTabsetPanel(session,'segrda_panels','pwRDA')
updp$df<-F
}
vals$plot_dp<-recordPlot()
})
output$dp_extract<-renderUI({
req(input$dp_view=='DP results')
dp<-getDP()
fluidRow(
column(12,renderPrint({dp}))
#renderPrint({vals$splitBP})
)
})
output$dp_view_dp<-renderUI({
req(input$dp_view=='DP results')
tipify(
actionLink(
ns('downcenter_dp_smw'),span("+ Download",icon("fas fa-table")), style="button_active"
),
"Download DP results"
)
})
observeEvent(ignoreInit = T,input$dp_seq.sig,{
vals$dp_seq.sig<-input$dp_seq.sig
})
output$side_dp<-renderUI({
if(is.null(vals$dp_seq.sig)){
vals$dp_seq.sig<-3
}
validate(need(length(vals$smw_dp)>0,"You need to run SMW analysis first"))
fluidRow(
class="map_control_style",style="color: #05668D",
div(
span(span(tipify(icon("fas fa-question-circle", style="color: gray"),"A target window size from which results will be extracted. If empty return z-scores averaged over the set of window sizes"),'+ w:'),
inline(
numericInput(ns("dp_w"),NULL, value=NULL, width="75px")
)
)
),
div(
span(span(actionLink(ns("dp_index_help"),tipify(icon("fas fa-question-circle"),"The result to be extracted. Click for details")),'+ index:'),
inline(
pickerInput(ns("dp_index"),NULL,choices=c("dp","rdp","md","sd","oem","osd","params"), width="75px")
)
)
),
div(
span(span(actionLink(ns("dp_sig_help"),tipify(icon("fas fa-question-circle"),"Significance test for detecting dissimilarity values that differs significantly from those appearing in a random pattern. Click for details")),'+ sig'),
inline(
pickerInput(ns("dp_sig"),NULL,choices=c("z","sd","sd2","tail1"), width="75px")
)
)
),
div(
span(span(tipify(icon("fas fa-question-circle", style="color: gray"),"The critical value for the significance of z-values"),"+ z:"),
inline(
numericInput(ns("dp_z"),NULL, value=1.85,step=0.01, width="75px")
)
)
),
div(
span(span(tipify(icon("fas fa-question-circle", style="color: gray"),"Defines if the breakpoints should be chosen as those sample positions corresponding to the maximum dissimilarity in a sequence of significant values (max) or as those sample positions corresponding to the median position of the sequence (median). Defaults to BPs=max. If empty the breakpoints are not computed"),"+ BPs:"),
inline(
pickerInput(ns("dp_BPs"),NULL,choices=c("","max","median"), selected = "max", width="75px")
)
)
),
div(
span(span(tipify(icon("fas fa-question-circle", style="color: gray"),"The maximum length of consecutive, significant values of dissimilarity that will be considered in defining the community breakpoints"),"+ seq.sig:"),
inline(
numericInput(ns("dp_seq.sig"),NULL, value=vals$dp_seq.sig,step=1, width="75px", min=1)
)
)
),
div(uiOutput(ns("dp_view_dp"))),
div(uiOutput(ns("dp_view_plot")))
)
})
output$dp_view_plot<-renderUI({
req(input$dp_view=='Plot')
div(
div(class="palette",
span("+ Palette",
inline(
pickerInput(inputId=ns("dp_palette"),
label = NULL,
choices = vals$colors_img$val[getgrad_col()],
choicesOpt = list(content = vals$colors_img$img[getgrad_col()]), width="75px")
)
)
),
div(
span("+ size",
inline(
numericInput(ns("dp_cex"), NULL,value=1,min=0.1,step=0.1, width="75px")
)
)
),
div(
span("+ diss col",
inline(
pickerInput(inputId=ns("dp_dcol"),
label = NULL,
choices = vals$colors_img$val[getsolid_col()],
choicesOpt = list(content = vals$colors_img$img[getsolid_col()]), width="75px")
)
)
),
div(
span("+ sig col",
inline(
pickerInput(inputId=ns("dp_scol"),
label = NULL,
choices = vals$colors_img$val[getsolid_col()],
choicesOpt = list(content = vals$colors_img$img[getsolid_col()]), selected= vals$colors_img$val[getsolid_col()][4], width="75px")
)
)
),
div(
span("+ bp col",
inline(
pickerInput(inputId=ns("dp_bcol"),
label = NULL,
choices = vals$colors_img$val[getsolid_col()],
choicesOpt = list(content = vals$colors_img$img[getsolid_col()]), selected= vals$colors_img$val[getsolid_col()][3], width="75px")
)
)
),
div(
span("+ bg",
inline(numericInput(ns("dp_bg"), NULL,value=0.5,min=0,max=1,step=0.05, width="75px"))
)
),
tipify(
actionLink(
ns('downp_dp'),span("+ Download",icon("fas fa-download"))
),
"Download plot"
)
)
})
output$segrda_dp<-renderUI({
validate(need(length(vals$smw_dp)>0,"You need to run SMW analysis first"))
fluidRow(
column(12,
div(strong("Dissimilary Profile"),
inline(uiOutput(ns("save_breakpoints")))),),
sidebarLayout(
sidebarPanel(uiOutput(
ns("side_dp")
)),
mainPanel(
tabsetPanel(id=ns("dp_view"),selected=vals$dp_view,
tabPanel("Plot", value="Plot",
plotOutput(ns("plot_dp"))),
tabPanel("DP results", value="DP results",
uiOutput(ns("dp_extract"))))
)
)
)
})
observeEvent(ignoreInit = T,input$dp_view,
vals$dp_view<-input$dp_view)
get_breaks_from_factor<-reactive({
x<-vals$saved_data[[input$segrda_X]]
y<-vals$saved_data[[input$segrda_Y]]
bp<-attr(x,"factors")[,input$bp_column]
xord<-x[order(as.numeric(bp)),]
yord<-y[order(as.numeric(bp)),]
breaks<-bp[order(as.numeric(bp))]
breaks<-which(diff(as.numeric(breaks))==1)
pw_in<-list(
breaks=breaks,
xord=xord,
yord=yord
)
})
observeEvent(ignoreInit = T,input$segrda_X,{
x<-attr(vals$saved_data[[input$segrda_X]],"factors")
})
choices_bp_column<-reactive({
req(input$segrda_X)
x<-attr(vals$saved_data[[input$segrda_X]],"factors")
colnames(x)
})
observeEvent(ignoreInit = T,input$bp_column,
vals$bp_column<-input$bp_column)
output$user_bp<-renderPrint(vals$bag_user_bp)
output$getDP<-renderPrint({
req(!isFALSE(vals$splitBP))
suppressWarnings(bp(getDP()))})
output$pw_out<-renderUI({
div(
span(
inline(
div(
tipify(icon("fas fa-question-circle",style="color: gray"),"Enter a vector of breakpoints (comma delimited, within the data range)"),
"+ Split reference [Y Datalist]",
pickerInput(ns('bp_column'), NULL,choices_bp_column() , width="200px", selected=vals$bp_column)
)
),
inline(numericInput(ns("pw_nrand"), 'n.rand', value=99, width="75px")),
inline(
div(id=ns('run_pwrda_button'),
actionButton(ns("run_pwrda"),strong(img(src=pw_icon,height='20',width='20'),"run pwRDA"), style="button_active")
)
),
inline(uiOutput(ns('pwrda_models_out'))),
),
uiOutput(ns("pwRDA_out"))
)
})
observe({
req(input$segrda_X)
req(input$pwrda_models)
req(input$run_pwrda)
if(!length(attr(vals$saved_data[[input$segrda_X]],"pwrda")[[input$pwrda_models]])>0){
addClass('run_pwrda_button',"save_changes")
} else{
removeClass('run_pwrda_button',"save_changes")
}
})
output$pwrda_models_out<-renderUI({
choices<-names(attr(vals$saved_data[[input$segrda_X]],"pwrda"))
req(length(choices)>0)
div(
inline(pickerInput(ns('pwrda_models'),"Results",choices, width="200px", selected=vals$pwrda_models)),
inline(uiOutput(ns('save_pw'))),
inline(actionButton(ns("delete_pwrda_models"),icon("fas fa-trash-alt")))
)
})
observeEvent(ignoreInit = T,input$delete_pwrda_models,{
attr(vals$saved_data[[input$segrda_X]],"pwrda")[[input$pwrda_models]]<-NULL
})
output$save_pw<-renderUI({
if(is.null(vals$bag_pw)){
class="novo"
} else{ class="save_changes"}
div(class=class,
actionButton(ns("save_pwrda"),icon("fas fa-save"), style="button_active")
)
})
observeEvent(ignoreInit = T,input$pwrda_models,
vals$pwrda_models<-input$pwrda_models)
observeEvent(ignoreInit = T,input$save_pwrda,{
vals$hand_save<-"Save pwRDA model in"
vals$hand_save2<-div(span("Target:",em(input$segrda_X,style="color: SeaGreen")))
vals$hand_save3<-NULL
showModal(module_desctools())
})
observeEvent(ignoreInit = T,input$disegrda_sp_summ,
vals$disegrda_sp_summ<-input$disegrda_sp_summ)
output$segrda_view_out<-renderUI({
req(input$segrda_view=='Summary')
div(
div(
span(
"+ Results:",
inline(
pickerInput(ns("disegrda_sp_summ"),NULL,choices=c('Summary stats','Importance (unconstrained)',"Importance (constrained)",'Variable scores','Observation scores','Linear constraints','Biplot'), selected=vals$disegrda_sp_summ, options=list(container="body"), width="200px")
)
)
),
div(
span("+ Axes",
inline(
numericInput(ns("segrda_axes"),NULL, value=2)
)
)
),
div(
tipify(
actionLink(
ns('downcenter_segrda'),span("+ Download",icon("fas fa-download")), style="button_active"
),
"Download selected results"
)
)
)
})
output$side_pw<-renderUI({
#req(length(vals$segrda_model)>0)
#req(length(vals$segrda_model$rda.pw)>0)
sidebarPanel(
fluidRow(class="map_control_style",style="color: #05668D",
uiOutput(ns("segrda_view_out")),
uiOutput(ns("segrda_view_plot"))
)
)
})
observeEvent(ignoreInit = T,input$segrda_view,
vals$segrda_view<-input$segrda_view)
output$segrda_sp_shape<-renderUI({
req(input$segrda_sp_display=='Shape')
inline(pickerInput(
inputId=ns("segrda_spshape"),
label = NULL,
choices = df_symbol$val,
options=list(container="body"),
selected=df_symbol$val[8],
choicesOpt = list(content = df_symbol$img),width='50px'
))
})
output$segrda_sp_on<-renderUI({
req(isTRUE(input$segrda_sp))
div(
div(
'+ Number:',
inline(numericInput(ns("segrda_spnum"),NULL, 10, step=1)),tipify(icon("fas fa-question-circle",style="color: gray"),"Show N variables with the highest scores", placement = "bottom")
),
div(
"+ Display:",
span(
inline(
pickerInput(ns("segrda_sp_display"),NULL,choices=c("Shape","Label"), width = "75px")
),
inline(uiOutput(ns("segrda_sp_shape")))
)
),
div(
span("+ Variable Color:",
inline(
tipify(
pickerInput(
inputId=ns("segrda_spcolor"),label = NULL,selected= 'firebrick',choices = vals$colors_img$val[getsolid_col()],choicesOpt = list(content = vals$colors_img$img[getsolid_col()]), options=list(container="body")), "Variable Color.")))
)
)
})
output$segrda_show_symbol_on<-renderUI({
req(isTRUE(input$segrda_show_symbols))
div(style="margin-left: 5px",
div(
span("+ Shape:",
inline(pickerInput(inputId=ns("segrda_symbol"),
label = NULL,
choices = df_symbol$val,
options=list(container="body"),
choicesOpt = list(content = df_symbol$img), width='75px')))),
div(
span("+ Size:",
inline(numericInput(ns("segrda_cexpoint"),NULL,value = 1,min = 0.1,max = 3,step = .1)
))
),
div(class="palette",
span("+ Color:",
inline(
tipify(
pickerInput(inputId=ns("segrda_colpalette"),label = NULL,choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),options=list(container="body"),selected=vals$colors_img$val[1]), "Symbol palette. Choose a gradient to color observations by a factor")))),
uiOutput(ns("segrda_fac_palette"))
)
})
output$segrda_show_labels_on<-renderUI({
req(isTRUE(input$segrda_show_labels))
div(style="margin-left: 5px",
div(span("+ Factor:",
inline(tipify(pickerInput(ns("segrda_labfactor"),NULL,choices = colnames(attr(vals$saved_data[[input$segrda_X]],"factors")), width="125px"), "label classification factor")
))),
div(span("+ Lab Color:",
inline(tipify(
pickerInput(
inputId=ns("segrda_labcolor"),
label = NULL,
selected= vals$colors_img$val[12],choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),width="75px",
options=list(container="body")
), "label classification factor"
)
))),
div(span("+ Lab adj:",
inline(
tipify(pickerInput(ns("segrda_labadj"),NULL,choices=c(1:4), options=list(containder="body")), "a position specifier for the text. If specified this overrides any adj value given. Values of 1, 2, 3 and 4, respectively indicate positions below, to the left of, above and to the right of the specified (x,y) coordinates.", placement = "right")
))),
div(span("+ Lab offset:",
inline(
tipify(numericInput(ns("segrda_offset"),NULL,value = 0,step = .1), "this value controls the distance ('offset') of the text label from the specified coordinate in fractions of a character width.")
))),
div(span("+ Size:",
inline(
tipify(numericInput(ns("segrda_cextext"),NULL,value = 1,min = 0.1,max = 3,step = .1), "label text size")
)))
)
})
output$segrda_view_plot<-renderUI({
req(input$segrda_view=='Plot')
div(
div(
'+ Scaling:',
inline(numericInput(ns("segrda_scaling"),NULL, 2, step=1, min=0, max=3)),tipify(icon("fas fa-question-circle",style="color: gray"),"Scaling for species and site scores. Either species (2) or site (1) scores are scaled by eigenvalues, and the other set of scores is left unscaled, or with 3 both are scaled symmetrically by square root of eigenvalues.", placement = "bottom")
),
div(span("+",checkboxInput(ns("segrda_biplot"), span("Biplot", pophelp(NULL,"show biplot arrows")), T))),
uiOutput(ns("segrda_biplot_options")),
div(span("+",checkboxInput(ns("segrda_sp"), span("Variables", pophelp(NULL,"Variables")), T))),
uiOutput(ns("segrda_sp_on")),
div(
span("+",
inline(checkboxInput(ns("segrda_show_symbols"),"Symbol" ,T)))),
uiOutput(ns("segrda_show_symbol_on")),
div(span("+",
inline(checkboxInput(ns("segrda_show_labels"),"Labels",F)
))),
uiOutput(ns("segrda_show_labels_on")),
div(
tipify(
actionLink(
ns('downp_pw'),span("+ Download",icon("fas fa-download")), style="button_active"
), "Download plot"
))
)
})
output$segrda_biplot_options<-renderUI({
req(isTRUE(input$segrda_biplot))
div(
span("+ Biplot Color:",
inline(
tipify(
pickerInput(
inputId=ns("segrda_biplotcolor"),label = NULL,selected="royalblue",choices = vals$colors_img$val[getsolid_col()],choicesOpt = list(content = vals$colors_img$img[getsolid_col()]), options=list(container="body")), "Variable Color.")))
)
})
output$segrda_fac_palette<-renderUI({
col<-getcolhabs(vals$newcolhabs,input$segrda_colpalette,2)
req(col[1]!=col[2])
div(
span("+ Factor:",
inline(tipify(pickerInput(ns("segrda_symbol_factor"),NULL,choices = rev(colnames(attr(vals$saved_data[[input$segrda_X]],"factors"))), width='125px'), "symbol classification factor"))))
})
output$segrda_print<-renderPrint({
req(input$segrda_view=='Summary')
segrda_summary()})
output$pwRDA_out<-renderUI({
# validate(need(length(getBP())>0, "No breakpoints found"))
fluidRow(
sidebarLayout(
uiOutput(ns("side_pw")),
mainPanel(
tabsetPanel(id=ns("segrda_view"),selected=vals$segrda_view,
tabPanel("Plot", value="Plot",
uiOutput(ns("ppwRDA"))),
tabPanel("Summary", value="Summary",
verbatimTextOutput(ns("segrda_print"))))
)
)
)
})
max_seq<-reactive({
validate(need(any(DP_smw()[,5]!='ns'),"DP without any significant dissimilarity value: no breakpoint could be determined."))
df<-DP_smw()[,c(1,5)]
colnames(df)<-c("a","b")
new=transform(df, Counter = ave(a, rleid(b), FUN = seq_along))
max_seq<-max(new[new[,2]=="*",3])
attr(max_seq,"min")<-if(max_seq!=1){
min( new[new[,2]=="*",3][new[new[,2]=="*",3]!=1])} else{
min(new[new[,2]=="*",3])
}
vals$max_seq<-max_seq
})
DP_smw<-reactive({
req(input$dp_BPs)
req(input$dp_w)
# savereac()
smw<- vals$smw_dp
dp_BPs<-if(input$dp_BPs==""){NULL} else{input$dp_BPs}
dp_w<-if(is.na(input$dp_w)){NULL} else{input$dp_w}
res<-suppressWarnings(
suppressMessages(
extract(smw,w=dp_w,
index=input$dp_index,
sig=input$dp_sig,
z=input$dp_z,
BPs=dp_BPs,
seq.sig=input$dp_seq.sig)
)
)
vals$dp_smw<-res
vals$dp_smw
})
getDP<-reactive({
smw<- vals$smw_dp
dp_BPs<-if(input$dp_BPs==""){NULL} else{input$dp_BPs}
dp_w<-if(is.na(input$dp_w)){NULL} else{input$dp_w}
dp<-DP_smw()
colnames(dp)[2]<-"SampleID"
sim1o<-getord()
yo<-data.frame(sim1o$yo)
bps<-suppressWarnings(bp(dp))
to_split<-c(1,rep(1:(length(bps)+1),
diff(c(1,bps, nrow(yo)))))
splits<-lapply(split(yo,to_split),function(x) rownames(x))
data_empty<-data.frame(attr(vals$saved_data[[input$segrda_X]],"factors")[,1,drop=F])
data_empty[,1]<-as.numeric(data_empty[,1])
for(i in 1:length(splits)){
data_empty[splits[[i]],1]<-i
}
vals$splitBP<- data_empty
dp
})
getord<-reactive({
req(input$segrda_Y)
req(input$segrda_X)
req(input$axis_ord_segrda)
x<-as.matrix(vals$saved_data[[input$segrda_X]])
colnames(x)<-colnames(vals$saved_data[[input$segrda_X]])
y<-na.omit(as.matrix(vals$saved_data[[input$segrda_Y]][rownames(x),,drop=F]))
colnames(y)<-colnames(vals$saved_data[[input$segrda_Y]])
x<-na.omit(x[rownames(y),])
if(isTRUE(input$segrda_ord)){
sim1o<-OrdData(x=y,y=x, axis=input$axis_ord_segrda,scale=input$segrda_scale)} else{
sim1o<-list()
sim1o$xo<-y
sim1o$yo<-x
}
sim1o
})
save_bpfac<-reactive({
vals$bagbp0<-vals$bagbp0+1
dp<-getDP()
newfac<-vals$splitBP
factors<-attr(vals$saved_data[[input$segrda_X]],"factors")
if(input$hand_save=="create") {
attr(vals$saved_data[[input$segrda_X]],"factors")[rownames(vals$splitBP),input$newdatalist]<-as.factor(vals$splitBP[,1])
} else{
attr(vals$saved_data[[input$segrda_X]],"factors")[rownames(vals$splitBP),input$over_datalist]<-as.factor(vals$splitBP[,1])
}
vals$bag_smw<-NULL
})
getBP<-reactive({
pw_in<-get_breaks_from_factor()
breaks=pw_in$breaks
breaks
})
segrda_summary<-reactive({
res<-summary(vals$segrda_model$rda.pw)
res<-switch(input$disegrda_sp_summ,
"Summary stats"= vals$segrda_model$summ,
"Variable scores"=res$species,
"Observation scores"=res$sites,
"Linear constraints"=res$constraints,
"Biplot"=res$biplot,
"Importance (unconstrained)"=res$cont$importance,
"Importance (constrained)"=res$concont$importance
)
vals$segrda_summary<-res[,1:input$segrda_axes]
vals$segrda_summary
})
observeEvent(ignoreInit = T,input$segrda_panels,{
vals$segrda_panels<-input$segrda_panels
})
observeEvent(ignoreInit = T,input$dp_index_help,{
showModal(
modalDialog(
column(12,
p(strong("dp:"),span("The dissimilarity profile (DP) table containing significant discontinuities and suggested breakpoints")),
p(strong("rdp:"),span("data frame containing the randomized DP;")),
p(strong("md:"),span("mean dissimilarity of the randomized DP")),
p(strong("sd:"),span("standard deviation for each sample position")),
p(strong("ospan:"),span("overall expected mean dissimilarity;")),
p(strong("osd:"),span("average standard deviation for the dissimilarities;")),
p(strong("params:"),span("list with input arguments"))
),
easyClose = T,
size="m",
title="index for extracting SMW results"
)
)
})
observeEvent(ignoreInit = T,input$dp_sig_help,{
showModal(
modalDialog(
column(12,
p(strong("z:"),span("consider normalized dissimilarity (z-scores) discontinuities that exceed a z critical value")),
p(strong("sd:"),span("consider dissimilarity discontinuities that exceed mean plus one standard deviation")),
p(strong("sd2:"),span("consider dissimilarity discontinuities that exceed mean plus two standard deviation")),
p(strong("tail1:"),span("Consider dissimilarity discontinuities that exceed 95 percent confidence limits"))
),
easyClose = T,
size="m",
title="Significance test fort the SMW results"
)
)
})
observeEvent(ignoreInit = T,input$inc_bp,{ vals$bag_user_bp<-c(vals$bag_user_bp,input$pw_user)
})
observeEvent(ignoreInit = T,input$remove_breaks,{ vals$bag_user_bp<-NULL
})
observeEvent(ignoreInit = T,input$downcenter_segrda,{
vals$hand_down<-"segRDA"
module_ui_downcenter("downcenter")
mod_downcenter <- callModule(module_server_downcenter, "downcenter", vals=vals)
})
observeEvent(ignoreInit = T,input$go_smw,{
vals$window_pool<-getpool()
updateTabsetPanel(session,"smw_panels",selected='swm_2')
vals$bag_smw<-T
bag_smw$df<-T
req(length(vals$window_pool)>0)
sim1o<-getord()
xo<-sim1o$xo ## ordered explanatory matrix.
yo<-sim1o$yo ## ordered community matrix (untransformed)
y=yo;ws=vals$window_pool; dist=input$smw_dist;rand=input$smw_rand;n.rand=input$smw_nrand
if (n.rand < 2) {
stop("number of randomizations not alowed")
}
if (any(ws%%2 == 1)) {
stop("all Window sizes must be enven")
}
rand<-match.arg(rand, c("shift", "plot"))
argg<-c(as.list(environment()), list())
smw<-list()
withProgress(message = paste0("SMW analysis (", 1, "/", length(ws), "); w =",
ws[1]),
min = 1,
max = length(ws),
{
for (j in 1:length(ws)) {
w1<-ws[j]
DPtable<-smw.root2(yo, w1, dist)
OB<-DPtable[, 3]
rdp<-data.frame(rep(NA, length(OB)))
seq_yo<-1:nrow(yo)
withProgress(message="randomizing",min = 1,
max = n.rand,{
for (b in 1:n.rand) {
if (rand == "shift") {
comm.rand<-apply(yo, 2, function(sp) sp[sample(seq_yo)])
rdp[b]<-smw.root2(data.frame(comm.rand),
w1, dist)[3]
} else if (rand == "plot") {
comm.rand<-t(apply(yo, 1, function(sp) sp[sample(seq_yo)]))
rdp[b]<-smw.root2(data.frame(comm.rand),
w1, dist)[3]
}
incProgress(1)
}
})
rownames(rdp)<-DPtable[,1]
Dmean<-apply(rdp, 1, mean)
SD<-apply(rdp, 1, sd)
oem<-sum(Dmean)/(nrow(yo) - w1)
osd<-sum(SD)/(nrow(yo) - w1)
Dz<-(OB - oem)/osd
DPtable$zscore<-Dz
smw[[j]]<-list(dp = data.frame(DPtable), rdp = matrix(rdp),
md = Dmean, sd = SD, oem = oem, osd = osd, params = argg)
class(smw[[j]])<-c("smw")
incProgress(1, message=paste0("SMW analysis (", j+1, "/", length(ws), "); w =",
ws[j+1]))
}
})
names(smw)<-paste("w", ws, sep = "")
class(smw)<-c("smw")
vals$smw_dp<-isolate(smw)
})
observeEvent(ignoreInit = T,input$downcenter_dp_smw,{
vals$hand_down<-"DP smw"
module_ui_downcenter("downcenter")
mod_downcenter <- callModule(module_server_downcenter, "downcenter", vals=vals)
})
observeEvent(ignoreInit = T,input$tools_saveBP,{
vals$hand_save<-"Create factor using breakpoints from the dissimilarity profile"
vals$hand_save2<-div(span("Target:",em(input$segrda_X,style="color: SeaGreen")))
vals$hand_save3<-NULL
showModal(module_desctools())
})
savenames<-reactive({
switch(
vals$hand_save,
"Create factor using breakpoints from the dissimilarity profile"= {c(paste0("BP_"),nlevels(as.factor(vals$splitBP[,1])))},
"Save pwRDA model in"={
paste0(input$segrda_X,"~",input$segrda_Y,"[factor:",input$bp_column,"]")
})
})
observeEvent( input$data_confirm,{
req(!is.null(vals$hand_save))
switch(
vals$hand_save,
"Create factor using breakpoints from the dissimilarity profile"= {save_bpfac()},
"Save pwRDA model in"={
save_pwrda()
})
removeModal()
})
save_pwrda<-reactive({
attr(vals$saved_data[[input$segrda_X]],"pwrda")[[input$newdatalist]]<-vals$segrda_model
attr(vals$saved_data[[input$segrda_X]],"pwrda")[["pwRda (unsaved)"]]<-NULL
updatePickerInput(session,'pwrda_models', selected=input$newdatalist)
vals$bag_pw<-NULL
})
observeEvent(ignoreInit = T,input$downp_we,{
vals$hand_plot<-"we"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observeEvent(ignoreInit = T,input$downp_dp,{
vals$hand_plot<-"dp"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observeEvent(ignoreInit = T,input$downp_pw,{
vals$hand_plot<-"segrda"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observe({
req(input$pwrda_models)
names(attr(vals$saved_data[[input$segrda_X]],"pwrda"))
vals$segrda_model<-attr(vals$saved_data[[input$segrda_X]],"pwrda")[[input$pwrda_models]]
})
observeEvent(ignoreInit = T,input$run_pwrda,{
vals$bag_pw<-T
pw_in<-get_breaks_from_factor()
breaks=pw_in$breaks
sim1o<-list()
sim1o$xo<-pw_in$yord
sim1o$yo<-pw_in$xord
model=suppressMessages(
try(
pwRDA2(sim1o$xo,sim1o$yo , BPs=breaks,n.rand = input$pw_nrand)
)
)
if(is.null(attr(vals$saved_data[[input$segrda_X]],"pwrda"))){
attr(vals$saved_data[[input$segrda_X]],"pwrda")<-list()
}
attr(vals$saved_data[[input$segrda_X]],"pwrda")[["pwRda (unsaved)"]]<-model
})
savereac<-reactive({
tosave<-isolate(reactiveValuesToList(vals))
tosave<-tosave[-which(names(vals)%in%c("saved_data","newcolhabs",'colors_img'))]
tosave<-tosave[-which(unlist(lapply(tosave,function(x) object.size(x)))>1000)]
tosave$saved_data<-vals$saved_data
tosave$newcolhabs<-vals$newcolhabs
tosave$colors_img<-vals$colors_img
saveRDS(tosave,"savepoint.rds")
saveRDS(reactiveValuesToList(input),"input.rds")
beep()
})
segrda_symbol_factor<-reactive({
req(input$segrda_symbol_factor)
col<-getcolhabs(vals$newcolhabs,input$segrda_colpalette,2)
symbol_factor<-if(col[1]!=col[2]){
data = vals$saved_data[[input$segrda_X]]
res<-attr(data,"factors")[rownames(data), input$segrda_symbol_factor]
names(res)<-rownames(data)
res
}else{NULL}
symbol_factor
})
segrda_text_factor<-reactive({
text_factor<-if(isFALSE(input$segrda_show_labels)){NULL} else{
data = vals$saved_data[[input$segrda_X]]
res<-attr(data,"factors")[, input$segrda_labfactor]
names(res)<-rownames(data)
res
}
text_factor
})
segrda_symbol<-reactive({
rda_symbol<-if(isFALSE(input$segrda_show_symbols)){NA}else{as.numeric(input$segrda_symbol)}
})
output$ppwRDA<-renderUI({
req(input$segrda_X)
req(input$pwrda_models)
all_models<-attr(vals$saved_data[[input$segrda_X]],"pwrda")
req(length(all_models)>0)
pwrda_model<-all_models[[input$pwrda_models]]
symbol_factor<-segrda_symbol_factor()
rda_symbol<-segrda_symbol()
text_factor<-segrda_text_factor()
text_factor<-text_factor[rownames(scores(vals$segrda_model$rda.pw)$sites)]
symbol_factor<-symbol_factor[rownames(scores(vals$segrda_model$rda.pw)$sites)]
args<-list(
model=pwrda_model$rda.pw,
key = symbol_factor,
points =input$segrda_show_symbols,
text = input$segrda_show_labels,
biplot=input$segrda_biplot,
keytext=text_factor,
col.arrow= input$segrda_biplotcolor,
show.sp=input$segrda_sp,
sp.display=input$segrda_sp_display,
n.sp=input$segrda_spnum,
palette = input$segrda_colpalette,
cex.points = input$segrda_cexpoint,
cex.text = input$segrda_cextext,
pch=rda_symbol,
col.sp=getcolhabs(vals$newcolhabs,input$segrda_spcolor,1),
pch.sp=as.numeric(input$segrda_spshape),
lwd_arrow=1,
textcolor=input$segrda_labcolor,
scaling=input$segrda_scaling,
newcolhabs=vals$newcolhabs,
pos=input$segrda_labadj,
offset=input$segrda_offset
)
#args<-readRDS("args.rds")
vals$seg_rda_plot<-do.call(plot_segrda,args)
renderPlot(replayPlot( vals$seg_rda_plot))
})
#####
## RDA
##
##
output$rda_fac_palette<-renderUI({
col<-getcolhabs(vals$newcolhabs,input$rda_colpalette,2)
req(col[1]!=col[2])
div(
span("+ Factor:",
inline(tipify(pickerInput(ns("rda_symbol_factor"),NULL,choices = rev(colnames(attr(vals$saved_data[[input$rda_X]],"factors"))), width='125px'), "symbol classification factor"))))
})
output$stats_crda<-renderUI({
validate(need(length(vals$saved_data)>1, "This functionality requires at least two datalist as explanatory and response data."))
column(12,style="background: white",
p(strong("Redundancy Analysis")),
span(
inline(
span(style="width: 150px",
inline(uiOutput(ns("rda_Y")))
)
),
inline(uiOutput(ns("rda_X")))
)
)
})
output$rda_X<-renderUI({
pickerInput(ns("rda_Y"),span("~ X Data", tiphelp("Predictors")), choices=names(vals$saved_data), selected=vals$cur_rda_Y)
})
output$rda_Y<-renderUI({
req(input$rda_Y)
pickerInput(ns("rda_X"),span("Y Data", tiphelp("Response data")), choices=names(vals$saved_data), selected=vals$cur_rda_X)
})
output$rda_options<-renderUI({
req(input$rda_view=='Plot')
div(
div(
span("+",
inline(checkboxInput(ns("rda_show_symbols"),"Symbol" ,T, width='75px')))),
uiOutput(ns("rda_show_symbols_out")),
div(span("+",
inline(checkboxInput(ns("rda_show_labels"),"Labels",F)
))),
uiOutput(ns("rda_show_labels_out")),
div(
actionLink(
ns('rda_downp'),span("+ Download",icon("fas fa-download")), style="button_active"
)
)
)
})
output$rda_show_symbols_out<-renderUI({
req(isTRUE(input$rda_show_symbols))
div(style="margin-left: 5px",
div(
span("+ Shape:",
inline(pickerInput(inputId=ns("rda_symbol"),
label = NULL,
choices = df_symbol$val,
options=list(container="body"),
choicesOpt = list(content = df_symbol$img), width='75px')))),
div(
span("+ Size:",
inline(numericInput(ns("rda_cexpoint"),NULL,value = 1,min = 0.1,max = 3,step = .1, width='75px')
))
),
div(class="palette",
span("+ Color:",
inline(
tipify(
pickerInput(inputId=ns("rda_colpalette"),label = NULL,choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),options=list(container="body"),selected=vals$colors_img$val[1], width='120px'), "Symbol palette. Choose a gradient to color observations by a factor")))),
uiOutput(ns("rda_fac_palette"))
)
})
rda_symbol_factor<-reactive({
req(input$rda_symbol_factor)
col<-getcolhabs(vals$newcolhabs,input$rda_colpalette,2)
if(col[1]!=col[2]){
data = vals$saved_data[[input$rda_X]]
attr(data,"factors")[rownames(data), input$rda_symbol_factor]
}else{NULL}
})
output$rda_show_labels_out<-renderUI({
req(isTRUE(input$rda_show_labels))
column(12,
div(span("+ Factor:",
inline(tipify(pickerInput(ns("rda_labfactor"),NULL,choices = colnames(attr(vals$saved_data[[input$rda_X]],"factors")), width="125px"), "label classification factor")
))),
div(span("+ Lab Color:",
inline(tipify(
pickerInput(
inputId=ns("rda_labcolor"),
label = NULL,
selected= vals$colors_img$val[12],choices = vals$colors_img$val,choicesOpt = list(content = vals$colors_img$img),width="75px",
options=list(container="body")
), "label classification factor"
)
))),
div(span("+ Lab adj:",
inline(
tipify(pickerInput(ns("rda_labadj"),NULL,choices=c(1:4), width="75px", options=list(containder="body")), "a position specifier for the text. If specified this overrides any adj value given. Values of 1, 2, 3 and 4, respectively indicate positions below, to the left of, above and to the right of the specified (x,y) coordinates.", placement = "right")
))),
div(span("+ Lab offset:",
inline(
tipify(numericInput(ns("rda_offset"),NULL,value = 0,step = .1, width="75px"), "this value controls the distance ('offset') of the text label from the specified coordinate in fractions of a character width.")
))),
div(span("+ Size:",
inline(
tipify(numericInput(ns("rda_cextext"),NULL,value = 1,min = 0.1,max = 3,step = .1), "label text size")
)))
)
})
output$rda_view_plot<-renderUI({
req(input$rda_view=='Plot')
div(
div(
'+ Scaling:',
inline(numericInput(ns("rda_scaling"),NULL, 2, step=1, min=0, max=3)),tipify(icon("fas fa-question-circle",style="color: gray"),"Scaling for species and site scores. Either species (2) or site (1) scores are scaled by eigenvalues, and the other set of scores is left unscaled, or with 3 both are scaled symmetrically by square root of eigenvalues.", placement = "bottom")
),
div(span("+",checkboxInput(ns("biplot_rda"), span("Biplot", pophelp(NULL,"show biplot arrows")), T))),
div(span("+",checkboxInput(ns("sp_rda"), span("Variables", pophelp(NULL,"Variables"))))),
uiOutput(ns("sp_rda_out"))
)
})
output$rda_sp_display<-renderUI({
req(input$rda_sp_display=='Shape')
inline(pickerInput(
inputId=ns("rda_spshape"),
label = NULL,
choices = df_symbol$val,
options=list(container="body"),
selected=df_symbol$val[8],
choicesOpt = list(content = df_symbol$img)))
})
observeEvent(ignoreInit = T,input$disp_rda_summ,
vals$disp_rda_summ<-input$disp_rda_summ)
output$rda_view_summary<-renderUI({
req(input$rda_view=='Summary')
div(
div(
span(
"+ Results:",
inline(
pickerInput(ns("disp_rda_summ"),NULL,choices=c('Importance (unconstrained)',"Importance (constrained)",'Variable scores','Observation scores','Linear constraints','Biplot'), selected=vals$disp_rda_summ, options=list(container="body"), width="200px")
)
)
),
div(
span("+ Axes",
inline(
numericInput(ns("rda_axes"),NULL, value=2)
)
)
),
div(
tipify(
actionLink(
ns('downcenter_rda'),span("+ Download",icon("fas fa-download")), style="button_active"
),
"Download selected results"
)
)
)
})
output$sp_rda_out<-renderUI({
req(isTRUE(input$sp_rda))
div(style="margin-left: 5px",
div(
'+ Number:',
inline(numericInput(ns("rda_spnum"),NULL, 10, step=1, width="100px")),tipify(icon("fas fa-question-circle",style="color: gray"),"Show N variables with the highest scores", placement = "bottom")
),
div(
"+ Display:",
span(
inline(
pickerInput(ns("rda_sp_display"),NULL,choices=c("Label","Shape"), width="100px")
),
inline(
uiOutput(ns("rda_sp_display"))
)
)
),
div(
span("+ Variable Color:",
tipify(
pickerInput(
inputId=ns("rda_spcolor"),
label = NULL,
selected= vals$colors_img$val[getsolid_col()][4],
choices = vals$colors_img$val[getsolid_col()],
choicesOpt = list(
content = vals$colors_img$img[getsolid_col()]
),
options=list(container="body")
), "Variable Color."))
)
)
})
output$rda_plot<-renderPlot({
prda(rda_model(),
key = rda_symbol_factor(),
points =input$rda_show_symbols,
text = input$rda_show_labels,
palette = input$rda_colpalette,
cex.points = input$rda_cexpoint,
cex.text = input$rda_cextext,
pch=c(rda_symbol(),3),
keytext=rda_text_factor(),
biplot=input$biplot_rda,
show.sp=input$sp_rda,
n.sp=input$rda_spnum,
sp.display=input$rda_sp_display,
pch.sp=as.numeric(input$rda_spshape),
col.sp=getcolhabs(vals$newcolhabs,input$rda_spcolor,1),
textcolor=input$rda_labcolor,
scaling=input$rda_scaling,
newcolhabs=vals$newcolhabs,
pos=input$rda_labadj,
offset=input$rda_offset
)
vals$rda_plot<-recordPlot()
rda
})
output$stats_rda<-renderUI({
req(input$rda_X)
validate(need(!anyNA(vals$saved_data[[input$rda_X]]), "This functionality does not support missing values; Please use the transformation tool to the handle missing values."))
mainPanel(
tabsetPanel(id=ns("rda_view"),selected=vals$rda_view,
tabPanel("Plot", value="Plot",
plotOutput(ns("rda_plot"))),
tabPanel("Summary", value="Summary",
verbatimTextOutput(ns("rda_print"))))
)
})
output$rda_print<-renderPrint({ rda_summary()})
output$orda_options<-renderUI({
div(
div(
span("+",
checkboxInput(ns("rda_scale"),span("Scale variables",tiphelp("Scale variables to unit variance (like correlations)")), value=T)
)
),
uiOutput(ns("rda_view_summary")),
uiOutput(ns("rda_view_plot")),
)
})
observeEvent(ignoreInit = T,input$rda_view,
vals$rda_view<-input$rda_view)
rda_text_factor<-reactive({
if(isFALSE(input$rda_show_labels)){NULL} else{
data = vals$saved_data[[input$rda_X]]
attr(data,"factors")[rownames(data), input$rda_labfactor]}
})
rda_model<-reactive({
data<-
vals$saved_data[[input$rda_X]]
x<-as.matrix(data)
colnames(x)<-colnames(data)
if(length(input$rda_Y)>0){
y<-na.omit(vals$saved_data[[input$rda_Y]][rownames(x),,drop=F])
colnames(y)<-colnames(vals$saved_data[[input$rda_Y]])
x<-na.omit(x[rownames(y),])
dim(data.frame(y))
dim(x)
model=vegan::rda(x~.,data=data.frame(y) ,scale=input$rda_scale)
} else{model= vegan::rda(x,scale=input$rda_scale)}
model})
rda_summary<-reactive({
res<-summary(rda_model())
res<-switch(input$disp_rda_summ,
"Variable scores"=res$species,
"Observation scores"=res$sites,
"Linear constraints"=res$constraints,
"Biplot"=res$biplot,
"Importance (unconstrained)"=res$cont$importance,
"Importance (constrained)"=res$concont$importance
)
vals$rda_summary<-res[,1:input$rda_axes]
vals$rda_summary
})
rda_symbol<-reactive({
if(isFALSE(input$rda_show_symbols)){NA}else{as.numeric(input$rda_symbol)}
})
observeEvent(ignoreInit = T,input$rda_downp,{
vals$hand_plot<-"rda"
module_ui_figs("downfigs")
mod_downcenter<-callModule(module_server_figs, "downfigs", vals=vals)
})
observeEvent(ignoreInit = T,input$rda_X,
vals$cur_rda_X<-input$rda_X)
observeEvent(ignoreInit = T,input$rda_Y,
vals$cur_rda_Y<-input$rda_Y)
data_overwritte<-reactiveValues(df=F)
data_store<-reactiveValues(df=F)
newname<-reactiveValues(df=0)
get_newname<-reactive({
req(!is.null(vals$hand_save))
newname$df<-switch(
vals$hand_save,
"Create Datalist: Niche results"={name_niche()},
"Create factor using breakpoints from the dissimilarity profile"= {c(paste0("BP_"),nlevels(as.factor(vals$splitBP[,1])))},
"Save pwRDA model in"={
paste0(input$segrda_X,"~",input$segrda_Y,"[factor:",input$bp_column,"]")
}
)})
output$data_over<-renderUI({
data_overwritte$df<-F
data<-vals$saved_data[[input$data_descX]]
choices<-c(names(vals$saved_data))
if(vals$hand_save=="Create factor using breakpoints from the dissimilarity profile"){choices<-colnames(attr(data,"factors"))}
req(input$hand_save=="over")
res<-pickerInput(ns("over_datalist"), NULL,choices, width="350px")
data_overwritte$df<-T
inline(res)
})
output$data_create<-renderUI({
req(newname$df!=0)
data_store$df<-F
req(input$hand_save=="create")
res<-textInput(ns("newdatalist"), NULL, newname$df, width="350px")
data_store$df<-T
inline(res)
})
observeEvent( input$data_confirm,{
req(!is.null(vals$hand_save))
switch(
vals$hand_save,
"Create Datalist: Niche results"={saveniche()},
"Create factor using breakpoints from the dissimilarity profile"= {save_bpfac()},
"Save pwRDA model in"={save_pwrda()}
)
removeModal()
})
module_desctools <- function() {
ns <- session$ns
modalDialog(
uiOutput(ns("databank_storage")),
title=strong(icon("fas fa-save"),'Save'),
footer=column(12,
fluidRow(modalButton(strong("cancel")),
inline(uiOutput(ns("save_confirm")))
)
),
easyClose = T
)
}
output$databank_storage<-renderUI({
req(!is.null(vals$hand_save))
newname$df<-0
get_newname()
div(
column(12,
div(strong("action:"),em("*",vals$hand_save,style="color: SeaGreen")),
div(vals$hand_save2,style="color: gray"),
div(vals$hand_save3)),
column(12,style="margin-top: 20px",
radioButtons(ns("hand_save"),NULL,
choiceNames= list(div(style="height: 40px",span("Create", style="margin-right: 15px"), inline(uiOutput(ns("data_create")))),
div(style="height: 40px",span("Overwrite", style="margin-right: 15px"), inline(uiOutput(ns("data_over"))))),
choiceValues=list('create',"over"), width="800px")
)
)
})
output$save_confirm<-renderUI({
req(isTRUE(data_store$df)|isTRUE(data_overwritte$df))
actionButton(ns("data_confirm"),strong("confirm"))
})
}
|
3cb3323139607841e8263a2297e29d3a8a68698c | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/gbp/R/gbp1d_cpp_rd.r | 0b2f144b49868f1a54ec063dae96f802ecbd1b12 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,816 | r | gbp1d_cpp_rd.r | #' gbp1d
#' @aliases
#' gbp1d Rcpp_gbp1d Rcpp_gbp1d-class
#' @description
#' generalized bin packing problem in 1 dimension, a.k.a knapsack 0-1 problem.
#' @details
#' gbp1d init a profit vector p, a weight vector w, and a weight constraint c,
#' gbp1d solver would solve
#'
#' maximize sum_{j=1}^{n} p_{j} x_{j}
#'
#' subject to sum_{j=1}^{n} w_{j} x_{j} leq c
#' x_{j} in {0, 1}, j = 1, ...., n
#'
#' and instantiate a gbp1d object with a selectin vector x and an objective z.
#'
#' gbp1d is implemented as rcpp class, an instantiate can be solved by calling
#' gbp1d_solver_dpp(p, w, c) and gbp1d_solver_min(p, w, c)
#' @family gbp1d
#' @rdname gbp1d
#' @docType class
"gbp1d"
#' gbp1d_solver_dpp
#' @description
#' solve gbp1d via dynamic programming simple - adagio::knapsnak()
#' @details
#' a dynamic programming solver on gbp1d instantiate - knapsack 0-1 problem, see gbp1d.
#'
#' gbp1d init a profit vector p, a weight vector w, and a weight constraint c,
#' gbp1d solver would solve
#'
#' maximize sum_{j=1}^{n} p_{j} x_{j}
#'
#' subject to sum_{j=1}^{n} w_{j} x_{j} leq c
#' x_{j} in {0, 1}, j = 1, ...., n
#'
#' and instantiate a gbp1d object with a selectin vector x and an objective z.
#'
#' gbp1d is implemented as rcpp class, an instantiate can be solved by calling
#' gbp1d_solver_dpp(p, w, c) and gbp1d_solver_min(p, w, c)
#'
#' @param p
#' p profit <vector>::<numeric>
#' @param w
#' w weight <vector>::<integer>
#' @param c
#' c constraint on weight <integer>
#' @return gbp1d
#' a gbp1d instantiate with p profit, w weight, c constraint on weight,
#' k selection, o objective, and ok an indicator of all fit or not.
#' @family gbp1d
#' @rdname gbp1d_solver_dpp
"gbp1d_solver_dpp"
|
847509956691b55d888318fcdf81557aae677520 | b5ba5c578810105c9148fecadc61f124ae68118c | /man/maxccf.Rd | eebbc2d4d569ebecfbb6ffdb3ad47b6e393f741c | [] | no_license | dangulod/ECTools | cce57dfe0189ee324922d4d014cb7a72bd97817d | a927092249a92ced28c6c50fe7b26588049a07d0 | refs/heads/master | 2021-01-25T10:51:04.021720 | 2018-05-16T10:31:25 | 2018-05-16T10:31:25 | 93,886,888 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 648 | rd | maxccf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/maxcorlag.R
\name{maxccf}
\alias{maxccf}
\title{maxccf}
\usage{
maxccf(x = x, y = y, lag.max = 6, allow.negative = T, abs = T)
}
\arguments{
\item{x}{vector}
\item{y}{vector}
\item{lag.max}{(Optional) maximum lag}
\item{allow.negative}{(Optional) logical, if negative lags are allowed, by default TRUE}
\item{abs}{(Optional) logical, Should be the maximum in absolute value, by defaukt TRUE}
}
\value{
Return the lag with the maximum/minimum correlations bewteen two time series
if x is a data frame or matrix, u must use maxccfdf fucntion
}
\description{
maxccf
}
|
1f7de2364ff32df6e9c5aab2ffb7743cebca4fd9 | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /B_analysts_sources_github/GuangchuangYu/bioc-release/ClassGeneClusterSet.R | fce1c732db59c44dcc0006fc1d2e406f2f90e406 | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955742 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,251 | r | ClassGeneClusterSet.R | setClass("GeneClusterSet", representation(GeneClusters="list"))
setMethod(
f= "sim",
signature= "GeneClusterSet",
definition=function(object, params){
if (length(params@combine)==0) {
stop("Using setCombineMethod(\"Params\") to specify which method to combine.")
}
size <- length(object@GeneClusters)
cluster_gos=list()
for(i in 1:size){
cluster_gos[[i]]=sapply(object@GeneClusters[[i]], gene2GO, params)
}
assign("GOSemSimCache", new.env(hash=TRUE),envir=.GlobalEnv)
simScores <- matrix(NA, nrow=size, ncol=size)
rownames(simScores) <- names(object@GeneClusters)
colnames(simScores) <- names(object@GeneClusters)
for (i in seq(along=object@GeneClusters)) {
for (j in 1:i) {
gos1 <- unlist(cluster_gos[[i]])
gos2 <- unlist(cluster_gos[[j]])
gos1 <- gos1[!is.na(gos1)]
gos2 <- gos2[!is.na(gos2)]
if (length(gos1) == 0 || length(gos2)== 0) {
simScores[i,j] <- NA
} else {
goids <- new("GOSet", GOSet1=gos1, GOSet2=gos2)
simScores[i,j] = sim(goids, params)
}
if (i != j ){
simScores[j,i] <- simScores[i,j]
}
}
}
remove("GOSemSimCache", envir=.GlobalEnv)
removeNA <- apply(!is.na(simScores), 1, sum)>0
return(simScores[removeNA, removeNA])
}
) |
941225985c413ba13ff4ddcb7b98bcfa0e8c3cf7 | 06c3bb38a86470847c0d56ded74126889e8762ce | /merge_HTcounts_Matrix_V1.2.R | c4e5cfacf8429b185ff316df3af2e973b436db8d | [] | no_license | haojiang9999/R_Data_analysis | 7bc10f7babe3b2a6a9bf81d9618471c7712fef27 | 882c7b4a1519cd9f5d6071808a34b600d075aa90 | refs/heads/master | 2020-03-29T01:04:07.581137 | 2019-11-09T02:31:45 | 2019-11-09T02:31:45 | 149,369,687 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 87 | r | merge_HTcounts_Matrix_V1.2.R | all.exp<-readRDS("FANTOM_E_MTAB_3929_exp.rds")
all.exp[1530,1530]
row.names(all.exp)
# |
4f611229da7eb2baf1846db54c882555f75812c2 | d38abc97785eb0296a1bcc0764edf1df9af7bcad | /support/array/general.R | 29cf23c7ea9399e05783f3aaab47dc7a17fe066d | [
"MIT"
] | permissive | lnsongxf/R4Econ | 0f0e1ebbaab669a8d00cc7c0d9b4c06e2bd02207 | cc50184e5650e94ea5dfa62cbb61d32e6ada3493 | refs/heads/master | 2021-02-06T06:08:31.951801 | 2020-02-26T04:08:48 | 2020-02-26T04:08:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 113 | r | general.R | # Remove last element of array
vars.group.bydf <- c('23','dfa', 'wer')
vars.group.bydf[-length(vars.group.bydf)]
|
7752c98e21e03dc8a619f84760aa51fd4af061da | 7d31f360f1ece69b09a4b51e3986ac44025efc7c | /package/clinUtils/man/formatTableLabel.Rd | fd73f6e3561d22e1db353ca459e201b0e3e64efa | [] | no_license | Lion666/clinUtils | a0500a773225ffafc29b7d7f7bcc722dd416743c | dc6118f32d311657d410bdeba02f3720f01d62df | refs/heads/master | 2023-08-12T08:48:42.923950 | 2021-09-21T14:56:18 | 2021-09-21T16:14:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 478 | rd | formatTableLabel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/label.R
\name{formatTableLabel}
\alias{formatTableLabel}
\title{Concatenate and format text strings to a label of a table}
\usage{
formatTableLabel(...)
}
\arguments{
\item{...}{string to be concatenated to form label}
}
\value{
String with chunk label
}
\description{
This function concatenates and formats
text strings to a label of a table for \code{bookdown} package
}
\author{
Laure Cougnaud
}
|
0968fe8028774f37a92536b75e1edcc1c998b5ae | 32e9a6eb06e58bafd7ec0ea1232f850db6455542 | /plot2.r | 01a7e97d1571b24c50f7837a9cdfac439611f4d8 | [] | no_license | Nempecovest1/ExData_Plotting1 | c1a7d4219e73b544547188ea53aafd84284737b8 | 4c79fdbdf744e2c2ebe4c6d60b9d89f9418e4ceb | refs/heads/master | 2021-01-18T11:49:23.029293 | 2014-12-03T18:00:03 | 2014-12-03T18:00:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 538 | r | plot2.r | # Load data
data_orig = read.table("household_power_consumption.txt", header=T, sep=";", na.strings="?")
data_orig$Date = as.Date(data_orig$Date, format="%d/%m/%Y")
data = data_orig[data_orig$Date == "2007-02-01" | data_orig$Date == "2007-02-02",]
rm(data_orig)
data$DateTime = paste(data$Date, data$Time)
data$DateTime = strptime(data$DateTime, format="%Y-%m-%d %H:%M:%S")
# Plot 2
png("plot2.png", width=480, height=480)
plot(data$DateTime, data$Global_active_power, type="l", xlab="", ylab="Global Active Power (kilowatts)")
dev.off() |
c814df991b94c679763f82fef40b470586b68278 | 21aed1a841b080bb19b900dc0afd0bb21692d72a | /bayes_crank.R | 2f54eca11adf40b8984278744dfd125e35c9ffb1 | [] | no_license | anthonyjp87/Teach_Bayes_datacamp | dbaad3bef013ff382dbea440af6587afa5e7b60b | b47858c4c5a8a7f2ca23868ba341981c59dabb74 | refs/heads/master | 2021-01-21T18:29:08.948781 | 2017-05-22T14:16:33 | 2017-05-22T14:16:33 | 92,051,492 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,080 | r | bayes_crank.R | ##Thist first example is for generating all of the values for the Bayesian Crank. This is a function in the Teach Bayes library which calculates the Posterior based on the Prior and the Likelihood. It also includes the vector P which contains all of the possible values. In this case, only five values are possible, .1-.5
# Define the values of the proportion: P. These are Discrete, which will rarely be the case, but it is helpful.
P <- c(0.1, 0.2, 0.3, 0.4, 0.5)
# Define a Prior:
Prior <- c(0.3, 0.3, 0.2, 0.1, 0.1)
# Compute the likelihoods: Likelihood
Likelihood <- dbinom(8, size = 20, prob = P)
# Create Bayes data frame: bayes_df
bayes_df <- data.frame(P, Prior, Likelihood)
print(bayes_df)
# Compute and print the posterior probabilities: bayes_df
bayes_df <- bayesian_crank(bayes_df)
#Bayesian Crank is fairly simple--just the Prior*Likelihood/Sum of Product:
# function (d)
# {
# d$Product <- d$Likelihood * d$Prior
# d$Posterior <- d$Product/sum(d$Product)
# d
# }
# Graphically compare the prior and posterior
prior_post_plot(bayes_df)
print(bayes_df)
|
67548116cc821f9c4973f59e0e39a6d00bde4306 | ef1be2cb902701ee410945a94a2626cc12fdcbeb | /cachematrix.R | eae25a3d56c376304790f86bd8d620ac017d5854 | [] | no_license | dckly1976/R-Programming | f578f06ef5f8cb68aee0629635237c16ef9a3d20 | 02a8e26502f9c5b725b82959df3075a8517cade5 | refs/heads/main | 2023-08-29T17:31:58.345233 | 2021-11-12T16:45:56 | 2021-11-12T16:45:56 | 427,429,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,251 | r | cachematrix.R | ## This script store matrix in a environment with its inverted value
## so, this way, you just need to calculate the inverted matrix one time
## and obtain this info from cache.
## makeCacheMatrix is the first function, it store the original matrix
## and create functions to get it, modify and assign and inverted matrix
makeCacheMatrix <- function(x = matrix()) {
inverted_mtx = NULL
# subfunction if it needed to modify stored matrix
set_new_matrix <- function(new_matrix){
x <<- new_matrix
inverted_mtx <<- NULL
}
# subfunction to get original matrix
get <- function() x
# subfunction to assing a value to inverted matrix
set_inverted <- function(inverted) inverted_mtx <<- inverted
# subfunction to get only inverted matrix, using original matrix as key
get_inverted <- function() inverted_mtx
list(set_inverted = set_inverted,
get = get, get_inverted = get_inverted)
}
## cacheSolve is a function that will retrieve matrixes stored in cache, or,
## if its first time, will compute inverted matrix and store in cache for
## further uses
cacheSolve <- function(x, ...) {
# first, we check if original matrix (x) already as a value for inverted on cache
## to that, we use get_inverse() that is assign to x object (original matrix)
get_inv_mtx = x$get_inverted()
## if has an value, retrun inverted matrix
if(!is.null(get_inv_mtx)) {
message("getting cached inverted matrix")
return(get_inv_mtx)
}
# if not, compute inverted matrix for the first time and store in cache
original_mtx = x$get()
inverted_mtx = solve(original_mtx, ...)
x$set_inverted(inverted_mtx)
inverted_mtx
## Return a matrix that is the inverse of 'x'
}
## tests
A <- matrix( c(5, 1, 0,
3,-1, 2,
4, 0,-1), nrow=3, byrow=TRUE)
# creating stores for matrix a, inside b variable
b = makeCacheMatrix(A)
# get matrix (equals to A)
b$get()
# get inverse matrix before have it (must be NULL)
b$get_inverted()
# Caching inversed matrix for B (1st time, so must calculate)
cacheSolve(b)
# matrix remains the same
b$get()
# inversed matrix now as a value
b$get_inverted()
# getting inversed matrix from cache
cacheSolve(b)
|
66f2c5e094f833cf4829fdbda1acf986317cc986 | 5bffae3f3c1f40e6cba7dfe334a1c0b220c13613 | /man/ui-server.Rd | 76414016ae9c33bcfffac0f5de0b7c1add14d87a | [
"MIT"
] | permissive | curso-r/auth0 | f1218c40b9a9509d202f3c48866dd73af33d9e50 | 19d90e7b533e020449b54f67b93cfedcc30303bb | refs/heads/master | 2023-07-14T19:57:10.362245 | 2023-03-22T18:55:50 | 2023-03-22T18:55:50 | 154,844,030 | 139 | 29 | NOASSERTION | 2023-06-21T21:57:55 | 2018-10-26T14:05:40 | R | UTF-8 | R | false | true | 1,073 | rd | ui-server.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shiny.R
\name{ui-server}
\alias{ui-server}
\alias{auth0_ui}
\alias{auth0_server}
\title{Modifies ui/server objects to authenticate using Auth0.}
\usage{
auth0_ui(ui, info)
auth0_server(server, info)
}
\arguments{
\item{ui}{\code{shiny.tag.list} object to generate the user interface.}
\item{info}{object returned from \link{auth0_info}. If not informed,
will try to find the \verb{_auth0.yml} and create it automatically.}
\item{server}{the shiny server function.}
}
\description{
These functions can be used in a ui.R/server.R framework, modifying the
shiny objects to authenticate using Auth0 service with no pain.
}
\examples{
\donttest{
# first, create the yml file using use_auth0() function
if (interactive()) {
# ui.R file
library(shiny)
library(auth0)
auth0_ui(fluidPage(logoutButton()))
# server.R file
library(auth0)
auth0_server(function(input, output, session) {})
# console
options(shiny.port = 8080)
shiny::runApp()
}
}
}
\seealso{
\link{auth0_info}.
}
|
2043e07632a388263c875a6bb03cf6dfae4ca1f3 | 66317f3e1ba137b5a16e339e358350587cc8ad85 | /R/convert_flow_unit.R | 9496fe54e24b39d95ecf89e36547f2c6fe65fe29 | [] | no_license | cran/clinPK | 2dd3c8f90cc711186003a47a229c2153f272be8f | 935e5cd7f2e0877814a1ce7347da48fc0ffda0a6 | refs/heads/master | 2022-05-16T16:11:20.119203 | 2022-05-09T07:10:05 | 2022-05-09T07:10:05 | 94,758,508 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,403 | r | convert_flow_unit.R | #' Convert flow (e.g. clearance) from / to units
#'
#' Flow units are expected to be specified as a combination
#' of volume per time units, potentially specified per kg
#' body weight, e.g. "mL/min", or "L/hr/kg".
#'
#' Accepted volume units are "L", "dL", and "mL".
#' Accepted time units are "min", "hr", and "day".
#' The only accepted weight unit is "kg".
#'
#' The function is not case-sensitive.
#'
#' @param value flow value
#' @param from from flow unit, e.g. `L/hr`.
#' @param to to flow unit, e.g. `mL/min`
#' @param weight for performing per weight (kg) conversion
#'
#' @examples
#'
#' ## single values
#' convert_flow_unit(60, "L/hr", "ml/min")
#' convert_flow_unit(1, "L/hr/kg", "ml/min", weight = 80)
#'
#' ## vectorized
#' convert_flow_unit(
#' c(10, 20, 30),
#' from = c("L/hr", "mL/min", "L/hr"),
#' to = c("ml/min/kg", "L/hr", "L/hr/kg"),
#' weight = c(70, 80, 90))
#'
#' @export
convert_flow_unit <- function(
value = NULL,
from = "l",
to = "ml",
weight = NULL) {
## Input checks:
if(is.null(from)) {
stop("`from` argument not specified.")
}
if(is.null(to)) {
stop("`to` argument not specified.")
}
if(length(from) != 1 && length(from) != length(value)) {
stop("`from` argument should be either a single value or a vector of the same length as the `value` argument.")
}
if(length(to) != 1 && length(to) != length(value)) {
stop("`to` argument should be either a single value or a vector of the same length as the `value` argument.")
}
## Clean up the from/to units:
from <- gsub("\\/", "_", tolower(from))
to <- gsub("\\/", "_", tolower(to))
## Definition of the units:
volume_units <- list(
"ml" = 1/1000,
"dl" = 1/10,
"l" = 1)
time_units <- list(
"min" = 1/60,
"hr" = 1,
"day" = 24)
## Calculate volume conversion factors
tryCatch({
from_volume_factor <- as.numeric(vapply(from, FUN = function(x) {
find_factor(x, units = volume_units, "^") # volume is always at the start, hence ^
}, 1))
}, error = function(e) { stop("Volume unit not recognized in `from` argument.") })
tryCatch({
to_volume_factor <- as.numeric(vapply(to, FUN = function(x) {
find_factor(x, units = volume_units, "^") # volume is always at the start, hence ^
}, 1))
}, error = function(e) { stop("Volume unit not recognized in `to` argument.") })
## Calculate per time conversion factors
tryCatch({
from_time_factor <- 1/as.numeric(vapply(from, FUN = function(x) {
find_factor(x, units = time_units, "_") # time is never at the start, always after "/" or "_"
}, 1))
}, error = function(e) { stop("Time unit not recognized in `from` argument.") })
tryCatch({
to_time_factor <- 1/as.numeric(vapply(to, FUN = function(x) {
find_factor(x, units = time_units, "_") # time is never at the start, always after "/" or "_"
}, 1))
}, error = function(e) { stop("Time unit not recognized in `to` argument.") })
## Calculate weight conversion factors
from_weight <- as.logical(vapply(from, function(x) { length(grep("_kg", x, value=F))>0 }, TRUE))
to_weight <- as.logical(vapply(to, function(x) { length(grep("_kg", x, value=F))>0 }, TRUE))
if((any(from_weight) || any(to_weight))) {
if(is.null(weight)) stop("Weight required for weight-based conversion of flow rates.")
if(length(weight) != 1 && length(weight) != length(value)) {
stop("`weight` argument should be either a single value or a vector of the same length as the `value` argument.")
}
}
from_weight_factor <- ifelse(from_weight, weight, 1)
to_weight_factor <- ifelse(to_weight, weight, 1)
## Combine factors and return
value *
(from_volume_factor * from_weight_factor * from_time_factor) /
(to_volume_factor * to_time_factor * to_weight_factor)
}
#' Helper function to grab the conversion factor from an input unit and given list
#'
#' @param full_unit full unit, e.g. "mL/min/kg"
#' @param units unit specification list, e.g. `list("ml" = 1/1000, "dl" = 1/10, "l" = 1)`
#' @param prefix prefix used in matching units, e.g. "^" only matches at start of string while "_" matches units specified as "/"
find_factor <- function(full_unit, units = NULL, prefix = "^") {
unlist(units[vapply(names(units), function(x) { grepl(paste0(prefix, x), full_unit) }, FUN.VALUE = logical(1))], use.names = FALSE)
}
|
82522af2f1db681c21062fd1c06b7bb9983c70b3 | 7efe27117099680642ffb3dc2fd9d9cbf6bf3b95 | /decision.R | 54cd5e2879f36532e3cfbb6b9e5edc6706e9351c | [] | no_license | rashmibhle/dsr_lab | ff075b40835dbbe4f64090e0471d15e86c30f5c4 | 9b0a2fa799832fc5074ce83d720ad0300bb4f789 | refs/heads/master | 2020-08-22T05:04:47.311810 | 2020-02-03T17:22:20 | 2020-02-03T17:22:20 | 216,323,263 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 328 | r | decision.R |
library(rpart)
library(rpart.plot)
dataset = read.csv("F:/7th sem/7th SEM/DSR/Dataset/Dataset/Mail_Respond.csv")
dataset
part = rpart(Outcome~District+House.Type+Income+Previous_Customer,control = rpart.control(minsplit = 1), parms= list(split="information"), data = dataset)
part
rpart.plot(part, type= 2,extra=4) |
0733ea64479c3435b15b7ea6885dda1558cf86a4 | 1839b1bc21a43384e9c169f0bf5fd0a3e4c68b0a | /w18/man/getRandomPWMsAndFilts.Rd | 9e70021989d94f2dcb8aac821cf985be2c0db2df | [] | no_license | CarlosMoraMartinez/worm19 | b592fa703896e1bbb6b83e41289674c63a046313 | 99fb3ef35d13739ee83f08b2ac1107179ea05ee2 | refs/heads/master | 2020-07-18T23:25:13.542031 | 2019-07-03T14:53:04 | 2019-07-03T14:53:04 | 206,333,433 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 774 | rd | getRandomPWMsAndFilts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getRandomPWMsAndFilts.R
\name{getRandomPWMsAndFilts}
\alias{getRandomPWMsAndFilts}
\title{getRandomPWMsAndFilts}
\usage{
getRandomPWMsAndFilts(PWMs, filt, n = 10)
}
\arguments{
\item{PWMs}{list of matrixes, with names}
\item{filt}{list of filters (regular expression-like, eg. [AT]TTG[CG]A)}
\item{n}{number of permutated lists (defaults to n=10).}
}
\value{
n lists of PWMs with shuffled columns (1 shuffled PWM per
originalPWM) and its corresponding filters (a list of lists, with n
permutations of each PWM.)
}
\description{
Given a set of PWMs and filter (eg. [AT]TTG[CG]A), returns n lists of PWMs
with shuffled columns and its corresponding filters.
}
\keyword{PWMs,}
\keyword{filters}
|
7c9bafec9fef7c894a1c42526dd7ad53bd5f3052 | 31362fdab2193f92b64f9a82b0fe1ca732fcf6df | /Eumaeus/ui.R | 5ff7a7740aab910fcd3a79a7fa773e5d89697c1b | [] | no_license | OHDSI/ShinyDeploy | a5c8bbd5341c96001ebfbb1e42f3bc60eeceee7c | a9d6f598b10174ffa6a1073398565d108e4ccd3c | refs/heads/master | 2023-08-30T17:59:17.033360 | 2023-08-26T12:07:22 | 2023-08-26T12:07:22 | 98,995,622 | 31 | 49 | null | 2023-06-26T21:07:33 | 2017-08-01T11:50:59 | R | UTF-8 | R | false | false | 13,353 | r | ui.R | library(shiny)
library(DT)
shinyUI(
fluidPage(style = 'width:1500px;',
titlePanel(
title = div(img(src = "logo.png", height = 50, width = 50), "Evaluating Use of Methods for Adverse Event Under Surveillance (EUMAEUS)"),
windowTitle = "EUMAEUS"
),
tabsetPanel(
tabPanel("About",
br(),
p("For review purposes only. Do not use.")
),
tabPanel("Effect-size-estimate-based metrics",
fluidRow(
column(2,
selectInput("exposure", label = div("Vaccine", actionLink("vaccineInfo", "", icon = icon("info-circle"))), choices = exposure$exposureName),
selectInput("calibrated", label = div("Empirical calibration:", actionLink("calibrationInfo", "", icon = icon("info-circle"))), choices = c("Uncalibrated", "Calibrated")),
selectInput("database", label = div("Database:", actionLink("databaseInfo", "", icon = icon("info-circle"))), choices = database$databaseId),
selectInput("timeAtRisk", label = div("Time at risk", actionLink("timeAtRiskInfo", "", icon = icon("info-circle"))), choices = timeAtRisks),
selectInput("trueRr", label = div("True effect size:", actionLink("trueRrInfo", "", icon = icon("info-circle"))), choices = trueRrs),
checkboxGroupInput("method", label = div("Methods:", actionLink("methodsInfo", "", icon = icon("info-circle"))), choices = unique(analysis$method), selected = unique(analysis$method))
),
column(10,
tabsetPanel(type = "pills",
tabPanel("Per period",
selectInput("period", label = div("Time period", actionLink("periodInfo", "", icon = icon("info-circle"))), choices = timePeriod$label[timePeriod$exposureId == exposure$exposureId[1]]),
dataTableOutput("performanceMetrics"),
uiOutput("tableCaption"),
conditionalPanel(condition = "output.details",
div(style = "display:inline-block", h4(textOutput("details"))),
tabsetPanel(id = "perPeriodTabSetPanel",
tabPanel("Estimates",
uiOutput("hoverInfoEstimates"),
plotOutput("estimates",
height = "270px",
hover = hoverOpts("plotHoverInfoEstimates",
delay = 100,
delayType = "debounce")),
div(strong("Figure 1.1."),"Estimates with standard errors for the negative and positive controls, stratified by true effect size. Estimates that fall above the red dashed lines have a confidence interval that includes the truth. Hover mouse over point for more information.")),
tabPanel("ROC curves",
plotOutput("rocCurves",
height = "420px"),
div(strong("Figure 1.2."),"Receiver Operator Characteristics curves for distinguising positive controls from negative controls. Negative controls not powered for positive control synthesis have been removed.")),
tabPanel("Diagnostics",
sliderInput("minRateChange", "Minimum relative rate change (%)", min = 0, max = 100, value = 50),
plotOutput("monthlyRates",
height = "420px"),
div(strong("Figure 1.3."),"Monthly incidence rates across the historic and current time windows. Only those outcomes having a relative change greater than the selected threshold are shown.")
)
)
)),
tabPanel("Across periods",
dataTableOutput("performanceMetricsAcrossPeriods"),
uiOutput("tableAcrossPeriodsCaption"),
conditionalPanel(condition = "output.detailsAcrossPeriods",
div(style = "display:inline-block", h4(textOutput("detailsAcrossPeriods"))),
tabsetPanel(
tabPanel("Estimates",
plotOutput("estimatesAcrossPeriods"),
div(strong("Figure 1.3."),"Effect-size estimates for the negative and positive controls across time, stratified by true effect size. Closed dots indicate statistical signficance (two-sides) at alpha = 0.05. The red dashed line indicates the true effect size.")
)
)
)
),
tabPanel("Across periods & methods",
fluidRow(
column(5, radioButtons("metricAcrossMethods", label = "Performance metrics", choices = c("Sensitivity & specificity", "AUC"))),
column(5, radioButtons("inputAcrossMethods", label = "Decision input (rule)", choices = c("P-value (< 0.05)", "Point estimate (> 1)", "Lower bound of 95% CI (> 1)")))
),
plotOutput("sensSpecAcrossMethods",
height = "800px"),
dataTableOutput("analysesDescriptions")
)
)
)
)
),
tabPanel("MaxSPRT-based metrics",
fluidRow(
column(2,
selectInput("exposure2", label = div("Vaccine", actionLink("vaccineInfo2", "", icon = icon("info-circle"))), choices = exposure$exposureName),
selectInput("database2", label = div("Database:", actionLink("databaseInfo2", "", icon = icon("info-circle"))), choices = database$databaseId),
textInput("minOutcomes", label = div("Minimum outcomes", actionLink("minimumOutcomesInfo2", "", icon = icon("info-circle"))), value = 1),
selectInput("timeAtRisk2", label = div("Time at risk", actionLink("timeAtRiskInfo2", "", icon = icon("info-circle"))), choices = timeAtRisks),
selectInput("trueRr2", label = div("True effect size:", actionLink("trueRrInfo2", "", icon = icon("info-circle"))), choices = trueRrs),
checkboxGroupInput("method2", label = div("Methods:", actionLink("methodsInfo2", "", icon = icon("info-circle"))), choices = unique(analysis$method), selected = unique(analysis$method))
),
column(10,
tabsetPanel(type = "pills",
tabPanel("Per method",
dataTableOutput("performanceMetrics2"),
uiOutput("table2Caption"),
conditionalPanel(condition = "output.details2",
div(style = "display:inline-block", h4(textOutput("details2"))),
tabsetPanel(
tabPanel("Log Likelihood Ratios",
uiOutput("hoverInfoLlrs"),
plotOutput("llrs",
height = "650px",
hover = hoverOpts("plotHoverInfoLlrs",
delay = 100,
delayType = "debounce")),
div(strong("Figure 2.1."),"Log likelihood ratios (LLR) (left axis) for the negative and positive controls at various points in time, stratified by true effect size. Closed dots indicate the LLR in that period exceeded the critical value. The critical value depends on sample size within and across periods, and is therefore different for each control. The yellow area indicates the cumulative number of vaccinations (right axis). Hover mouse over point for more information.")),
tabPanel("Sensitivity / Specificity",
plotOutput("sensSpec",
height = "800px"),
div(strong("Figure 2.2."),"Sensitivity and specificity per period based on whether the log likehood ratio for a negative or positive control exceeded the critical value in that period or any before. Negative controls not powered for positive control synthesis have been removed."))
)
)
),
tabPanel("Across methods",
fluidRow(
column(5, radioButtons("metricAcrossMethods2", label = "Performance metrics", choices = c("Sensitivity & specificity", "AUC")))
),
plotOutput("sensSpecAcrossMethods2",
height = "800px"),
dataTableOutput("analysesDescriptions2")
)
)
)
)
),
tabPanel("Database information",
plotOutput("databaseInfoPlot", height = "650px"),
div(strong("Figure 3.1."),"Overall distributions of key characteristics in each database."),
dataTableOutput("databaseInfoTable"),
div(strong("Table 3.2."),"Information about each database.")
)
)
)
)
|
025eac216b01dd7cb403dbc128da875b571299c4 | c05349aeb7e205ebac614d59d9cc4c3f14ead36f | /stochastic_simulations/analysis_dir/02-create_rds_dataset.R | 16ccf151fdedd3e36d6df87de7565c8915e57728 | [] | no_license | T-Heide/reply_to_tarabichi_et_al_ng2018 | d24327a594ab00745f326cd1dbbf2eeeadb0e180 | e6629d74573a70549e688e7755b256c81ce4005e | refs/heads/master | 2020-03-19T13:48:02.302694 | 2018-10-30T15:04:43 | 2018-10-30T15:04:43 | 136,595,654 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,699 | r | 02-create_rds_dataset.R | ################################################################################
# FILENAME: '02-create_rds_dataset.R'
################################################################################
# Options:
simResultDir <- "./results/simulations" # Dir containing the simulation results.
datasetDir <- "./results/datasets"
# Libs: ########################################################################
library(neutralitytestr)
library(dplyr)
# Functions: ###################################################################
paramsFromFileNames <- function(x) {
exprI <- "([[:digit:]]*)"
exprF <- "([[:digit:].]*)"
expr <- sprintf("^simulation-mmr_%s-mbr_%s-seed_%s-cst_%s-[[:print:]]*$",
exprI, exprF, exprI, exprI)
base <- basename(x)
match <- regexec(expr, base)
params <- do.call(rbind, regmatches(base, match))
# Restructure parameter matrix:
colnames(params) <- c("file","sc_mutation_rate","sc_deltaS","seed","clst")
rownames(params) <- gsub("-simulated_sequencing[.]tsv$", "", params[,"file"])
params <- params[,-1, drop=FALSE]
storage.mode(params) <- "numeric"
params <- data.frame(params)
return(params)
}
parseSimFiles <- function(f, ...) {
# Extract the params from file names:
cat("- Extracting parameters from file names.\n")
params <- paramsFromFileNames(f)
# Neutrality testing:
cat("- Loading the data.\n")
data <- lapply(lapply(lapply(f, read.delim), "[", "VAF"), unlist)
cat("- Performing neutrality tests.\n")
rsqs <- list()
wh <- sapply(data, function(x) sum(between(x, 0.12, 0.24)) >= 11)
test <- lapply(data[wh], neutralitytestr::neutralitytest, ...)
rsqs[wh] <- lapply(test, function(x) unlist(x$rsq["metric"])[1])
params$rsq <- sapply(rsqs, function(x) { if (is.null(x)){return(NA)}
else {return(x)}})
params$non_neutral <- params$rsq < 0.98
return(params)
}
parseInBatches <- function(files, batchSiz=200, ...) {
Nf <- length(files)
Nb <- ceiling(Nf / batchSiz)
index <- head(rep(seq_len(Nb), each=batchSiz), Nf)
splFiles <- split(files, index)
cat(sprintf("Loading %d simulation result files in %d batch(es):\n\n",Nf,Nb))
res_batches <- lapply(seq_along(splFiles), function(i) {
cat(sprintf("Batch %d/%d:\n", i, Nb))
res <- parseSimFiles(splFiles[[i]], ...)
cat("\n")
return(res)
})
return(do.call(rbind, res_batches))
}
# Main: ########################################################################
# Detect cell count files:
countFileMt <- "^simulation[[:print:]]*-cell_number[.]tsv$"
countFiles <- list.files(simResultDir, countFileMt, rec=1, full=1)
baseCountFiles <- basename(countFiles)
# Load cell count data:
cellCountData <- do.call(rbind, lapply(countFiles, read.delim))
cellCountData$subcloneFrac <- cellCountData$clone2 / cellCountData$total
cellCountData$simID <- gsub("-cell_number[.]tsv$", "", baseCountFiles)
cellCountData <- cbind(cellCountData, paramsFromFileNames(countFiles))
# Detect sequencing result files:
resFileMt <- "^simulation[[:print:]]*-simulated_sequencing[.]tsv$"
resFiles <- list.files(simResultDir, resFileMt, rec=1, full=1)
# Load result data:
resultData <- parseInBatches(resFiles)
resultDataExt <- parseInBatches(resFiles,fmin=0.025, fmax=0.45)
# Save as rds files:
dir.create(datasetDir, showWarnings=FALSE, recursive=TRUE)
saveRDS(resultData, file.path(datasetDir, "1f_model_fits.rds"))
saveRDS(resultDataExt, file.path(datasetDir, "1f_model_fits_ext.rds"))
saveRDS(cellCountData, file.path(datasetDir,"cell_counts.rds"))
|
f9c243755c3a373d319fea407572c2ee4101cdc8 | 70700fe9f3712da839680c1d7bd0085d8b3ef9f2 | /Durbin-Watson statistic.R | 77c071749f7b234dd5d8de8163f1417a4403131f | [] | no_license | sharyjose/Regression-Analysis | dfc4f210150f904575a175afd19ec961f7f73a50 | a87ce17923872f54d72c046ea01c7600de1df83a | refs/heads/main | 2023-08-28T04:28:16.821442 | 2021-09-14T00:02:59 | 2021-09-14T00:02:59 | 406,164,935 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,626 | r | Durbin-Watson statistic.R | options(digits=3)
########################################################
## A classic example: Anscombe's quartet!
anscombe <- read.csv("D:/stat 315/anscombe.csv")
summary(lm(y1~x1, data=anscombe))
summary(lm(y2~x2, data=anscombe))
summary(lm(y3~x3, data=anscombe))
summary(lm(y4~x4, data=anscombe))
# but are the 4 datasets really all the same?
# set-up space for 4 plots on a page:
par(mfrow=c(2,2))
plot(y1~x1, data= anscombe)
plot(y2~x2, data= anscombe)
plot(y3~x3, data= anscombe)
plot(y4~x4, data= anscombe)
########################################################
## A classic example from the first class: Age vs. Money
x1 <- c(82, 45, 71, 22, 29, 9, 12, 18, 24)
x2 <- c(22, 44, 31, 122, 20, 0, 2, 10, 35)
y <- c(71, 54, 43, 45, 21, 11, 30, 45, 10)
lm(y~x1+x2)
lm(y~I(log(x1))+x2)
plot(y~(x1))
plot(y~log(x1))
plot(log(y)~x1)
plot(log(y)~log(x1))
########################################################
## Example for serial correlation: prices over time
price_data <- read.csv("D:/stat 315/price_data.csv")
n <- dim(price_data)[1]
plot(y~location, data=price_data)
lmod <- lm(y~location, data=price_data)
summary(lmod)
res <- lmod$residuals
plot(res~time,data= price_data)
## shift by one timepoint:
cbind(res[-1],res[-n])
## correlation at a lag of one timepoint
cor1 <- cor(res[-1],res[-n])
cor1
sum(res[-1]*res[-n])/sqrt(sum(res[-1]^2)*sum(res[-n]^2))
## scatterplot
plot(cbind(res[-1],res[-n]))
DW <- sum((res[-1] - res[-n])^2)/sum(res^2)
DW
#approx equal to:
2-2*cor1
library(lmtest)
dwtest(lmod)
.
|
6ad696ab5268b2da20b8c1cec99a3f60b6590791 | 9fd399fc293811236c60b7f63e11090a0cbb5914 | /data-raw/create_academic_badges.R | ebf83b26d64c4bb18a859f6731636c74c3d5aca8 | [] | no_license | beatrizmilz/shinyresume | e6b3dc667a37ca73f7d5c7e85ec8f7b909cea2aa | 5454278435567185a29e504d91115e7b01158878 | refs/heads/master | 2023-06-13T07:44:01.271243 | 2021-07-03T21:08:32 | 2021-07-03T21:08:32 | 365,636,375 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,539 | r | create_academic_badges.R | library(magrittr, include.only = "%>%")
academic <- readr::read_csv2("data/academic_dataset.csv")
source("R/add_url_to_authors.R", encoding = "UTF-8")
academic_badges <- academic %>%
dplyr::mutate(
status_badges = dplyr::case_when(
status == "Presented" ~ glue::glue(
""
),
status == "Published" ~ glue::glue(
""
),
status == "Submitted" ~ glue::glue(
""
),
status == "Approved" ~ glue::glue(
""
),
TRUE ~ glue::glue(
""
)
),
type_of_publication_badges = dplyr::case_when(
type_of_publication == "Book chapter" ~ glue::glue(
""
),
type_of_publication == "Journal Editorial" ~ glue::glue(
""
),
type_of_publication == "Journal" ~ glue::glue(
""
),
type_of_publication == "Conference presentation" ~ glue::glue(
""
),
TRUE ~ glue::glue(
""
)
),
url_text_badges = dplyr::case_when(
!is.na(url_text) ~ glue::glue(
"[]({url_text})"
),
TRUE ~ glue::glue("")
),
url_code_badges = dplyr::case_when(
!is.na(url_code) ~ glue::glue(
"[]({url_code})"
),
TRUE ~ glue::glue("")
),
url_slides_badges = dplyr::case_when(
!is.na(url_slides) ~ glue::glue(
"[]({url_slides})"
),
TRUE ~ glue::glue("")
),
url_youtube_badges = dplyr::case_when(
!is.na(url_youtube) ~ glue::glue(
"[]({url_youtube})"
),
TRUE ~ glue::glue("")
),
) %>%
dplyr::mutate(authors_link = add_url_to_authors(authors),
item_info_link = add_url_to_authors(item_info),
)
academic_text <- academic_badges %>%
dplyr::mutate(
ano_previsao = dplyr::case_when(
status == "Submitted" ~ glue::glue("Not published yet"),
status == "Approved" & type_of_publication == "Conference presentation" ~ glue::glue("Not presented yet"),
TRUE ~ glue::glue("{year}")
),
text =
glue::glue(
"- {status_badges} {type_of_publication_badges} <br> {url_text_badges} {url_slides_badges} {url_code_badges} {url_youtube_badges} \n - {ano_previsao}. {authors_link}. {title}. {item_info_link}. \n\n \n\n \\<br>"
)
)
academic_text %>%
readr::write_csv2("data/academic_badges.csv")
|
75d4d0ad946261accd8b0fc1e100b4c47ce0f142 | 8ecba2046e47303dfde07cfe0e70886df5f41235 | /number_years_in_league_by_player.R | 9680722c596036716c5c8e2fa6e666fa5806781d | [] | no_license | drivergit/longest-tenured-college-on-NFL-team | b813bba3378357054bffff85debc7bd1cac7a607 | 96992121c4d6fbf8123da8c91b5db632bd8044ff | refs/heads/master | 2021-01-17T20:55:55.597472 | 2015-03-30T00:30:41 | 2015-03-30T00:30:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,222 | r | number_years_in_league_by_player.R |
#Load complete roster info
roster.master.list<-read.csv('complete-roster-info.csv')
#eliminates the first column, which was previously the number
roster.master.list<-roster.master.list[,-1]
#eliminate the first row, which was the 'x' initialization placeholder
roster.master.list<-roster.master.list[-1,]
#remove the period in column names, as reading from CSV does not retain format
# of original col names
colnames(roster.master.list)<-gsub('\\.',' ',colnames(roster.master.list))
library(dplyr)
#grab the name, DOB and college for each position on the roster
players.years.active<-select(roster.master.list,name,DOB,college,`roster year`)%>%
#group by name DOB and college (should be a unique identifier for each player)
group_by(name,DOB,college)%>%
#counts the number of occurances for roster years for times appearing on roster
summarise(roster.years=n())%>%
#ungroup so data can be sorted by roster years
ungroup%>%
#sort by player count, in descending order
arrange(desc(roster.years))
#historgram of how many years a player plays in league
hist(players.years.active$roster.years)
#summary of how many years a player plays in league
summary(players.years.active$roster.years)
|
08282d23f413f9f42ae824cfa4313b66b5bd616e | 61b4adde63a7b434e028488d2158ef23014c4cfc | /man/pdf_diff.Rd | f143167be2882b22da3c80a0d2e145490f720574 | [] | no_license | SVA-SE/mill | a165deeae9612c1448d287caea73f5de6e87d02a | b5faa7738d6b475759c7f2f980e30628d7f15f35 | refs/heads/master | 2021-05-10T09:19:22.504502 | 2020-06-15T12:05:40 | 2020-06-15T12:05:40 | 103,141,739 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 722 | rd | pdf_diff.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/images.R
\name{pdf_diff}
\alias{pdf_diff}
\title{pdf_diff}
\usage{
pdf_diff(reference, new, dir = tempdir())
}
\arguments{
\item{reference}{Path to a pdf file}
\item{new}{Path to a pdf file}
\item{dir}{directory to perform the comparison inside}
}
\value{
data.frame A data.frame with 3 columns: page,
percent_diff, composite.
}
\description{
Difference of two pdf:s
}
\details{
Submitt two pdfs of the same number of pages to this function and
get back a data.frame with the same number of rows as the pdf with
the percent pixel difference for each page and a path to a
composed image of each page highlighting the differences in red.
}
|
2de0c2f66602f792b3d56e8765724c3d0f4d2e9c | 63a770312db431190f9bf7db60dacdb86134fa76 | /src_tidy/1.1_norm3groups.R | 52907b8ec854ecb4f740877e38d0693d4a1be3b4 | [] | no_license | zhilongjia/nCoV2019 | 0ee4aab7dcc35a4273a36dd4be97e9b6d85c55f3 | 57b616e83aa638fbfcdd4be09101e0c4331eb0e0 | refs/heads/master | 2023-02-20T11:17:52.551457 | 2020-07-25T08:00:21 | 2020-07-25T08:00:21 | 236,764,272 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,290 | r | 1.1_norm3groups.R |
load("../results/sample_count_df_symbol.RData")
library(edgeR)
library(limma)
################################################################################
g3_names <- c("Healthy", "Others", "Viral-like", "nCoV" )
subsample_pheno <- dplyr::filter(sample_meta, Types %in% g3_names )
subsample_pheno$Types <- factor(subsample_pheno$Types, levels = g3_names )
Expdesign <- model.matrix(~subsample_pheno$Types)
S_raw <- as.matrix(dplyr::select(sample_count_df_symbol, -SYMBOL) )
rownames(S_raw) <- sample_count_df_symbol$SYMBOL
dge <- DGEList(counts=S_raw)
subsample_dge <- dge[,subsample_pheno$NewID]
# filter and norm
keep <- filterByExpr(subsample_dge, Expdesign)
subsample_dge <- subsample_dge[keep,,keep.lib.sizes=FALSE]
subsample_dge <- calcNormFactors(subsample_dge)
v <- voom(subsample_dge, Expdesign, plot=FALSE, normalize="quantile")
nCoV_pneu_Heal_norm_symbol_GE <- v$E
################################################################################
#PCA 3 groups
load("../results/DEA_pneu_list.RData")
DEG_nCoV_Heal <- rownames(DEA_list[["limma_DE"]][["nCoV_Heal"]])
DEG_pneuVir_Heal <- rownames(DEA_list[["limma_DE"]][["Vir_Heal"]])
DEG_pneuBac_Heal <- rownames(DEA_list[["limma_DE"]][["Others_Heal"]])
DEG_nCoV_pneuVir <- rownames(DEA_list[["limma_DE"]][["nCoV_Vir"]])
DEG_united <- unique(c(DEG_nCoV_Heal, DEG_pneuVir_Heal, DEG_pneuBac_Heal))
library(ggfortify)
# all union genes
autoplot(prcomp(t(v$E[intersect(DEG_united, rownames(v$E) ),]), scale=F),
data=subsample_pheno, colour = "Types",
size = 5, label = F, label.colour="black", ts.colour="black" )
autoplot(prcomp(t(v$E[intersect(DEG_united, rownames(v$E) ),]), scale=F), x=1,y=3,
data=subsample_pheno, colour = "Types",
size = 5, label = F, label.colour="black", ts.colour="black" )
autoplot(prcomp(t(v$E[intersect(DEG_united, rownames(v$E) ),]), scale=F), x=1,y=2,
data=subsample_pheno, colour = "virus",
size = 5, label = F, label.colour="black", ts.colour="black" )
readr::write_tsv(tibble::rownames_to_column(as.data.frame(v$E)), path="../results/nCoV_pneu_Heal_norm_symbol_GE.tsv")
save.image("../results/1.1_norm3groups.RData")
save(nCoV_pneu_Heal_norm_symbol_GE, sample_meta, file="../results/nCoV_pneu_Heal_norm_symbol_GE.RData")
|
5a241970de240863575b2e262e745ddbfa4fea1c | 714e7c6736a2e3d8fd07634427c4a8bb3cef2d61 | /R/map_colour_text.R | 813ab5f0aff736066694e11b0fd1272982ccb4b8 | [
"MIT"
] | permissive | flaneuse/llamar | da7cb58a03b2adbffb6b2fe2e57f3ffeede98afb | ea46e2a9fcb72be872518a51a4550390b952772b | refs/heads/master | 2021-01-18T00:10:00.797724 | 2017-10-24T13:41:21 | 2017-10-24T13:41:21 | 48,335,371 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,034 | r | map_colour_text.R | #' Modifies a data frame to determine color to overlay on a colored background
#'
#' Takes a data frame with a value to map to a fill colour and determines whether
#' light or dark text should be used as the label on top of the fill. For use with
#' \code{ggplot2::scale_colour_identity()} downstream.
#'
# @import dplyr
#'
#' @param df data frame containing the data
#' @param bckgrnd_column string containing the name of the column to map to fill values
#' @param colour_palette colour palette specification (list of hex values). Can use \code{RColorBrewer::brewer.pal} to generate
#' @param limits (optional) limits for the fill color palette mapping
#' @param sat_threshold (optional) breakpoint between the light and dark text color. 50 percent saturation, by default
#' @param dark_colour (optional) dark color to overlay on low fill values
#' @param light_colour (optional) light color to overlay on high fill values
#'
#' @examples {
#' # Define a Color Brewer palette
#' library(RColorBrewer)
#' # Generate random data
#' df = data.frame(x = 1:9, y = 1:9)
#' pal = 'Reds'
#'
#' limits = c(0,15)
#' df = map_colour_text(df, 'x', brewer.pal(9, pal), limits)
#'
#' library(ggplot2)
#' ggplot(df, aes(x = x, y = y, fill = x, colour = text_colour, label = round(hsv.s,2))) +
#' geom_point(size = 10, shape = 21) +
#' geom_text() +
#' scale_fill_gradientn(colours = brewer.pal(9, pal), limits = limits) +
#' scale_colour_identity() +
#' theme_blank()
#' }
#'
#' @seealso \code{\link{scale_colour_text}}
map_colour_text = function(df,
bckgrnd_column,
colour_palette,
limits = c(min(df[[bckgrnd_column]]), max(df[[bckgrnd_column]])),
sat_threshold = 0.5,
dark_colour = grey90K,
light_colour = 'white') {
# -- Create a color palette --
# Returns RGB values
ramp = colorRamp(colour_palette)
# -- convert background values to colors --
# Adjust to between 0 and 1
df = df %>%
mutate_(.dots = setNames(paste0('(', bckgrnd_column, '-', limits[1],')/(',limits[2], '-', limits[1], ')'), 'bckgrnd')) %>%
mutate(bckgrnd = ifelse(is.na(bckgrnd), 0,
ifelse(bckgrnd < 0, 0,
ifelse(bckgrnd > 1, 1, bckgrnd))))
# Check if any values are NA; replace w/ 0
mapped_colours = ramp(df$bckgrnd)
# convert to HSV
mapped_colours = rgb2hsv(t(mapped_colours))
mapped_colours = data.frame('hsv' = t(mapped_colours))
if(all(round(mapped_colours$hsv.s, 1) == 0)) {
# greyscale: use values
df = df %>%
bind_cols(mapped_colours) %>%
mutate(text_colour = ifelse(hsv.v > sat_threshold, dark_colour, light_colour))
} else {
# colors: use saturation
# pull out the saturation
df = df %>%
bind_cols(mapped_colours) %>%
mutate(text_colour = ifelse(hsv.s < sat_threshold, dark_colour, light_colour))
}
return(df)
}
|
ccf46a72b7c1fd15f1206b31be206f1ac96bb635 | da0634866b3d3cb67e1770c08e925d8ec30d0714 | /app/app_heatmap.R | 1db598f7b6dfe2b7168b06555a9bafbdcd6ce306 | [] | no_license | TZstatsADS/Spr2017-proj2-grp8 | d9796c7c1bcebdd7ac3dfa1b47aea1b5ea8b3cbb | 3ef36d5a630d3d4fb4c5f5e0404d79ba8dd17ef4 | refs/heads/master | 2021-01-18T19:12:06.178211 | 2017-02-24T21:22:06 | 2017-02-24T21:22:06 | 80,873,376 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 7,540 | r | app_heatmap.R |
library(shiny)
library(ggplot2)
library(ggmap)
library(choroplethrZip)
library(dtplyr)
library(dplyr)
library(DT)
library(lubridate)
# Define UI for application that draws a histogram
ui <- shinyUI(navbarPage("Perfect City Go", theme="black.css",
# heatmap TAB
tabPanel('heatmap',
titlePanel(
h2("heatmap of your city"
)),
sidebarLayout(
sidebarPanel(
fixed=TRUE,draggable=TRUE,
top=60,left="auto",right=20,bottom="auto",
width=330,height="auto",
selectInput("city",
label = "Where are you living?",
choices = c("New York" = "New York" ,
"Los Angeles" = "Los Angeles",
"San Francisco" = "San Francisco",
"Austin" = "Austin",
"Chicago" = "Chicago")
),
selectInput("asp",
label = "Which aspect you want to learn about?",
choices = c("Population" = 1,
"crime rate" = 2,
"library" = 3,
"Restaurant" = 4,
"park" = 5,
"health care" =6)
)
),
mainPanel(
plotOutput("heatmap")
)
)))
)
server <- shinyServer(function(input, output){
data("zip.regions")
map <- reactive({
if (input$asp == 1){
d <- read.csv("~/GitHub/Spr2017-proj2-proj2_grp8/data/All-in/Population.csv")
d <- d[d$city == input$city,]
d <- na.omit(d)
d_map <- get_map(location = input$city ,maptype = "terrain" , zoom = 12)
map <- ggmap(d_map, extent = "device") +
geom_density2d(data = d,
aes(x = Lon, y = Lat), size = 0.3) +
stat_density2d(data = d,
aes(x = Lon, y = Lat, fill = ..level.., alpha = ..level..), size = 0.01,
bins = 16, geom = "polygon") + scale_fill_gradient(low = "green", high = "red") +
scale_alpha(range = c(0, 0.3), guide = FALSE)
}
if(input$asp == 2 ){
crime <- read.csv(paste("~/GitHub/Spr2017-proj2-proj2_grp8/data/crime/",input$city,".csv",sep = ""))
crime <- na.omit(crime)
crime_map <- get_map(location = input$city,maptype = 'terrain',zoom = 12)
map <- ggmap(crime_map, extent = "device") +
geom_density2d(data = crime,
aes(x = Lon, y = Lat), size = 0.3) +
stat_density2d(data = crime,
aes(x = Lon, y = Lat, fill = ..level.., alpha = ..level..), size = 0.01,
bins = 16, geom = "polygon") + scale_fill_gradient(low = "green", high = "red") +
scale_alpha(range = c(0, 0.3), guide = FALSE)
}
if(input$asp == 3){
lib <- read.csv(paste("~/GitHub/Spr2017-proj2-proj2_grp8/data/City Raw/",input$city, "/Library.csv", sep = ""))
lib=
lib%>%
filter(lib$ZIP>0)%>%
mutate(region=as.character(ZIP))
lib=
lib%>%
group_by(region)%>%
summarise(
value=n()
)
map <- zip_choropleth(lib,
title = paste("Library in ", input$city, sep = ""),
legend = "Number of Libraries",
zip_zoom = lib$region)
}
if (input$asp == 4){
res <- read.csv(paste("~/GitHub/Spr2017-proj2-proj2_grp8/data/City Raw/",input$city, "/Restaurant.csv", sep = ""))
res=
res%>%
filter(res$ZIP>0)%>%
mutate(region=as.character(ZIP))
res=
res%>%
group_by(region)%>%
summarise(
value=n()
)
map <- zip_choropleth(res,
title = paste("Restaurant in ", input$city, sep = ""),
legend = "Number of restaurants",
zip_zoom = res$region[res$region %in% zip.regions$region])
}
if (input$asp == 5){
res <- read.csv(paste("~/GitHub/Spr2017-proj2-proj2_grp8/data/City Raw/",input$city, "/Park.csv", sep = ""))
res=
res%>%
filter(res$ZIP>0)%>%
mutate(region=as.character(ZIP))
res=
res%>%
group_by(region)%>%
summarise(
value=n()
)
map <- zip_choropleth(res,
title = paste("Park in ", input$city, sep = ""),
legend = "Number of Parks",
zip_zoom = res$region[res$region %in% zip.regions$region])
}
if (input$asp == 6){
res <- read.csv(paste("~/GitHub/Spr2017-proj2-proj2_grp8/data/City Raw/",input$city, "/Health.csv", sep = ""))
res=
res%>%
filter(res$ZIP>0)%>%
mutate(region=as.character(ZIP))
res=
res%>%
group_by(region)%>%
summarise(
value=n()
)
map <- zip_choropleth(res,
title = paste("Health care in ", input$city, sep = ""),
legend = "Number of Health cares",
zip_zoom = res$region[res$region %in% zip.regions$region])
}
return(map)
})
output$heatmap <- renderPlot({
map()
}
)
})
shinyApp(ui=ui, server = server)
|
164c6dae08a05d01ac2b9a1691dbae4ca72da5e4 | 547cee3ec07bbe9ea5708651caafb9b569dee332 | /filter_runs_avg.R | f6a09142c4243e782015d1314ca6a9a11e287b6e | [] | no_license | ckrosslowe/sr15-scenarios | 1d6c4da9aa35255fe70cc525228dc97f06191496 | 613b03a85e352c6e092a0859d385dc4a33f9ac0a | refs/heads/master | 2021-05-26T09:55:11.324015 | 2021-01-12T19:56:48 | 2021-01-12T19:56:48 | 254,086,065 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,943 | r | filter_runs_avg.R | # Function to filter runs
require(tidyverse)
# Inputs: run sample, warming categories, region, sustainability limits, models to exclude if region selected
filter_runs_avg <- function(runs, temp_cats, reg="World", limits, ms_exclude) {
# Select runs that meet temp criteria
runs <- runs %>% filter(category %in% temp_cats)
# calculate 2040-2060 average
runs_avg <- runs %>%
filter(Year>=2040,
Year<=2060,
Region %in% "World") %>%
select(mod_scen,
CarbonSequestration.CCS.Biomass,
CarbonSequestration.LandUse,
PrimaryEnergy.Biomass) %>%
#replace(is.na(.),0) %>%
group_by(mod_scen) %>%
summarise(avg_beccs = mean(CarbonSequestration.CCS.Biomass, na.rm=T),
avg_af = mean(CarbonSequestration.LandUse, na.rm=T),
avg_bio = mean(PrimaryEnergy.Biomass, na.rm=T)) %>%
replace(is.na(.), 0)
# --- BECCS & Bioenergy - global
bio_lim <- limits["bio"]
beccs_lim <- limits["beccs"]
af_lim <- limits["af"]
# Which models meet these criteria at a global level in 2050?
#keep_ms <- runs$mod_scen[runs$Year==2050 & runs$Region %in% "World" & runs$CarbonSequestration.CCS.Biomass<=beccs_lim & runs$PrimaryEnergy.Biomass<=bio_lim]
#keep_ms <- runs$mod_scen[runs$Year==2050 & runs$Region %in% "World" & runs$CarbonSequestration.CCS.Biomass<=beccs_lim & runs$CarbonSequestration.LandUse <= af_lim]
keep_ms <- runs_avg$mod_scen[runs_avg$avg_beccs <= beccs_lim & runs_avg$avg_bio <= bio_lim]
# --- FILTER runs
runs <- filter(runs, mod_scen %in% keep_ms, Region %in% reg)
# --- REMOVE runs that don't include regional breakdowns
# TEST shows which runs don't have Electricity in 2050 (All do in world, after filters)
#table(runs$mod_scen[runs$Year==2050], !is.na(runs$SecondaryEnergy.Electricity[runs$Year==2050]))
if (!reg %in% "World") runs <- filter(runs, !mod_scen %in% ms_exclude)
return(runs)
} |
bb33f29d86901d09d4aacdb86d5f157013ab6081 | b6ca4c890d29aa7085b47421bc770d37755e3061 | /complete.R | bd265c9e9b0d6a3aa1bcbe62b4dff2fcfdecfd9e | [] | no_license | alanmyers/R_Programming | 7d72d52a1214d1a008988c20662ff526c3f27cd4 | f8d586ca45086570030f34487c5a64e3e3892324 | refs/heads/master | 2021-03-12T20:45:51.031486 | 2015-02-15T21:35:58 | 2015-02-15T21:35:58 | 30,843,044 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 972 | r | complete.R | complete <- function(directory, id = 1:332, printit=TRUE) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'id' is an integer vector indicating the monitor ID numbers
## to be used
## Return a data frame of the form:
## id nobs
## 1 117
## 2 1041
## ...
## where 'id' is the monitor ID number and 'nobs' is the
## number of complete cases
df = data.frame()
for (i in id) {
if (i < 10) {
fname <- sprintf("%s/00%d.csv", directory, i)
} else if (i < 100) {
fname <- sprintf("%s/0%d.csv", directory, i)
} else {
fname = sprintf("%s/%d.csv", directory, i)
}
Data = read.csv(fname)
nobs <- nrow(subset(Data, Data[2]>= 0 & Data[3]>= 0))
df = rbind(df, c(i, nobs))
str = sprintf("Id: %d, nobs: %d", i, nobs)
if (printit) {
print(str)
}
}
colnames(df) <- c("id", "nobs")
df
} |
5f0dfd1fe63e0fc55df4c820ea8e8abe859c1608 | 0edde7dc03658b64303ffc0d4da539f858cf77b9 | /R/match_azure_with_cv2.R | 34f71452938779e432740bcfca5b2cbe5bb70b3a | [
"MIT"
] | permissive | Atan1988/alvision | 8ff114dbff8297768b0e8ef6196d0355b89b412e | 15f771d24f70353c81fa62c7461c4e602dc75b01 | refs/heads/master | 2021-07-11T00:52:34.072235 | 2020-12-11T05:43:09 | 2020-12-11T05:43:09 | 221,738,729 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,476 | r | match_azure_with_cv2.R | #' @title azure lines into cv2 bounding boxes
#' @param bounds_df bounds_df result from crop_out_boxes function
#' @param res_lines lines result from the azure api reading the whole page
#' @export
az_to_cv2_box <- function(bounds_df, res_lines) {
if (nrow(bounds_df) == 0) {
match_idx <- rep(NA, length(res_lines))
} else {
bounds_list <- bbox_df_to_c(bounds_df)
match_idx <- res_lines %>% purrr::map(~pts_to_wh(.$boundingBox)) %>%
purrr::map_dbl(function(x) {
res <- bounds_list %>% purrr::map_lgl(~chk_box_in(., x, 10)) %>% which(.)
if (length(res) == 0) return(NA)
return(res[1])
})
bounds_df$az <- 1:nrow(bounds_df) %>% purrr::map(
function(x) {
idx <- which(match_idx == x)
if (length(idx) == 0) return(list())
return(res_lines[idx])
}
)
}
not_matched_idx <- which(is.na(match_idx))
not_matched_bounds_df <- not_matched_idx %>%
purrr::map_df(function(x) {
box_ref <- res_lines[[x]]$boundingBox %>% pts_to_wh() %>% t()
box_ref <- tibble::as_tibble(box_ref)
names(box_ref) <- c('x', 'y', 'w', 'h')
#box_ref$az <- list(res_lines[[x]])
return(box_ref)
})
not_matched_bounds_df$az <- not_matched_idx %>% purrr::map(~res_lines[.])
bounds_dfb <- dplyr::bind_rows(bounds_df, not_matched_bounds_df)
bounds_df1 <- add_rc_bbox(bbox_df = bounds_dfb)
return(bounds_df1)
}
|
36fe45701220fca499d4523ddd341dd39f65471c | 15e70518da836ba65181d1e0b2f1ef5acc0f7983 | /man/multiPredict.Rd | cfe6494b111f277fd4615362c38435c44132e543 | [] | no_license | razielmelchor/caretEnsemble | 03742a489ff021d7ac0edf5c15109568ad91013c | 66f647e1f7994886ba0274dbb8de4124dd9f7dd3 | refs/heads/master | 2021-01-21T02:35:51.343915 | 2014-04-22T14:12:45 | 2014-04-22T14:12:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 520 | rd | multiPredict.Rd | \name{multiPredict}
\alias{multiPredict}
\usage{
multiPredict(list_of_models, type, newdata = NULL, ...)
}
\arguments{
\item{list_of_models}{a list of caret models to make
predictions for}
\item{type}{Classification or Regression}
\item{...}{additional arguments to pass to predict.train.
DO NOT PASS the "type" argument. Classsification models
will returns probabilities if possible, and regression
models will return "raw".}
}
\description{
Make a matrix of predictions from a list of caret models
}
|
01dc3d897dd7fd157a4e1f25f29fff3e895b28b5 | baa9f522320d708c4ac95e75e63849642a83a124 | /man/noop.Rd | 02289678145bead5e6c9d6b95bd9761b120ba069 | [] | no_license | cbaumbach/miscFun | d9faba0e7f00c3a341c747f8288e8e92c70aae15 | b7fb44d006b83655bc9b3a87eebf6cd04899ba01 | refs/heads/master | 2021-01-17T10:23:14.645478 | 2017-01-23T11:21:17 | 2017-01-23T11:21:17 | 30,698,538 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 279 | rd | noop.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/miscFun.R
\name{noop}
\alias{noop}
\title{Do nothing}
\usage{
noop(...)
}
\arguments{
\item{\dots}{Ignored}
}
\value{
None.
}
\description{
Do nothing
}
\examples{
noop(ignore_whatever_is_in_here)
}
|
3536f420b1e8d273c108715c29143837e0ccc023 | 3e4acee05323c4195a69109151ab1925688b1033 | /11_COS_example.R | ecddbcccdf066ac22511542e2376c3f312f39bf5 | [] | no_license | costlysignalling/TheorBiol2020 | 1a83c95aa76400e208613ea07213b14f33d92338 | 23e4bea87cb63d5672fb279f869a778e04cb6b7b | refs/heads/main | 2023-04-01T17:02:51.703110 | 2021-04-13T17:58:48 | 2021-04-13T17:58:48 | 300,630,454 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,044 | r | 11_COS_example.R | library(rethinking)
#Corridor of stability example
#Create the dataset, where you know the correct answer
h<-rnorm(10000,0,1)
w<-h*0.2+rnorm(10000,0,0.6)
#Thei function gives you the sequence of beta estimations with random gradually growing sample
#Set the ns, you are willing to consider
ns<-5:200
#Define the function that will start at 5, estimate the beta (we know the correct answer, because we created the original dataset), add another participant, estimate the beta and iterate until n=max
givebet<-function(){
sam<-sample(1:10000)
ds<-list(h=h[sam],w=w[sam])
betas<-NA
counter<-1
for(n in ns){
dsub<-list(h=ds$h[1:n],w=ds$w[1:n])
m<-quap(alist(
w~dnorm(mu,sigma),
mu<-a+b*h,
a~dnorm(0,0.2),
b~dnorm(0,0.5),
sigma~dexp(1)
),data=dsub,start=list(a=0,b=0,sigma=1))
betas[counter]<-precis(m)[[1]][2]
print(n)
counter<-counter+1
}
return(betas)
}
#You need to create multiple curves like this. The more, the better (but it takes more computation time)
runs<-20
curves<-replicate(runs,givebet())
str(curves)
#Set the COS width, where you wish to land
wd<-0.10
plot(ns,curves[,1],type="n",ylim=c(-0.2,0.5))
for(i in 1:runs){
lines(ns,curves[,i],col="#00000080")
}
#You can higlight single simulation run like this
lines(ns,curves[,1],col="#0000FF")
#Plot the correct estimate and the corridor of stability
abline(h=0.2,col=2)
abline(h=0.2+c(-1,1)*wd,col=2,lty=2)
#The stability is usually defined as staynw within the corridor
#First you need to define, if iven point along each curve is within the corridor
between<-curves>0.2-wd&curves<0.2+wd
str(between)
#Then you need to check whether the given point is not just within the corridor, but also that given curve does not fluctuate outside the COS until the end of the simulation. This is achieved by comparison of the cummulative sum of the reversed T/F vector of being within the corridor with the vector corresponding to the order of the used semple size
#This is the procedure for the fourth curve
i<-4
rev(cumsum(rev(between[,i]))==c(1:length(ns)))
#This is the procedure for all of them (matrix is returned)
ins<-sapply(1:20,function(i){rev(cumsum(rev(between[,i]))==c(1:poc))})
#Another arbitrary parameter you need to define (besides COS width, wd in this script) is the Proportion of curves you want to securely keep within the COS until the end of the simulation (until maximum n is reached). 80% was selected here
security<-0.80
#Cacluate the proportion of your simulation runs, where the estimate stays within the corridor until the end of the simulation
proport<-rowSums(ins)/runs
#Result is the threshold. The first point, where the requested proportion of estimates stay within the requested corridor.
tt<-ns[min(which((proport>=security)==T))]
tt
#You can put it as a vertical line to the plot.
abline(v=tt,col=3)
|
6c9585cc1ca0ffc243bab701df4bf83d1d9269ee | f1d2bfa8addbbb74cc8a14269c417667884f7672 | /R/emisja.R | 5a8dad73d7fe2d2a89a123cc6c637a87837e700a | [
"MIT"
] | permissive | prusakk/KPFuels | 21f162446a3a538ef77bb626c7fc1111a473a0e7 | 91d6663eee4520d1f7579cab40e820af62accdad | refs/heads/main | 2023-01-30T03:47:38.998695 | 2020-12-13T21:45:43 | 2020-12-13T21:45:43 | 319,713,311 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,479 | r | emisja.R | #' Funckja do obliczania genrowanych emisji przez pojazdy spalinowe
#'
#' @param dane dataframe - dane wejลciowe
#' @param kategoria character - kategoria pojazdu np. Passenger Cars, Heavy Duty Trucks itd.
#' @param euro character - norma emisji spalin np. Euro II, Euro III itd.
#' @param mode character - tryb jazdy pojazdu np. Highway, Urban Peak itd.
#' @param substancja character - nazwa substacji emisyjnej np. CH4, N2O
#'
#' @return dataframe
#'
#' @import dplyr
#' @export
#'
#' @details Wzรณr wykorzystywany do obliczenia emsji
#'
#' (Alpha x Procent^2 + Beta x Procent + Gamma + (Delta/Procent)/
#' (Epsilon x Procent^2 + Zita x Procent + Hta) x (1-Reduction)
#'
emisja <- function(dane = input,
kategoria = "Passenger Cars",
euro = "Euro 4",
mode = "",
substancja = "CO") {
out <- wskazniki %>%
filter(Category %in% kategoria) %>%
filter(Euro.Standard %in% euro) %>%
filter(Pollutant %in% substancja) %>%
filter(Mode %in% mode)
out <- inner_join(x = out, y = input, by = c("Segment", "Fuel", "Technology"))
out <- out %>%
mutate(Emisja = Nat * ((Alpha * Procent ^ 2 + Beta * Procent + Gamma + (Delta/Procent))/
(Epsilon * Procent ^ 2 + Zita * Procent + Hta) * (1-Reduction))
) %>%
select(Category, Fuel, Euro.Standard, Technology, Pollutant, Mode, Segment, Nat, Emisja)
out[!duplicated(out), ] -> out
return(out)
}
|
0f84b1ec3350f7445a7c3d300fb62ebfebe62fd5 | b733d3f7e67a62c34d4889c561d2388da835d451 | /tests/testthat/test-createLocationID.R | a8e4869a43d20aa35042cc22b29440e76345ffdc | [] | no_license | cran/MazamaCoreUtils | 7c3c4c71d2667b4512f203ca5ba7c67df773dc9d | 15f2b32ed32835229b1df8cf74243d745ea7fd16 | refs/heads/master | 2023-09-05T17:48:57.276030 | 2023-08-29T21:50:02 | 2023-08-29T23:30:40 | 154,902,175 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,092 | r | test-createLocationID.R | test_that("algorithms work", {
# Setup
longitude <- -120:-110
latitude <- 30:40
# Default to "digest"
expect_identical(
createLocationID(longitude, latitude),
c("2579cca9bc8bb160","0bc60b264bab6c8f","242c3d44df97de47","891fb5f2df4f8a39",
"1e9bb5a927f39726","890cb1a66d1e9e9d","e2105228a0188686","f61bfb636bba4233",
"c60fc5cd3450730d","ef89fa02bbd43fb5","c389bbe887dcf75f")
)
# Explicit "digest"
expect_identical(
createLocationID(longitude, latitude),
c("2579cca9bc8bb160","0bc60b264bab6c8f","242c3d44df97de47","891fb5f2df4f8a39",
"1e9bb5a927f39726","890cb1a66d1e9e9d","e2105228a0188686","f61bfb636bba4233",
"c60fc5cd3450730d","ef89fa02bbd43fb5","c389bbe887dcf75f")
)
# Explicit "geohash"
expect_identical(
createLocationID(longitude, latitude, "geohash"),
c("9m6dtm6dtm","9me2k56u54","9msn4c7j88","9mug9x7nym","9qj92me2k5","9qnpp5e9cb",
"9qquvceepq","9qxdkxeut5","9wb25msn4c","9wcjf5sr2w","9x1g8cu2yh")
)
# Stop on unexpected algorithm
expect_error(createLocationID(longitude, latitude, "paste"))
})
|
e9e56bbd33ed0eed28bdc84461cec58893d5fdcb | 71342669c1ecd5246822806ba02dcfd2886324b2 | /run_analysis.R | 1ec7611b909df7d06dbfd3ce48497c48b7febe98 | [] | no_license | zephyr213/GCD_courseproject | 49895cf710ab8cb8c074a210e8a7cc90fc659ebd | 051390d299c9a5737d9291728d6a1ee926147507 | refs/heads/master | 2021-01-10T20:35:16.754484 | 2014-10-26T23:34:55 | 2014-10-26T23:34:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,082 | r | run_analysis.R | # first read in the train, test data
testx <- read.table("./UCI HAR Dataset/test/X_test.txt", header = F)
testy <- read.table("./UCI HAR Dataset/test/y_test.txt", header = F)
tests <- read.table("./UCI HAR Dataset/test/subject_test.txt", header = F)
trainx <- read.table("./UCI HAR Dataset/train/X_train.txt", header = F)
trainy <- read.table("./UCI HAR Dataset/train/y_train.txt", header = F)
trains <- read.table("./UCI HAR Dataset/train/subject_train.txt", header = F)
# read in the variable descriptions
features <- read.table("./UCI HAR Dataset/features.txt")
# find only the mean() and std() variable index and create new index
meanindex <- grep("-mean()", features$V2, fixed = T)
stdindex <- grep("-std()", features$V2, fixed = T)
newindex <- sort(c(meanindex, stdindex))
# subset x data
testx1 <- testx[ ,newindex]
trainx1 <- trainx[ ,newindex]
# include label in y data and
testnew <- cbind(testx1, testy, tests)
trainnew <- cbind(trainx1, trainy, trains)
#merge test and train
testnew$group <- "test"
trainnew$group <- "train"
vnames1 <- featurex$V2[newindex]
vnames <- c(vnames1, "activity", "subject", "group") # descriptive variable name
names(testnew) <- vnames
names(trainnew) <- vnames
newdata <- rbind(testnew, trainnew)
#substitute label into activity
newdata$activity <- as.numeric(newdata$activity)
for (i in 1:length(newdata$activity)) {
if (newdata$activity[i] == 1) newdata$activity[i] <- "WALKING"
if (newdata$activity[i] == 2) newdata$activity[i] <- "WALKING_UPSTAIRS"
if (newdata$activity[i] == 3) newdata$activity[i] <- "WALKING_DOWNSTAIRS"
if (newdata$activity[i] == 4) newdata$activity[i] <- "SITTING"
if (newdata$activity[i] == 5) newdata$activity[i] <- "STANDING"
if (newdata$activity[i] == 6) newdata$activity[i] <- "LAYING"
}
# now newdata is the merged data set
# now create a second tidy dataset
library(reshape2)
tmpdata <- melt(newdata, id = c("activity", "subject"), measure.vars = vnames1)
newdata_2 <- dcast(tmpdata, activity + subject ~ variable, mean)
# now newdata_2 is the data set required for step 5 |
37cea900d2d7c2d2b8acf709028c3e9f92cd1e94 | 1cd5be99e42382f8b7c8aea6b89b59870144b0f4 | /codes/plot2Shape.R | 0b4a7042e1c29d7025113a10d305b623710998e1 | [] | no_license | acdantas/mef2-selexseq | 89812eba9216c1277357db2007820dd68f66a276 | 768f6d2b8b108fedd56429be60db2662e34ba4f5 | refs/heads/master | 2022-11-22T02:22:22.472942 | 2020-07-28T06:16:32 | 2020-07-28T06:16:32 | 143,241,809 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,402 | r | plot2Shape.R | plot2Shape <- function (shapeMatrix, background = NULL, colDots = rgb(0, 0,
1, 0.1), colDotsBg = rgb(0, 0, 0, 0.1), colLine = "steelblue",
colLineBg = "gray50", cex = 0.5, lwd = 4, ylim, ...)
{
n <- nrow(shapeMatrix)
mu <- colMeans(shapeMatrix, na.rm = TRUE)
m <- length(mu)
span <- round(m/2)
if (is.null(background)) {
if (missing(ylim))
ylim <- range(mu, na.rm = TRUE)
plot(mu, col = colDots, pch = 19, cex = cex, xaxt = "n",
xlab = "", #ylab = paste0("Mean value (n=", n, ")"),
ylim = ylim, ...)
# axis(1, at = c(0, m), labels = c(-span, paste0("+", span)))
# abline(v = span, lty = 2, col = "gray30")
lines(lowess(mu, f = 1/10), col = colLine, lwd = lwd)
}
else {
mu1 <- mu
mu2 <- colMeans(background, na.rm = TRUE)
if (missing(ylim))
ylim <- range(mu1, mu2, na.rm = TRUE)
plot(mu1, col = colDots, pch = 19, cex = cex, xaxt = "n",
xlab = "", #ylab = paste0("Mean value (n=", n, ")"),
ylim = ylim, ...)
points(mu2, pch = 19, cex = cex, col = colDotsBg)
# axis(1, at = c(0, m), labels = c(-span, paste0("+", span)))
# abline(v = span, lty = 2, col = "gray30")
lines(lowess(mu1, f = 1/10), col = colLine, lwd = lwd)
lines(lowess(mu2, f = 1/10), col = colLineBg, lwd = lwd)
}
}
|
70dec6aeaf7a3535d54f4fe39894ad45ec676003 | ff6cd64471c3dd38fb4b8ed3d5b5f816e9450063 | /rangesurvey/R/check_groups_recorded.R | eebae5979fd0eafb9d527ba9587efac69af462c0 | [
"MIT"
] | permissive | JamieCranston/RangeShift_survey | c9dcc97e9a433079138633f23f97b59c39b777e0 | 71f9318f650bc9e02389c227c77b16cfda2305cb | refs/heads/main | 2023-04-14T02:46:13.075353 | 2022-03-20T12:08:55 | 2022-03-20T12:08:55 | 471,427,098 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 926 | r | check_groups_recorded.R | #' check_groups_recorded
#'
#' @param data respondent character table
#' @param config config file (for paths to validated groups recorded csv)
#'
#' @return respondent data with a column added for the groups the respondent's reported recording as listed in text field of the other option on the groups recorded question.
#' range-shifting species
#' @export
check_groups_recorded <- function(data, config) {
#species_group_val <- readr::read_csv(config$validation_dirs$species_group_val, col_types = readr::cols(id = "c"))
print("please see our imputation for respondent answers about other groups they recorded")
print(species_group_val)
other_validated <- data %>%
dplyr::left_join(
x = .,
y = species_group_val %>%
dplyr::select(-.data$OtherGroups),
by = "id"
) %>%
dplyr::select(-.data$OtherGroups) %>%
dplyr::mutate(.data$imputed_group)
return(other_validated)
}
|
9e2ab7a8a521d7cf2677a950e3e1c7791a3e0f34 | 2e1a9e1d0d038293bc8dba83d0473e9dc2f7f93e | /stats/scripts/altmetrics.analysis/man/write.table.gzip.Rd | 2532a050220daab774f812cc42b3ac736c5999ed | [
"MIT",
"CC0-1.0"
] | permissive | neostoic/plos_altmetrics_study | 92821fa2634e75235f0fc5603b7ee7e25d298c91 | 5d4bd840763286c77cb834ef351e137eefb7946b | refs/heads/master | 2020-12-25T08:29:47.940176 | 2011-03-22T15:00:48 | 2011-03-22T15:00:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 248 | rd | write.table.gzip.Rd | \name{write.table.gzip}
\alias{write.table.gzip}
\title{write table gzip}
\usage{
write.table.gzip(data, basedir, filename)
}
\arguments{
\item{data}{
}
\item{basedir}{
}
\item{filename}{
}
}
\author{Jason Priem and Heather Piwowar}
|
6aeeb6d6be19a0d3d807e3d6bd6d96a6ee3c0050 | eb661d348facb5aee7240fbbedf6e615b7dcb3c3 | /plot3.R | 3a1e5ecda6ece871d3734bd5a5a96dc47eaea8cd | [] | no_license | swoldetsadick/COUREDAPRO2 | 9169f4c564530b1619d851022b829d842e01c348 | 3d2dec9bec6e2d19cd12478aca88a505c98fa43c | refs/heads/master | 2021-01-19T17:17:41.004522 | 2014-09-20T16:04:51 | 2014-09-20T16:04:51 | 24,243,472 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,575 | r | plot3.R | # Downloading in current working directory (CWD) and Loading data sets
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(url, "exdata-data-NEI_data.zip", mode="wb")
unzip("exdata-data-NEI_data.zip")
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# Subsetting data related only to Baltimore city and saving it to dataint
# Loading plyr library to use ddply for summing amount of PM2.5 emission in tons
# by year and type of source. data obtained is stored in new dataset called data2
# whose first column shows the year, the second type of source and last total
# emission of above pollutant that year. Columns are labeled accordingly.
library(plyr)
library(ggplot2)
dataint <- subset(NEI, as.factor(NEI$fips) == 24510)
data3 <- ddply(dataint, .(as.factor(year),as.factor(type)), summarize,tot=sum(as.numeric(Emissions)))
names(data3)[1] <- "Year"
names(data3)[2] <- "Type"
# plot3 uses ggplot plotting system and plots a titled lines plot with duely labeled x - and y -
# axis. A legend is included on the plot. The plot is then saved in CWD under
# png file named plot3.png.
png("./plot3.png")
plot.title="Total Emissions in Baltimore City from 1999 to 2008 from PM"
plot.subtitle="by type of source"
plot3 <- ggplot(data3, aes(Year, tot, group = Type))
plot3 <- plot3 + geom_line(aes(color = Type)) + labs(y=expression("Total Emissions in Tons from PM"[2.5]))
plot3 <- plot3 + ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) + labs(x="Years")
plot3
dev.off() |
0a4859f9f1ec02057e86ceb03053298241585f8c | 81467b617a2cc6e211d0dc0c735a251445f4a163 | /uberdata/man/multinomialHierBayesModel.Rd | e8d083b08da2d497e7f51efdb979336437bb0254 | [] | no_license | adam-sullivan/uberproject | 664d1813296907f0aa148c9848ebdcb252ff7f7e | b0f68bca5b8f7d5df57215300a9fa046b02f4dcd | refs/heads/master | 2016-09-06T07:51:59.261832 | 2015-04-04T22:39:20 | 2015-04-04T22:39:20 | 33,405,636 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 752 | rd | multinomialHierBayesModel.Rd | % Generated by roxygen2 (4.1.0.9001): do not edit by hand
% Please edit documentation in R/prediction.R
\name{multinomialHierBayesModel}
\alias{multinomialHierBayesModel}
\title{Bayesian Hiearchial multinomial logistic regression}
\usage{
multinomialHierBayesModel(testTrip)
}
\arguments{
\item{testTrip}{an input of the shortened feature vector}
}
\value{
outMCMCs The multinomial Bayesian model chains for the estimates for beta.
}
\description{
Exploratory function. No guarantee on code safety, included for demonstration.
This function was my top pick for being able to model the dropoff location.
It creates a list structure (one for each unique ID) as the input and predictor variable.
The output is MCMC samples for the estimates for beta.
}
|
6ef665665b8ab8b86141b2892c1cd4fa25930493 | 1d9593d1031d38caef98783af997de9a2c02b901 | /R/get_results_aj.R | 81ec61531180b31bcaf1f57cc6e37012eaed5dda | [] | no_license | antonmalko/ibextor | 6b079cf5e5aac0f07e4ba238bfbefbb8e4e120f1 | 1396d025907985929f9763f2ae6d07a41e76823c | refs/heads/master | 2021-09-09T16:37:23.259768 | 2018-03-18T04:42:33 | 2018-03-18T04:42:33 | 125,311,591 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,779 | r | get_results_aj.R | get_results_aj <- function(file_name,
elem_number = NULL,
del_col = NULL, del_mode = "auto",
col_names = NULL, partial_names = TRUE,
col_classes = NULL, partial_classes = TRUE,
short_subj_ids = TRUE,
...) {
#' @rdname get_results
#' @export
if (is.null(col_names)){
col_names <- c("question",
"answer",
"is_correct",
"quest_rt",
"sentence")
}
if (is.null(col_classes)){
col_classes <- c("character",
"character",
"numeric",
"numeric",
"character")
}
res <- read_ibex(file_name, ...)
res <- subset_ibex(res, controller = "AcceptabilityJudgment", elem_number = elem_number)
res <- res[, 1:11]
# Reading odd (first line of code) and even (second line) raws.
# They contain different types of info.
res_FlashSentence <- res[(seq(1, NROW(res), by=2)), 8 ]
if (NROW(res_FlashSentence)==0) stop ("Subsetting for sentences data failed")
res <- res[(seq(2, nrow(res), by=2)), ]
if(NROW(res)==0) stop ("Subsetting for questions data failed")
rownames(res) <- NULL
#add FlashSentence info into the 12th column, after the main data
res[,12] <- res_FlashSentence
droplevels(res)
res <- format_ibex(res,
col_names = col_names, partial_names = partial_names,
col_classes = col_classes, partial_classes = partial_classes)
res <- recode_subjects(res, short_ids = short_subj_ids)
res <- delete_columns(res, del_col, del_mode)
return(res)
}
|
04c1d2cddcf9cb41f7c2e1b948a8a63cf9001bea | 2f92299aa6ba0f054d59a07ea71b755b0a472bdc | /GitHub Setup v1.R | 7208619e9129a0d204f4d7743608bf1e5bf7c630 | [] | no_license | MichaelLandivar/GitHub-Setup | bb44d8c1ac4a119121ab9ea947492a8e5e0700e1 | 4c6eb0d841cef35120836eae5e055b3cddd0f4ab | refs/heads/master | 2021-01-11T01:50:20.126299 | 2016-11-21T15:54:59 | 2016-11-21T15:54:59 | 70,844,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,927 | r | GitHub Setup v1.R | #Install R
#https://www.r-project.org/
#Install RStudio
#https://www.rstudio.com/
#Install Git
#https://git-scm.com/downloads
#Sign up for & install GitHub
#https://github.com/
#Installing & Configuring Git
#A. Login to github.com and navigate to repositories
#1. Select "New"
#2. Provide a repository name
#3. Provide a description (optional)
#4. Select privacy if applicable
#5. Check box "Initialize this repository with a README" that provides initial repository information
#6. Select "Create repository"
#B. Configure Git on your computer
#1. Open "Start" and search for "GitHub" and "Git Shell"
#2. Pin both to desktop or other location for easy access
#3. Open Git shell and enter the following shell commands:
#git config --global user.name "YOUR NAME"
#git config --global user.email "YOUR EMAIL"
#git config --list
#The last command verifies the information entered
#C. Open RStudio
#1. Go to Tools > Global Options > Git/SVN
#2. Check box "Enable version control interface for RStudio projects"
#3. Provide file path for Git executable (e.g., C:/Program Files/Git/bin/git.exe)
#Be sure that when installing git you keep track of where the program files are saved
#4. Select "Ok"
#5. Restart RStudio
#6. Go to File > New Project > Version Control > Git
#7. Provide Repository URL from github account
# Login to github
#Go to Your Profile > Repositories
#Select the repository you would like to connect to
#Go to Clone or download
#Copy URL and paste in RStudio Provide Repository URL dialogue
#8. Create project as subdirectory of...
#D. Create New File > R Script
#1. Write codes
#2. Go to tools > Version Control > Commit...
#3. Select "Show staged"
#3. Check "Staged" box for desired R file(s) and for the GitHub Project
#4. Type a commit message description of the action on the repository
#5. Select "Commit"
#You will be prompted to enter your githum.com credentials
#Example of successful commit:
#[master YOURHEXADECIMAL] YOUR COMMIT MESSAGE
#2 files changed, 63 insertions(+)
#create mode 100644 YOURRSCRIPTFILENAME
#create mode 100644 YOURGITREPOSITORYNAME
#6. Select "Push"
#Example of successful branch push:
#To https://github.com/YOURGITUSERNAME/YOURGITREPOSITORYNAME
#YOURHEXADECIMAL master -> master
#7 Go to github.com and refresh page
#8 Updates now located in repository > commits
#E. Check for track changes/Update with changes
#1. Go to Tools > Version Control > Diff ""
#2. Type a commit message description of the action on the repository
#3. Select "Commit"
|
7b4db94bf81a259dcef03561b974f2e779e21b99 | 86b56702a9041a8a17e60d95c212978f388311ad | /follow_seq2ASV.R | 7c934154ebe3356f7f9f34d70582fa900ca50d86 | [] | no_license | AMCMC/ITS_seq_ASVmapping | c166c72eb4c86b97a409a941dcad486bf6eacea4 | b7630c8f083eabe311b85214411f654ee6158226 | refs/heads/master | 2020-03-27T16:31:50.353858 | 2018-09-04T14:51:09 | 2018-09-04T14:51:09 | 146,789,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,407 | r | follow_seq2ASV.R | library(dada2);packageVersion("dada2")
library(phyloseq);packageVersion("phyloseq")
library(ggplot2);packageVersion("ggplot2")
source("../../MicrobiotaCentre/MiCA_ITS/Scripts/taxa_facet_barplot_asv.R")
#### regular bigdata approach ####
fqs <- list.files("./", pattern = "I.*fastq.gz")
dereps <- list()
for (file in fqs){
dereps[[file]] <- derepFastq(file)
}
names(dereps) <- gsub("_.*","",names(dereps))
err8 <- readRDS("ITS_0008.R1.RDS")
err9 <- readRDS("ITS_0009.R1.RDS")
ddF <- list()
for (i in names(dereps)){
if (substr(i,2,2)==8){
ddF[[i]] <- dada(derep = dereps[[i]], err = err8)
}else{
ddF[[i]] <- dada(derep = dereps[[i]], err = err9)
}
}
topx=10
topxseqs <- c()
for (i in dereps){
topxseqs <- c(topxseqs,names(i$uniques[1:topx]))
}
length(unique(topxseqs)) # track a total of 13 sequences
table(table(topxseqs)) #6 sequences are in the top10 in all samples
#build dataframe
df <- data.frame()
for(i in names(dereps)){
df <- rbind(df, data.frame(sequence=names(dereps[[i]]$uniques[1:topx]),
Abundance=unname(dereps[[i]]$uniques[1:topx]),
ASV=ddF[[i]]$sequence[ddF[[i]]$map[1:topx]],
SampleID=i))
}
df$ASV2 <- df$ASV
levels(df$ASV2) <- paste0("ASV-",1:length(levels(df$ASV2)))
df$sequence2 <- df$sequence
levels(df$sequence2) <- paste0("sequence-",1:length(levels(df$sequence2)))
df$ASVrep <- as.character(df$sequence)==as.character(df$ASV)
df$ASVrep2[df$ASVrep] <- as.character(df$ASV2[df$ASVrep])
ggplot(df, aes(x = sequence2, y = Abundance, fill=ASV2)) +
geom_bar(stat="identity") +
facet_grid(SampleID ~ ., scale="free_y") +
theme(axis.text.x = element_text(angle=90, hjust=1))
ggplot(df, aes(x = sequence2, y = Abundance, fill=ASVrep2)) +
geom_bar(stat="identity") +
facet_grid(SampleID ~ ., scale="free_y") +
theme(axis.text.x = element_text(angle=90, hjust=1))
### identical sequences in the various samples get assigned to different ASVs
#### try to resolve this issue with priors ####
priors <- unique(topxseqs)
ddF2 <- list()
for (i in names(dereps)){
if (substr(i,2,2)==8){
ddF2[[i]] <- dada(derep = dereps[[i]], err = err8, priors = priors)
}else{
ddF2[[i]] <- dada(derep = dereps[[i]], err = err9, priors = priors)
}
}
#build dataframe
df2 <- data.frame()
for(i in names(dereps)){
df2 <- rbind(df2, data.frame(sequence=names(dereps[[i]]$uniques[1:topx]),
Abundance=unname(dereps[[i]]$uniques[1:topx]),
ASV=ddF2[[i]]$sequence[ddF2[[i]]$map[1:topx]],
SampleID=i))
}
df2$ASV2 <- df2$ASV
levels(df2$ASV2) <- paste0("ASV-",1:length(levels(df2$ASV2)))
df2$sequence2 <- df2$sequence
levels(df2$sequence2) <- paste0("sequence-",1:length(levels(df2$sequence2)))
df2$ASVrep <- as.character(df2$sequence)==as.character(df2$ASV)
df2$ASVrep2[df2$ASVrep] <- as.character(df2$ASV2[df2$ASVrep])
ggplot(df2, aes(x = sequence2, y = Abundance, fill=ASV2)) +
geom_bar(stat="identity") +
facet_grid(SampleID ~ ., scale="free_y") +
theme(axis.text.x = element_text(angle=90, hjust=1))
ggplot(df2, aes(x = sequence2, y = Abundance, fill=ASVrep2)) +
geom_bar(stat="identity") +
facet_grid(SampleID ~ ., scale="free_y") +
theme(axis.text.x = element_text(angle=90, hjust=1))
#### try to resolve this issue with 1 specific prior ####
priors <- as.character(df2$sequence[df2$sequence2=="sequence-4"][1])
ddF3 <- list()
for (i in names(dereps)){
if (substr(i,2,2)==8){
ddF3[[i]] <- dada(derep = dereps[[i]], err = err8, priors = priors)
}else{
ddF3[[i]] <- dada(derep = dereps[[i]], err = err9, priors = priors)
}
}
#build dataframe
df3 <- data.frame()
for(i in names(dereps)){
df3 <- rbind(df3, data.frame(sequence=names(dereps[[i]]$uniques[1:topx]),
Abundance=unname(dereps[[i]]$uniques[1:topx]),
ASV=ddF3[[i]]$sequence[ddF3[[i]]$map[1:topx]],
SampleID=i))
}
df3$ASV2 <- df3$ASV
levels(df3$ASV2) <- paste0("ASV-",1:length(levels(df3$ASV2)))
df3$sequence2 <- df3$sequence
levels(df3$sequence2) <- paste0("sequence-",1:length(levels(df3$sequence2)))
df3$ASVrep <- as.character(df3$sequence)==as.character(df3$ASV)
df3$ASVrep2[df3$ASVrep] <- as.character(df3$ASV2[df3$ASVrep])
ggplot(df3, aes(x = sequence2, y = Abundance, fill=ASV2)) +
geom_bar(stat="identity") +
facet_grid(SampleID ~ ., scale="free_y") +
theme(axis.text.x = element_text(angle=90, hjust=1))
ggplot(df3, aes(x = sequence2, y = Abundance, fill=ASVrep2)) +
geom_bar(stat="identity") +
facet_grid(SampleID ~ ., scale="free_y") +
theme(axis.text.x = element_text(angle=90, hjust=1))
#### how to the sequences relate ?
levels(df3$sequence)[c(1:7)]
# they clearly differ in the length of the homopolymer
#### is the issue due to the indel?
nwalign(names(dereps[[1]]$uniques)[2],names(dereps[[1]]$uniques)[3])
drmod <- dereps[[1]]
#make a substitution rather than an indel
names(drmod$uniques)[2] <- "AAAAGTCGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTAAAGAAATTTAATAATTTTGAAAATGGATTTTTTTTTTTAGTTTTGGCAAGAGCATGAGAGCTTTTACTGGGC"
ddFmod <- dada(derep = drmod, err = err8)
ddFref <- dada(derep = dereps[[1]], err = err8)
#### truncate homopolymers in forward read ####
fqs <- list.files("./", pattern = "I.*fastq.gz")
dereps <- list()
for (file in fqs){
dereps[[file]] <- derepFastq(file)
}
names(dereps) <- gsub("_.*","",names(dereps))
err8 <- readRDS("ITS_0008.R1.RDS")
err9 <- readRDS("ITS_0009.R1.RDS")
ddF <- list()
for (i in names(dereps)){
if (substr(i,2,2)==8){
ddF[[i]] <- dada(derep = dereps[[i]], err = err8)
}else{
ddF[[i]] <- dada(derep = dereps[[i]], err = err9)
}
}
for (i in names(ddF)){
ddF[[i]]$sequence <- gsub("AAAAAA*","AAAAAA",ddF[[i]]$sequence)
ddF[[i]]$sequence <- gsub("TTTTTT*","TTTTTT",ddF[[i]]$sequence)
ddF[[i]]$sequence <- gsub("CCCCCC*","CCCCCC",ddF[[i]]$sequence)
ddF[[i]]$sequence <- gsub("GGGGGG*","GGGGGG",ddF[[i]]$sequence)
}
topx=10
topxseqs <- c()
for (i in dereps){
topxseqs <- c(topxseqs,names(i$uniques[1:topx]))
}
table(table(topxseqs)) #6 sequences are in the top10 in all samples
#build dataframe
df <- data.frame()
for(i in names(dereps)){
df <- rbind(df, data.frame(sequence=names(dereps[[i]]$uniques[1:topx]),
Abundance=unname(dereps[[i]]$uniques[1:topx]),
ASV=ddF[[i]]$sequence[ddF[[i]]$map[1:topx]],
SampleID=i))
}
df$ASV2 <- df$ASV
levels(df$ASV2) <- paste0("ASV-",1:length(levels(df$ASV2)))
df$sequence2 <- df$sequence
levels(df$sequence2) <- paste0("sequence-",1:length(levels(df$sequence2)))
df$ASVrep <- as.character(df$sequence)==as.character(df$ASV)
df$ASVrep2[df$ASVrep] <- as.character(df$ASV2[df$ASVrep])
ggplot(df, aes(x = sequence2, y = Abundance, fill=ASV2)) +
geom_bar(stat="identity") +
facet_grid(SampleID ~ ., scale="free_y") +
theme(axis.text.x = element_text(angle=90, hjust=1))
ggplot(df, aes(x = sequence2, y = Abundance, fill=ASVrep2)) +
geom_bar(stat="identity") +
facet_grid(SampleID ~ ., scale="free_y") +
theme(axis.text.x = element_text(angle=90, hjust=1))
st <- makeSequenceTable(ddF)
st2 <- st
colnames(st2) <- gsub("AAAAAA*","AAAAAA",colnames(st2))
colnames(st2) <- gsub("TTTTTT*","TTTTTT",colnames(st2))
colnames(st2) <- gsub("CCCCCC*","CCCCCC",colnames(st2))
colnames(st2) <- gsub("GGGGGG*","GGGGGG",colnames(st2))
st2 <- collapseNoMismatch(st2)
tt <- cbind(make.unique(substr(colnames(st),1,10)),colnames(st))
rownames(tt) <- tt[,2]
ps <- phyloseq(otu_table(st, taxa_are_rows = F),
sample_data(data.frame(row.names=names(ddF),
Sample_Namex=names(ddF),
Lib=substr(names(ddF),1,2),
Polymerase=c("T","P","T","P","T","P"))),
tax_table(tt)
)
ps.rare <- rarefy_even_depth(ps)
ord <- ordinate(ps.rare, method = "PCoA", distance = "bray")
plot_ordination(ps, ord, label = "Sample_Namex", color="Polymerase")
ps.temp <- prune_taxa(colnames(ps.rare@otu_table) %in% colnames(ps.rare@otu_table)[1:10], ps.rare)
plot_bar(ps.temp, fill = "ta1")
ps.rare <- rarefy_even_depth(ps)
ord <- ordinate(ps.rare, method = "PCoA", distance = "bray")
plot_ordination(ps, ord, label = "Sample_Namex", color="Polymerase")
tt <- cbind(make.unique(substr(colnames(st2),1,10)),colnames(st2))
rownames(tt) <- tt[,2]
ps <- phyloseq(otu_table(st2, taxa_are_rows = F),
sample_data(data.frame(row.names=names(ddF),
Sample_Namex=names(ddF),
Lib=substr(names(ddF),1,2),
Polymerase=c("T","P","T","P","T","P"))),
tax_table(tt)
)
ps.rare <- rarefy_even_depth(ps)
ord <- ordinate(ps.rare, method = "PCoA", distance = "bray")
plot_ordination(ps, ord, label = "Sample_Namex", color="Polymerase")
ps.temp <- prune_taxa(colnames(ps.rare@otu_table) %in% colnames(ps.rare@otu_table)[1:10], ps.rare)
plot_bar(ps.temp, fill = "ta1")
#### where to collapse the homopolyer and nomismatch in the workflow? ####
fqs <- list.files("./", pattern = "I.*fastq.gz")
dereps <- list()
for (file in fqs){
dereps[[file]] <- derepFastq(file)
}
names(dereps) <- gsub("_.*","",names(dereps))
err8 <- readRDS("ITS_0008.R1.RDS")
err9 <- readRDS("ITS_0009.R1.RDS")
ddF <- list()
for (i in names(dereps)){
if (substr(i,2,2)==8){
ddF[[i]] <- dada(derep = dereps[[i]], err = err8)
}else{
ddF[[i]] <- dada(derep = dereps[[i]], err = err9)
}
}
for (i in names(ddF)){
ddF[[i]]$sequence <- gsub("AAAAAA*","AAAAAA",ddF[[i]]$sequence)
ddF[[i]]$sequence <- gsub("TTTTTT*","TTTTTT",ddF[[i]]$sequence)
ddF[[i]]$sequence <- gsub("CCCCCC*","CCCCCC",ddF[[i]]$sequence)
ddF[[i]]$sequence <- gsub("GGGGGG*","GGGGGG",ddF[[i]]$sequence)
}
table(ddF[[1]]$map)
aggregate(ddF[[i]]$denoised, by=list(ddF[[i]]$sequence), FUN=sum)
aggregate(ddF[[i]]$denoised, by=list(ddF[[i]]$sequence), FUN=sum)
aggregate(ddF[[i]]$denoised, by=list(ddF[[i]]$sequence), FUN=sum)
aggregate(ddF[[i]]$denoised, by=list(ddF[[i]]$sequence), FUN=sum)
aggregate(ddF[[i]]$denoised, by=list(ddF[[i]]$sequence), FUN=sum)
x <- c()
for (i in names(dereps)){
x[i] <- length(dereps[[i]]$uniques)/sum(dereps[[i]]$uniques)
}
ps@sam_data$seqcom <- 1-x
sequecne() |
40491ead1ceda4fd5ed5863066ba135a39e2005e | 4cc92a349885a505896de9056887465f5db40c76 | /code/NC13GroomR2.r | 6b5dddabafbea3af70f500553d3ce1328fe6a8eb | [] | no_license | guanjiahui/Social-Network_rhesus-macaques | cdaba33cbc333c00e67963e7dffd2c622d66e333 | aee1f7583c168ca17a9c83bc0659d1e645cb96e6 | refs/heads/master | 2020-04-18T14:39:45.612811 | 2019-01-25T18:46:08 | 2019-01-25T18:46:08 | 167,594,532 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,509 | r | NC13GroomR2.r | NC13_RU2_Grooming_Matrix <- read.csv("~/Dropbox/Research/SNH_health profile data for Fushing-selected/NC13_RU2_Grooming_Matrix.csv")
NC13GroomR2=as.matrix(NC13_RU2_Grooming_Matrix [,-1])
colnames(NC13GroomR2)=NC13_RU2_Grooming_Matrix[,1]
rownames(NC13GroomR2)=NC13_RU2_Grooming_Matrix[,1]
##############
win2=conductance(NC13GroomR2,maxLength = 4)
win_prob2=win2$p.hat
temp=c(0.0045,0.07,0.15,0.3,0.8,1)
Ens13.groom2=Eigen.plot2(temp, selected.id=c(1,2,3,4,5,6),win_prob2)
DCG13.groom2=DCGtree.plot(num.clusters.selected=c(1,1,2,3,4,6),
"NC13GroomR2 tree",Ens13.groom2,temp)
plot(DCG13.groom2,hang=-1,main="NC13GroomR2 tree")
G2=cutree(DCG.groom2,k=5)
############################
Eigen.plot2=function(tempinv,selected.id,D){
tempinv.selected <- tempinv[selected.id]
ensM<- list() # your ensemble matrices at each temperature.
for ( i in 1:length(selected.id))
ensM[[i]]=EstClust(GetSim2(D,tempinv.selected[i]), MaxIt=1000, m=5)
#check eigenvalues
par(mfrow=c(2,3))
for (j in 1:length(selected.id)){
Ens=ensM[[j]]
N <- nrow(Ens)
Dinvsqrt <- diag(sapply(1:N, function(i) 1/sqrt(sum(Ens[i,]))))
Lsym <- diag(N) - Dinvsqrt %*% Ens %*% Dinvsqrt
Eigen <- eigen(Lsym)$values
Eigen <- sort(1 - Eigen/Eigen[1], decreasing=TRUE)
#cat(Eigen[1:25],"\n")
cat("difference","\n",diff(Eigen[1:20]),"\n")
plot(Eigen[1:20],type="b",main=j)
}
return(ensM)
} |
5b7ebc73b980bee0227e7f4f5eaa8709c0f3aca5 | cc7ce923db8885f1b340384a35fd96eb9d0ed797 | /R/filter_reformat_vcf_df.R | 4d940dc5e5cad878977106625551eac43f24fe09 | [
"MIT"
] | permissive | collaborativebioinformatics/snpReportR | 0886c8d2924bff70f766948081f523bcbd9c43ca | 48066a3a5ca9002d03b5b7a93009207a50d31b11 | refs/heads/main | 2023-04-09T15:59:09.877204 | 2021-04-22T23:59:49 | 2021-04-22T23:59:49 | 324,649,334 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,726 | r | filter_reformat_vcf_df.R | #Jenny Smith
#Jan. 8, 2021
#Purpose: reformat the VCF output from v2.5 CTAT Mutations Pipeline
#' Filter and Reformat the VCF from v2.5 CTAT Mutations Pipeline
#'
#' @param vcf.df is a dataframe derived from vcfR package
#' @param vcf.s4 is a s4 vectors object from bioconductor VariantAnnotation package
#'
#' @return
#' @export
#'
#' @examples
#'\dontrun{
#' vcf <- vcfR::read.vcfR("/path/to/vcf")
#' vcf.df <- cbind(as.data.frame(vcfR::getFIX(vcf)), vcfR::INFO2df(vcf))
#' vcf.s4 <- VariantAnnotation::readVcf("/path/to/vcf")
#' vcf.filtered <- filter_ctat_vcf(vcf.df,vcf.s4)
#'}
#'
#' @import dplyr
filter_ctat_vcf <- function(vcf.df, vcf.s4){
#Define annotations in VCF header lines
functional_annots_names <- VariantAnnotation::header(vcf.s4) %>%
VariantAnnotation::info(.) %>%
as.data.frame(.)
functional_annots_names <- functional_annots_names["ANN","Description"] %>%
gsub("^.+\\'(.+)\\'","\\1",.) %>%
stringr::str_split(., pattern = "\\|") %>%
unlist() %>%
gsub("\\s", "", .)
functional_annots_names <- functional_annots_names[-length(functional_annots_names)]
# expland the annotations from ANN attribute of the VCF file for the first 3 transcripts.
# https://www.biostars.org/p/226965/
functional_annots.df <- data.frame(do.call(rbind, strsplit(as.vector(vcf.df$ANN), split = "\\|")))
functional_annots.df <- functional_annots.df[,1:45] #keep only the first 3 transcripts
colnames(functional_annots.df) <- paste(functional_annots_names,rep(1:3, each=15), sep="_")
#Run the filtering function
vcf.df.filter <- vcf.df %>%
mutate(S4_Vector_IDs=names(SummarizedExperiment::rowRanges(vcf.s4))) %>%
bind_cols(., functional_annots.df) %>%
mutate(rsID=ifelse(!is.na(RS), paste0("rs", RS), RS)) %>%
mutate_at(vars(chasmplus_pval,vest_pval), ~as.numeric(.)) %>%
group_by(GENE) %>%
mutate(Number_SNVs_per_Gene=n()) %>%
ungroup() %>%
dplyr::select(GENE,Number_SNVs_per_Gene, COSMIC_ID,
rsID,CHROM:ALT,
FATHMM,SPLICEADJ,
matches("chasmplus_(pval|score)"),
matches("vest_(pval|score)"),
TISSUE,TUMOR,
Annotation_1,Annotation_Impact_1,Feature_Type_1,
Transcript_BioType_1,
coding_DNA_change_1=HGVS.c_1,
protein_change_1=HGVS.p_1,
-ANN, everything(), ANN) %>%
dplyr::filter(grepl("PATHOGENIC", FATHMM) | !is.na(SPLICEADJ)) %>%
dplyr::filter(grepl("HIGH|MODERATE",Annotation_Impact_1) | !is.na(SPLICEADJ)) %>%
arrange(desc(chasmplus_score), desc(vest_score),
desc(Number_SNVs_per_Gene), Annotation_Impact_1)
return(vcf.df.filter)
}
|
7f42c8ded979f948d2aabdda7b844fe092f392ae | 1540706522486b205bb278399ba86986e264f906 | /plot3.R | 42bed4ba70ca700590126bb846cd03163dd7f124 | [] | no_license | wunzeco/exdata-project2 | 136d021bb95f5b6303b9994390de54c862d1d435 | 54681ddb1385990195252633c6c8f07da6178e3d | refs/heads/master | 2016-09-09T21:13:08.006451 | 2014-08-29T06:46:59 | 2014-08-29T06:46:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 970 | r | plot3.R | ## Question 3.
## Of the four types of sources indicated by the type (point, nonpoint, onroad,
## nonroad) variable, which of these four sources have seen decreases in emissions
## from 1999โ2008 for Baltimore City? Which have seen increases in emissions from
## 1999โ2008?
## Use the ggplot2 plotting system to make a plot to answer this question.
library(plyr)
library(ggplot2)
NEI <- readRDS("../summarySCC_PM25.rds")
SCC <- readRDS("../Source_Classification_Code.rds")
## Baltimore NEI dataset
bNEI <- subset(NEI, fips == "24510")
## Summarised NEI: Total emissions from 1999 to 2008 in Baltimore City
sNEI <- ddply(bNEI, .(year, type), summarise, total = sum(Emissions))
## plot graph to png file
png(filename = "plot3.png", width = 960, height = 480)
qplot(year, total, data = sNEI, geom = c('point','line'), facets = . ~ type,
xlab = "Year", ylab = "PM2.5 Emissions",
main = "Baltimore City - Total emissions (1999 to 2008)")
dev.off()
|
44cd1175b96702899ebf389fd40eeb602c845030 | 065495790d7e78412f26434f8dbe3d67eb48c4ba | /R/testmaf.R | 6ba8555a004772820dc85b43a927427cfc095539 | [] | no_license | lculibrk/Ploidetect | 8202834315ec7c6861ed5332233498c7b65c8930 | 0bf8e1f8f670717adde284f7d0cc47f162150d98 | refs/heads/main | 2023-05-12T14:09:58.112373 | 2023-05-05T20:32:30 | 2023-05-05T20:35:59 | 224,064,456 | 6 | 0 | null | 2023-05-05T20:36:00 | 2019-11-26T00:01:31 | R | UTF-8 | R | false | false | 1,784 | r | testmaf.R | #' @export
testMAF <- function(CN, tp){
## This special case results in division by zero
if(CN == 0 & tp == 1){
return(c("0" = 0.5))
}
#if(CN < 0 | CN > 11){
# stop("Please enter a CN between 0 and 8")
#}
np <- 1-tp
halfcn <- ceiling(CN/2)
if(CN < 15){
major_allele_possibilities = seq(from = 0, to = min(10, CN), by = 1)
}
else if(CN < 60){
major_allele_possibilities = c(seq(from = 0, to = 10, by = 1), seq(from = 15, to = min(50, CN), by = 5))
}
else if(CN < 150){
major_allele_possibilities = c(seq(from = 0, to = 10, by = 1), seq(from = 15, to = 50, by = 5), seq(from = 60, to = min(140, CN), by = 10))
}
else if(CN < 250){
major_allele_possibilities = c(seq(from = 0, to = 10, by = 1), seq(from = 15, to = 50, by = 5), seq(from = 60, to = 140, by = 10), seq(from = 150, to = CN, by = 50))
}
else{
major_allele_possibilities = c(seq(from = 0, to = 10, by = 1), seq(from = 15, to = 50, by = 5), seq(from = 60, to = 140, by = 10), seq(from = 150, to = 240, by = 50), seq(from = 250, to = CN, by = 100))
}
output <- c()
for(al in major_allele_possibilities){
majoraf <- ((al * tp) + (1 * np))/((CN * tp) + (2 * np))
output[as.character(al)] <- majoraf
}
return(output)
}
#' @export
testMAF_sc <- function(CN, tp){
CN <- max(CN, 0)
fraction = CN - floor(CN)
if(fraction == 0){
return(testMAF(CN, tp))
}
np = 1-tp
base_cn <- floor(CN)
which_fraction = c(0, 1)
major_allele_possibilities = seq(from = 0, to = base_cn, by = 1)
major_allele_possibilities = sort(c(major_allele_possibilities, major_allele_possibilities + fraction))
output <- c()
for(al in major_allele_possibilities){
output[paste0(al)] <- ((al * tp) + (1 * np))/((CN * tp) + (2 * np))
}
return(output)
}
|
95acf8576640395f515f2984f06d423b987c0003 | 77bbd565e1da3809dbc758dcf75d90ab9b31c855 | /man/get_created.Rd | b5c5be4aa8c97d96976ca44b6659e1ee4f4ea4cc | [] | no_license | gmyrland/fduper | 21c285e7a7282e41854aafbd084e665c6cccf249 | 8faba6c9fca284a0016bcdc57f946286080772f7 | refs/heads/master | 2021-08-14T20:03:03.558646 | 2017-11-16T16:32:46 | 2017-11-16T16:32:46 | 110,996,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 339 | rd | get_created.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/file_properties.R
\name{get_created}
\alias{get_created}
\title{Get file created date}
\usage{
get_created(path)
}
\arguments{
\item{path}{The path to a file}
}
\value{
The file created date of the file
}
\description{
Returns the file created date of a file
}
|
29137183eed6a1fccb02e866bd84603370c729ac | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/miceadds/examples/library_install.Rd.R | 53df2194a76541839b09234d93b70ef2e918213f | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 331 | r | library_install.Rd.R | library(miceadds)
### Name: library_install
### Title: R Utilities: Loading a Package or Installation of a Package if
### Necessary
### Aliases: library_install
### Keywords: R utilities
### ** Examples
## Not run:
##D # try to load packages PP and MCMCglmm
##D library_install( pkg=c("PP", "MCMCglmm") )
## End(Not run)
|
891c37ce23282e87426fe68f671d2c2e195353c6 | 2e6555b08b874efe0a455a3bd284c715a7cff976 | /man/mergedCEdata.Rd | 014aa69263deeb1639e192e0313440f7a9e72abe | [] | no_license | dutchjes/MSMSsim | 99d17ead953cb382392cad70eef3a7877e1ffe20 | 89ced2837f6f597c79198d8fe152fa5d23a6e2ad | refs/heads/master | 2022-01-12T11:12:22.893585 | 2019-06-26T13:43:59 | 2019-06-26T13:43:59 | 75,643,765 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 561 | rd | mergedCEdata.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mergedCEdata_func.R
\name{mergedCEdata}
\alias{mergedCEdata}
\title{Merged CE data for comparison}
\usage{
mergedCEdata(all.frag, pairs, dmz = 0.001, intensity.type)
}
\arguments{
\item{all.frag}{}
\item{pairs}{}
\item{dmz}{}
\item{intensity.type}{}
}
\value{
list of two lists, first with merged spectra of parent, second with merged spectra of TP. New function mergedSpectra
is better because doesn't require input of the pairs.
}
\description{
Merged CE data for comparison
}
|
63b1039dd582e299962eec30601612c5c8e2f0f3 | 593420234664a284d6919ccc97d4c5fe0dc396fd | /section_6_1_1/section_6_1_1_tests.R | 037cfcabddb247116b94c955cd77e15ae7cd8af4 | [] | no_license | bgs25/scope-experiments | db3c7edb03d193fb580a6ce2765b89a12ad96d67 | aea4872ffc5504c37262fa37f5c58e700d59329e | refs/heads/main | 2023-04-20T12:01:34.768821 | 2021-05-06T13:45:15 | 2021-05-06T13:45:15 | 362,241,334 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,271 | r | section_6_1_1_tests.R |
source("casanovafit.factor.crossval.R")
library(CatReg)
library(bazar)
library(randomForest)
library(tictoc)
library(DMRnet)
library(rpart)
library(effectFusion)
# This contains the lists that results will be stored in
scopecv_model_list = list()
scopes_model_list = list()
scopem_model_list = list()
scopel_model_list = list()
acc_model_list = list()
rf_model_list = list()
cart_model_list = list()
dmr_model_list = list()
bayes_model_list = list()
train.list = list()
response.list = list()
# signal regime 1, 2
# noise regime 1, 2, 3, 4
# Just fit models, on monday we'll compute prediction error, estimation error, signal proportion, clustering, et cetera
#with this regime, probability of not seeing category in training set is < 0.00001 in 1000 replicates so we ignore that it could happen... running it on loads of computers anyway so not an issue
n_jobs = 250
gammaseq = c(0.001, 2^(0:7), 1000) #for the cross-validating gamma
gamsol = list()
bestcv = rep(0, length(gammaseq))
grid.safe = 200
print("Pausing...")
Sys.sleep(rexp(1,0.1))
print("Go!")
for(job_id in 1:n_jobs) {
model.index = 1
# Main code to run. This should write to a file job.txt in the Results directory
for ( setting in 1:3 ) {
train.design = train.list[[ setting ]]
for ( noise.level in 1:5 ) {
# Now actually fit the models
y = response.list[[ 5*(setting - 1) + noise.level ]]
Sys.sleep(rexp(1,0.5))
print(setting)
print(noise.level)
# Script for cross-validating gamma. Fixes random elements of the
# cross-validation process to ensure that coordinate descent cycle order
# is preserved, otherwise the randomness in the different CV curves depending
# on this causes a lot of variance in the parameters selected.
print("scope CV")
cycleorder = sample(1:10)
cvfold = as.integer(sample(ceiling((1:100)*5/100)))
starttime = tic()
for ( gam in 1:length(gammaseq) ) {
print(paste0("gamma = ", gammaseq[gam]))
gamsol[[ gam ]] = scope(y, data.frame(matrix(1, 500, 1) train.design), interceptxlinear = T, gamma = gammaseq[gam], default.length = 150, BICterminate = 40, simply.cross.validated = TRUE, silent = TRUE, blockorder = cycleorder, FoldAssignment = cvfold)
bestcv[ gam ] = min(gamsol[[ gam ]][ , 1 ])
}
bestgamind = which.min(bestcv)
bestgam = gammaseq[ bestgamind ]
bestlam = t(gamsol[[ bestgamind ]][ , -1 ])
terminationpoint = which.min(gamsol[[ bestgamind ]][ , 1 ])
grid.safe = 200
bestlam = data.frame(matrix(0, 10, grid.safe), bestlam)
lambdaratio = bestlam[ 1, grid.safe + 1 ] / bestlam[ 1, grid.safe + 2 ]
for ( i in seq(grid.safe, 1, -1) ) {
bestlam[ , i ] = lambdaratio * bestlam[ , i + 1 ]
}
bestlam = bestlam[ , 1:(terminationpoint + grid.safe) ]
print("Cross-validated gamma and lambda, now fitting final version")
solution = scope(y, data.frame(matrix(1, 500, 1), train.design), interceptxlinear = T, gamma = bestgam, blockorder = cycleorder, FoldAssignment = cvfold, BICterminate = 40)
stoptime = tic()
duration = stoptime - starttime
scopecv_model_list[[ model.index ]] = list(solution, bestgam, bestcv, duration)
print("scope small gamma")
starttime = tic()
solution = scope(y, data.frame(matrix(1, 500, 1), train.design), interceptxlinear = T, default.length = 150, BICterminate = 40)
stoptime = tic()
duration = stoptime - starttime
scopes_model_list[[ model.index ]] = list(solution, duration)
print("scope medium gamma")
starttime = tic()
solution =scope(y, data.frame(matrix(1, 500, 1), train.design), interceptxlinear = T, default.length = 150, BICterminate = 40, gamma = 16)
stoptime = tic()
duration = stoptime - starttime
scopem_model_list[[ model.index ]] = list(solution, duration)
print("scope large gamma")
solution =scope(y, data.frame(matrix(1, 500, 1), train.design), interceptxlinear = T, default.length = 150, BICterminate = 40, gamma = 32)
stoptime = tic()
duration = stoptime - starttime
scopel_model_list[[ model.index ]] = list(solution, duration)
print("Random Forest")
starttime = tic()
solution = randomForest(y ~ ., data = data.frame(y, train.design))
stoptime = tic()
duration = stoptime - starttime
rf_model_list[[ model.index ]] = list(solution, duration)
print("CART")
starttime = tic()
cpsolution = rpart(y ~ ., data = data.frame(y, train.design))
cptable = printcp(cpsolution)
minerror = which.min(cptable[ , 4 ])
minthresh = cptable[ minerror, 4 ] + cptable[ minerror, 5 ] # This is using the 1-SE rule for pruning these trees
bestcp = min(which(cptable[ , 4 ] < minthresh))
if ( bestcp > 1 ) {
cpthresh = 0.5*(cptable[ bestcp, 1 ] + cptable[ bestcp - 1, 1 ])
} else {
cpthresh = 1
}
solution = prune(cpsolution, cp = cpthresh)
stoptime = tic()
duration = stoptime - starttime
cart_model_list[[ model.index ]] = list(solution, cpsolution, duration)
print("DMRnet")
starttime = tic()
if ( noise.level == 1 ) {
print("adding small noise to y")
cvy = y + 0.1 * rnorm(500) # This is required because DMRnet often errors in exact noiseless case due to diving through by 0 somewhere
} else {
cvy = y
}
print("fitting cv solution")
cvsolution = cv.DMRnet(train.design, cvy, nfolds = 5)
cvmaxp = cvsolution$df.min - 1
print(cvmaxp)
print("fitting full solution")
solution = DMRnet(train.design, cvy, maxp = cvmaxp)
solution = solution$beta[ , 1 ]
stoptime = tic()
duration = stoptime - starttime
dmr_model_list[[ model.index ]] = list(solution, cvsolution, duration)
print("Effect fusion")
starttime = tic()
if ( ( noise.level == 1 ) || ( noise.level == 5 ) ) {
solution = NULL
} else {
solution = effectFusion(y, train.design, types = rep("n", 10), method = "FinMix")
solution = solution$refit$beta
}
# Modelling category levels in a bayesian way as a sparse finite gaussian mixture model
stoptime = tic()
duration = stoptime - starttime
bayes_model_list[[ model.index ]] = list(solution, duration)
print("2-stage adaptive casanova")
starttime = tic()
if ( ( noise.level == 1 ) || ( noise.level == 5 ) ) {
solution = NULL
} else {
solution = casanovafit(y, matrix(1, 500, 1), train.design, interceptxlinear = T, prev.coefficients = unlist(cas_model_list[[ model.index ]][[ 1 ]][[ 1 ]]), BICterminate = 50)
}
stoptime = tic()
duration = stoptime - starttime
acc_model_list[[ model.index ]] = list(solution, duration)
model.index = model.index + 1
}
}
save(lm_model_list, ols_model_list, cas_model_list, acc_model_list, scopecv_model_list, scopes_model_list, scopem_model_list, scopel_model_list, rf_model_list, cart_model_list, dmr_model_list, bayes_model_list, train.list, response.list, file=paste0("section_6_1_1_raw.Rdata"))
}
|
e37a4e75aac508ccab5e5f3536b9685c121154e3 | 2ec9ffc060f300b96b34bbc994bcb7d85924c817 | /orientationwords/data-raw/make.R | 472723f40bba7e0c3d730804c17e0e599e4bba5e | [] | no_license | lupyanlab/orientation-words | da5a34c96f00c2f3c1c3f8410fb3f18caa14b8e6 | 1de56e4c7b3e1ac86c5a4e486380387b04637401 | refs/heads/master | 2021-01-10T07:42:57.938815 | 2015-11-30T03:33:44 | 2015-11-30T03:33:44 | 46,096,020 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 658 | r | make.R | library(devtools)
library(dplyr)
load_all()
make_unilateral <- function(overwrite = FALSE) {
unilateral_dir <- "data-raw/unilateral/"
make_unilateral_version <- function(version) {
regex_keys <- list("MOW1", "MOW3")
compile(unilateral_dir, regex_keys[[version]]) %>%
clean %>% recode %>% mutate(version = version)
}
unilateral <- plyr::rbind.fill(
make_unilateral_version(1),
make_unilateral_version(2)
)
use_data(unilateral, overwrite = overwrite)
}
make_bilateral <- function(overwrite = FALSE) {
bilateral <- compile("data-raw/bilateral/") %>%
clean %>% recode
use_data(bilateral, overwrite = overwrite)
}
|
6822f3b219b56a757960a640de1489e70a7bbd15 | abbc59b48a40e5190c6c32c86e902c1e986eed20 | /10 Basic Inferential Statistics/10c_analysis_of_survey_data.R | f0d19ff4e2d056986318572a5b9197532354c265 | [] | no_license | IonelaM/Data-Processing-Analysis-Science-with-R | f49788f5bea439894155d80e6df44cde534ac7a5 | cdc6086b0e2b9adf3750cc4682e3d38850389a90 | refs/heads/master | 2020-12-15T18:06:13.754626 | 2020-01-12T05:21:00 | 2020-01-12T05:21:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,085 | r | 10c_analysis_of_survey_data.R | ############################################################################
### Al.I. Cuza University of Iaศi ###
### Faculty of Economics and Business Administration ###
### Department of Accounting, Information Systems and Statistics ###
############################################################################
###
############################################################################
### Data Processing/Analysis/Science with R ###
############################################################################
### 10c. Analysis of Survey Data (Likert) - in Romanian (and English) ###
############################################################################
### See also the presentation:
### https://github.com/marinfotache/Data-Processing-Analysis-Science-with-R/blob/master/10%20Basic%20Inferential%20Statistics/10_basic_inferential_statistics.pptx
############################################################################
## last update: 2019-03-25
#install.packages("likert")
library(likert)
# citation('likert')
require(scales)
library(tidyverse)
############################################################################
### Download the necesary data sets for this script
############################################################################
# all the files needed o run this script are available at:
# https://github.com/marinfotache/Data-Processing-Analysis-Science-with-R/tree/master/DataSets
# Please download the files in a local directory (such as 'DataSets') and
# set the directory where you dowloaded the data files as the
# default/working directory, ex:
setwd('/Users/marinfotache/Google Drive/R(Mac)/DataSets')
############################################################################
############################################################################
### Load the data
load(file = 'chestionarSIA2013.RData')
#######################################################################################
### For variable visualization and analysis, see scrips 07e and 07f ###
#######################################################################################
##
## we not cover EDA here, but focus instead on likert data
#######################################################################################
### I. Evaluarea generala: program, profi, discipline (scala Likert) ###
#######################################################################################
# https://stackoverflow.com/questions/43646659/likert-in-r-with-unequal-number-of-factor-levels/43649056
#df <- rbind(c("Strongly agree","Strongly agree","Strongly agree","Strongly agree","Strongly agree","Strongly agree"),
# c("Neither agree nor disagree","Neither agree nor disagree","Neither agree nor disagree","Neither agree nor disagree","Neither agree nor disagree","Neither agree nor disagree"),
# c("Disagree","Strongly disagree","Neither agree nor disagree","Disagree","Disagree","Neither agree nor disagree"))
#df <- as.data.frame(df)
#colnames(df) <- c("Increased student engagement", "Instructional time effectiveness increased", "Increased student confidence", "Increased student performance in class assignments", "Increased learning of the students", "Added unique learning activities")
#lookup <- data.frame(levels = 1:5, mylabels = c('Strongly disagree', 'Disagree', 'Neither agree nor disagree', 'Agree', 'Strongly agree'))
#df.1 <- as.data.frame(apply(df, 2, function(x) match(x, lookup$mylabels)))
#df.new <- as.data.frame(lapply(as.list(df.1), factor, levels = lookup$levels, labels = lookup$mylabels))
names(evaluari.2)
from_ <- c('1', '2', '3', '4', '5')
niveluri = c("foarte scฤzut", "scฤzut", "mediu", "bun", "foarte bun")
niveluri.en = c("very poor", "poor", "average", "good", "very good")
atrib <- c("evMaster", "evProfi", "evDiscipline")
evGenerala <- evaluari.2 [, atrib]
#evGenerala <- as.data.frame(sapply(evGenerala, as.numeric))
str(evGenerala)
names(evGenerala) <- c('Master', 'Profesori', 'Discipline')
evGenerala[evGenerala == "1"] <- "foarte scฤzut"
evGenerala[evGenerala == "2"] <- "scฤzut"
evGenerala[evGenerala == "3"] <- "mediu"
evGenerala[evGenerala == "4"] <- "bun"
evGenerala[evGenerala == "5"] <- "foarte bun"
i <- 1
for (i in 1:ncol(evGenerala))
{
evGenerala[,i] = factor(evGenerala[,i],
levels=niveluri, ordered=TRUE)
}
names(evGenerala)
str(evGenerala)
###################################################################################
### I.a Vizualizare date Likert ###
###################################################################################
l.evGenerala = likert(evGenerala, nlevels = 5)
l.evGenerala
summary(l.evGenerala)
summary(l.evGenerala, center=2.5)
# grafic Likert
plot(l.evGenerala, text.size=4.5) +
ggtitle("Evaluare generalฤ: profesori, discipline ศi master") +
theme (plot.title = element_text (colour="black", size="16"))+
theme (axis.text.y = element_text (colour="black", size="14",
hjust=0))+
theme (axis.text.x = element_text (colour="black", size="14")) +
theme (legend.text = element_text (colour="black", size="14"))
# alta versiune a graficului
library(plyr)
plot(l.evGenerala, plot.percents=TRUE, plot.percent.low=FALSE,
plot.percent.high=FALSE, text.size=4.5, centered=FALSE) +
ggtitle("Evaluare generalฤ: profesori, discipline ศi master") +
theme (plot.title = element_text (colour="black", size=17))+
theme (axis.text.y = element_text (colour="black", size=14, hjust=0))+
theme (axis.text.x = element_text (colour="black", size=14)) +
theme (legend.text = element_text (colour="black", size=14))
# Heat map
plot(l.evGenerala, type='heat', wrap=30, text.size=4)
plot(l.evGenerala, type='heat', wrap=30, text.size=4) +
ggtitle("Diagrama heatmap a evaluarii \npersonalului didactic, programului si disciplinelor") +
theme (plot.title = element_text (colour="black", size="18"))+
theme (axis.text.y = element_text (colour="black", size="14", hjust=0))+
theme (axis.text.x = element_text (colour="black", size="12")) +
theme (legend.text = element_text (colour="black", size="12"))
# Density plot
plot(l.evGenerala, type='density')
plot(l.evGenerala, type='density', facet=FALSE)
###################################################################################
### I.b Vizualizare date Likert, cu gruparea rezultatelor dupa Gen ###
###################################################################################
names(evaluari.2)
evaluari.3 <- subset(evaluari.2, !is.na(sex))
evaluari.3$sex <- as.factor(evaluari.3$sex)
evaluari.3$evMaster <- as.numeric(evaluari.3$evMaster)
evaluari.3$evProfi <- as.numeric(evaluari.3$evProfi)
evaluari.3$evDiscipline <- as.numeric(evaluari.3$evDiscipline)
atrib <- c('evMaster', 'evProfi', 'evDiscipline', 'sex')
evGenerala <- evaluari.3 [, atrib]
names(evGenerala) <- c('Master', 'Profesori', 'Discipline', 'gen')
evGenerala[evGenerala == "1"] <- "foarte scฤzut"
evGenerala[evGenerala == "2"] <- "scฤzut"
evGenerala[evGenerala == "3"] <- "mediu"
evGenerala[evGenerala == "4"] <- "bun"
evGenerala[evGenerala == "5"] <- "foarte bun"
for (i in 1:(ncol(evGenerala)-1))
{
evGenerala[,i] = factor(evGenerala[,i], levels=niveluri, ordered=TRUE)
}
names(evGenerala)
#evGenerala <- as.data.frame(sapply(evGenerala, as.numeric))
str(evGenerala)
names(evGenerala) <- c('Master', 'Profesori', 'Discipline')
l.evGenerala.g1 <- likert(evGenerala[,1:3], grouping=evaluari.3$sex)
l.evGenerala.g1
summary(l.evGenerala.g1)
# Plots
plot(l.evGenerala.g1)
plot(l.evGenerala.g1, group.order=c('Feminin', 'Masculin'))
plot(l.evGenerala.g1, wrap=30, text.size=4.5,
panel.background = element_rect(size = 1, color = "grey70", fill = NA),
group.order=c('Feminin', 'Masculin')) +
ggtitle("Evaluare generalฤ: profesori, discipline ศi master,\npe genuri/sexe") +
theme (plot.title = element_text (colour="black", size=17))+
theme (axis.text.y = element_text (colour="black", size=14, hjust=0))+
theme (axis.text.x = element_text (colour="black", size=12)) +
theme (legend.text = element_text (colour="black", size=12)) +
theme(strip.text.x = element_text(size = 14, colour = "black", angle = 0))
#plot(l.evGenerala.g1, center=2.5, include.center=FALSE) ## asta e cel mai bun
#plot(l.evGenerala.g1, group.order=c('Feminin', 'Masculin'))
# Reordonarea grupurilor
# Curba densitatii
plot(l.evGenerala.g1, type='density')
# calcul medie evaluare, pentru cele doua sexe
evaluari.3 %>%
group_by(sex) %>%
dplyr::summarise(
mean.of.evMaster= mean(evMaster, na.rm = TRUE),
mean.of.evProfi= mean(evProfi, na.rm = TRUE),
mean.of.evDiscipline= mean(evDiscipline, na.rm = TRUE)
)
# calcul mediana evaluare, pentru cele doua sexe
evaluari.3 %>%
group_by(sex) %>%
dplyr::summarise(
median.of.evMaster= median(evMaster, na.rm = TRUE),
median.of.evProfi= median(evProfi, na.rm = TRUE),
median.of.evDiscipline= median(evDiscipline, na.rm = TRUE)
)
###################################################################################
### I.c Analiza datelor din evaluare ###
###################################################################################
names(evaluari.3)
#########
## Intrebare:
## Exista diferente semnificative intre absolventi si absolvente in ceea ce priveste
## evaluarea finala a masterului ?
# H0: Nu exista diferente semnificative intre absolventi si absolvente in ceea ce priveste
## evaluarea finala a masterului
wilcox.test(evMaster ~ sex, data=evaluari.3)
kruskal.test(evMaster ~ sex, data=evaluari.3)
# p-value = 0.2296; H0 nu este respinsa, deci, aparent, nu exista diferente semnificative
#install.packages('zoo')
#install.packages('coin')
library(coin)
wilcox_test(evMaster ~ sex, alternative="less", conf.int=TRUE,
distribution="exact", data=evaluari.3)
wilcox_test(evMaster ~ sex, alternative="greater", conf.int=TRUE,
distribution="exact", data=evaluari.3)
wilcox_test(evMaster ~ sex, alternative="two.sided", conf.int=TRUE,
distribution="exact", data=evaluari.3)
# effect size
# Z / sqrt(nrow(evaluari.3))
1.2015 / sqrt(nrow(evaluari.3))
# 0.1396715
## Intrebare:
## Exista diferente semnificative intre absolventi si absolvente in ceea ce priveste
## evaluarea profesorilor ?
# H0: Nu exista diferente semnificative intre absolventi si absolvente in ceea ce priveste
## evaluarea profesorilor
#evaluari.3$evProfi <- as.numeric(evaluari.3$evProfi)
wilcox.test(evProfi ~ sex, data=evaluari.3)
kruskal.test(evProfi ~ sex, data=evaluari.3)
# p-value = 0.336; H0 nu este respinsa, deci, aparent, nu exista diferente
## Intrebare:
## Exista diferente semnificative intre absolventi si absolvente in ceea ce priveste
## evaluarea disciplinelor masterului ?
# H0: Nu exista diferente semnificative intre absolventi si absolvente in ceea ce priveste
## evaluarea disciplinelor masterului
#evaluari.3$evDiscipline <- as.numeric(evaluari.3$evDiscipline)
wilcox.test(evDiscipline ~ sex, data=evaluari.3)
kruskal.test(evDiscipline ~ sex, data=evaluari.3)
# p-value = 0.4791; H0 nu este respinsa, deci, aparent, nu exista diferente
wilcox_test(evDiscipline ~ sex, alternative="greater", conf.int=TRUE,
distribution="exact", data=evaluari.3)
# effect size
# Z / sqrt(nrow(evaluari.3))
1.2015 / sqrt(nrow(evaluari.3))
# 0.139
# Testele de mai sus sunt discutabile datorita compararii medianelor
# pentru date definite pe scala likert; de aceea, cream un atribut compozit, numeric
evaluari.3$PunctajProgram <- (
ifelse(is.na(evaluari.3$evMaster), 0, evaluari.3$evMaster) +
ifelse(is.na(evaluari.3$evProfi), 0, evaluari.3$evProfi) +
ifelse(is.na(evaluari.3$evDiscipline), 0, evaluari.3$evDiscipline) ) /
( ifelse(is.na(evaluari.3$evMaster), 0, 1) +
ifelse(is.na(evaluari.3$evProfi), 0, 1) +
ifelse(is.na(evaluari.3$evDiscipline), 0, 1) )
atrib <- c("evMaster", "evProfi", "evDiscipline", "PunctajProgram")
evaluari.3[atrib]
# calcul medie si mediana punctaj global, pentru cele doua sexe
evaluari.3 %>%
group_by(sex) %>%
dplyr::summarise(
medie.punctaj = mean(PunctajProgram, na.rm = TRUE),
mediana.punctaj = median(PunctajProgram, na.rm = TRUE)
)
# Density plots with semi-transparent fill
ggplot(evaluari.3, aes(x=PunctajProgram, fill=sex)) +
geom_density(alpha=.3) +
ggtitle("Punctaj compozit master,\npe genuri/sexe")
## Intrebare:
## Exista diferente semnificative intre absolventi si absolvente in ceea ce priveste
## evaluarea generala (compozita) a masterului ?
# H0: Nu exista diferente semnificative intre absolventi si absolvente in
# ceea ce priveste punctajul compozit acordat masterului
wilcox.test(PunctajProgram ~ sex, data=evaluari.3)
kruskal.test(PunctajProgram ~ sex, data=evaluari.3)
# p-value = 0.2223; H0 nu este respinsa, deci, aparent, nu exista diferente
wilcox_test(PunctajProgram ~ sex, alternative="two.sided", conf.int=TRUE,
distribution="exact", data=evaluari.3)
# Z = -1.2205, p-value = 0.2248
# 95 percent confidence interval: [-0.6666667, 0.0000000]
wilcox_test(PunctajProgram ~ sex, alternative="greater", conf.int=TRUE,
distribution="exact", data=evaluari.3)
# p-value = 0.8886; H0 nu este respinsa, deci, aparent, nu exista diferente
# effect size
# Z / sqrt(nrow(evaluari.3))
1.2205 / sqrt(nrow(evaluari.3))
# 0.1418
#######################################################################################
### II. Evaluarea, la momentul curent (2013), a utilitatii disciplinelor ###
#######################################################################################
names(evaluari)
atribute = names(evaluari)
atribute_moment_actual <- atribute[which(str_detect(atribute, "MomActual"))]
evalUtilitActuala = subset(evaluari, , select = atribute_moment_actual)
head(evalUtilitActuala)
evalUtilitActuala = evalUtilitActuala[, -(1:2)]
head(evalUtilitActuala)
str(evalUtilitActuala)
evalUtilitActuala[evalUtilitActuala == "1"] <- "foarte scฤzut"
evalUtilitActuala[evalUtilitActuala == "2"] <- "scฤzut"
evalUtilitActuala[evalUtilitActuala == "3"] <- "mediu"
evalUtilitActuala[evalUtilitActuala == "4"] <- "bun"
evalUtilitActuala[evalUtilitActuala == "5"] <- "foarte bun"
for (i in 1:ncol(evalUtilitActuala))
{
evalUtilitActuala[,i] = factor(evalUtilitActuala[,i],
levels=niveluri, ,ordered=TRUE)
}
atribute = names(evalUtilitActuala)
nume.noi = str_replace(atribute, 'vUtilitateMomActual', '')
nume.noi = str_replace(nume.noi, '^e', '')
names(evalUtilitActuala) = nume.noi
l.evalUtilitActuala = likert(evalUtilitActuala)
l.evalUtilitActuala
summary(l.evalUtilitActuala)
summary(l.evalUtilitActuala, center=2.5)
plot(l.evalUtilitActuala, text.size=4) +
ggtitle("Evaluare actualฤ a utilitฤศii disciplinelor (domeniilor)") +
theme (plot.title = element_text (colour="black", size="18"))+
theme (axis.text.y = element_text (colour="black", size="12", hjust=0))+
theme (axis.text.x = element_text (colour="black", size="10")) +
theme (legend.text = element_text (colour="black", size="11"))
plot(l.evalUtilitActuala, ordered=FALSE,
group.order=names(evalUtilitActuala)) # specificare ordine de pe axa y
plot(l.evalUtilitActuala, centered=FALSE, wrap=30)
plot(l.evalUtilitActuala, center=2.5, wrap=30)
plot(l.evalUtilitActuala, center=2.5, include.center=FALSE, wrap=30)
plot(l.evalUtilitActuala, center=2.5, include.center=FALSE, wrap=20)
plot(l.evalUtilitActuala, plot.percents=TRUE, plot.percent.low=FALSE, plot.percent.high=FALSE)
# Density plot
plot(l.evalUtilitActuala, type='density', facet=FALSE)
# Heat map
plot(l.evalUtilitActuala, type='heat', wrap=30, text.size=4.5)
|
a353cad0abed1a017d27fa4f017a18ab2508ee45 | 42d6315be4acce738f7838c5ed6ee06ed3059a43 | /R/plot3.R | c3d7508546c60f9222098992adafd823515e365a | [] | no_license | cesarggtid/ExData_Plotting1 | 044aef8f33ba3dcb1ad7839459f9fb55fc057f8d | 50bd5e35716312e44ed5c70c768b12f709918f8f | refs/heads/master | 2020-12-27T09:33:38.619403 | 2014-10-12T20:39:32 | 2014-10-12T20:39:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 882 | r | plot3.R | # Set working directory to current R script directory
setwd("D:/workspace-R/coursera/")
# Load R file containing the getData function
source('getData.R')
# Get Tidy data
hpc <- getData("D:/workspace-R/coursera/data/", "household_power_consumption.txt")
# Choose png graphics device and set image dimensions
png(filename = "plot3.png", width = 480, height = 480)
# Plot the first line graph (submetering_1)
with(hpc, plot(Date.Time, Sub_metering_1, type = "l", xlab="", ylab="Energy Sub Metering"))
# Add the second line graph (submetering_2)
with(hpc, lines(Date.Time, Sub_metering_2, col="red"))
# Add the third line graph (submetering_3)
with(hpc, lines(Date.Time, Sub_metering_3, col="blue"))
# Add the legend
legend("topright", lty=1, col=c("black","red","blue"), legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), cex = 0.6)
# Close graphics device
dev.off() |
a03d5fcf45f48a16be6f9778e5e12040bd0df47a | c71056ad296a655e334ea59206f647f3e7e4a070 | /Scripts/R/Quarterly/quarterly_chol.R | b0f4919c0e7005af861705a52b6310bff74ed5b4 | [] | no_license | Allisterh/VAR_SVAR-MscThesisICEF | 4ed4db1a84f28d8ac40494da3c93ce6a5868865a | 22220c3844e66532b12ccf5227f3b21708969083 | refs/heads/main | 2023-05-31T15:52:43.410287 | 2021-06-10T16:01:41 | 2021-06-10T16:01:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,731 | r | quarterly_chol.R | library(seasonal)
library(rio)
library(lubridate)
library(bvarsv)
library(VARsignR)
library(vars)
library(svars)
library(tsDyn)
library(ggplot2)
library(mFilter)
library(BVAR)
dat <- import("/Users/rutra/ะะจะญ/ะะฐะณะธัััะฐัััะฐ/Thesis/Data/Aggregated data/Data Quarterly.xlsx", sheet=3)
dat <- na.omit(dat[dat$date > as.Date("2005-05-01"),])
dat <- dat[NROW(dat):1,]
#Deseasonalization
to_deseasonalize <- c("gdp_nominal_index", "imp_price_qoq", grep("cpi.*", colnames(dat), value=T))
dat_unseas <- dat
for(colname in to_deseasonalize){
current_ts <- ts(dat[,colname], start=c(2005, 2), frequency=4)
current_seas <- seas(current_ts, transform.function="none",
#regression.aictest=NULL, outlier=NULL,
#automdl=NULL,
seats.noadmiss="yes")
dat_unseas[,colname] <- as.numeric(final(current_seas))
}
#Output gap
dat_unseas$nominal_gdp_gap <- hpfilter(dat_unseas$gdp_nominal_index, freq=1600)$cycle
dat_unseas$real_gdp_gap <- hpfilter(dat_unseas$gdp_real_SA_index, freq=1600)$cycle
#dat_unseas$d_real_gdp_gap <- c(NA, diff(dat_unseas$real_gdp_gap))
#dat_unseas <- na.omit(dat_unseas)
#dat_unseas$miacr_90 <- c(NA, diff(dat_unseas$miacr_90))
#dat_unseas <- na.omit(dat_unseas)
plot(dat_unseas$date, dat_unseas$real_gdp_gap)
#Cholesky decomposition
data_to_model <- dat_unseas[,c(
"gdp_real_SA_qoq",
"oil_USD_qoq",
"miacr_31",
"neer_qoq",
"cpi_all_qoq"
)]
data_chol <- data_to_model
#data_chol <- dat_unseas[,c("oil_USD_qoq", "imp_price_qoq", "reserves_USD_qoq",
# "miacr_31", "neer_qoq", "d_real_gdp_gap", "cpi_all_qoq")]
data_chol$neer_qoq <- data_chol$neer_qoq * -1
#data_chol$real_usd_qoq <- data_chol$real_usd_qoq * -1
#data_chol$nom_usd_qoq <- data_chol$nom_usd_qoq * -1
VARselect(data_chol, lag.max=4)$selection
model_VAR <- VAR(data_chol, p = 1, type = "const")
choldec <- id.chol(model_VAR)
irf_choldec <- irf(choldec, n.ahead = 4, ortho=TRUE)
#plot(irf_choldec)
#REER gives closer results
#Oil
sum(irf_choldec$irf$`epsilon[ oil_USD_qoq ] %->% cpi_all_qoq`) /
sum(irf_choldec$irf$`epsilon[ oil_USD_qoq ] %->% neer_qoq`)
#MIACR 31
sum(irf_choldec$irf$`epsilon[ miacr_31 ] %->% cpi_all_qoq`) /
sum(irf_choldec$irf$`epsilon[ miacr_31 ] %->% neer_qoq`)
#NEER
sum(irf_choldec$irf$`epsilon[ neer_qoq ] %->% cpi_all_qoq`) /
sum(irf_choldec$irf$`epsilon[ neer_qoq ] %->% neer_qoq`)
#Output
sum(irf_choldec$irf$`epsilon[ gdp_real_SA_qoq ] %->% cpi_all_qoq`) /
sum(irf_choldec$irf$`epsilon[ gdp_real_SA_qoq ] %->% neer_qoq`)
#CPI
sum(irf_choldec$irf$`epsilon[ cpi_all_qoq ] %->% cpi_all_qoq`) /
sum(irf_choldec$irf$`epsilon[ cpi_all_qoq ] %->% neer_qoq`)
#Cholesky decomposition (dollars as exchange rate)
data_chol_usd <- dat_unseas[,c("oil_USD_qoq", "imp_price_qoq", "reserves_USD_qoq",
"miacr_31", "nom_usd_qoq", "gdp_real_SA_qoq", "cpi_all_qoq")]
data_chol_usd$nom_usd_qoq <- data_chol_usd$nom_usd_qoq * -1
VARselect(data_chol_usd, lag.max = 4)$selection
model_VAR_usd <- VAR(data_chol_usd, p = 4, type = "const")
choldec_usd <- id.chol(model_VAR_usd)
irf_choldec_usd <- irf(choldec_usd, n.ahead = 4, ortho=TRUE)
plot(irf_choldec_usd)
#Reserves
sum(irf_choldec_usd$irf$`epsilon[ reserves_USD_qoq ] %->% cpi_all_qoq`) /
sum(irf_choldec_usd$irf$`epsilon[ reserves_USD_qoq ] %->% nom_usd_qoq`)
#Oil
sum(irf_choldec_usd$irf$`epsilon[ oil_USD_qoq ] %->% cpi_all_qoq`) /
sum(irf_choldec_usd$irf$`epsilon[ oil_USD_qoq ] %->% nom_usd_qoq`)
#MIACR 31
sum(irf_choldec_usd$irf$`epsilon[ miacr_31 ] %->% cpi_all_qoq`) /
sum(irf_choldec_usd$irf$`epsilon[ miacr_31 ] %->% nom_usd_qoq`)
#NEER
sum(irf_choldec_usd$irf$`epsilon[ nom_usd_qoq ] %->% cpi_all_qoq`) /
sum(irf_choldec_usd$irf$`epsilon[ nom_usd_qoq ] %->% nom_usd_qoq`)
#Output
sum(irf_choldec_usd$irf$`epsilon[ gdp_nominal_qoq ] %->% cpi_all_qoq`) /
sum(irf_choldec_usd$irf$`epsilon[ gdp_nominal_qoq ] %->% nom_usd_qoq`)
#Smooth transition
model_cv <- id.st(model_VAR, c_lower=-5, c_upper=5, c_step=0.02, nc=8, c_fix=25)#!!!
irf_cv <- irf(model_cv, n.ahead = 4, ortho=TRUE)
#plot(irf_cv)
#Oil
sum(irf_cv$irf$`epsilon[ oil_USD_qoq ] %->% cpi_all_qoq`) /
sum(irf_cv$irf$`epsilon[ oil_USD_qoq ] %->% neer_qoq`)
#Import prices
sum(irf_cv$irf$`epsilon[ imp_price_qoq ] %->% cpi_all_qoq`) /
sum(irf_cv$irf$`epsilon[ imp_price_qoq ] %->% neer_qoq`)
#MIACR 31
sum(irf_cv$irf$`epsilon[ miacr_31 ] %->% cpi_all_qoq`) /
sum(irf_cv$irf$`epsilon[ miacr_31 ] %->% neer_qoq`)
#NEER
sum(irf_cv$irf$`epsilon[ neer_qoq ] %->% cpi_all_qoq`) /
sum(irf_cv$irf$`epsilon[ neer_qoq ] %->% neer_qoq`)
#Output
sum(irf_cv$irf$`epsilon[ gdp_real_SA_qoq ] %->% cpi_all_qoq`) /
sum(irf_cv$irf$`epsilon[ gdp_real_SA_qoq ] %->% neer_qoq`)
|
91bd8e7c426d8432fa1d5e20620b6d96b3c8dbd1 | 178086eeb8b4158d45b705428d5bf4e3b41d6da4 | /demos/nnet_demo.R | 1a7335933c73e8595722dd2547272224fa071356 | [] | no_license | CameronMSeibel/info201a_final_project | 3a90c9a5741f2c276bb3478d65b9c9d361808e8e | 50190e57408f203872b34e20162e00f72b85a54d | refs/heads/master | 2020-03-17T16:02:51.955682 | 2018-05-31T19:37:02 | 2018-05-31T19:37:02 | 133,734,059 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,432 | r | nnet_demo.R | # Cameron Seibel, INFO 201
# NNET Proof of Concept
#
# This file serves as a proof of concept for using the nnet package to solve classification
# problems by exploring one of R's given datasets, iris.
# Be sure that nnet is already installed on your machine!
library(nnet)
library(dplyr)
# This sets the size of the hidden layer of the neural net; ultimately determines the
# accuracy of classifications, but there is danger of "overfitting" training data.
HIDDEN_LAYER_SIZE = 10
iris_df <- iris
# Train on a sample of the iris data
iris_subset <- sample_n(iris_df, 50)
# Test on data not in the training set
iris_test <- iris_df %>%
anti_join(iris_subset)
# Instantiation of the neural net; where Species is a function of the other features,
# the network should be trained on the subset of training data, and the number of perceptrons
# in the hidden layer is set to some value, where larger values will merit greater accuracy,
# but slower performance.
iris_classifier <- nnet(Species ~ ., data = iris_subset, size = HIDDEN_LAYER_SIZE)
# Output the predictions for the test set to this table.
predictions <- data.frame(iris_test$Species, predict(iris_classifier, iris_test, type = "class"))
colnames(predictions) <- c("Species", "Prediction")
n_wrong <- predictions %>%
filter(Species != Prediction) %>%
count()
print(paste("The network was able to classify the data with", n_wrong, "percent innaccuracy."))
|
99ff35a84cd29d6e9125b09f844bb961e50e4db8 | f17de11f2aa5ba013b442c1fff1ab794b984a792 | /man/ros_ping.Rd | ef8a7acb57110cd05149bce3ee01f20a58820cca | [] | no_license | ktargows/rosette | 27d136225717254919f8ad0f791ac4fe53a056b4 | caea1ecd62fdef278e8927a7eafd043332e7ac19 | refs/heads/master | 2020-12-30T12:10:45.021008 | 2016-10-11T22:06:08 | 2016-10-11T22:06:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 214 | rd | ros_ping.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ping.r
\name{ros_ping}
\alias{ros_ping}
\title{Rosette API availability}
\usage{
ros_ping()
}
\description{
Rosette API availability
}
|
e1e982411f84b7110ebb14b285c8cddac0bb396e | a6035147e4078ec16659573ee7c7fc89f78efc3d | /R/BPbasis.R | ea86111c8978ebbb3549b3caa847ccc4b7a81069 | [] | no_license | Li-Syuan/BayesBP | 8b04899d22d6a73b1f10eae1d4c2b9e6c0fb4cd3 | 182a7eb8d5de2809c8c778bd31725051b0678ca8 | refs/heads/main | 2022-02-14T01:47:44.753106 | 2022-01-26T01:50:06 | 2022-01-26T01:50:06 | 213,273,058 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,109 | r | BPbasis.R | #'Bernstein polynomial basis.
#'@description This function build two dimensional Bernstein polynomial basis.
#'@param ages Range of ages.
#'@param years Range of years.
#'@param n0 Upper bound of possion random variable.
#'@param N Lower bound of possion random variable.
#'@return Bernstein basis.
#'@examples
#'ages <- 35:85
#'years <- 1988:2007
#'list.basis <- BPbasis(ages,years,10)
#'list.basis
#'@family Bernstein basis
#'@export BPbasis
BPbasis <- function(ages, years, n0, N = 1) {
x <- scale_to_01(ages)
y <- scale_to_01(years)
xy <- expand.grid(x, y)
basis_list <- list()
for (n in N:n0) {
i <- j <- 0:n
g <- array(0, dim = c(n + 1, n + 1, nrow(xy)))
for (k in seq_len(nrow(xy))) {
g[, , k] <- outer(i, j, function(i, j) {
bin(n, i, xy[k, 1]) * bin(n, j, xy[k, 2])
})
}
parameter <- matrix(as.vector(g), nrow = (n + 1)^2, ncol = nrow(xy))
basis_list[[length(basis_list) + 1]] <- parameter
}
class(basis_list) <- "BPbasis"
return(basis_list)
}
|
4a3bb2137eca361fe3d1f77c15044f1de5addb99 | 4f9015f385c8a02ff258414ba931952afb1e6fac | /R-libraries/spm/R/spm.remove.last.words.R | a816d0c52b416926a509d64fe0b2cf4b55d27d5d | [] | no_license | NIWAFisheriesModelling/SPM | 0de0defd30ccc92b47612fa93946ef876196c238 | 0412f06b19973d728afb09394419df582f1ecbe4 | refs/heads/master | 2021-06-06T00:15:07.548068 | 2021-05-27T06:07:46 | 2021-05-27T06:07:46 | 21,840,937 | 6 | 3 | null | null | null | null | UTF-8 | R | false | false | 255 | r | spm.remove.last.words.R | #' utility function
#'
#' @author Alistair Dunn
#'
"spm.remove.last.words"<-
function(string, words = 1)
{
temp <- spm.unpaste(string, sep = " ")
to.drop <- length(temp) - (0:(words - 1))
paste(unlist(temp[ - to.drop]), collapse = " ")
}
|
d21d5b3820eec1e3f37a65e144b228d0ba7c126e | 305cf81d42d09a94f7948a0a47a7a8d58e03f845 | /R/base_pymolr.r | 5f8793b0935e1ac23719d04364b0de8911ac37fd | [] | no_license | StefansM/pymolr | 8c56df35df19024c3dcce058e671faa360159f23 | 38e946f07f8724ded51788e62662f1598fb4c24f | refs/heads/master | 2021-04-27T10:51:43.971589 | 2018-02-22T23:30:03 | 2018-02-22T23:30:03 | 122,548,133 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,949 | r | base_pymolr.r | #' Control PyMol from R.
#'
#' Pymolr makes all PyMol commands available from R, and provides tools to
#' manipulate on PyMol selections.
#'
#' Use the \code{\linkS4class{Pymol}} class to interact with Pymol and the
#' \code{\linkS4class{Selection}} class to create PyMol selections.
"_PACKAGE"
#' Base class for PyMol connections.
#'
#' This base class implements all PyMol commands, but directly returns the data
#' returned by PyMol. The derived class \code{\link{Pymol}} performs
#' post-processing on certain methods and is the recommended interface.
BasePymol <- setRefClass("BasePymol",
fields=list(pid="integer",
executable="character",
args="character",
url="character"))
BasePymol$methods(
initialize = function(executable=Sys.which("pymol"), show.gui=FALSE,
rpc.port=9123) {
"Initialise a new Pymol class."
rpc.server <- system.file("extdata", "pymol_xmlrpcserver.py",
package="pymolr")
.self$executable <<- executable
.self$args <<- c("-q",
if(!show.gui) "-c",
rpc.server,
if(show.gui) "--rpc-bg",
"--rpc-port", rpc.port)
.self$url <<- paste0("http://localhost:", rpc.port, "/RPC2")
# Before we start a pymol server, make sure that there is not one already
# running on this port.
if(tryCatch(.self$is.connected(), error=function(...) "err") != "err"){
stop(paste("A process is already running on port", rpc.port))
}
.self$pid <<- sys::exec_background(.self$executable, .self$args)
# Loop until the RPC server comes up. PyMol can take quite a long time to
# start, so we might have to Sys.sleep() a few times until it comes up.
exit.status <- NA
max.tries <- 10
connection.tries <- 0
while(TRUE){
exit.status <- sys::exec_status(.self$pid, wait=FALSE)
if(!is.na(exit.status)
|| tryCatch(.self$is.connected(), error=function(cond) FALSE)
|| connection.tries == max.tries) {
break
}
Sys.sleep(1)
connection.tries <- connection.tries + 1
}
if(!is.na(exit.status)){
stop(paste("Unable to start PyMol process. Exit status:", exit.status))
}else if(connection.tries == max.tries){
tools::pskill(.self$pid)
stop("Couldn't connect to PyMol XMLRPC server.")
}
},
finalize = function() {
"Closes PyMol when this class is garbage collected."
.self$quit()
},
is.connected = function() {
"Check that the PyMol server is active."
.self$.rpc("ping") == "pong"
},
.rpc = function(method, ...) {
"Call a remote PyMol method."
XMLRPC::xml.rpc(.self$url, method, ...)
}
)
|
01924e99890df83b3b26db2c0bae4e66814cef2c | 8694cb6d3889d64d8b4d6977774512771002a40f | /IntroR/pollutantmean.r | 5b32e03b59896534e1dfa5477696acbea88ed376 | [] | no_license | jbewald/R | 2abee7df1ea6447dc4981a43317d39979cc0f5e8 | c838707a810995b674679c7c87f2fbba1d94491f | refs/heads/master | 2021-01-10T08:55:57.045066 | 2016-01-29T16:32:41 | 2016-01-29T16:32:41 | 50,674,756 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,655 | r | pollutantmean.r | pollutantmean <- function(directory, pollutant, id = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'pollutant' is a character vector of length 1 indicating
## the name of the pollutant for which we will calculate the
## mean; either "sulfate" or "nitrate".
## 'id' is an integer vector indicating the monitor ID numbers
## to be used
## Return the mean of the pollutant across all monitors list
## in the 'id' vector (ignoring NA values)
## NOTE: Do not round the result!
## Function call example: pollutantmean('c:/data/specdata', 'sulfate', 2:3)
#print(id)
#print (class(id))
# Get the list of files
filelist <- list.files(directory, pattern='.csv', full.names = TRUE)
#subset(dataset, ID == 1)
#################### Create a Dataframe from all Observastions ####################
for (file in filelist){
# if the merged dataset doesn't exist, create it
if (!exists("dataset")){
dataset <- read.csv(file) }
else { temp_dataset <-read.csv(file)
dataset<-rbind(dataset, temp_dataset)
rm(temp_dataset)
}
}
#mean(dataset[1:1000,2], na.rm = TRUE)
#mean(dataset[dataset$ID > 1 & dataset$ID <= 3,2], na.rm = TRUE)
if (pollutant == "nitrate") {col = 3}
if (pollutant == "sulfate") {col = 2}
#Calculate Mean
#mu <- mean(dataset[dataset$ID == id, 2], na.rm = TRUE)
#sub <- dataset[dataset$ID == id, 2]
#sub <- dataset[dataset$ID %in% id, col]
mean(dataset[dataset$ID %in% id, col], na.rm = TRUE)
} |
a9aa45f2b74eddf6149a009d5c188192796da508 | 19ece375ca4095b5578efe5332e7f79137435b0c | /man/SNR.Rd | 2458bfd72eb6a69fcd5066ab61da282e78f9bea0 | [] | no_license | rscharpf/crlmmCompendium | 547d80cc477b1eb29f3e7764bb37b5fe23fa83c6 | 1901b99953ad932c64fb12ffd80bf1e68785bb00 | refs/heads/master | 2020-04-08T22:57:53.965914 | 2012-05-03T19:02:29 | 2012-05-03T19:02:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 632 | rd | SNR.Rd | \name{SNR}
\alias{SNR}
\docType{data}
\title{
Signal to noise ratio estimated from the CRLMM algorithm.
}
\description{
The signal to noise ratio (SNR) is an overall measure of the separation of the
genotype clusters for a particular sample. The SNR can be useful as a
measure of quality control. For Affymetrix 6.0, we generally suggest
dropping samples with a SNR below 5.
}
\usage{data(SNR)}
\format{
The format is:
num [1:1258] 6.6 7.05 7.83 7.35 6.47 ...
}
\source{
HapMap phase III
}
\examples{
data(SNR)
\dontrun{
##for an object of class \code{CNSet}, the SNR can be extracted by
object$SNR[]
}
}
\keyword{datasets}
|
c13fe031f38712d7842ef7ad9f584fac42dbada7 | 35de14603463a45028bd2aca76fa336c41186577 | /R/find_consensus_SNPs_no_PolyFun.R | 75b899460b6d167ccb4d28d387deeacb015a8a3b | [
"MIT"
] | permissive | UKDRI/echolocatoR | e3cf1d65cc7113d02b2403960d6793b9249892de | 0ccf40d2f126f755074e731f82386e4e01d6f6bb | refs/heads/master | 2023-07-14T21:55:27.825635 | 2021-08-28T17:02:33 | 2021-08-28T17:02:33 | 416,442,683 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 471 | r | find_consensus_SNPs_no_PolyFun.R | find_consensus_SNPs_no_PolyFun <- function(finemap_dat,
verbose=T){
printer("Identifying UCS and Consensus SNPs without PolyFun",v=verbose)
newDF <- find_consensus_SNPs(finemap_dat,
exclude_methods = "POLYFUN_SUSIE",
sort_by_support = F)
finemap_dat$Consensus_SNP_noPF <- newDF$Consensus_SNP
finemap_dat$Support_noPF <- newDF$Support
return(finemap_dat)
}
|
768173f124ea69e4b1426eb59e150a329b3aa34c | a6ca6b4d428124461ef4184f32e35961b8a4ce9e | /02_rprogramming/assignment3/best.R | 81c9608cc1705b50eb11546039a0e0fbf6b0626f | [] | no_license | ryanmcdonnell/datasciencecoursera | c81a95beb7afc05fad209dc3f1d858f489de7d4a | 1d005de1d3f29a30cc31a5a0114d99cb7895a995 | refs/heads/master | 2016-08-07T21:51:58.802527 | 2015-08-10T23:08:05 | 2015-08-10T23:08:05 | 38,704,568 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,250 | r | best.R | best <- function(state, outcome) {
## Read outcome data
## Check that state and outcome are valid
## Return hospital name in that state with lowest 30-day death
## rate
# Validate the outcome argument
validOutcomes = c("heart attack", "heart failure", "pneumonia")
if(!(outcome %in% validOutcomes)) {
stop("invalid outcome")
}
# Load outcome data
data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
# Reduce data to just the columns necessary and rename columns
data <- data[c(2, 7, 11, 17, 23)]
names(data)[1] <- "name"
names(data)[2] <- "state"
names(data)[3] <- "heart attack"
names(data)[4] <- "heart failure"
names(data)[5] <- "pneumonia"
# Validate the state argument
states <- unique(data$state)
if(!(state %in% states)) {
stop("invalid state")
}
# Narrow down data to state and outcome
stateData <- data[data$state == state & data[outcome] != 'Not Available', ]
# Coerce outcome data to numeric
stateData[, outcome] <- as.numeric(stateData[, outcome])
lowest <- min(stateData[, outcome])
hospitals <- stateData[stateData[outcome] == lowest, 1]
sorted <- sort(hospitals)
sorted[1]
} |
17ce26b3c8eb6f03b3035660ffd74653c0f19002 | a8c143e36e191984fca19f9f421b0231fbc7bc18 | /man/pick.batch.sizes.Rd | b8c00f4f6c7d62916c4a57bb45baf591c45ce2b6 | [] | no_license | bschiffthaler/BatchMap | 3d74d8315dee0d5ae00413fbfed740134c9a064f | 21806c09de4b8839b0625d8b73047bc57c4a02ae | refs/heads/master | 2021-01-17T23:33:10.251330 | 2019-12-10T13:49:21 | 2019-12-10T13:49:21 | 84,226,774 | 3 | 3 | null | null | null | null | UTF-8 | R | false | true | 1,069 | rd | pick.batch.sizes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/overlapping.batches.R
\name{pick.batch.sizes}
\alias{pick.batch.sizes}
\title{Picking optimal batch size values}
\usage{
pick.batch.sizes(input.seq, size = 50, overlap = 15, around = 5)
}
\arguments{
\item{input.seq}{an object of class \code{sequence}.}
\item{size}{The center size around which an optimum is to be searched}
\item{overlap}{The desired overlap between batches}
\item{around}{The range around the center which is maximally allowed
to be searched.}
}
\value{
An integer value for the size which most evenly divides batches. In
case of ties, bigger batch sizes are preferred.
}
\description{
Suggest an optimal batch size value for use in
\code{\link[BatchMap]{map.overlapping.batches}}
}
\examples{
\dontrun{
LG <- structure(list(seq.num = seq(1,800)), class = "sequence")
batchsize <- pick.batch.sizes(LG, 50, 19)
}
}
\seealso{
\code{\link[BatchMap]{map.overlapping.batches}}
}
\author{
Bastian Schiffthaler, \email{bastian.schiffthaler@umu.se}
}
\keyword{utilities}
|
7c000d67249182a910864f7a139a476873433fde | e158e9992ab1d0510134ecc123aac7220c612df0 | /R/utilityFunctions.R | 78091cb630b2a3202fdb00879392b532e19eff04 | [] | no_license | grimbough/biomaRt | a7e2a88276238e71898ea70fcf5ac82e1e043afe | 8626e95387ef269ccc355dbf767599c9dc4d6600 | refs/heads/master | 2023-08-28T13:03:08.141211 | 2022-11-01T14:38:39 | 2022-11-01T14:38:39 | 101,400,172 | 26 | 8 | null | 2022-05-17T08:10:51 | 2017-08-25T12:08:50 | R | UTF-8 | R | false | false | 14,729 | r | utilityFunctions.R |
## sometimes results can be returned by getBM() in a different order to we
## asked for them, which messes up the column names. Here we try to match
## results to known attribute names and rename accordingly.
.setResultColNames <- function(result, mart, attributes, bmHeader = FALSE) {
## get all available attributes and
## filter only for the ones we've actually asked for
att <- listAttributes(mart, what = c("name", "description"))
att <- att[which(att[,'name'] %in% attributes), ]
if(length(which(duplicated(att[,'description']))) >
length(which(duplicated(att)))) {
warning("Cannot unambiguously match attribute names
Ignoring bmHeader argument and using biomart
description field")
return(result)
}
resultNames = colnames(result)
## match the returned column names with the attribute names
matches <- match(resultNames, att[,2], NA)
if(any(is.na(matches))) {
warning("Problems assigning column names.",
"Currently using the biomart description field.",
"You may wish to set these manually.")
return(result)
}
## if we want to use the attribute names we specified, do this,
## otherwise we use the header returned with the query
if(!bmHeader) {
colnames(result) = att[matches, 1]
}
## now put things in the order we actually asked for the attributes in
result <- result[, match(att[matches,1], attributes), drop=FALSE]
return(result)
}
## BioMart doesn't work well if the list of values provided to a filter is
## longer than 500 values. It returns only a subset of the requested data
## and does so silently! This function is designed to take a list of provided
## filters, and split any longer than 'maxChunkSize'. It operates recursively
## incase there are multiple filters that need splitting, and should ensure
## all possible groupings of filters are retained.
.splitValues <- function(valuesList, maxChunkSize = 500) {
vLength <- vapply(valuesList[[1]], FUN = length, FUN.VALUE = integer(1))
if(all(vLength <= maxChunkSize)) {
return(valuesList)
} else {
## pick the next filter to split
vIdx <- min(which(vLength > maxChunkSize))
nchunks <- (vLength[vIdx] %/% maxChunkSize) + 1
splitIdx <- rep(1:nchunks, each = ceiling(vLength[vIdx] / nchunks))[ 1:vLength[vIdx] ]
## a new list we will populate with the chunks
tmpList <- list()
for(i in 1:nchunks) {
for( j in 1:length(valuesList) ) {
listIdx <- ((i - 1) * length(valuesList)) + j
tmpList[[ listIdx ]] <- valuesList[[j]]
tmpList[[ listIdx ]][[ vIdx ]] <- tmpList[[ listIdx ]][[ vIdx ]][which(splitIdx == i)]
}
}
## recursively call the function to process next filter
valuesList <- .splitValues(tmpList, maxChunkSize = maxChunkSize)
}
return(valuesList)
}
## Creating the filter XML for a single chunk of values. Returns a character
## vector containing the XML lines for all specified filters & their
## attributes spliced together into a single string.
.createFilterXMLchunk <- function(filterChunk, mart) {
individualFilters <- vapply(names(filterChunk),
FUN = function(filter, values, mart) {
## if the filter exists and is boolean we do this
if(filter %in% listFilters(mart, what = "name") &&
grepl('boolean', filterType(filter = filter, mart = mart)) ) {
if(!is.logical(values[[filter]]))
stop("'", filter,
"' is a boolean filter and needs a ",
"corresponding logical value of TRUE or FALSE to ",
"indicate if the query should retrieve all data that ",
"fulfill the boolean or alternatively that all data ",
"that not fulfill the requirement should be retrieved.",
call. = FALSE)
val <- ifelse(values[[filter]], yes = 0, no = 1)
val <- paste0("\" excluded = \"", val, "\" ")
} else {
## otherwise the filter isn't boolean, or doesn't exist
if(is.numeric(values[[filter]]))
values[[filter]] <- as.integer(values[[filter]])
val <- paste0(values[[filter]], collapse = ",")
## convert " to ' to avoid truncating the query string
val <- gsub(x = val, pattern = "\"", replacement = "'", fixed = TRUE)
val <- paste0('" value = "', val, '" ')
}
filterXML <- paste0("<Filter name = \"", filter, val, "/>")
return(filterXML)
}, FUN.VALUE = character(1),
filterChunk, mart,
USE.NAMES = FALSE)
filterXML <- paste0(individualFilters, collapse = "")
return(filterXML)
}
.generateFilterXML <- function(filters = "", values, mart, maxChunkSize = 5000) {
## return empty string if no filter specified & this isn't ensembl
## specifying no filter is generally bad, as it will get 'everything'
## and we might encounter the time out problem
if(filters[1] == "") {
return("")
}
## if we have multiple filters, the values must be specified as a list.
if(length(filters) > 1 && class(values) != "list") {
stop("If using multiple filters, the 'value' has to be a list.",
"\nFor example, a valid list for 'value' could be: list(affyid=c('1939_at','1000_at'), chromosome= '16')",
"\nHere we select on Affymetrix identifier and chromosome, only results that pass both filters will be returned");
}
## it's easy to not realise you're passing a data frame here, so check
if(is.data.frame(values) && ncol(values == 1)) {
values <- values[,1]
}
if(!is.list(values)){
values <- list(values)
}
names(values) <- filters
values <- .splitValues(list(values), maxChunkSize = maxChunkSize)
filterXML_list <- lapply(values, .createFilterXMLchunk, mart)
return(filterXML_list)
}
#' it seems like pretty common practice for users to copy and paste the host
#' name from a browser if they're not accessing Ensembl. Typically this will
#' include the "http://" and maybe a trailing "/" and this messes up our
#' paste the complete URL strategy and produces something invalid.
#' This function tidies that up to catch common variants.
.cleanHostURL <- function(host, warn = TRUE) {
parsed_url <- httr::parse_url(host)
## just supplying 'ensembl.org' is no longer handled correctly
## stick 'www' in front if we see this
if( parsed_url$path == "ensembl.org" ) {
parsed_url$path = "www.ensembl.org"
}
## only prepend http if needed
if(is.null(parsed_url$scheme)) {
parsed_url$scheme <- "http"
parsed_url$hostname <- parsed_url$path
parsed_url$path <- ""
}
## warn about Ensembl HTTPS here - later we'll force the change
if(grepl("ensembl", parsed_url$hostname) &&
parsed_url$scheme != "https" &&
warn == TRUE) {
warning(
"Ensembl will soon enforce the use of https.\n",
"Ensure the 'host' argument includes \"https://\"",
call. = FALSE)
}
host <- httr::build_url(parsed_url)
## strip trailing slash
host <- gsub(pattern = "/$", replacement = "", x = host)
return(host)
}
.createErrorMessage <- function( error_code, host = "" ) {
## if we encounter internal server error, suggest using a mirror
if( error_code == 500) {
err_msg <- 'biomaRt has encountered an unexpected server error.'
} else if ( error_code == 509) {
err_msg <- 'biomaRt has exceeded the bandwidth allowance with this server.'
} else {
err_msg <- paste0('biomaRt has encountered an unknown server error. HTTP error code: ', error_code,
'\nPlease report this on the Bioconductor support site at https://support.bioconductor.org/')
}
if( grepl("ensembl", x = host) ) {
err_msg <- c(err_msg, '\nConsider trying one of the Ensembl mirrors (for more details look at ?useEnsembl)')
}
return(err_msg)
}
.submitQueryXML <- function(host, query, httr_config) {
res <- httr::POST(url = host,
body = list('query' = query),
config = httr_config,
timeout(300))
if( httr::http_error(res) ) {
err_msg <- .createErrorMessage( error_code = status_code(res), host = host )
stop(err_msg, call. = FALSE)
}
## content() prints a message about encoding not being supplied
## for ensembl.org - no default, so we suppress it
return( suppressMessages(content(res)) )
}
#' if parsing of TSV results fails, try this
.fetchHTMLresults <- function(host, query, httr_config) {
query = gsub(x = query, pattern = "TSV", replacement = "HTML", fixed = TRUE)
html_res <- .submitQueryXML(host, query, httr_config)
XML::readHTMLTable(html_res, stringsAsFactors = FALSE)[[1]]
}
#' @param postRes Character vector of length 1 returned by server. We expect
#' this to be a tab delimited string that comprises the whole table of results
#' including column headers.
.processResults <- function(postRes, mart, hostURLsep = "?",
fullXmlQuery, quote = "\"", numAttributes) {
## we expect only a character vector of length 1
if(!(is.character(postRes) && (length(postRes)==1L))) {
stop("The query to the BioMart webservice returned an invalid result\n",
"biomaRt expected a character string of length 1.\n",
"Please report this on the support site at http://support.bioconductor.org")
}
if(grepl(pattern = "^Query ERROR", x = postRes))
stop(postRes)
## convert the serialized table into a dataframe
result <- tryCatch(read.table(text = postRes, sep="\t", header = TRUE, quote = quote,
comment.char = "", stringsAsFactors = FALSE, check.names = FALSE),
error = function(e) {
## if the error relates to number of element, try reading HTML version
if(grepl(x = e, pattern = "line [0-9]+ did not have [0-9]+ elements"))
.fetchHTMLresults(host = paste0(martHost(mart), hostURLsep),
query = fullXmlQuery,
httr_config = martHTTRConfig(mart))
else
stop(e)
}
)
if(!(is(result, "data.frame") && (ncol(result) == numAttributes))) {
stop("The query to the BioMart webservice returned an invalid result.\n",
"The number of columns in the result table does not equal the number of attributes in the query.\n",
"Please report this on the support site at http://support.bioconductor.org")
}
return(result)
}
##############################################
## searching Attributes, Filters, and Datasets
##############################################
#' given a data.frame, searches every column for
#' the value in 'pattern'
#' returns index of rows containing a match
.searchInternal <- function(pattern, data) {
colIdx <- vapply(data,
FUN = stringr::str_detect,
FUN.VALUE = logical(length = nrow(data)),
pattern = pattern)
rowIdx <- apply(colIdx, 1, any)
## return either the matching rows, or NULL
if(any(rowIdx)) {
return(data[rowIdx,])
} else {
message('No matching datasets found')
return(NULL)
}
}
searchDatasets <- function(mart, pattern) {
if(missing(mart))
stop("Argument 'mart' must be specified")
if(missing(pattern))
pattern = ".*"
datasets <- listDatasets(mart)
res <- .searchInternal(pattern = pattern, data = datasets)
if(is.null(res))
invisible(res)
else
res
}
searchAttributes <- function(mart, pattern) {
if(missing(mart))
stop("Argument 'mart' must be specified")
if(missing(pattern))
pattern = ".*"
attributes <- listAttributes(mart)
res <- .searchInternal(pattern = pattern, data = attributes)
if(is.null(res))
invisible(res)
else
res
}
searchFilters <- function(mart, pattern) {
if(missing(mart))
stop("Argument 'mart' must be specified")
if(missing(pattern))
pattern = ".*"
filters <- listFilters(mart)
res <- .searchInternal(pattern = pattern, data = filters)
if(is.null(res))
invisible(res)
else
res
}
## Some filters have a predefined list of options that can be selected.
## This function lets us search those values, given a specified filter.
searchFilterOptions <- function(mart, filter, pattern) {
if(missing(mart))
stop("Argument 'mart' must be specified")
if(missing(filter))
stop("Argument 'filter' must be specified")
if(missing(pattern))
pattern = ".*"
## first get all filters & their options, then reduce to what's requested
filters <- listFilters(mart, what = c("name", "options"))
filters <- filters[ filters$name == filter, ]
if(nrow(filters) == 0) {
stop("Filter '", filter, "' not found.")
}
options <- gsub(filters$options, pattern = "^\\[|\\]$", replacement = "")
options <- strsplit(options, split = ",", fixed = TRUE)[[1]]
res <- grep(x = options, pattern = pattern,
ignore.case = TRUE, value = TRUE)
if(length(res) == 0)
message('No matching values found')
else
res
}
searchFilterValues <- function(mart, filter, pattern) {
.Deprecated(new = "listFilterOptions",
msg = c("This function has been renamed searchFilterOptions()",
"\nsearchFilterValues() is deprecated and will be removed in the future."))
searchFilterOptions(mart, filter, pattern = pattern)
}
listFilterOptions <- function(mart, filter) {
searchFilterOptions(mart = mart, filter = filter)
}
listFilterValues <- function(mart, filter) {
.Deprecated(new = "listFilterOptions",
msg = c("This function has been renamed listFilterOptions()",
"\nlistFilterValues() is deprecated and will be removed in the future."))
listFilterOptions(mart, filter)
}
|
ff83ea80ff6ffdb0bee875b0068f1a6726ee6493 | 358fac5c4137d512c32806b994dccd62a0167352 | /R/assetservers.R | 957ec0ab8090545b717704f505211b421236659c | [] | no_license | paleolimbot/piplyr | b8a8bcf2bcabbfab5ae982517061eeb2ceae6fd0 | 87758e0390fa8d23b59c9c64c1208e917281f729 | refs/heads/master | 2020-08-21T08:56:14.859525 | 2019-10-19T19:22:06 | 2019-10-19T19:22:06 | 216,126,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 791 | r | assetservers.R |
#' Retrieve a list of all Asset Servers known to this service
#'
#' @inheritParams pi_get
#' @param webId The ID of the server.
#' @param ... Passed to [pi_get()]
#'
#' @export
#'
#' @references
#' https://devdata.osisoft.com/piwebapi/help/controllers/assetserver/actions/list
#' https://devdata.osisoft.com/piwebapi/help/controllers/assetserver/actions/get
#'
#' @examples
#' con <- pi_connect_public()
#' pi_assetserver_list(con)
#' pi_assetserver(con, "F1RSIRAQC7zjPUOfBqai218IAwUElTUlYx")
#'
pi_assetserver_list <- function(.con, ...) {
pi_get(.con, "assetservers", ...)
}
#' @rdname pi_assetserver_list
#' @export
pi_assetserver <- function(.con, webId, ...) {
webId <- pi_web_id(webId)
pi_get(
.con,
glue::glue("assetservers/{webId}"),
webId = webId,
...
)
}
|
f3b9716883e23da355271b6901b40688e0d0a70c | b3acea392da5c57fd91bd14ece661853deed1c05 | /Hibridacion_monolitica/Hibridacion_monolitica.R | c73b5359608c7987bfbc36307a066450f93b07dd | [] | no_license | Artcs1/SistemasdeRecomendacion_T2 | 5c674c9dc590903a4ea29dd142505180f61680ea | 976e099f5e656d350aba43d19abb1f7ef70dc08f | refs/heads/master | 2020-03-18T12:56:32.713718 | 2018-06-22T04:47:37 | 2018-06-22T04:47:37 | 134,751,809 | 0 | 3 | null | 2018-06-03T21:14:35 | 2018-05-24T18:04:35 | R | ISO-8859-1 | R | false | false | 5,238 | r | Hibridacion_monolitica.R | FBC.genres.sim <- function(dataset,testset,genreset) {
dataset = as.matrix(dataset)
testset = as.matrix(testset)
genreset = as.matrix(genreset)
testset = testset[,2:3]
nmovies = max(c(dataset[,2],testset[,2])) # nรบmero de filmes
A = list("Animation"=1,"Adventure"=2,"Comedy"=3,"Action"=4,"Drama"=5,"Thriller"=6,"Crime"=7,"Romance"=8,"Children's"=9,"Documentary"=10,"Sci-Fi"=11,"Horror"=12,"Western"=13,"Mystery"=14,"Film-Noir"=15,"War"=16,"Musical"=17,"Fantasy"=18)
genres = matrix(rep(0,18*nmovies),18,nmovies) # generos
#construรงรฃo da matriz gรฉnero vs filme
for(i in 1:nmovies) {
g = strsplit(genreset[i,3],split = "|" , fixed = TRUE )[[1]]
for(j in 1:length(g)) {
value = as.numeric(A[g[j]])
genres[value,i]=1;
}
}
#Calculo das similiraridades entre os filmes respeito a seus generos con o mรฉtodo Jaccard
library(proxy)
sim = simil(t(genres), method="Jaccard")
sim = as.matrix(sim)
sim[is.na(sim)==T] = 0
return (sim)
}
computeTFIDF <- function(row) { # TF - IDF
df = sum(row[1:3564] > 0)
w = rep(0, length(row))
w[row > 0] = (1 + log2(row[row > 0])) * log2(3564/df)
return(w)
}
FBC.metadados.sim <- function(dataset,testset,reviewset) {
dataset = as.matrix(dataset)
testset = as.matrix(testset)
reviewset = as.matrix(reviewset)
testset = testset[,2:3]
nmovies = max(c(dataset[,2],testset[,2])) # nรยบmero de filmes
library(tm)
library(SnowballC)
reviews = c()
for(i in 1:nmovies) {
reviews = c(reviews , paste(reviewset[which(as.numeric(reviewset[,1]) == i),2],collapse = " ")) # Concatenando todos os textos
}
reviewList = as.list(reviews) # Transformando o vetor para uma lista
nDocs = length(reviewList)
reviewList = VectorSource(reviewList)
corpus = Corpus(reviewList)
corpus = tm_map(corpus, removePunctuation) # Tokenizaรยงรยฃo
corpus = tm_map(corpus, content_transformer(tolower))# Normalizaรยงรยฃo de termos
corpus = tm_map(corpus, stripWhitespace) # Normalizaรยงรยฃo de termos
tdm = TermDocumentMatrix(corpus, control=list(stopwords=TRUE,stemWords=TRUE,wordLengths=c(1,15)))
#Remoรยงรยฃo de stopwords
#Radicalizaรยงรยฃo
m = as.matrix(tdm) # matrix do vocabulario vs termos
n = t(apply(m, 1, FUN=computeTFIDF)) # fazer TF - IDF
n = scale(n, center=FALSE, scale=sqrt(colSums(n^2))) # normalizaรยงรยฃo
sim = t(n) %*% n # Calculo das similaridades
return (sim)
}
HM.model <- function(dataset,testset,genreset,reviewset) {
users = dataset[,1] #ID dos usuรยกrios
movies = dataset[,2] #ID dos filmes
ratings = dataset[,3] # Ratings
nusers = max(c(dataset[,1],testset[,2])) # nรยบmero de usuarios
nmovies = max(c(dataset[,2],testset[,3])) # nรยบmero de filmes
scores = matrix(rep(0,nusers*nmovies),nusers,nmovies) # interacciรยณn usuario filme
for(i in 1:length(users))
scores[users[i],movies[i]] = ratings[i] #construรยงao da matriz de interaรยงรยฃo usuario filme
sim.genres = FBC.genres.sim(dataset,testset,genreset)
sim.metadatos = FBC.metadados.sim(dataset,testset,reviewset)
sim = 0.385*sim.genres+0.615*sim.metadatos
model = list(score = scores,sim = sim)
return (model)
}
HM.predict <- function(model, user , movie, K) {
simil = as.matrix(model$sim)
score = as.matrix(model$score)
similar.movies = order(-simil[movie,])# Ordenando as similiaridades de um filme en forma decrescente
rated.movies = which(score[user,] > 0) # Escolher os items que o usuario avalio
most.similar.rated = intersect(similar.movies, rated.movies)[1:min(K,length(rated.movies))] #interseรยงรยฃo
#Calculo de la prediรยงรยฃo
sumSim = 0
sumWeight = 0
if(is.na(most.similar.rated[1])) {#Se la interseรยงรยฃo e vacia retorna a media
return (3.603814)
}
#Calculo de prediรยงรยฃo con os k vizinhos mais proximos
for(j in most.similar.rated) {
sumSim = sumSim + simil[movie, j]
sumWeight = sumWeight + simil[movie, j] * score[user, j]
}
sumSim=sumSim+1e-12 # Em caso sumSim seja 0
return(sumWeight/sumSim)
}
HM.test <- function(model,testset,vizinhos,name) {
testset = as.matrix(testset[,2:3])
testUser = testset[,1] #Usuarios
testMovie = testset[,2] #Filmes
tam = length(testUser)
ids = (1:(tam))
ids = ids-1
ratings = rep(0,tam) #vetor para os ratings
for ( i in 1:tam) {
ratings[i] = HM.predict(model,testUser[i],testMovie[i],vizinhos) #prediรงรฃo
}
my.dataset <- data.frame(id = ids, rating = ratings) #Criaรงรฃo de um dataframe
write.csv(my.dataset,name,row.names=F) #Exportacion
}
HM.pretest <- function(model,testset)
{
testset = as.matrix(testset[,2:4])
testUser = testset[,1] #Usuarios
testMovie = testset[,2] #Filmes
tam = length(testUser)
ids = (1:(tam))
ids = ids-1
ratings = rep(0,tam) #vetor para os ratings
error = c()
for(i in 1:30) # testando os k vizinhos
{
for(j in 1:tam)
{
ratings[j] = HM.predict(model,testUser[j],testMovie[j],i) #prediรงรฃo
}
r = ratings;
print(paste("HM_",i,sep=""))
e = RMSE(r,testset[,3]) #calculo de Error
print(e)
error = c(error,e) # lista de errors
}
return (error/sum(error)) # normalizaรงรฃo
}
|
add1d2008f6f729425bb169d467493991db2762d | 84ddf0c12885c83d2c7a32791447f8a56fbb8fa2 | /Data_Analysis/downloading file from url.R | 5aceb08134df002c419d30691658602a353d3ed6 | [] | no_license | Haineycf/R | 60fb12c7c12583908663247ccd550a413cbf99f0 | 468a2c74649cc8a424b86061a92971a108eae5a0 | refs/heads/master | 2021-01-13T14:47:57.754348 | 2017-12-11T23:35:12 | 2017-12-11T23:35:12 | 76,572,656 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 221 | r | downloading file from url.R | # download a file from a url pate
fileUrl <- "https://data.baltimorecity.gov/api/views/dz54-2aru/rows.csv?accessType=DOWNLOAD"
download.file(fileUrl, destfile = "./data/cameras.csv", method = "curl")
list.files("./data") |
8bda21fa5b25d154f6f812767c95994e4cc025f4 | 04983b845e7fbde889d642890935b8e397cc41ad | /01 carga y limpieza.R | 2c71132c448786ecc9afb0bc2292050745079546 | [] | no_license | pmtempone/DM_CUP | a0a30ae4d31768443cd84171caeccaf2c03345e9 | 47ba3b065652c4bff7cf406da71e51906d9b55ff | refs/heads/master | 2021-01-19T13:47:32.960958 | 2017-05-14T18:54:16 | 2017-05-14T18:54:16 | 88,108,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,416 | r | 01 carga y limpieza.R | library(ggplot2)
library(dplyr)
library(data.table)
library(dtplyr)
library(funModeling)
library(doMC)
train <- read.delim("/Volumes/Disco_SD/Datamining Cup/DMC_2017_task/train.csv",header = TRUE,sep = "|")
items <- read.delim("/Volumes/Disco_SD/Datamining Cup/DMC_2017_task/items.csv",header = TRUE,sep = "|")
check.integer <- function(N){
!grepl("[^[:digit:]]", format(N, digits = 20, scientific = FALSE))
}
train <-
train %>% left_join(items, by = "pid")
# Tabla con los NA
na_count <- data.frame(sapply(train, function(y) sum(length(which(is.na(y))))))
data_profile <- df_status(train)
# na_count
#100.687
# CONSTRUCCIรN DE VARIABLES
#Construir units
train <- train %>%
mutate(units = revenue/price)
#Contruir dรญa de la semana
train <- train %>%
mutate(dia_semana = day %% 7)
#Contruir diferencia porcentual con el competidor
train <- train %>%
mutate(compar_compet = (competitorPrice - price) / price)
#Contruir la comparaciรณn con el precio de referencia
train <- train %>%
mutate(compar_ref = (rrp - price) / price)
train <- train %>%
mutate(price_changed = !(check.integer(train$revenue/train$price)))
distinct_days = train %>% distinct(day)
train$orden_dia_producto <- 0
#Porcentaje de acciones que terminan en compra
prop.table(table(train$order))
#Ad Flag presente significa que se vende mรกs
prop.table(table(train$adFlag,train$order),1)
#Availability: Availability 4 casi no se vende!!! A menor avail. mรกs se vende
prop.table(table(train$availability,train$order),1)
#La comparaciรณn con el precio del competidor no parece influyente
prop.table(table(train$compar_compet > 0,train$order),1)
#unit. Parece determinante
prop.table(table(train$unit,train$order),1)
#generico. Se vende mucho mรกs.
prop.table(table(train$genericProduct,train$order),1)
#sales index. No se quรฉ es.
prop.table(table(train$salesIndex,train$order),1)
#category. Es factor. Demasiados levels.
train$category <- as.factor(train$category)
prop.table(table(train$category,train$order),1)
#Campaign index. NA se parece mucho a B.
prop.table(table(train$campaignIndex,train$order),1)
# Podrรญa ser significativa.
prop.table(table(train$dia_semana,train$order),1)
#La comparaciรณn con el precio del competidor no parece influyente
prop.table(table(train$orden_dia_producto,train$order),1)
#Campaign index. NA se parece mucho a B.
prop.table(table(train$campaignIndex,train$order),1)
|
f844c116bdf377d9221b96dd66ca3fc8bb49f167 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/FateID/examples/reclassify.Rd.R | 846f2cfa22078363eb883e2d0a2c858436b4766a | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 258 | r | reclassify.Rd.R | library(FateID)
### Name: reclassify
### Title: Reclassification of cells
### Aliases: reclassify
### ** Examples
x <- intestine$x
y <- intestine$y
tar <- c(6,9,13)
rc <- reclassify(x,y,tar,z=NULL,nbfactor=5,use.dist=FALSE,seed=NULL,nbtree=NULL,q=.9)
|
4bde17e4d3eb3849b774254859dd06a45245165b | d3681eaac1eeb5dea3f30aabeb21029d7ad8feaf | /inst/doc/examples/ssqtest.R | e75f009aa24eb5596f18bf19037774b60358bcc5 | [] | no_license | cran/optimx | 01f3b3546a1c4f0780063d8f541d8f2680830f9e | 675f08649655da273365f1efcc0996b39dc8bb46 | refs/heads/master | 2023-08-17T00:12:18.490641 | 2023-08-14T07:10:12 | 2023-08-14T08:30:59 | 17,698,097 | 3 | 7 | null | null | null | null | UTF-8 | R | false | false | 665 | r | ssqtest.R | # ssqbtest.R -- a simple sum of squares showing differences between
# opm() and optimx()
## author: John C. Nash
rm(list=ls())
require(optimx)
sessionInfo()
ssqb.f<-function(x){
nn<-length(x)
yy <- 1:nn
f<-sum((yy-x)^2)
f
}
ssqb.g <- function(x){
nn<-length(x)
yy<-1:nn
gg<- 2*(x - yy)
}
ssqb.h <- function(x){
nn<-length(x)
hh<- 2*diag(nn)
}
xx <- rep(pi, 4)
all4b <- opm(xx, ssqb.f, ssqb.g, hess=ssqb.h, method="ALL")
summary(all4b, order=value)
all4bx <- optimx(xx, ssqb.f, ssqb.g, control=list(all.methods=TRUE))
summary(all4bx, order=value)
cat("\n\nShow structure differences in solution of opm and optimx\n\n")
str(all4b)
str(all4bx)
|
841f0a19e56b7d7335dfb2c81e84b1f773dbc862 | 2802979852f33dc4336c0e0fbc6a601a928efc5e | /R/cutoffs.R | 0becf27bef4f1b298381456d8fe2c2497bc785bc | [] | no_license | cran/netgwas | 05ee21591f4bc89b295b4d7d6754ec9fb5cc7225 | e661e37640b335d4fa515f03411e08bb12b795fa | refs/heads/master | 2023-08-31T22:02:45.223899 | 2023-08-07T14:40:02 | 2023-08-07T16:35:15 | 112,773,132 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 804 | r | cutoffs.R | #-------------------------------------------------------------------------------#
# Package: Network-Based Genome-Wide Association Studies #
# Author: Pariya Behrouzi #
# Emails: <pariya.Behrouzi@gmail.com> #
# Date: Nov 21th 2017 #
#-------------------------------------------------------------------------------#
cutoffs = function(y){
p<-ncol(y)
n<-nrow(y)
k<-unique(sort(unlist(y)))
n.levels<-length(k)
q<-matrix(nrow=p,ncol=n.levels)
for(i in 1:p){
X=factor(y[,i],levels=k)
No<-tabulate(X, nbins=n.levels)
q[i,]<-qnorm(cumsum(No)/n)
}
q[ ,n.levels] <- Inf
q<-cbind(-Inf,q)
return(q)
} |
a594795b8d3389bdb13c9367c70b1cb95fcbfcc5 | 3134e07d1cf55e5ee931f036c67c8749e9682428 | /R/camel.tiger.clime.mfista.R | 0655813888e841232a0f2717114b7d64be9ecdb7 | [] | no_license | cran/camel | 462474e8df3bde111fae225d4067741ab852e063 | 1b480116cd340bfad76b5a411c05a02acc1a3574 | refs/heads/master | 2020-12-24T12:02:01.521103 | 2013-09-09T00:00:00 | 2013-09-09T00:00:00 | 17,694,927 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,223 | r | camel.tiger.clime.mfista.R | #----------------------------------------------------------------------------------#
# Package: camel #
# camel.tiger.clime.hadm(): Coordinate descent method for sparse precision matrix #
# estimation #
# Author: Xingguo Li #
# Email: <xingguo.leo@gmail.com> #
# Date: Aug 23th, 2013 #
# Version: 0.1.0 #
#----------------------------------------------------------------------------------#
camel.tiger.clime.mfista <- function(Sigma, d, maxdf, mu, lambda, shrink, prec, max.ite){
d_sq = d^2
Y = diag(d)
# lambda = lambda-shrink*prec
nlambda = length(lambda)
L = eigen(Sigma)$values[1]^2
icov = array(0,dim=c(d,d,nlambda))
ite.ext = rep(0,d*nlambda)
obj = array(0,dim=c(max.ite,nlambda))
runt = array(0,dim=c(max.ite,nlambda))
x = array(0,dim=c(d,maxdf,nlambda))
col_cnz = rep(0,d+1)
row_idx = rep(0,d*maxdf*nlambda)
begt=Sys.time()
str=.C("tiger_clime_mfista", as.double(Y), as.double(Sigma), as.double(icov),
as.integer(d), as.double(mu), as.integer(ite.ext), as.double(lambda),
as.integer(nlambda), as.integer(max.ite), as.double(prec), as.double(L),
as.double(x), as.integer(col_cnz), as.integer(row_idx),PACKAGE="camel")
runt1=Sys.time()-begt
ite.ext = matrix(unlist(str[6]), byrow = FALSE, ncol = nlambda)
obj = 0
icov_list = vector("list", nlambda)
icov_list1 = vector("list", nlambda)
for(i in 1:nlambda){
icov_i = matrix(unlist(str[3])[((i-1)*d_sq+1):(i*d_sq)], byrow = FALSE, ncol = d)
icov_list1[[i]] = icov_i
icov_list[[i]] = icov_i*(abs(icov_i)<=abs(t(icov_i)))+t(icov_i)*(abs(t(icov_i))<abs(icov_i))
obj[i] = sum(abs(icov_i))
}
x = unlist(str[12])
col_cnz = unlist(str[13])
row_idx = unlist(str[14])
return(list(icov=icov_list, icov1=icov_list1,ite=ite.ext, obj=obj,runt=runt1,
x=x, col_cnz=col_cnz, row_idx=row_idx))
}
|
a14c3126a4d41f132360fa1fda2ba8ccbad17140 | 16b0ef0cfdebe10c0c37207f75c1c457dd452290 | /man/iascagpcp.Rd | 08f34c376cbd749e3959196876e188d664a14110 | [] | no_license | cran/mbgraphic | ad2c5b907e3dd4c026d6fff41d16212ca05f5f52 | 15acaaa26de781203c6e80c53d2ae8d4d83fdb57 | refs/heads/master | 2020-12-30T15:42:12.746005 | 2019-04-28T18:20:03 | 2019-04-28T18:20:03 | 91,166,050 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,486 | rd | iascagpcp.Rd | \name{iascagpcp}
\alias{iascagpcp}
\title{
Interactive parallel coordinate plots for exploring scagnostics results
}
\description{
An interactive parallel coordinate plot for exploring scagnostics results programmed with the package \pkg{shiny}. If \code{sdfdata} is generated by function \code{\link{sdf}}, \emph{Outliers} and \emph{Exemplars} can be explored separately. Selections within the parallel coordinate plot can be made by drawing boxes on the axes around the chosen line.
}
\usage{
iascagpcp(sdfdata)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{sdfdata}{
A list of class \code{"sdfdata"}.
}
}
\details{
For scaling the three options 'std' (every scagnostic individually by subtraction of mean and division by standard deviation), 'uniminmax' (every scagnostic individually to unit interval) and 'globalminmax' (no scaling) can be used. See also \code{\link[GGally]{ggparcoord}}.
}
\value{
A shiny app object.
}
\references{
W. Chang, J. Cheng, J. Allaire, Y. Xie and J. McPherson (2016) shiny: Web Application Framework for R. \url{https://cran.r-project.org/package=shiny}.
B. Schloerke et al. (2016) GGally: Extension to ggplot2. \url{https://cran.r-project.org/package=GGally}
}
\author{
Katrin Grimm
}
\seealso{
\code{\link{sdf}}, \code{\link{scag2sdf}}
}
\examples{
\dontrun{
data(Election2005)
# some demographic/economic variables
sdfdata <- sdf(Election2005[,5:40])
iascagpcp(sdfdata)
}
}
\keyword{interactive apps} |
22cb5863c752aa3b02ccd130d861634e56ededd4 | 3f887566cd63a82f13d2aae590cdea252f2532db | /backup/HotelR.R | 3c9a4c299266cd28d558b4a2d9fa3318bfc6f1e9 | [] | no_license | seaireal/hotel_rating | b4a9a7bbdee0b76caad3b4d6fd29a71cf9eba439 | 3e6ad9de14d05dfe0870f256c3c22865a497d585 | refs/heads/master | 2020-08-09T11:56:24.236767 | 2019-12-30T17:05:10 | 2019-12-30T17:05:10 | 214,081,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 879 | r | HotelR.R | # set working directory
setwd("/Users/huiwang/Downloads")
HotelReviews = read.csv('Hotel_Reviews.csv')
# choose and rename the predictors
HotelR = cbind(HotelReviews[,c(2,3,4,5,8,9,11,12,13,15,16,17)])
names(HotelR) = c("addscore", "date", "score", "hotel", "negative", "review",
"positive", "reviewers", "avescore", "day", "lat", "lng")
# deal with the last predictor to take it numeric without "days"
HotelR$day=as.numeric(HotelR$day)
head(HotelR)
# missing data
missing = is.na(HotelR)
sum(missing)
dim(HotelR)
str(HotelR)
# Average the observations based on the hotel name
Hotel = aggregate(cbind(addscore, score, negative, review,
positive, reviewers, avescore, day, lat, lng) ~ hotel, HotelR, mean)
head(Hotel)
# try regression~
lmod = lm(score ~ addscore + negative + review + positive + reviewers + day + lat + lng, Hotel)
summary(lmod)
|
590b533ed61bf538e79ede177fa5dad0cf5f5e06 | b0f7d5d2489e761646c6363d2958e9d3a1b75747 | /Analytics Edge/NUnit5_Assignment3.R | 3d9cef5a6fc89af3c480882ea73d789400487be9 | [] | no_license | krishnakalyan3/Edge | 4cd5cb55677ed662dda4d4acdf7fba4c7e813735 | ad070911bd36c26ff7c612b4adc4150a53579676 | refs/heads/master | 2021-01-21T04:46:50.714837 | 2016-07-03T10:48:34 | 2016-07-03T10:48:34 | 53,034,825 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,597 | r | NUnit5_Assignment3.R | getwd()
setwd("/Users/krishna/MOOC/Edge/Data")
email = read.csv("emails.csv",stringsAsFactors=FALSE)
# Problem 1.1 - Loading the Dataset
str(email)
# 'data.frame': 5728 obs. of 2 variables:
# Problem 1.2 - Loading the Dataset
sum(email$spam)
# 1368
# Problem 1.3 - Loading the Dataset
# subject
# Problem 1.4 - Loading the Dataset
# Yes
# Problem 1.5 - Loading the Dataset
max(nchar(email$text))
# Problem 1.6 - Loading the Dataset
which.min(nchar(email$text))
# Problem 2.1 - Preparing the Corpus
sparse = 1 - 0.05
corpus = Corpus(VectorSource(email$text))
corpus = tm_map(corpus , tolower)
corpus = tm_map(corpus, PlainTextDocument)
corpus = tm_map(corpus, removePunctuation)
corpus = tm_map(corpus, removeWords, stopwords('english'))
corpus = tm_map(corpus, stemDocument, language = 'english')
dtm = DocumentTermMatrix(corpus)
spdtm = removeSparseTerms(dtm, sparse)
spdtm
# 28687
# Problem 2.2 - Preparing the Corpus
# 330
# Problem 2.3 - Preparing the Corpus
emailsSparse = as.data.frame(as.matrix(spdtm))
colnames(emailsSparse) = make.names(colnames(emailsSparse))
which.max(colSums(emailsSparse))
#
# enron
# 92
# Problem 2.4 - Preparing the Corpus
emailsSparse$spam = email$spam
ham = subset(emailsSparse,spam ==0)
sort(colSums(ham))
# 6
# Problem 2.5 - Preparing the Corpus
spam = subset(emailsSparse,spam ==1)
(sort(colSums(spam)) )
#3
# Problem 3.1 - Building machine learning models
emailsSparse$spam = as.factor(emailsSparse$spam)
library(caTools)
set.seed(123)
spl = sample.split(emailsSparse$spam, 0.7)
train = subset(emailsSparse, spl ==T)
test = subset(emailsSparse, spl ==F)
spamLog = glm(spam ~ . , data = train , family = "binomial")
spamCART = rpart(spam ~ . , data = train, method="class")
library(randomForest)
spamRF = randomForest(spam ~ . , data = train)
predlog = predict(spamLog)
predlog
predCART = predict(spamCART)
predCART
predRF = predict(spamRF,train)
predRF
table(predlog < 0.00001)
table(predlog > 0.99999)
table(predlog >= 0.00001 & predlog <= 0.99999)
# Problem 3.2 - Building Machine Learning Models
summary(spamLog)
# 0
# Problem 3.3 - Building Machine Learning Models
prp(spamCART)
# 2
# Problem 3.4 - Building Machine Learning Models
cm =table(train$spam,predlog>=0.5)
TN = cm[1,1]
TP = cm[2,2]
FN = cm[2,1]
FP = cm[1,2]
Acc = (TP + TN)/sum(cm)
Acc
# 0.9990025
# Problem 3.5 - Building Machine Learning Models
library(ROCR)
predROCR = prediction(predlog,train$spam)
prefROCR = performance(predROCR,"tpr","fpr")
plot(prefROCR, colorize = T )
performance(predROCR,"auc")@y.values
# 0.9999959
# Problem 3.6 - Building Machine Learning Models
cm =table(train$spam,predCART[,2]>=0.5)
TN = cm[1,1]
TP = cm[2,2]
FN = cm[2,1]
FP = cm[1,2]
Acc = (TP + TN)/sum(cm)
Acc
# 0.942394
# Problem 3.7 - Building Machine Learning Models
library(ROCR)
predROCR = prediction(predCART[,2],train$spam)
prefROCR = performance(predROCR,"tpr","fpr")
plot(prefROCR, colorize = T )
performance(predROCR,"auc")@y.values
# Problem 3.8 - Building Machine Learning Models
cm =table(train$spam,predRF>=0.5)
TN = cm[1,1]
TP = cm[2,2]
FN = cm[2,1]
FP = cm[1,2]
Acc = (TP + TN)/sum(cm)
Acc
# Problem 3.9 - Building Machine Learning Models
library(ROCR)
predROCR = prediction(predRF,train$spam)
prefROCR = performance(predROCR,"tpr","fpr")
plot(prefROCR, colorize = T )
performance(predROCR,"auc")@y.values
# 0.9999928
# Problem 4.1 - Evaluating on the Test Set
cm =table(test$spam, predict(spamLog, test) >=0.5)
TN = cm[1,1]
TP = cm[2,2]
FN = cm[2,1]
FP = cm[1,2]
Acc = (TP + TN)/sum(cm)
Acc
# 0.9511059
# What is the testing set AUC of spamLog?
predROCR = prediction(predict(spamLog, test),test$spam)
prefROCR = performance(predROCR,"tpr","fpr")
plot(prefROCR, colorize = T )
performance(predROCR,"auc")@y.values
# 0.9767994
# Problem 4.3 - Evaluating on the Test Set
cm =table(test$spam, predict(spamCART, test)[,2] >=0.5)
TN = cm[1,1]
TP = cm[2,2]
FN = cm[2,1]
FP = cm[1,2]
Acc = (TP + TN)/sum(cm)
Acc
# 0.9394645
# Problem 4.4 - Evaluating on the Test Set
predROCR = prediction(predict(spamCART, test)[,2],test$spam)
prefROCR = performance(predROCR,"tpr","fpr")
plot(prefROCR, colorize = T )
performance(predROCR,"auc")@y.values
# Problem 4.5 - Evaluating on the Test Set
cm =table(test$spam, predict(spamRF, test) >=0.5)
TN = cm[1,1]
TP = cm[2,2]
FN = cm[2,1]
FP = cm[1,2]
Acc = (TP + TN)/sum(cm)
Acc
# 0.9743888
# Problem 4.6 - Evaluating on the Test Set
predROCR = prediction(predict(spamRF, test),test$spam)
prefROCR = performance(predROCR,"tpr","fpr")
plot(prefROCR, colorize = T )
performance(predROCR,"auc")@y.values
|
64c909034486b62c9ff11fad2c241fd9a3159dbd | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/metafor/examples/dat.pritz1997.Rd.R | 0bbdf83491659ce9aaad1b3bcced53a569f79e1f | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,242 | r | dat.pritz1997.Rd.R | library(metafor)
### Name: dat.pritz1997
### Title: Studies on the Effectiveness of Hyperdynamic Therapy for
### Treating Cerebral Vasospasm
### Aliases: dat.pritz1997
### Keywords: datasets
### ** Examples
### load data
dat <- get(data(dat.pritz1997))
### computation of "weighted average" in Zhou et al. (1999), Table IV
dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat, add=0)
theta.hat <- sum(dat$ni * dat$yi) / sum(dat$ni)
se.theta.hat <- sqrt(sum(dat$ni^2 * dat$vi) / sum(dat$ni)^2)
ci.lb <- theta.hat - 1.96*se.theta.hat
ci.ub <- theta.hat + 1.96*se.theta.hat
round(c(estimate = theta.hat, se = se.theta.hat, ci.lb = ci.lb, ci.ub = ci.ub), 4)
### this is identical to a FE model with sample size weights
rma(yi, vi, weights=ni, method="FE", data=dat)
### random-effects model with raw proportions
dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat)
res <- rma(yi, vi, data=dat)
predict(res)
### random-effects model with logit transformed proportions
dat <- escalc(measure="PLO", xi=xi, ni=ni, data=dat)
res <- rma(yi, vi, data=dat)
predict(res, transf=transf.ilogit)
### mixed-effects logistic regression model
res <- rma.glmm(measure="PLO", xi=xi, ni=ni, data=dat)
predict(res, transf=transf.ilogit)
|
a21ac82d8b3a6842998aed97e7384d0ae408a0b8 | dbff7481385f4c5e7ae9dbb726835de7970bb22c | /R/daqDarkThemeProvider.R | 36055f477cca785e4e4c38a9ab8f959b847f2210 | [
"MIT"
] | permissive | emilhe/dash-daq | f2056ef37eab04ba39c954c35faf96da2b7ebe88 | 34140134f740e5f5186af8e4727de9a5a0cbe917 | refs/heads/master | 2022-12-03T15:50:19.026762 | 2020-08-27T06:59:42 | 2020-08-27T06:59:42 | 288,721,296 | 0 | 0 | MIT | 2020-08-19T12:06:54 | 2020-08-19T12:06:53 | null | UTF-8 | R | false | false | 510 | r | daqDarkThemeProvider.R | # AUTO GENERATED FILE - DO NOT EDIT
daqDarkThemeProvider <- function(children=NULL, theme=NULL) {
props <- list(children=children, theme=theme)
if (length(props) > 0) {
props <- props[!vapply(props, is.null, logical(1))]
}
component <- list(
props = props,
type = 'DarkThemeProvider',
namespace = 'dash_daq',
propNames = c('children', 'theme'),
package = 'dashDaq'
)
structure(component, class = c('dash_component', 'list'))
}
|
68a93ee5a6390ff4cda4fd1b0bc0aeb6de1a2b6e | 242730ec15df34c6d3b4850831c44b5bfa13f3ec | /demos/2020-10-30.R | c8bd1054a8dd80069172a2234782644595524b0c | [] | no_license | tdhock/cs499-599-fall-2020 | bcf4d81e96b2685d7fcc22e30c9f95eee4ea959e | b270b5f94a3e36c57313d392176a1b6de455b82a | refs/heads/master | 2023-07-10T12:12:41.320347 | 2021-08-18T18:07:16 | 2021-08-18T18:07:16 | 255,656,952 | 1 | 5 | null | null | null | null | UTF-8 | R | false | false | 5,204 | r | 2020-10-30.R | loss.dt.list <- list()
change.dt.list <- list()
## penalized model selection / evaluation with Fpsn (dynamic
## programming).
data(neuroblastoma, package="neuroblastoma")
library(data.table)
all.profiles <- data.table(neuroblastoma$profiles)
all.labels <- data.table(neuroblastoma$annotations)
label.counts <- all.labels[, .(
positive.labels=sum(annotation=="breakpoint"),
negative.labels=sum(annotation=="normal")
), by=.(profile.id)]
label.counts[positive.labels==3 & negative.labels==3]
profile.id.show <- "2"
labels.show <- all.labels[profile.id==profile.id.show]
profiles.show <- all.profiles[
profile.id==profile.id.show & chromosome %in% labels.show$chromosome]
label.i.todo <- 1:nrow(labels.show)
label.i.done <- as.integer(unique(sub(" .*", "", names(loss.dt.list))))
label.i.new <- label.i.todo[! label.i.todo %in% label.i.done]
max.segments <- 10
for(label.i in label.i.new){
cat(sprintf("label.i=%d\n", label.i))
one.label <- labels.show[label.i]
select.dt <- one.label[, .(profile.id, chromosome)]
pro.dt <- all.profiles[select.dt, on=names(select.dt)]
## Code from demos about dynamic programming for changepoint
## detection (2020-10-16).
this.max <- if(nrow(pro.dt) < max.segments){
nrow(pro.dt)
}else{
max.segments
}
optimal.models <- jointseg::Fpsn(pro.dt$logratio, this.max)
segs.dt.list <- list()
for(n.segs in 1:this.max){
end <- optimal.models$t.est[n.segs, 1:n.segs]
start <- c(1, end[-length(end)]+1)
segs.dt.list[[paste(n.segs)]] <- data.table(start, end)[, .(
segments=n.segs,
mean=mean(pro.dt$logratio[start:end]),
algorithm="DP"
), by=.(start, end)]
}
segs.dt <- do.call(rbind, segs.dt.list)
for(col.name in c("start", "end")){
col.value <- segs.dt[[col.name]]
set(segs.dt, j=paste0(col.name, ".pos"),
value=pro.dt$position[col.value])
}
segs.dt[, end.before := c(NA, end.pos[-.N]), by=.(segments) ]
change.dt <- data.table(select.dt, segs.dt[1 < start])
change.dt[, changepoint := (start.pos+end.before)/2]
this.loss.dt <- data.table(
segments=1:this.max,
loss=optimal.models$J.est)
penalty <- 0.12
this.loss.dt[, crit.value := loss + penalty*segments]
loss.dt.list[[paste(label.i)]] <- data.table(
select.dt, this.loss.dt)
change.dt.list[[paste(label.i)]] <- change.dt[, data.table(
select.dt, changepoint, segments)]
}
change.dt <- do.call(rbind, change.dt.list)
loss.dt <- do.call(rbind, loss.dt.list)
## Compute model selection function, which maps penalty (lambda)
## values to model complexity (segments) values.
all.model.selection <- loss.dt[, penaltyLearning::modelSelection(
.SD, "loss", "segments"),
by=.(profile.id, chromosome)]
pred.penalty.dt <- loss.dt[, data.table(
pred.log.lambda=log(10)
), by=.(profile.id, chromosome)]
## Compute label error, fp/fn for each selected model.
error.list <- penaltyLearning::labelError(
models=all.model.selection,
labels=labels.show,
changes=change.dt,
problem.vars=c("profile.id", "chromosome"),
change.var="changepoint",
model.vars="segments")
error.list$model.errors[, .(
profile.id, chromosome, min.lambda, max.lambda, segments, fp, fn)]
## Compute ROC curve, FPR/TPR for every prediction threshold (default
## prediction threshold is zero).
roc.list <- penaltyLearning::ROChange(
error.list$model.errors,
pred.penalty.dt,
problem.vars=c("profile.id", "chromosome"))
roc.list$roc[, .(min.thresh, max.thresh, FPR, TPR, errors)]
## Visualize ROC curve.
library(animint2)
ggplot()+
geom_path(aes(
FPR, TPR),
data=roc.list$roc)
ggplot()+
geom_point(aes(
position, logratio),
data=profiles.show)+
geom_rect(aes(
xmin=min, xmax=max, fill=annotation),
ymin=-Inf,
ymax=Inf,
alpha=0.5,
data=labels.show)+
facet_grid(chromosome ~ .)
show.changes.list <- list()
show.roc.list <- list()
thresh.vec <- roc.list$roc[, seq(min(max.thresh), max(min.thresh), l=50) ]
for(thresh.i in seq_along(thresh.vec)){
thresh <- thresh.vec[[thresh.i]]
show.roc.list[[paste(thresh.i)]] <- data.table(
thresh.i,
roc.list$roc[min.thresh <= thresh & thresh < max.thresh])
pred.penalty.dt[, new.log.lambda := pred.log.lambda + thresh]
thresh.selected <- all.model.selection[pred.penalty.dt, on=.(
profile.id, chromosome,
min.log.lambda <= new.log.lambda, max.log.lambda > new.log.lambda)]
thresh.changes <- change.dt[
thresh.selected, nomatch=0L,
on=.(profile.id, chromosome, segments)]
if(nrow(thresh.changes)){
show.changes.list[[paste(thresh.i)]] <- data.table(
thresh.i, thresh.changes)
}
}
show.changes <- do.call(rbind, show.changes.list)
show.roc <- do.call(rbind, show.roc.list)
animint(
ggplot()+
geom_path(aes(
FPR, TPR),
data=roc.list$roc)+
geom_point(aes(
FPR, TPR),
clickSelects="thresh.i",
size=5,
alpha=0.55,
data=show.roc),
ggplot()+
geom_point(aes(
position, logratio),
data=profiles.show)+
geom_tallrect(aes(
xmin=min, xmax=max, fill=annotation),
alpha=0.5,
data=labels.show)+
geom_vline(aes(
xintercept=changepoint),
showSelected="thresh.i",
data=show.changes)+
facet_grid(chromosome ~ .)
)
|
9cffae9d756afbbb9cc163d55f5e2e2e605cba8a | f78cd948863b0d44fb817d10e28c52a885d425e5 | /ValoresHorariosCompletosMeteorologicosAemet.R | 8e3250b8982616806da7b1e5bdf73f02f6185aa7 | [] | no_license | GuilleHM/TFM_GuillermoHuerta_MasterBigData_2019_UNIR | 6044fdc94c32f01559b9ce1b64dac86c79b6172a | 6126784ba90740eded8711bb22517463bc2eeb28 | refs/heads/master | 2020-07-10T17:02:08.893045 | 2019-09-15T11:08:40 | 2019-09-15T11:08:40 | 204,317,561 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 4,031 | r | ValoresHorariosCompletosMeteorologicosAemet.R | # Este "script" sirve para complementar el archivo csv exportado desde la
# colecciรณn "meteorologicos" de la BBDD "aemet" de nuestro servidor mongodb.
# En esta colecciรณn se encuentran los valores meteorologicos horarios, obtenidos
# mediante llamada a la API OpenData de Aemet, para todas las estaciones meteorolรณgicas
# que forman parte de la red de la Agencia Estatal de Meteorologรญa.
# Es necesario complementar el archivo csv que exportamos desde mongodb ya que faltan
# algunos registros temporales y es necesario que estรฉn todos para poder realizar
# la correlaciรณn de dichos valores con los ofrecidos por la estaciรณn FROGGIT, para
# asรญ garantizar la bondad de las medidas de รฉsta รบltima.
# Utilizamos los valores de la estaciรณn con "idema":"5972X" (SAN FERNANDO), ya que
# es la que se ecuentra mรกs prรณxima a la estaciรณn FROGGIT. No obstante, podrรญamos
# emplear los registros de cualquier estaciรณn en este "script".
# NOTA IMPORTANTE: El archivo tiene que estar codificado en formato ANSI
# Establecemos el directorio de trabajo
setwd("C:\\Users\\GuilleHM\\TFM\\OpendataAEMET")
# Cargamos el paquete que necesitaremos para manipular las fechas
library(lubridate)
# Cargamos los datos provenientes de la colecciรณn
aemetorigin <- read.csv(file = "mongoexport_meteoro_5972X_VvFint_may19.csv", header = TRUE, sep = ",", dec = ".")
# Definimos e inicializamos las variables con las que trabajaremos
# ----------------------------------------------------------------
# Obtenemos la fecha de inicio (los valores en el csv son para cada mes)
Aรฑo <- substring(as.character(aemetorigin$fint[1]),1,4)
Mes <- substring(as.character(aemetorigin$fint[1]),6,7)
Dรญa <- substring(as.character(aemetorigin$fint[1]),9,10)
# Valor que servirรก para comprobar si falta un registro
FechaControl <- ISOdate(Aรฑo,Mes,Dรญa,00,00,00, tz="GMT")
# Formateamos para poder comparar con FechaControl
FechaOriginal <- ymd_hms(aemetorigin$fint, tz ="GMT")
# Creamos la tabla final sobre la que realizaremos el anรกlisis
# Damos a las columnas los mismos valores que los de la tabla final para los
# valores de la estaciรณn meteorolรณgica Froggit.
aemetfinal <- data.frame(FechaFinal = FechaOriginal[1], VelFinal = aemetorigin$vv[1])
# n -> Cuenta los registros horarios no existentes en el archivo original de aemet
# m -> Puntero para movernos por la tabla aemet final
# longitud -> Nรบmero de campos existentes en el archivo original de aemet
n <- m <- 0
longitud <- length(FechaOriginal)
# ----------------------------------------------------------------
# Recorremos la tabla original e incluimos en la tabla final los registros temporales
# que falten, con un valor NA para la velocidad del viento
for (i in 1:longitud){
if (i == 1) {
FechaControl <- FechaControl + hours(1)
next
}
if (FechaControl != FechaOriginal[i-n]){
n <- n + 1
aemetfinal <- rbind(aemetfinal, list(FechaControl, NA))
}
else{
aemetfinal <- rbind(aemetfinal, list(FechaControl, aemetorigin$vv[i-n]))
}
FechaControl <- FechaControl + hours(1)
}
# Actualizamos m con la posiciรณn desde donde continuar insertando campos
m <- longitud - n + 1
# Repetimos las inserciones hasta que no quede ningรบn registro sin incorporar
# a la tabla final de valores de aemet
while (n != 0) {
temp <- n
for (i in 1:n){
if (i == 1) {
n <- 0
}
if (FechaControl != FechaOriginal[m]){
aemetfinal <- rbind(aemetfinal, list(FechaControl, NA))
n <- n + 1
}
else{
aemetfinal <- rbind(aemetfinal, list(FechaControl, aemetorigin$vv[m]))
m <- m + 1
}
FechaControl <- FechaControl + hours(1)
}
longitud <- longitud + temp
}
# Guardamos los valores en el archivo que emplearemos para los anรกlisis
write.csv(aemetfinal, file = "SalidaScriptValoresHorariosCompletosAemetMay.csv", row.names = FALSE)
# Limpiamos el entorno borrando todas las variables
rm(list=ls())
|
23a0d392cdf10dd070ea0a5ff2e8c10c6bc38b39 | e279d4de1f3d9be6fcdd688f375e59535a44d18e | /cachematrix.R | 60146d63b9753acad04883d6208d3fb3d1060066 | [] | no_license | eniedling/ProgrammingAssignment2 | 4dbb0d96f5d6696a4ed09ed2a2af42a2f219dff6 | 92e21ac778b2ce9f91f510a6d99be09b1eeef7ec | refs/heads/master | 2021-01-09T06:44:09.437791 | 2014-09-21T14:48:27 | 2014-09-21T14:48:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,605 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
makeCacheMatrix <- function(x = matrix()) {
# initialize inverse matrix object
inv <- NULL
# define set function, which stores matrix internally AND resets inverse
set <- function(y) {
x <<- y
# any change to the matrix x, which is updated into the matrix object via $set(m)
# also leads to a reset of the inverse matrix
inv <<- NULL
}
get <- function() x
# store value of solve in object inv
setinverse <- function(solve) inv <<- solve
# returns value of inverse
getinverse <- function() inv
# defines matrix object as type list: try
# > mo <- makeCacheMatrix( matrix(1:4, nrow=2,ncol=2))
# > class(mo)
list(set = set, get = get,
setinverse = setinverse, getinverse = getinverse)
}
## Write a short comment describing this function
# determines the inverse of a matrix via the solve function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
# get the inverse calculated earlier, reading from cache
inv <- x$getinverse()
# check if Inv-object is not NULL/empty, and return result
if(!is.null(inv)) {
message("getting cached data")
return(inv)
}
# inv-object is NULL, hence the following code is executed to solve matrix for inverse
# 1) get the intial matrix from cache
data <- x$get()
# 2) determine the inverse via the solve function
inv <- solve(data, ...)
# 3) store result in cache with the setinverse function
x$setinverse(inv)
# 4) output result
inv
}
|
0354759d1ee2df3184f70f4c9e6becc61b33f681 | f28e53f5f9ad06bf51b69f45fd1425cf02c1709b | /man/granplot.Rd | 2bc7d20aead8b881ab26b4334b4b8509f361b789 | [] | no_license | gallonr/G2Sd | f42ac156bdab5dc1ded6b82563d864bd92245092 | 31325d40d14e2df08319ac66ddd9aed7c1f1686b | refs/heads/master | 2023-07-04T23:08:17.638661 | 2020-03-06T16:43:48 | 2020-03-06T16:43:48 | 170,348,255 | 4 | 2 | null | 2021-08-25T13:05:39 | 2019-02-12T16:07:25 | R | UTF-8 | R | false | false | 1,893 | rd | granplot.Rd | \encoding{UTF8}
\name{granplot}
\alias{granplot}
\title{
Histogram with a cumulative percentage curve
}
\description{
This function provides a histogram of the grain-size distribution with a cumulative percentage curve
}
\usage{
granplot(x, xc = 1, meshmin=1, hist = TRUE, cum = TRUE, main = "",
col.cum = "red", col.hist="darkgray", cexname=0.9,
cexlab=1.3,decreasing=FALSE)
}
\arguments{
\item{x}{
A numeric matrix or data frame (see the shape of data(granulo))
}
\item{xc}{
A numeric value or a numeric vector to define columns
}
\item{meshmin}{
Define the size of the smallest meshsize if it is 0 in raw data
}
\item{hist}{
If TRUE, display a histogram; if FALSE, do not display a histogram (only for only one column)
}
\item{cum}{
If TRUE, display a cumulative percentage curve; if FALSE do not display a cumulative percentage curve (only for only one column)
}
\item{main}{
Add a title to the current plot
}
\item{col.cum}{
Color in which cumulative percentage curve will be drawn
}
\item{col.hist}{
Color in which histogram will be drawn
}
\item{cexname}{
A numerical value giving the amount by which plotting text and symbols should be magnified
relative to the default.
}
\item{cexlab}{
A numerical value giving the amount by which axis labels should be magnified
relative to the default.
}
\item{decreasing}{
A logical value defining the order increasing or decreasing
}
}
\details{
The obtained graph is the most commonly used by Sedimentologists
}
\value{
A histogram with a cumulative percentage curve
}
\author{
Regis K. Gallon (MNHN) \email{reg.gallon@gmail.com},
Jerome Fournier (CNRS) \email{fournier@mnhn.fr}
}
\seealso{
\code{\link[G2Sd]{grandistrib}}
}
\examples{
data(granulo)
granplot(granulo,xc=1,hist=TRUE,cum=TRUE,main="Grain-size Distribution",
col.hist="gray",col.cum="red")
granplot(granulo,xc=2:4,main="Grain-size Distribution")
} |
184d92fec62d54cc579ab80ab498baba0f5fee4d | 313208dae9bd931bd221bfbbb53f97d29fa7433b | /man/GeneratRateMatrix.ProgressionGraph.Rd | e5952717c2339c4779f9f2c56b461bbbbf3e6d5e | [] | no_license | scientific-computing-solutions/badminton | 23cf0a4ad7b14fc001108e5e2a98d2b4d081dbcb | a89625d15aa13d3f7ceac8676cc4aef42fb2a4aa | refs/heads/master | 2020-12-25T13:44:47.001850 | 2016-11-23T19:43:24 | 2016-11-23T19:43:24 | 65,361,935 | 0 | 0 | null | 2016-11-23T19:43:25 | 2016-08-10T07:50:11 | R | UTF-8 | R | false | true | 699 | rd | GeneratRateMatrix.ProgressionGraph.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/progressionGraph.R
\name{GeneratRateMatrix.ProgressionGraph}
\alias{GeneratRateMatrix.ProgressionGraph}
\title{Outputs a matrix of rates from the edge Data of a \code{ProgressionGraph}
object}
\usage{
GeneratRateMatrix.ProgressionGraph(object)
}
\arguments{
\item{object}{A \code{ProgressionGraph} object}
}
\value{
A Matrix of transition rates
}
\description{
The matrix that is produced is such that M[i,j] = the rate
from node i to node j, where the nodes are numbered in creation
order (\code{nodes(object$graph))}. If the transition rates are
formula then these should be evaluated before calling this function
}
|
12e629e67f8d35908ca4f9c0bddb656b71f47da8 | 304e0912b221eca50a1f511525e196f9958bed7f | /capture_url_information.r | 225831eeb45e9e76811bbfac12a22052fba53a23 | [] | no_license | thefactmachine/ckan_fun | 52733d5863962b1c6e2a30e3c2251cb520c187c7 | 6b9f5ac0f6776a9a480df5d2b720b39f4b03ad78 | refs/heads/master | 2020-05-21T08:51:56.892383 | 2016-11-08T02:55:48 | 2016-11-08T02:55:48 | 69,430,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,193 | r | capture_url_information.r | # clear the decks
rm(list=ls())
options(stringsAsFactors=FALSE)
library(jsonlite)
library(dplyr)
# library(httr)
# read the json file amd parse it into an R list
# this is top CKAN url which returns a list of CKAN references to each data set.
# the length of the returned data set is equal to the number data sets.
lst_ckan <- jsonlite::fromJSON("http://data.gov.au/api/3/action/package_list")
# the "result" frame is the only one which is relevant.
vct_results <- lst_ckan$result
# load in a function to return some text for each url supplied.
source("fn_read_url.r")
# main CKAN stem for datq.gov.au
str_html_stem <- "http://data.gov.au/api/3/action/package_show?id="
# we now have a vector or urls
vct_urls <- paste0(str_html_stem, vct_results)
# declare a blank list to hold our results
lst_ckan_results <- list()
# cycle through each url and save the resultant json object in
# a list
for (i in 1:length(vct_urls)) {
lst_ckan_results[[vct_urls[i]]] <- fn_read_url(vct_urls[i])
print(i)
}
# ASSERT: number of list elements == number of urls.
length(lst_ckan_results) == length(vct_results)
# save the sucker for future processing.
save(lst_ckan_results, file = "results.rda") |
38f4c3663d88276cc8a381463ac659b9f75c7ff0 | c2d4519a1f951ac6b8acfa8810265334508ea20a | /man/BLRM.fit.mwg.Rd | 59a6d51e71fd9a379db29afbc70a5bd96116f571 | [] | no_license | lcw68/G3proj | ea56788ad44f9223426492b7416170821e9401a6 | a419894ba9dc20e5c13568221d53157c238e4496 | refs/heads/main | 2023-04-24T03:48:54.759692 | 2021-05-06T17:10:00 | 2021-05-06T17:10:00 | 359,679,879 | 0 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,307 | rd | BLRM.fit.mwg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppWrapper.R
\name{BLRM.fit.mwg}
\alias{BLRM.fit.mwg}
\title{Bayesian Logistic Regression Model (BLRM) training}
\usage{
BLRM.fit.mwg(
Y0,
X0,
PriorVar,
propSD0,
nMC = 1000,
nBI = 250,
thin = 5,
seed = 1
)
}
\arguments{
\item{Y0}{vector of responses}
\item{X0}{covariate matrix}
\item{PriorVar}{variance of prior distribution of beta}
\item{propSD0}{vector of standard deviations for normal proposal density}
\item{nMC}{number of MCMC samples}
\item{nBI}{number of burn-in samples}
\item{thin}{number of samples to skip over in thinning}
\item{seed}{set seed for random number generation}
}
\value{
a nested list of beta samples, and beta acceptance rates
}
\description{
Performs Bayesian Logistic Regression Model training by sampling beta
from posterior distribution with user specified parameters and data
}
\examples{
## simulate data;
set.seed(1);
N = 100;
p = 10;
X = matrix(data = rnorm(N*p), nrow=N, ncol=p)
beta_true = c(rep(1,p/2),rep(0,p/2))
eta = X \%*\% beta_true
pi = exp(eta) / (1 + exp(eta))
Y = rbinom(N,1,pi)
propSD = rep(1,p)
## fit model;
test1 <- G3proj::BLRM.fit.mwg(Y0 = Y, X0 = X, PriorVar = 1000, propSD0 = propSD,
nMC = 500, nBI = 100, thin = 5)
}
|
24e77dbc468047b8d08707e2ee7ed87ec9fbe4ba | 8f94ccd8d3aed33b418cb9639dc64a159931ae4e | /R/print.sc_rci.R | 0fc5579cbdb124323d2b1a56c5a6535e385e5081 | [] | no_license | cran/scan | 8c9d9b2dc44bbb8c339be3795a62bb5c49be87b0 | 860599c21c4c5e37746fa8e6234c6f6cc8028070 | refs/heads/master | 2023-08-22T17:47:22.450439 | 2023-08-08T13:00:02 | 2023-08-08T14:31:36 | 70,917,562 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 522 | r | print.sc_rci.R | #' @rdname print.sc
#' @export
#'
print.sc_rci <- function(x, ...) {
cat("Reliable Change Index\n\n")
cat("Mean Difference = ", x$descriptives[2, 2] - x$descriptives[1, 2], "\n")
cat("Standardized Difference = ", x$stand.dif, "\n")
cat("\n")
cat("Descriptives:\n")
print(x$descriptives)
cat("\n")
cat("Reliability = ", x$reliability, "\n")
cat("\n")
cat(x$conf.percent * 100, "% Confidence Intervals:\n")
print(x$conf)
cat("\n")
cat("Reliable Change Indices:\n")
print(x$RCI)
cat("\n")
}
|
d8f36b786598e218c32d7a58b627cfe037d92c7e | 362495bee5b074de6953d17ca22682829e66168d | /tests/testthat/test-pobierz_wartosci_wskaznikow_ewd.R | 741170e80cb31569917a2530595bced80d835c38 | [
"MIT"
] | permissive | tzoltak/EWDdane | fc8d59aa97d803b347e70a6f70df4154f82bd6b2 | 8eb5ef560e999cbde8d0dddb78012e4a52bc3480 | refs/heads/master | 2022-10-22T02:54:33.216359 | 2022-09-29T15:32:56 | 2022-09-29T15:32:56 | 29,207,245 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 592 | r | test-pobierz_wartosci_wskaznikow_ewd.R | context('pobierz_wartosci_wskaznikow_ewd') # cokolwiek - to siฤ po prostu wyลwietla na ekranie podczas uruchamiania testรณw jako taka wskazรณwka, co siฤ wลaลnie testuje
# testy grupujemy w nazwane grupy, ale znรณw od strony technicznej wszystko jedno, jak to zrobimy
# mogฤ
byฤ wszystkie w jednym test_that() rรณwnie dobrze jak kaลผdy w oddzielnym
test_that('pobierz_wartosci_wskaznikow_ewd dziaลa', {
dane = pobierz_wartosci_wskaznikow_ewd('T', 2013)
expect_is(dane, 'data.frame')
expect_equal(all(dane$typ_szkoly %in% 'T'), TRUE)
expect_equal(all(!is.na(dane$nazwa)), TRUE)
})
|
25f322a8088cf84e795c4e1bccad87adba571196 | ea859cacef237016e55cd5af3765fa8c39561367 | /R/global.R | 8f85314d0c085bb2b07beb2af1a11b50b4ecc9b7 | [] | no_license | Kadambala/GWSDAT | a0253617ff7527c508c82dda8c5b37b350ab88ea | 0c725e0273a102f51b5126ee3b32ba4696fd50f5 | refs/heads/master | 2023-08-22T17:00:30.324738 | 2021-07-30T15:05:52 | 2021-07-30T15:05:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 839 | r | global.R |
# This is the place in Shiny to define global _constant_ variables.
# Note that these variables become locked if the software is packaged.
#
# The binding can be unlocked, but instead of doing this rather complicated step
# I put all non-constant variables into the server() function (non-global) and pass them to
# the functions that needs it.
#
coord_units <- c("","metres", "feet")
conc_units <- c("ng/l", "ug/l", "mg/l", "Level",
"metres", # for GW (groundwater depth)
"feet", # for GW (groundwater depth)
"mm", # for NAPL thickness
"pH")
conc_flags <- c("", "E-acc", "Omit", "NotInNAPL", "Redox")
conc_header <- list("WellName", "Constituent", "SampleDate", "Result", "Units", "Flags")
well_header <- list("WellName", "XCoord", "YCoord", "Aquifer")
|
9ff99d7cd7113f608bdf70b1ed7111a087edf4a3 | da9922c616758fb2beced7407529d974eb892d3c | /R/pssbBind.R | 1cb8435382998ec48626f417ab1bee71af7f8bd6 | [] | no_license | kcstreamteam1/wlRd-package | 714d749b6480d86e5a1e3a0d81047e9ecbc5acf6 | 46683391740cedbd2c5b8bccef2fe90c2582a634 | refs/heads/master | 2021-01-01T18:49:53.992958 | 2017-07-31T18:17:50 | 2017-07-31T18:17:50 | 98,440,798 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,244 | r | pssbBind.R | pssbBind <- function(file.path, score.type = NULL, ambient = F) {
# validate lengths of file path vector and score type vector are the same
if (!is.null(score.type)) {
if (length(file.path) != length(score.type)) stop("The number of score types must be equal to the number of file paths")}
# loop through file paths, define anonymous function
all.list <- lapply(1:length(file.path), function(x) {
path.files <- list.files(file.path[x])
# read in files
list.with.each.file <- lapply(paste(file.path, list.files(file.path), sep = ''), function(y) read.delim(y, header=TRUE))
# append id to each file individually
if (!is.null(score.type)) {
list.with.each.file <- lapply(list.with.each.file, function(y) {
y$id <- score.type[x]
y})}
# bind all files from each file.path into data frames
do.call("rbind.data.frame", list.with.each.file)
})
# bind files from all file.paths into one data frame
bibi <- do.call("rbind.data.frame", all.list)
if (ambient == TRUE) {bibi <- droplevels(bibi[bibi$Agency=="King County - DNRP" & bibi$Project == 'Boise Ambient' | bibi$Project == 'Ambient Monitoring' | bibi$Project =='Vashon' | bibi$Project =='Seattle',])}
return(bibi)
}
|
767a1e0621fc5203cace01834d7444a332ac31f2 | e9f2bfee76e2d5a4de154bac6f0f1defcb8f5605 | /R/customLosses.R | 22af209140771d4d1363276e6cd782bf32f9b194 | [] | no_license | devhliu/ANTsRNet | f3f3357df2b4ac54b12a8c02243b4695649d8fde | ba02b69fcb3835d6b9ba178f88c0932d1f3a86f1 | refs/heads/master | 2020-06-23T08:08:48.638690 | 2019-07-22T18:41:06 | 2019-07-22T18:41:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,116 | r | customLosses.R | #' Model loss function for multilabel problems--- multilabel dice coefficient
#'
#' Based on the keras loss function (losses.R):
#'
#' \url{https://github.com/rstudio/keras/blob/master/R/losses.R}
#'
#' @param y_true True labels (Tensor)
#' @param y_pred Predictions (Tensor of the same shape as \code{y_true})
#'
#' @details Loss functions are to be supplied in the loss parameter of the
#' \code{compile()} function.
#'
#' Loss functions can be specified either using the name of a built in loss
#' function (e.g. \code{loss = binary_crossentropy}), a reference to a built in loss
#' function (e.g. \code{loss = binary_crossentropy()}) or by passing an
#' arbitrary function that returns a scalar for each data-point.
#' The actual optimized objective is the mean of the output array across all
#' datapoints.
#' @export
multilabel_dice_coefficient <- function( y_true, y_pred )
{
smoothingFactor <- 1.0
K <- keras::backend()
K$set_image_data_format( 'channels_last' )
y_dims <- unlist( K$int_shape( y_pred ) )
numberOfLabels <- y_dims[length( y_dims )]
# Unlike native R, indexing starts at 0. However, we are
# assuming the background is 0 so we skip index 0.
if( length( y_dims ) == 3 )
{
# 2-D image
y_true_permuted <- K$permute_dimensions(
y_true, pattern = c( 3L, 0L, 1L, 2L ) )
y_pred_permuted <- K$permute_dimensions(
y_pred, pattern = c( 3L, 0L, 1L, 2L ) )
} else {
# 3-D image
y_true_permuted <- K$permute_dimensions(
y_true, pattern = c( 4L, 0L, 1L, 2L, 3L ) )
y_pred_permuted <- K$permute_dimensions(
y_pred, pattern = c( 4L, 0L, 1L, 2L, 3L ) )
}
y_true_label <- K$gather( y_true_permuted, indices = c( 1L ) )
y_pred_label <- K$gather( y_pred_permuted, indices = c( 1L ) )
y_true_label_f <- K$flatten( y_true_label )
y_pred_label_f <- K$flatten( y_pred_label )
intersection <- y_true_label_f * y_pred_label_f
union <- y_true_label_f + y_pred_label_f - intersection
numerator <- K$sum( intersection )
denominator <- K$sum( union )
if( numberOfLabels > 2 )
{
for( j in 2L:( numberOfLabels - 1L ) )
{
y_true_label <- K$gather( y_true_permuted, indices = c( j ) )
y_pred_label <- K$gather( y_pred_permuted, indices = c( j ) )
y_true_label_f <- K$flatten( y_true_label )
y_pred_label_f <- K$flatten( y_pred_label )
intersection <- y_true_label_f * y_pred_label_f
union <- y_true_label_f + y_pred_label_f - intersection
numerator <- numerator + K$sum( intersection )
denominator <- denominator + K$sum( union )
}
}
unionOverlap <- numerator / denominator
return ( ( 2.0 * unionOverlap + smoothingFactor ) /
( 1.0 + unionOverlap + smoothingFactor ) )
}
attr( multilabel_dice_coefficient, "py_function_name" ) <-
"multilabel_dice_coefficient"
#' Multilabel dice loss function.
#'
#' @param y_true true encoded labels
#' @param y_pred predicted encoded labels
#'
#' @rdname loss_multilabel_dice_coefficient_error
#' @export
loss_multilabel_dice_coefficient_error <- function( y_true, y_pred )
{
return( -multilabel_dice_coefficient( y_true, y_pred ) )
}
attr( loss_multilabel_dice_coefficient_error, "py_function_name" ) <-
"multilabel_dice_coefficient_error"
#' Peak-signal-to-noise ratio.
#'
#' Based on the keras loss function (losses.R):
#'
#' \url{https://github.com/rstudio/keras/blob/master/R/losses.R}
#'
#' @param y_true True labels (Tensor)
#' @param y_pred Predictions (Tensor of the same shape as \code{y_true})
#'
#' @details Loss functions are to be supplied in the loss parameter of the
#' \code{compile()} function.
#'
#' @export
peak_signal_to_noise_ratio <- function( y_true, y_pred )
{
K <- keras::backend()
return( -10.0 * K$log( K$mean( K$square( y_pred - y_true ) ) ) / K$log( 10.0 ) )
}
attr( peak_signal_to_noise_ratio, "py_function_name" ) <-
"peak_signal_to_noise_ratio"
#' Peak-signal-to-noise ratio.
#'
#' @param y_true true encoded labels
#' @param y_pred predicted encoded labels
#'
#' @rdname loss_peak_signal_to_noise_ratio_error
#' @export
loss_peak_signal_to_noise_ratio_error <- function( y_true, y_pred )
{
return( -peak_signal_to_noise_ratio( y_true, y_pred ) )
}
attr( loss_peak_signal_to_noise_ratio_error, "py_function_name" ) <-
"peak_signal_to_noise_ratio_error"
#' Pearson correlation coefficient.
#'
#' Based on the code found here:
#'
#' \url{https://github.com/rstudio/keras/issues/160}
#'
#' @param y_true True labels (Tensor)
#' @param y_pred Predictions (Tensor of the same shape as \code{y_true})
#'
#' @details Loss functions are to be supplied in the loss parameter of the
#' \code{compile()} function.
#'
#' @export
pearson_correlation_coefficient <- function( y_true, y_pred )
{
K <- keras::backend()
N <- K$sum( K$ones_like( y_true ) )
sum_x <- K$sum( y_true )
sum_y <- K$sum( y_pred )
sum_x_squared <- K$sum( K$square( y_true ) )
sum_y_squared <- K$sum( K$square( y_pred ) )
sum_xy <- K$sum( y_true * y_pred )
numerator <- sum_xy - ( sum_x * sum_y / N )
denominator <- K$sqrt( ( sum_x_squared - K$square( sum_x ) / N ) *
( sum_y_squared - K$square( sum_y ) / N ) )
coefficient <- numerator / denominator
return( coefficient )
}
attr( pearson_correlation_coefficient, "py_function_name" ) <-
"pearson_correlation_coefficient"
#' Pearson correlation coefficient
#'
#' @param y_true true encoded labels
#' @param y_pred predicted encoded labels
#'
#' @rdname loss_pearson_correlation_coefficient_error
#' @export
loss_pearson_correlation_coefficient_error <- function( y_true, y_pred )
{
return( -pearson_correlation_coefficient( y_true, y_pred ) )
}
attr( loss_pearson_correlation_coefficient_error, "py_function_name" ) <-
"pearson_correlation_coefficient_error"
#' Loss function for the SSD deep learning architecture.
#'
#' Creates an R6 class object for use with the SSD deep learning architecture
#' based on the paper
#'
#' W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C-Y. Fu, A. Berg.
#' SSD: Single Shot MultiBox Detector.
#'
#' available here:
#'
#' \url{https://arxiv.org/abs/1512.02325}
#'
#' @docType class
#'
#' @section Usage:
#' \preformatted{ssdLoss <- LossSSD$new( dimension = 2L, backgroundRatio = 3L,
#' minNumberOfBackgroundBoxes = 0L, alpha = 1.0,
#' numberOfClassificationLabels )
#'
#' ssdLoss$smooth_l1_loss( y_true, y_pred )
#' ssdLoss$log_loss( y_true, y_pred )
#' ssdLoss$compute_loss( y_true, y_pred )
#' }
#'
#' @section Arguments:
#' \describe{
#' \item{ssdLoss}{A \code{process} object.}
#' \item{dimension}{image dimensionality.}
#' \item{backgroundRatio}{The maximum ratio of background to foreround
#' for weighting in the loss function. Is rounded to the nearest integer.
#' Default is 3.}
#' \item{minNumberOfBackgroundBoxes}{The minimum number of background boxes
#' to use in loss computation *per batch*. Should reflect a value in
#' proportion to the batch size. Default is 0.}
#' \item{alpha}{Weighting factor for the localization loss in total loss
#' computation.}
#' \item{numberOfClassificationLabels}{number of classes including background.}
#' }
#'
#' @section Details:
#' \code{$smooth_l1_loss} smooth loss
#'
#' \code{$log_loss} log loss
#'
#' \code{$compute_loss} computes total loss.
#'
#' @author Tustison NJ
#'
#' @return an SSD loss function
#'
#' @name LossSSD
NULL
#' @export
LossSSD <- R6::R6Class( "LossSSD",
public = list(
dimension = 2L,
backgroundRatio = 3L,
minNumberOfBackgroundBoxes = 0L,
alpha = 1.0,
numberOfClassificationLabels = NULL,
tf = tensorflow::tf,
initialize = function( dimension = 2L, backgroundRatio = 3L,
minNumberOfBackgroundBoxes = 0L, alpha = 1.0,
numberOfClassificationLabels = NULL )
{
self$dimension <- as.integer( dimension )
self$backgroundRatio <- self$tf$constant( backgroundRatio )
self$minNumberOfBackgroundBoxes <-
self$tf$constant( minNumberOfBackgroundBoxes )
self$alpha <- self$tf$constant( alpha )
self$numberOfClassificationLabels <-
as.integer( numberOfClassificationLabels )
},
smooth_l1_loss = function( y_true, y_pred )
{
y_true <- self$tf$cast( y_true, dtype = "float32" )
absoluteLoss <- self$tf$abs( y_true - y_pred )
squareLoss <- 0.5 * ( y_true - y_pred )^2
l1Loss <- self$tf$where( self$tf$less( absoluteLoss, 1.0 ),
squareLoss, absoluteLoss - 0.5 )
return( self$tf$reduce_sum( l1Loss, axis = -1L, keepdims = FALSE ) )
},
log_loss = function( y_true, y_pred )
{
y_true <- self$tf$cast( y_true, dtype = "float32" )
y_pred <- self$tf$maximum( y_pred, 1e-15 )
logLoss <- -self$tf$reduce_sum( y_true * self$tf$log( y_pred ),
axis = -1L, keepdims = FALSE )
return( logLoss )
},
compute_loss = function( y_true, y_pred )
{
y_true$set_shape( y_pred$get_shape() )
batchSize <- self$tf$shape( y_pred )[1]
numberOfBoxesPerCell <- self$tf$shape( y_pred )[2]
indices <- 1:self$numberOfClassificationLabels
classificationLoss <- self$tf$to_float( self$log_loss(
y_true[,, indices], y_pred[,, indices] ) )
indices <- self$numberOfClassificationLabels + 1:( 2 * self$dimension )
localizationLoss <- self$tf$to_float( self$smooth_l1_loss(
y_true[,, indices], y_pred[,, indices] ) )
backgroundBoxes <- y_true[,, 1]
if( self$numberOfClassificationLabels > 2 )
{
foregroundBoxes <- self$tf$to_float( self$tf$reduce_max(
y_true[,, 2:self$numberOfClassificationLabels],
axis = -1L, keepdims = FALSE ) )
} else {
foregroundBoxes <- self$tf$to_float( self$tf$reduce_max(
y_true[,, 2:self$numberOfClassificationLabels],
axis = -1L, keepdims = TRUE ) )
}
numberOfForegroundBoxes <- self$tf$reduce_sum(
foregroundBoxes, keepdims = FALSE )
if( self$numberOfClassificationLabels > 2 )
{
foregroundClassLoss <- self$tf$reduce_sum(
classificationLoss * foregroundBoxes, axis = -1L, keepdims = FALSE )
} else {
foregroundClassLoss <- self$tf$reduce_sum(
classificationLoss * foregroundBoxes, axis = -1L, keepdims = TRUE )
}
backgroundClassLossAll <- classificationLoss * backgroundBoxes
nonZeroIndices <-
self$tf$count_nonzero( backgroundClassLossAll, dtype = self$tf$int32 )
numberOfBackgroundBoxesToKeep <- self$tf$minimum( self$tf$maximum(
self$backgroundRatio * self$tf$to_int32( numberOfForegroundBoxes ),
self$minNumberOfBackgroundBoxes ), nonZeroIndices )
f1 = function()
{
return( self$tf$zeros( list( batchSize ) ) )
}
f2 = function()
{
backgroundClassLossAll1d <-
self$tf$reshape( backgroundClassLossAll, list( -1L ) )
topK <- self$tf$nn$top_k(
backgroundClassLossAll1d, numberOfBackgroundBoxesToKeep, FALSE )
values <- topK$values
indices <- topK$indices
backgroundBoxesToKeep <- self$tf$scatter_nd(
self$tf$expand_dims( indices, axis = 1L ),
updates = self$tf$ones_like( indices, dtype = self$tf$int32 ),
shape = self$tf$shape( backgroundClassLossAll1d ) )
backgroundBoxesToKeep <- self$tf$to_float(
self$tf$reshape( backgroundBoxesToKeep,
list( batchSize, numberOfBoxesPerCell ) ) )
return( self$tf$reduce_sum( classificationLoss * backgroundBoxesToKeep,
axis = -1L, keepdims = FALSE ) )
}
backgroundClassLoss <- self$tf$cond( self$tf$equal(
nonZeroIndices, self$tf$constant( 0L ) ), f1, f2 )
classLoss <- foregroundClassLoss + backgroundClassLoss
localizationLoss <- self$tf$reduce_sum(
localizationLoss * foregroundBoxes, axis = -1L, keepdims = FALSE )
totalLoss <- ( classLoss + self$alpha * localizationLoss ) /
self$tf$maximum( 1.0, numberOfForegroundBoxes )
return( totalLoss )
}
)
)
|
7db0882a15150b63bce78dc09dfcd8bd3d8b9402 | 846791f0492405acbf9a8eae8d7cd4fa8a43d03b | /R/read_meso_region_bg.R | ac053ad49d2eb056eb2307ee2f4d6d9727869a74 | [] | no_license | Prof-Rodrigo-Silva/geobage | 1d523c480bd2850556d873282a51dd077ae636ac | aaec7e8e3f537f99b646669dff00898d371a573d | refs/heads/master | 2022-11-15T23:25:28.965541 | 2022-07-26T03:28:33 | 2022-07-26T03:28:33 | 275,835,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 462 | r | read_meso_region_bg.R | #' Meso region the municipality of Bage
#'
#' Function returns the meso region in which the municipality of Bage is inserted. Data at scale 1:250,000, using Geodetic reference system "SIRGAS2000" and CRS(4674)
#'
#' @export
#' @family general area functions
#' @examples \dontrun{
#'
#' library(geobage)
#'
#' c <- read_meso_region_bg()
#'
#' }
read_meso_region_bg <- function(){
objeto <- geobr::read_meso_region(code_meso=4306)
objeto
}
|
cff0749b4b66416cc5ecfe4d1e4d7643675fbc13 | 09f9121232947f5e8eec489b5db880e3f5a5fc06 | /inst/extra_scripts/build_data.R | d3679c63495631f7423d58abe9123adc40a001c7 | [] | no_license | mejihero/clustext | 9ac986476509e3ef233011858ff2ca6023af08e5 | 19963bc5a63148aa40f29f32bee3430128206215 | refs/heads/master | 2020-03-12T00:06:49.552509 | 2017-04-14T16:42:47 | 2017-04-14T16:42:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,081 | r | build_data.R | pacman::p_load(clustext, dplyr)
x <- presidential_debates_2012 %>%
mutate(tot = gsub("\\..+$", "", tot)) %>%
textshape::combine() %>%
filter(person %in% c("ROMNEY", "OBAMA")) %>%
with(data_store(dialogue, stopwords = tm::stopwords("english"), min.char = 3))
set.seed(10)
kmeans_assignment <- kmeans_cluster(x, 50) %>%
assign_cluster(myfit2)
set.seed(10)
nmf_assignment <- nmf_cluster(x, 50) %>%
assign_cluster(myfit2)
set.seed(10)
skmeans_assignment <- skmeans_cluster(x, 50) %>%
assign_cluster(myfit2)
hierarchical_assignment <- hierarchical_cluster(x) %>%
assign_cluster(k=50)
assignments <- list(
hierarchical_assignment = hierarchical_assignment,
kmeans_assignment = kmeans_assignment,
skmeans_assignment = skmeans_assignment,
nmf_assignment =nmf_assignment
)
assignments <- lapply(assignments, function(x) {
attributes(x)[['data_store']] <- NULL
attributes(x)[['model']] <- NULL
attributes(x)[['join']] <- NULL
x
})
lapply(assignments, function(x) {names(attributes(x))})
pax::new_data(assignments)
|
2e33a5cab2c0b96783a79b3c31c3c403bfca9229 | b5ad091aca9e037cdd7750c7c016a9fbc41cc1b1 | /FunctionAddNum.R | 3f632faaefd313b6005897045dc887396a5097d4 | [] | no_license | avdharwadkar/LiveSessionDemoProject | 4c2f9f8a577e81e2d1809407046ff42519baa165 | 548222bbef8425388096383731839947b85f3f9d | refs/heads/master | 2021-01-12T15:40:24.868656 | 2016-10-25T00:44:32 | 2016-10-25T00:44:32 | 71,843,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 84 | r | FunctionAddNum.R | # Function to Add two number
AddNumbers <- function(x, y){
z = x + y
return(z)
} |
08804c1316562c62b1041498be4a120962802453 | 295d1939656d1b5d20b4eade3e2f9eb622558bec | /scripts/1_MultipleSites_NHA_TemplateGenerator.R | a4ccdf40bbbd15c086f48c2c8738d4be76734814 | [] | no_license | PNHP/NHA_newTemplate | 1959ad006d0c6af2f93c1cf7a7acb96253d70004 | 18a0da80db93f0ef8e6e10bb2587d1759133d833 | refs/heads/master | 2020-04-27T09:30:47.335200 | 2020-01-06T20:45:01 | 2020-01-06T20:45:01 | 174,217,624 | 2 | 2 | null | 2019-06-24T17:27:46 | 2019-03-06T20:40:53 | R | UTF-8 | R | false | false | 23,801 | r | 1_MultipleSites_NHA_TemplateGenerator.R | #-------------------------------------------------------------------------------
# Name: NHA_TemplateGenerator.r
# Purpose: Create a Word template for NHA content for multiple sites at once
# Author: Anna Johnson
# Created: 2019-10-16
# Updated:
#
# Updates:
#
# To Do List/Future ideas:
#
#-------------------------------------------------------------------------------
####################################################
#Set up libraries, paths, and settings
# check and load required libraries
if (!requireNamespace("here", quietly = TRUE)) install.packages("here")
require(here)
if (!requireNamespace("arcgisbinding", quietly = TRUE)) install.packages("arcgisbinding")
require(arcgisbinding)
if (!requireNamespace("RSQLite", quietly = TRUE)) install.packages("RSQLite")
require(RSQLite)
if (!requireNamespace("knitr", quietly = TRUE)) install.packages("knitr")
require(knitr)
if (!requireNamespace("xtable", quietly = TRUE)) install.packages("xtable")
require(xtable)
if (!requireNamespace("flextable", quietly = TRUE)) install.packages("flextable")
require(flextable)
if (!requireNamespace("dplyr", quietly = TRUE)) install.packages("dplyr")
require(dplyr)
if (!requireNamespace("dbplyr", quietly = TRUE)) install.packages("dbplyr")
require(dbplyr)
if (!requireNamespace("rmarkdown", quietly = TRUE)) install.packages("rmarkdown")
require(rmarkdown)
if (!requireNamespace("tmap", quietly = TRUE)) install.packages("tmap")
require(tmap)
if (!requireNamespace("OpenStreetMap", quietly = TRUE)) install.packages("OpenStreetMap")
require(OpenStreetMap)
if (!requireNamespace("openxlsx", quietly = TRUE)) install.packages("openxlsx")
require(openxlsx)
if (!requireNamespace("sf", quietly = TRUE)) install.packages("sf")
require(sf)
# if (!requireNamespace("odbc", quietly = TRUE)) install.packages("odbc")
# require(odbc)
# note: we need to install 64bit java: https://www.java.com/en/download/manual.jsp
# load in the paths and settings file
source(here::here("scripts", "0_PathsAndSettings.r"))
####################################################
# Select focal NHAs
#Load list of NHAs that you wish to generate site reports for
NHA_list <- read.csv(here("_data", "sourcefiles", "NHAs_SWCounties.csv")) #download list that includes site names and/or (preferably) NHA Join ID
#if you are just running a few sites, you can select individual site by name or NHA join id:
#selected_nhas <- arc.select(nha, where_clause="SITE_NAME='White's Woods' AND STATUS = 'NP'")
#selected_nhas <- arc.select(nha, where_clause="NHA_JOIN_ID IN('alj82942')")
#Select larger number of sites
#Method A) If using site names (but this gets hung up on apostrophes)
NHA_list <- NHA_list[order(NHA_list$Site.Name),] #order alphabetically
Site_Name_List <- as.vector(NHA_list$Site.Name)
Site_Name_List <- as.list(Site_Name_List)
SQLquery_Sites <- paste("SITE_NAME IN(",paste(toString(sQuote(Site_Name_List)),collapse=", "), ") AND STATUS IN('NP','NR')") #use this to input vector of site names to select from into select clause.
#Method B) Or use NHA join ID
#Site_NHAJoinID_List <-as.character(NHA_list$NHA.Join.ID)
#SQLquery_Sites <- paste("NHA_Join_ID IN(",paste(toString(sQuote(Site_NHAJoinID_List)),collapse=", "), ") AND STATUS IN('NP','NR')")
serverPath <- paste("C:/Users/",Sys.getenv("USERNAME"),"/AppData/Roaming/ESRI/ArcGISPro/Favorites/PNHP.PGH-gis0.sde/",sep="")
nha <- arc.open(paste(serverPath,"PNHP.DBO.NHA_Core", sep=""))
selected_nhas <- arc.select(nha, where_clause=SQLquery_Sites)
dim(selected_nhas) #check how many records are returned to ensure it meets expectations
selected_nhas <- selected_nhas[order(selected_nhas$SITE_NAME),]#order alphabetically
####
#manual check to ensure that your original list of NHAs and the selected NHA data frame both have sites in the same order
identical(selected_nhas$SITE_NAME, as.character(NHA_list$Site.Name))
####
####################################################
## Build the Species Table #########################
# open the related species table and get the rows that match the NHA join ids from the selected NHAs
nha_relatedSpecies <- arc.open(paste(serverPath,"PNHP.DBO.NHA_SpeciesTable", sep=""))
selected_nha_relatedSpecies <- arc.select(nha_relatedSpecies)
Site_ID_list <- as.list(unique(selected_nhas$NHA_JOIN_ID)) #added in unique for occasions where a site might be in the import list multiple times (e.g. when it crosses county lines and we want to talk about it for all intersecting counties)
#open linked species tables and select based on list of selected NHAs
species_table_select <- list()
for (i in 1:length(Site_ID_list)) {
species_table_select[[i]] <- selected_nha_relatedSpecies[which(selected_nha_relatedSpecies$NHA_JOIN_ID==Site_ID_list[i]),]
}
species_table_select #list of species tables
#merge species lists w/ EO information from Point Reps database
#create one big data frame first of all the EOIDs across all the selected NHAs
speciestable <- bind_rows(species_table_select, .id = "column_label")
SQLquery_pointreps <- paste("EO_ID IN(",paste(toString(speciestable$EO_ID),collapse=", "), ")") #don't use quotes around numbers
pointreps <- arc.open("W:/Heritage/Heritage_Data/Biotics_datasets.gdb/eo_ptreps")
selected_pointreps <- arc.select(pointreps, c('EO_ID', 'EORANK', 'GRANK', 'SRANK', 'SPROT', 'PBSSTATUS', 'LASTOBS', 'SENSITV_SP', 'SENSITV_EO'), where_clause=SQLquery_pointreps) #select subset of columns from EO pointrep database
#if this select command does not work (which sometimes happens to me?), try this method, which will work
#selected_pointreps <- arc.select(pointreps, c('EO_ID', 'EORANK', 'GRANK', 'SRANK', 'SPROT', 'PBSSTATUS', 'LASTOBS', 'SENSITV_SP', 'SENSITV_EO'))
#selected_pointreps <- subset(selected_pointreps, selected_pointreps$EO_ID %in% speciestable$EO_ID)
dim(selected_pointreps)
speciestable <- merge(speciestable,selected_pointreps, by="EO_ID")
names(speciestable)[c(15:22)] <- c("EORANK","GRANK","SRANK","SPROT","PBSSTATUS","LASTOBS","SENSITIVE","SENSITIVE_EO") #should rewrite this to be resilient to changing order of data frames
species_table_select<- split(speciestable, speciestable$column_label) #split back into a list of species tables
namevec <- NULL #name species tables so that you can tell if they end up in a weird order
for (i in seq_along(species_table_select)){
namevec[i] <- species_table_select[[i]]$NHA_JOIN_ID[1]}
names(species_table_select) <- namevec
#Make a list of all the ELCODES within all the species tables, to pull further info out from databases
SD_specieslist <- lapply(seq_along(species_table_select),
function(x) species_table_select[[x]][,c("ELCODE")])
SD_specieslist <- unlist(SD_specieslist)
#Connect to database and merge ElSubID into species tables
TRdb <- dbConnect(SQLite(), dbname=TRdatabasename) #connect to SQLite DB
Join_ElSubID <- dbGetQuery(TRdb, paste0("SELECT ELSubID, ELCODE FROM ET"," WHERE ELCODE IN (", paste(toString(sQuote(SD_specieslist)), collapse = ", "), ");"))
dbDisconnect(TRdb)
SD_speciesTable <- lapply(seq_along(species_table_select),
function(x) merge(species_table_select[[x]], Join_ElSubID, by="ELCODE"))# merge in the ELSubID until we get it fixed in the GIS layer
names(SD_speciesTable) <- namevec #keep names associated with list of tables
#add a column in each selected NHA species table for the image path, and assign image.
#Note: this uses the EO_ImSelect function, which I modified in the source script to work with a list of species tables
for (i in 1:length(SD_speciesTable)) {
for(j in 1:nrow(SD_speciesTable[[i]])){
SD_speciesTable[[i]]$Images <- EO_ImSelect(SD_speciesTable[[i]][j,])
}
}
# modify image assignments to account for finer groupings of the inverts--this part is not working right, come back to later
#for (i in 1:length(SD_speciesTable)) {
# for(j in 1:nrow(SD_speciesTable[[i]])){
# SD_speciesTable[[i]]$Images <- EO_ImFix(SD_speciesTable[[i]][j,])
# }
#}
############################################
# write species table to the SQLite database
speciesTable4db <- SD_speciesTable
for (i in 1:length(speciesTable4db)){
speciesTable4db[[i]] <- cbind(selected_nhas$NHA_JOIN_ID[i], speciesTable4db[[i]])
}
for (i in 1:length(speciesTable4db)){
names(speciesTable4db[[i]])[1] <- "NHA_JOIN_ID"
speciesTable4db[[i]]$NHA_JOIN_ID <- as.character(speciesTable4db[[i]]$NHA_JOIN_ID)
}
db_nha <- dbConnect(SQLite(), dbname=nha_databasename) # connect to the database
# delete existing threats and recs for this site if they exist
for (i in 1:length(selected_nhas$NHA_JOIN_ID)){
dbExecute(db_nha, paste("DELETE FROM nha_species WHERE NHA_JOIN_ID = ", sQuote(selected_nhas$NHA_JOIN_ID[i]), sep=""))
}
# add in the new data
for (i in 1:length(speciesTable4db)){
dbAppendTable(db_nha, "nha_species", speciesTable4db[[i]])
}
dbDisconnect(db_nha)
#################################################
### Pull out info from Biotics for each site
eoid_list <- list() #list of EOIDs to pull Biotics records with
for (i in 1: length(SD_speciesTable)){
eoid_list[[i]] <- paste(toString(SD_speciesTable[[i]]$EO_ID), collapse = ",")
} # make a list of EOIDs to get data from
ptreps <- arc.open(paste(biotics_gdb,"eo_ptreps",sep="/"))
ptreps_selected <- list() #list of EO records for each selected NHA
for (i in 1:length(eoid_list)){
ptreps_selected[[i]] <- arc.select(ptreps, fields=c("EO_ID", "SNAME", "EO_DATA", "GEN_DESC","MGMT_COM","GENERL_COM"), where_clause=paste("EO_ID IN (", eoid_list[[i]], ")",sep="") )
}
################################################
# calculate the site significance rank based on the species present at the site
source(here::here("scripts","nha_ThreatsRecDatabase","2_loadSpeciesWeights.r"))
#check whether there are multiple EOs in the species table for the same species, and only keep one record for each species, the most recently observed entry
for (i in 1:length(SD_speciesTable)) {
duplic_Spp <- SD_speciesTable[[i]]
duplic_Spp <- duplic_Spp[order(duplic_Spp$LASTOBS, decreasing=TRUE),]
SD_speciesTable[[i]] <- duplic_Spp[!duplicated(duplic_Spp[1]),]
}
sigrankspecieslist <- SD_speciesTable #so if things get weird, you only have to come back to this step
#remove species which are not included in thesite ranking matrices--GNR, SNR, SH/Eo Rank H, etc.
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) sigrankspecieslist[[x]][which(sigrankspecieslist[[x]]$GRANK!="GNR"&!is.na(sigrankspecieslist[[x]]$EORANK)),]) #remove EOs which are GNR
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) sigrankspecieslist[[x]][which(sigrankspecieslist[[x]]$GRANK!="GNA"&!is.na(sigrankspecieslist[[x]]$EORANK)),]) #remove EOs which are GNA
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) sigrankspecieslist[[x]][which(sigrankspecieslist[[x]]$SRANK!="SNR"&!is.na(sigrankspecieslist[[x]]$EORANK)),]) #remove EOs which are SNR
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) sigrankspecieslist[[x]][which(sigrankspecieslist[[x]]$SRANK!="SH"&!is.na(sigrankspecieslist[[x]]$EORANK)),]) #remove EOs which are SH
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) sigrankspecieslist[[x]][which(sigrankspecieslist[[x]]$EORANK!="H"),]) #remove EOs w/ an H quality rank
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) sigrankspecieslist[[x]][which(sigrankspecieslist[[x]]$SRANK!="SU"&!is.na(sigrankspecieslist[[x]]$EORANK)),]) #remove EOs which are SU
#Merge rounded S, G, and EO ranks into individual species tables
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) merge(sigrankspecieslist[[x]], rounded_grank, by="GRANK"))
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) merge(sigrankspecieslist[[x]], rounded_srank, by="SRANK"))
sigrankspecieslist <- lapply(seq_along(sigrankspecieslist),
function(x) merge(sigrankspecieslist[[x]], nha_EORANKweights, by="EORANK"))
#Calculate rarity scores for each species within each table
RarityScore <- function(x, matt) {
matt <- nha_gsrankMatrix
if (nrow(x) > 0) {
for(i in 1:nrow(x)) {
x$rarityscore[i] <- matt[x$GRANK_rounded[i],x$SRANK_rounded[i]] }}
else {
"NA"
}
x$rarityscore
}
res <- lapply(sigrankspecieslist, RarityScore) #calculate rarity score for each species table
sigrankspecieslist <- Map(cbind, sigrankspecieslist, RarityScore=res) #bind rarity score into each species table
names(sigrankspecieslist) <- namevec #reassign the names
#Adjust site significance rankings based on presence of G1, G2, and G3 EOs
#create flags for sites with a G3 species (which should automatically be at least regional)
G3_regional <- lapply(seq_along(sigrankspecieslist),
function(x) "G3" %in% sigrankspecieslist[[x]]$GRANK_rounded)
#create flags for sites with a G1 or G2 species (which should automatically be a global site)
G1_global <- lapply(seq_along(sigrankspecieslist),
function(x) "G1" %in% sigrankspecieslist[[x]]$GRANK_rounded)
G2_global <- lapply(seq_along(sigrankspecieslist),
function(x) "G2" %in% sigrankspecieslist[[x]]$GRANK_rounded)
#Calculate scores for each site, aggregating across all species and assign significance rank category. Skip any remaining NA values in the rarity scores
TotalScore <- lapply(seq_along(sigrankspecieslist),
function(x) sigrankspecieslist[[x]]$RarityScore[!is.na(sigrankspecieslist[[x]]$RarityScore)] * sigrankspecieslist[[x]]$Weight) # calculate the total score for each species
SummedTotalScore <- lapply(TotalScore, sum)
SummedTotalScore <- lapply(SummedTotalScore, as.numeric)
SiteRank <- list() #create empty list object to write into
for (i in seq_along(SummedTotalScore)) {
if(SummedTotalScore[[i]]==0|is.na(SummedTotalScore[[i]])){
SiteRank[[i]] <- "Local"
} else if(is.na(SummedTotalScore[[i]])){
SiteRank[[i]] <- "Local"
} else if(SummedTotalScore[[i]]>0 & SummedTotalScore[[i]]<=152) {
SiteRank[[i]] <- "State"
} else if(SummedTotalScore[i]>152 & SummedTotalScore[[i]]<=457) {
SiteRank[[i]] <- "Regional"
} else if (SummedTotalScore[[i]]>457) {
SiteRank[[i]] <- "Global"
}
}
#manual check step, take a look if you want to see where things are mismatched--do any sites need to have ranks overriden?
check <- as.data.frame(cbind(SiteRank, SummedTotalScore, G3_regional, G2_global, G1_global, namevec, selected_nhas$NHA_JOIN_ID))
#Do the site ranking overrides automatically
for (i in seq_along(SiteRank)) {
if(G3_regional[[i]]=="TRUE") {
SiteRank[[i]] <-"Regional"
} else if(G2_global[[i]]=="TRUE"){
SiteRank[[i]] <- "Global"
} else if(G1_global[[i]]=="TRUE"){
SiteRank[[i]] <- "Global"
}
}
#reorder the sites
selected_nhas <- selected_nhas[match(namevec, selected_nhas$NHA_JOIN_ID),]#order to match order of species tables
#ensure that both data frames have sites in the same order
identical(selected_nhas$NHA_JOIN_ID, namevec)
#merge significance data into NHA table
selected_nhas$site_score <- unlist(SiteRank) #add site significance rankings to NHA data frame
selected_nhas$site_rank <- unlist(SummedTotalScore) #add site significance score to NHA data frame
summary(as.factor(selected_nhas$site_score)) #manual check step: take a look at distribution of significance ranks
#########################################################
#Build pieces needed for each site report
#generate list of folder paths and file names for selected NHAs
nha_foldername_list <- list()
for (i in 1:length(Site_Name_List)) {
nha_foldername_list[[i]] <- gsub(" ", "", Site_Name_List[i], fixed=TRUE)
nha_foldername_list[[i]] <- gsub("#", "", nha_foldername_list[i], fixed=TRUE)
nha_foldername_list[[i]] <- gsub("'", "", nha_foldername_list[i], fixed=TRUE)
}
nha_foldername_list <- unlist(nha_foldername_list) #list of folder names
nha_filename_list <- list()
for (i in 1:length(nha_foldername_list)) {
nha_filename_list[i] <- paste(nha_foldername_list[i],"_",gsub("[^0-9]", "", Sys.Date() ),".docx",sep="")
}
nha_filename_list <- unlist(nha_filename_list) #list of file names
#generate URLs for each EO at site
URL_EOs <- list()
for (i in 1:length(ptreps_selected)){
URL_EOs[[i]] <- lapply(seq_along(ptreps_selected[[i]]$EO_ID), function(x) paste("https://bioticspa.natureserve.org/biotics/services/page/Eo/",ptreps_selected[[i]]$EO_ID[x],".html", sep=""))
URL_EOs[[i]] <- sapply(seq_along(URL_EOs[[i]]), function(x) paste("(",URL_EOs[[i]][x],")", sep=""))
}
Sname_link <- list()
for (i in 1:length(ptreps_selected)){
Sname_link[[i]] <- sapply(seq_along(ptreps_selected[[i]]$SNAME), function(x) paste("[",ptreps_selected[[i]]$SNAME[x],"]", sep=""))
}
Links <- mapply(paste, Sname_link, URL_EOs, sep="") #for R markdown, list of text plus hyperlinks to create links to biotics page for each EO at each site
# set up the directory folders where site account pieces go
NHAdest1 <- sapply(seq_along(nha_foldername_list), function(x) paste(NHAdest,"DraftSiteAccounts",nha_foldername_list[x],sep="/"))
sapply(seq_along(NHAdest1), function(x) dir.create(NHAdest1[x], showWarnings=FALSE)) # make a folder for each site, if those folders do not exist already
sapply(seq_along(NHAdest1), function(x) dir.create(paste(NHAdest1[x],"photos", sep="/"), showWarnings = F)) # make a folder for each site, for photos
#######################################################################
#Pull out species-specific threats/recs from the database for each site
TRdb <- dbConnect(SQLite(), dbname=TRdatabasename) #connect to SQLite DB
ElementTR <- list() #
ThreatRecTable <- list()
ET <- list()
for (i in 1:length(SD_speciesTable)){
ElementTR[[i]] <- dbGetQuery(TRdb, paste0("SELECT * FROM ElementThreatRecs"," WHERE ELSubID IN (", paste(toString(sQuote(SD_speciesTable[[i]]$ELSubID)), collapse = ", "), ");"))
ThreatRecTable[[i]] <- dbGetQuery(TRdb, paste0("SELECT * FROM ThreatRecTable"," WHERE TRID IN (", paste(toString(sQuote(ElementTR[[i]]$TRID)), collapse = ", "), ");"))
ET[[i]] <- dbGetQuery(TRdb, paste0("SELECT SNAME, ELSubID FROM ET"," WHERE ELSubID IN (", paste(toString(sQuote(ElementTR[[i]]$ELSubID)), collapse = ", "), ");"))
}
#join general threats/recs table with the element table
ELCODE_TR <- list() #create list of threat rec info to print for each site, to call in R Markdown
for (i in 1:length(ElementTR)){
ELCODE_TR[[i]] <- ElementTR[[i]] %>%
inner_join(ET[[i]]) %>%
inner_join(ThreatRecTable[[i]])
}
######################################################
# make the maps
#convert geometry to simple features for the map
slnha <- list()
nha_sf_list <- list()
nha_sf_list <- arc.data2sf(selected_nhas)
a <- st_area(nha_sf_list) #calculate area
a <- a*0.000247105 #convert m2 to acres
selected_nhas$Acres <- as.numeric(a)
mtype <- 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}?'
basetiles <- sapply(seq_along(nha_sf_list$geom), function(x) tmaptools::read_osm(nha_sf_list$geom[x], type=mtype, ext=1.5, use.colortable=FALSE))
# plot the maps
nha_map <- list()
for (i in 1:length(nha_sf_list$geom)) {
tmap_mode("plot")
nha_map[[i]] <- tm_shape(basetiles[[i]], unit="m") +
tm_rgb() +
tm_shape(nha_sf_list[i,]) +
tm_borders("red", lwd=1.5)+
tm_legend(show=FALSE) +
tm_layout(attr.color="white") +
tm_compass(type="arrow", position=c("left","bottom")) +
tm_scale_bar(position=c("center","bottom"))
tmap_save(nha_map[[i]], filename=paste(NHAdest1[i], "/", nha_foldername_list[[i]],"_tempmap.png",sep=""), units="in", width=7)
}
###################################################################
#Write the output R markdown document for each site, all at once
for (i in 1:length(nha_filename_list)) {
NHAdest2 <- NHAdest1[i]
selectedNhas <- selected_nhas[i,]
speciesTable <- SD_speciesTable[[i]]
ptrepsSelected <- ptreps_selected[[i]]
ELCODETR <- ELCODE_TR[[i]]
nhaFoldername <- nha_foldername_list[[i]]
LinksSelect <- Links[[i]]
SiteRank1 <- SiteRank[[i]]
rmarkdown::render(input=here::here("scripts","template_NHAREport_part1v2.Rmd"), output_format="word_document",
output_file=nha_filename_list[[i]],
output_dir=NHAdest1[i])
}
# delete the map, after its included in the markdown
for (i in 1:length(nha_filename_list)){
fn <- paste(NHAdest1[i], "/", nha_foldername_list[i],"_tempmap.png",sep="")
if (file.exists(fn)) #Delete file if it exists
file.remove(fn)
}
####################################################
#output data about NHAs with completed templates to database and summary sheets
# insert the NHA data into a sqlite database
nha_data <- NULL
nha_data <- selected_nhas[,c("SITE_NAME","SITE_TYPE","NHA_JOIN_ID","site_rank","site_score","BRIEF_DESC","COUNTY","Muni","USGS_QUAD","ASSOC_NHA","PROTECTED_LANDS")]
nha_data$nha_filename <- unlist(nha_filename_list)
nha_data$nha_folderpath <- NHAdest1
nha_data$nha_foldername <- unlist(nha_foldername_list)
db_nha <- dbConnect(SQLite(), dbname=nha_databasename) # connect to the database
dbAppendTable(db_nha, "nha_main", nha_data)
dbDisconnect(db_nha)
#Create record of NHA creation for organizing writing and editing tasks
#create some summary stats describing EOs at each site by taxa group, to help with determining who should write site accounts
#build functions
plantperc <- function(x) {
p <- nrow(species_table_select[[x]][species_table_select[[x]]$ELEMENT_TYPE == 'P',])
pt <- nrow(species_table_select[[x]])
p/pt
}
musselperc <- function(x){
u <- nrow(species_table_select[[x]][species_table_select[[x]]$ELEMENT_TYPE == 'U',])
ut <- nrow(species_table_select[[x]])
u/ut
}
insectperc <- function(x){
i <- nrow(species_table_select[[x]][species_table_select[[x]]$ELEMENT_TYPE %in% c('IM','IB','IA','ID','IT'),])
it <- nrow(species_table_select[[x]])
i/it
}
herpperc <- function(x){
h <- nrow(species_table_select[[x]][species_table_select[[x]]$ELEMENT_TYPE %in% c('R','A'),])
ht <- nrow(species_table_select[[x]])
h/ht
}
#calculate for each spp table, using functions
PlantEO_percent <- unlist(lapply(seq_along(species_table_select),
function(x) plantperc(x)))
MusselEO_percent <- unlist(lapply(seq_along(species_table_select),
function(x) musselperc(x)))
InsectEO_percent <- unlist(lapply(seq_along(species_table_select),
function(x) insectperc(x)))
HerpEO_percent <- unlist(lapply(seq_along(species_table_select),
function(x) herpperc(x)))
nEOs <- unlist(lapply(seq_along(species_table_select),
function(x) nrow(species_table_select[[x]]))) #number of total EOs at site
EO_sumtable <- as.data.frame(cbind(nEOs, PlantEO_percent,MusselEO_percent,InsectEO_percent,HerpEO_percent)) #bind summary stats into one table together
db_nha <- dbConnect(SQLite(), dbname=nha_databasename)
nha_data$Template_Created <- as.character(Sys.Date())
nha_sum <- nha_data[,c("NHA_JOIN_ID","SITE_NAME","COUNTY","nha_folderpath", "site_score")]
nha_sum <- cbind(nha_sum, EO_sumtable)
dbAppendTable(db_nha, "nha_sitesummary", nha_sum)
dbDisconnect(db_nha) #disconnect
## For now, you should hand copy and paste the new rows into the NHA site summary Excel worksheet. I created an exports folder within the database folder where .csv versions can periodically be sent, as batches of NHA templates are created.
########################
|
f3e380886d5aee530c110ae538171c51d89a4a52 | 17902a8ed8ac24eaa620ff8da7441c0554d5e06d | /Examples/GSE52870_Analysis.R | aef558c1bf146a7783cb87e9e9638ad61581ce42 | [] | no_license | soulj/PhenomeExpress | deb4a9d2a2df9b631a1b6595e6ef22b7eeb502d8 | cdc15f801361a791b55c9e7455444c7192ee0593 | refs/heads/master | 2021-01-18T22:41:11.506795 | 2015-04-30T11:03:43 | 2015-04-30T11:03:43 | 24,099,237 | 6 | 2 | null | null | null | null | UTF-8 | R | false | false | 7,405 | r | GSE52870_Analysis.R | #Analysis of GSE52870 PAX5 dataset with PhenomeExpress
#takes around 15 mins to run
require("Matrix")
require("igraph")
require("data.table")
require("DESeq2") # for the RNA-seq analysis
require("BioNet") # for comparison purposes - not needed by PhenomeExpress
require("VennDiagram") # for making the Venn diagram figures
require("RCytoscape") # also requires cytoscape v2.8 to be open with the Cytoscape RPC plugin active
setwd("~/PhenomeExpress")
#source the methods
source("./src/HeterogeneousNetwork.R")
source("./src/RHWN.R")
source("./src/runGIGA.R")
source("./src/runPhenoExpress.R")
#calculate the FPKM using the effective gene length and the counts per gene
GSE52870_Pax5Restoration.GenewiseCounts <- read.delim("./GSE52870/GSE52870_Pax5Restoration-GenewiseCounts.txt")
countmatrix=GSE52870_Pax5Restoration.GenewiseCounts[,3:8]
rownames(countmatrix)=GSE52870_Pax5Restoration.GenewiseCounts$EntrezID
genelength=GSE52870_Pax5Restoration.GenewiseCounts$GeneLength
FPKMtable=(countmatrix * 10^9) /(colSums(countmatrix) * genelength)
FPKMtable=ifelse(FPKMtable>1,1,0)
countmatrix=countmatrix[Matrix::rowSums(FPKMtable)>2,]
#use DESeq2 to analyse the raw data
colData=data.frame(colnames=colnames(countmatrix),condition=c(rep("PAX5KD",3),rep("PAX5Rescue",3)))
dds=DESeqDataSetFromMatrix(countData=countmatrix,colData=colData,design=~condition)
dds$condition=factor(dds$condition, levels =c ( "PAX5KD","PAX5Rescue" ))
dds2=DESeq(dds)
#get the expression table with the fold changes and p values
res=results(dds2)
dt=as.data.frame(res[order (res$log2FoldChange),])
dt$EntrezID=rownames(dt)
#Anotate the genes with SwissProt names to match the network node names
Young_EnteztoSwiss_via_Uniprot <- read.delim("./GSE52870/GenenamesEntreztoUniprot_via_UniProt.txt")
Young_EnteztoSwiss_via_David <- read.delim("./GSE52870/GenenamesEntreztoUniprot_via_David.txt", dec=",")
Young_EnteztoSwiss_via_David=Young_EnteztoSwiss_via_David[,1:2]
Young_EnteztoSwiss=rbind(Young_EnteztoSwiss_via_David,Young_EnteztoSwiss_via_Uniprot)
Young_EnteztoSwiss=Young_EnteztoSwiss[!duplicated(Young_EnteztoSwiss),]
#note 1 entrez gene maps to more than one protein
dt=merge(dt,Young_EnteztoSwiss,by.x="EntrezID",by.y="From")
dt=na.omit(dt)
colnames(dt)[8]="name"
#load the high confidence mouse PPI network from STRING
load("./Networks/HCString_Mouse_Graph.RData")
presentList=na.omit(match(dt$name,V(HCString_Mouse)$name))
#Use pre-existing networks filter based on genes found in the transcriptomics experiment
pax5.network=induced.subgraph(HCString_Mouse,presentList)
pax5.network=decompose.graph(pax5.network)[[1]]
presentList=na.omit(match(V(pax5.network)$name,dt$name))
#filter the expression data based on proteins present in the network
dt=dt[presentList,]
dt=na.omit(dt)
#calculate the Pi value for use in the node scoring stage
dt$Pi=abs(dt$log2FoldChange)*-log10(dt$padj)
dt$absFC=abs(dt$log2FoldChange)
#select the phenotypes from the UberPheno ontology - the Phenomiser tool and manual searching of the ontolgy by relevent keywords is helpful for this
Phenotypes=c("HP:0004812","MP:0012431","HP:0012191","MP:0008211","MP:0008189")
#run Phenome Express
LeukResults=runPhenomeExpress(pax5.network,dt,Phenotypes,"Mouse")
#retrieve the significant sub-networks
subnetworks=LeukResults[[1]]
#retrieve the table of p-values
sigTable=LeukResults[[2]]
#collapse all the nodes in the subnetworks from PhenomeExpress
nodes=c()
for(i in 1:length(subnetworks)) {
tempGraph=subnetworks[[i]]
nodes=c(nodes,V(tempGraph)$name)
}
#load the results from JActiveModules and GIGA - run externally, subnetworks >= 5 nodes kept
leukJAM <-read.table("./JActiveModules/leukJM2107", quote="\"")
leukJAM=leukJAM[!duplicated(leukJAM$V1),]
GIGA <- read.delim("./GIGA/leukGIGA.txt", header=F)
#run BioNet for comparison
pval=dt$pvalue
names(pval)=dt$name
b <- fitBumModel(pval, plot = FALSE)
scores <- scoreNodes(network = pax5.network, fb = b,fdr = 1e-25) #FDR produces similar sized module to max sized PhenomeExpress sub-network
module <- runFastHeinz(pax5.network, scores)
#count the number of seed Phenotype annotated proteins present in all the sub-networks for each tool
#First get the gene to phenotype associations for labelling seed nodes
z=getHeterogeneousNetwork(pax5.network,"Mouse")[["genePheno"]] # note contains all proteins - including ones not present in network
phenoAnnotated=z[rownames(z) %in% Phenotypes,]
phenoAnnotated=phenoAnnotated[,colSums(phenoAnnotated)>0]
phenoAnnotated=colnames(phenoAnnotated)
#calculate the number of seed phenotype annotated genes for each tool
no.Seeds.PhenomeExpress=table(ifelse(nodes %in% phenoAnnotated,1,0))
no.Seeds.leukJAM=table(ifelse(leukJAM %in% phenoAnnotated,1,0))
no.Seeds.GIGA=table(ifelse(GIGA$V2 %in% phenoAnnotated,1,0))
no.Seeds.BioNet=table(ifelse(V(module)$name %in% phenoAnnotated,1,0))
#make a Venn diagram of protein in subnetworks from each tool
nodeList=list(PhenomeExpress=nodes,JActivemodules=leukJAM,GIGA=GIGA$V2,BioNet=V(module)$name)
venn.diag=venn.diagram(nodeList,fill = c("red", "green","blue","purple"),alpha = c(0.5, 0.5,0.5,0.5), cex = 2,cat.fontface = 4,lty =2, fontfamily =3, filename=NULL )
grid.draw(venn.diag)
#send all the sub-networks from PhenomeExpress to cytoscape
#colours the nodes according to the fold change
#black border if directly annotated to seed phenotype
#useful to assign the node with the entrez ID as well - for downstream analysis in cytoscape i.e mapping to genenames or functional annotation
V(pax5.network)$EntrezID=as.character(dt$EntrezID)
for(i in 1:length(subnetworks)) {
presentList=na.omit(match(V(subnetworks[[i]])$name,V(pax5.network)$name))
tempGraph=induced.subgraph(pax5.network,presentList)
FC=dt[na.omit(match(V(tempGraph)$name,dt$name)),]
V(tempGraph)$logFC=FC$log2FoldChange
seedAnnotatedGenes=ifelse(V(tempGraph)$name %in% phenoAnnotated,1,0)
V(tempGraph)$Seed=seedAnnotatedGenes
#do the network creation stuff
#convert the igraph object to a graphNEL object and intialise the attributes
tempGraph.NEL=igraph.to.graphNEL(tempGraph)
tempGraph.NEL=initEdgeAttribute(tempGraph.NEL,"Confidence","numeric",0)
tempGraph.NEL=initEdgeAttribute(tempGraph.NEL,"weight","numeric",0)
tempGraph.NEL=initNodeAttribute(tempGraph.NEL,"logFC","numeric",0)
tempGraph.NEL=initNodeAttribute(tempGraph.NEL,"Seed","numeric",0)
tempGraph.NEL=initNodeAttribute(tempGraph.NEL,"EntrezID","char",0)
nodeDataDefaults(tempGraph.NEL, "label") <- "name"
nodeData(tempGraph.NEL,V(tempGraph)$name,"label") = V(tempGraph)$name
tempGraph.NEL=initNodeAttribute(tempGraph.NEL,"label","char","name")
#Open the cytoscape window and send the graph
cw1 <- new.CytoscapeWindow (paste("PhenoExpress",as.character(i),sep=""), graph=tempGraph.NEL)
#display the graph
displayGraph (cw1)
#select the layout
layoutNetwork (cw1, layout.name='force-directed')
#colour according to the logFC
control.points <- c(-5,0,5)
node.colors <- c ("#00AA00", "#00FF00", "#FFFFFF", "#FF0000", "#AA0000")
setNodeColorRule (cw1, node.attribute.name='logFC', control.points, node.colors, mode='interpolate')
setDefaultBackgroundColor (cw1, '#FFFFFF')
#set the nodeborder to correspond to the seed phenotype annotated genes
data.values <- c ("1", "0")
line.widths = c ("15","1")
setNodeBorderWidthRule (cw1, 'Seed', data.values, line.widths)
}
|
7c110f21cfb8f21761d5e1f3a0779f20a3177c77 | 419f499346f60b2f341a0a57d5e6842a5ab9b565 | /code/09b_ranks_map.R | 004bb7dbb61fe6b7aeae46b24f2df49758ded26f | [] | no_license | rhrzic/TreatableMortality | b0d04b6ea44ccd8aa1f4ce238e8947af126ea997 | c3cb9f950303687adb7192105fe701c91103878f | refs/heads/master | 2022-12-30T06:23:47.796564 | 2020-10-23T10:37:04 | 2020-10-23T10:37:04 | 275,123,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,121 | r | 09b_ranks_map.R | require(eurostat)
require(tidyverse)
require(sf)
male_ranks_overall$Sex = "Male"
female_ranks_overall$Sex = "Female"
gisco0 <- get_eurostat_geospatial(output_class = "sf", resolution = "03", nuts_level = "0", year = "2016", cache = TRUE)
per_country <- rbind(select(male_ranks_overall, Country, Sex, MAD),
select(female_ranks_overall, Country, Sex, MAD)) %>%
filter(str_length(Country) == 2)
map <- left_join(per_country, gisco0, by = c("Country" = "id"))
map1 <- ggplot(map) +
geom_sf(aes(fill = MAD, geometry = geometry), colour = "transparent") +
scale_fill_gradient2(low = "white", mid = "gainsboro", high = "black")+
coord_sf(xlim = c(-10, +30), ylim = c(35, 70)) +
theme(panel.grid.major = element_line(colour = 'transparent'),
panel.background = element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
labs(fill = "Mean absolute\ndifference\nin rank")+
facet_grid(. ~ Sex)
ggsave('plots/m1.png', map1, scale = 2, width = 5, height = 2, units = "in")
|
1166c138cc6e2432d1ef342f1e5fd1a49be318b2 | 5f29b8d3189a6526bb6ee0b4dc8e05ab3d2d3069 | /001_regularGrid.R | 9bcfdfa9fb08e069799ca4af5aa43f0f1e91e48b | [] | no_license | curdon/linguisticDensity_ProcB_derungsEtAl | cb6f6b8acec7b86181146bd00167f539beb79542 | 318f4ca4e64db4782677e7f189692c3bb35d6fa0 | refs/heads/master | 2020-03-20T08:56:14.958289 | 2018-06-14T08:43:18 | 2018-06-14T08:43:18 | 137,323,409 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,425 | r | 001_regularGrid.R | ##################################################
## Name: 001_regularGrid
## Script purpose: Creating an even distribution of grid points for different spatial resolutions
## Date: 2018
## Author: Curdin Derungs
##################################################
library(geosphere)
library(rgdal)
library(plyr)
rm(list=ls())
##three spatial resolutions are defined as numbers of regions used in the following function
#the script has the be run for each resolution seperately
res<-30 #appr. 1000 grid points
# res<-15 #300
# res<-50 #3000
##rading continental shapes
#even grid points are only distributed over continental land masses
cont <- readOGR("input", "continents_simple")
proj4string(cont)<-CRS("+proj=longlat +datum=WGS84")
##creating regular points using the regularCoordinates() function from the geosphere package
#the function solves the Thompson Problem
reg.coords<-regularCoordinates(res)
##converting coordinates to spatial points
reg.spdf<-SpatialPointsDataFrame(SpatialPoints(reg.coords),data.frame(id=1:nrow(reg.coords)))
#defining the spatial projection
proj4string(reg.spdf)<-CRS("+proj=longlat +datum=WGS84")
##intersecting evenly distributed points with the continental polygons
ov<-over(reg.spdf,cont)
#filtering for points on continents
reg.spdf<-reg.spdf[!is.na(ov$OBJECTID),]
#save the grids
save(reg.spdf,file=paste("output/001_regularGrid/randPts_",nrow(reg.spdf),".Rdata",sep=""))
|
ca5dcaab800eb32ab58810e151a465e6acaf7efd | 9217ebebf4325621f8726001596dbf57e922548f | /inst/scripts/make-metadata.R | 59bf067a2a805d214b64395fde535e7b5ac1be38 | [] | no_license | DKMS-LSL/ipdDb | a851b4ee44a8fab168e59804f12c26ff11d80a0f | 57a6201b7fb4337bb3bf1ea5c31f79fa8fb90bbf | refs/heads/master | 2020-03-21T17:46:38.184184 | 2018-10-18T13:20:45 | 2018-10-18T13:20:45 | 138,853,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,238 | r | make-metadata.R | hlaMetadata <- data.frame(
Title = "Allele data from the IPD IMGT/HLA database version 3.32.0",
Description = paste0("Data for all alleles of selected HLA loci (HLA-A, -B,
-C, -DPB1, -DQB1 and -DRB1). The allele annotation, sequence, gene structure
and the (sequence-based) closest allele in full-length is stored.
Reference:
Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P,
De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA
KIR Nomenclature in non-human species Immunogenetics (2018), in preparation"),
BiocVersion = "3.8",
Genome = "no genome data",
SourceType = "Zip",
SourceUrl = "https://github.com/ANHIG/IMGTHLA/blob/Latest/xml/hla.xml.zip",
SourceVersion = "3.32.0",
Species = "Homo sapiens",
TaxonomyId = 9606,
Coordinate_1_based = TRUE,
DataProvider = "EMBL-EBI",
Maintainer = "Steffen Klasberg <klasberg@dkms-lab.de>",
RDataClass = "data.frame, DNAStringSet, GRanges",
DispatchClass = "SQLiteFile",
RDataPath = "ipdDb/ipdHLA_3.32.0.sqlite",
Tags = "ipd:hla:IMGT/HLA:alleles"
)
hlaMetadata <- rbind(hlaMetadata, data.frame(
Title = "Allele data from the IPD IMGT/HLA database version 3.33.0",
Description = paste0("Data for all alleles of selected HLA loci (HLA-A, -B,
-C, -DPB1, -DQB1 and -DRB1). The allele annotation, sequence, gene structure
and the (sequence-based) closest allele in full-length is stored.
Reference:
Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P,
De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA
KIR Nomenclature in non-human species Immunogenetics (2018), in preparation"),
BiocVersion = "3.8",
Genome = "no genome data",
SourceType = "Zip",
SourceUrl = "https://github.com/ANHIG/IMGTHLA/blob/Latest/xml/hla.xml.zip",
SourceVersion = "3.33.0",
Species = "Homo sapiens",
TaxonomyId = 9606,
Coordinate_1_based = TRUE,
DataProvider = "EMBL-EBI",
Maintainer = "Steffen Klasberg <klasberg@dkms-lab.de>",
RDataClass = "data.frame, DNAStringSet, GRanges",
DispatchClass = "SQLiteFile",
RDataPath = "ipdDb/ipdHLA_3.33.0.sqlite",
Tags = "ipd:hla:IMGT/HLA:alleles"
))
kirMetadata <- data.frame(
Title = "Allele data from the IPD KIR database version 2.7.1",
Description = paste0("Data for the alleles of all KIR loci in the database.
The allele annotation, sequence, gene structure and the (sequence-based)
closest allele in full-length is stored.
Reference:
Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P,
De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA
KIR Nomenclature in non-human species Immunogenetics (2018), in preparation"),
BiocVersion = "3.8",
Genome = "no genome data",
SourceType = "Zip",
SourceUrl = "https://github.com/ANHIG/IPDKIR/blob/Latest/KIR.dat",
SourceVersion = "2.7.1",
Species = "Homo sapiens",
TaxonomyId = 9606,
Coordinate_1_based = TRUE,
DataProvider = "EMBL-EBI",
Maintainer = "Steffen Klasberg <klasberg@dkms-lab.de>",
RDataClass = "data.frame, DNAStringSet, GRanges",
DispatchClass = "SQLiteFile",
RDataPath = "ipdDb/ipdKIR_2.7.1.sqlite",
Tags = "ipd:kir:alleles"
)
write.csv(rbind(hlaMetadata, kirMetadata), "inst/extdata/ipd_metadata.csv")
|
af3a31bfa1f624311a70768ed0f88f0955fa38b9 | 6e3fa6b477380f245abb385f9c30b1f601f0aa82 | /inst/tinytest/test_gdns.R | dee5f4e47e38b04605c86934b5c2a60942b418a9 | [] | no_license | cran/gdns | 4435597cb607ae897bb597ce02b2bb8d5c677a75 | 6a2388a6c687f3dc1df3cfc55e8331d7728037bb | refs/heads/master | 2020-05-21T08:53:14.863944 | 2020-05-15T13:00:03 | 2020-05-15T13:00:03 | 69,889,209 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 486 | r | test_gdns.R | library(gdns)
if (at_home()) {
expect_true(length(gdns::query("example.com")) > 0)
doms <- c("example.com", "example.org", "example.net")
qry <- gdns::bulk_query(doms)
expect_true(nrow(qry) > 0)
}
expect_true(is_soft_fail("v=spf1 include:_spf.apple.com include:_spf-txn.apple.com ~all"))
expect_false(is_hard_fail("v=spf1 include:_spf.apple.com include:_spf-txn.apple.com ~all"))
expect_false(passes_all("v=spf1 include:_spf.apple.com include:_spf-txn.apple.com ~all"))
|
e7aeb016269c92d32599713feae0c7dde4b37ab4 | 4d265c3f4046c3edd1bac44a9894d466526d9d1d | /chap01/10_string.R | a339f4765d5ac23bba7ed99224f32eb4d72a71f5 | [] | no_license | kjy3309/R_study | 3caa3c0753c3f32b8aefe69afbb9ae97ed7288ce | 6ff03f9b4940116a551aaa5e186559400c7746d0 | refs/heads/master | 2023-01-22T06:07:11.477448 | 2020-11-12T04:49:27 | 2020-11-12T04:49:27 | 312,169,482 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,844 | r | 10_string.R | library(plotly)
library(dplyr)
# ์ค์
๋ฅ ๋ฐ์ดํฐ ๋ผ์ธ์ผ๋ก ๊ทธ๋ฆฌ๊ธฐ
View(economics)
plot_ly(economics,x=~date, y=~unemploy) %>% add_lines() %>%
layout(title='์ค์
๋ฅ ์ถ์ด', xaxis=list(title='์ฐ๋'), yaxis=list(title='์ค์
์ ์'))
## ์งํ์ฒ ์ด ์น๊ฐ ์ ์ผ์๋ณ ๊ทธ๋ํ
subway <- read.csv('./data/ch02/202006_SUBWAY.csv', stringsAsFactors = F)
View(subway)
class(subway)
ex <- subway %>% group_by(์ฌ์ฉ์ผ์) %>% summarise(์ด์น๊ฐ์ = sum(์น์ฐจ์ด์น๊ฐ์+ํ์ฐจ์ด์น๊ฐ์))
View(ex)
ex$์ฌ์ฉ์ผ์ <- ifelse(ex$์ฌ์ฉ์ผ์>20200600, ex$์ฌ์ฉ์ผ์-20200600,ex$์ฌ์ฉ์ผ์)
# ์์ธ ์ค์ ์ https://plotly.com/r
plot_ly(ex, x=~์ฌ์ฉ์ผ์, y=~์ด์น๊ฐ์) %>% add_lines() %>%
layout(title='6์ ์น๊ฐ ์ถ์ด', xaxis=list(title='๋ ์ง', autotick=FALSE), yaxis=list(title='์ด์ฉ๊ฐ ์'))
## ๊ฐ ํธ์ ๋ณ ๋ ์ง๋ณ ์ด์ฉ ์น๊ฐ
list <- subway %>% group_by(์ฌ์ฉ์ผ์, ๋
ธ์ ๋ช
) %>% summarise(total=sum(์น์ฐจ์ด์น๊ฐ์+ํ์ฐจ์ด์น๊ฐ์))
list$์ฌ์ฉ์ผ์ <- ifelse(list$์ฌ์ฉ์ผ์>20200600, list$์ฌ์ฉ์ผ์-20200600,list$์ฌ์ฉ์ผ์)
write.csv(file='C:/R/chap01/sample.csv',list)
list <- read.csv('C:/R/chap01/sample.csv')
plot_ly(list, x=~์ฌ์ฉ์ผ์, y=~total) %>% add_lines(linetype=~๋
ธ์ ๋ช
)
View(list)
# 1ํธ์ ~ 9ํธ์ ๋ง ๋ํ๋ด๊ธฐ
library(stringr) # ๋ฌธ์์ด์ ๋ค๋ฃจ๋ ๋ผ์ด๋ธ๋ฌ๋ฆฌ
g <- list %>% filter(str_detect(๋
ธ์ ๋ช
,'^1ํธ์ ')|str_detect(๋
ธ์ ๋ช
,'^2ํธ์ ')|
str_detect(๋
ธ์ ๋ช
,'^3ํธ์ ')|str_detect(๋
ธ์ ๋ช
,'^4ํธ์ ')|
str_detect(๋
ธ์ ๋ช
,'^5ํธ์ ')|str_detect(๋
ธ์ ๋ช
,'^6ํธ์ ')|
str_detect(๋
ธ์ ๋ช
,'^6ํธ์ ')|str_detect(๋
ธ์ ๋ช
,'^8ํธ์ ')|
str_detect(๋
ธ์ ๋ช
,'^9ํธ์ '))
View(g)
plot_ly(g, x=~์ฌ์ฉ์ผ์, y=~total) %>% add_lines(linetype=~๋
ธ์ ๋ช
)
|
0d383538551fe4da4be299692957d8d7b9c46372 | 8a6fb400998956cec1dd817a74b90c34c62c6f29 | /1st.Version/GP_Simulate.R | 34e4171ce77a20fb7fb540dd4db5a2bb179fc8a0 | [] | no_license | XGerade/GaussianProcessDynamicSystem | 9faed684f141e55aec6b0a417ea26971b697ebad | cc4444eec8c74772f86be9afbd0bafcf36277cd0 | refs/heads/master | 2021-01-23T11:48:10.335574 | 2014-04-23T16:28:58 | 2014-04-23T16:28:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,114 | r | GP_Simulate.R | load("Simulate_Results.RData")
source("GPRegression.R")
tao = 0.02
k1 = -1
k2 = -1
k3 = 1
k4 = -0.1
theta_initial <- -0.15
theta_1_initial <- -1
x_initial <- 1
x_1_initial <- 3
timeLength <- 300
state <- matrix(0, timeLength, 5)
sigma.squared1 = 0.01
sigma.squared2 = 0.01
Fm = 2.5
#K.xx <- calculateCovariance(x.data, x.data)
for (i in 1: timeLength) {
print(i)
state[i, 1] <- theta_initial
state[i, 2] <- theta_1_initial
state[i, 3] <- x_initial
state[i, 4] <- x_1_initial
F <- Fm * sign(k1 * x_initial+ k2 * x_1_initial+ k3 * theta_initial+ k4 * theta_1_initial)
state[i, 5] <- F
results1 <- GP.reg(x.data, theta_2_out, matrix(state[i,], 1, 5), sigma.squared1, K.xx)
theta_2 <- results1$post.mean[1]
results2 <- GP.reg(x.data, x_2_out, matrix(state[i,], 1, 5), sigma.squared2, K.xx)
x_2 <- results2$post.mean[1]
x_initial <- x_initial + tao * x_1_initial
x_1_initial <- x_1_initial + tao * x_2
theta_initial <- theta_initial + tao * theta_1_initial
theta_1_initial <- theta_1_initial + tao * theta_2
}
save(state, file = "GP_Results.RData")
|
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