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# Make the canvas | |
plot(1:10,1:10,xlim=c(-5,5),ylim=c(0,10),type="n",xlab="",ylab="",xaxt="n",yaxt="n") | |
# Make the branches | |
rect(-1,0,1,2,col="tan3",border="tan4",lwd=3) | |
polygon(c(-5,0,5),c(2,4,2),col="palegreen3",border="palegreen4",lwd=3) | |
polygon(c(-4,0,4),c(3.5,5.5,3.5),col="palegreen4",border="palegreen3",lwd=3) | |
polygon(c(-3,0,3),c(5,6.5,5),col="palegreen3",border="palegreen4",lwd=3) | |
polygon(c(-2,0,2),c(6.25,7.5,6.25),col="palegreen4",border="palegreen3",lwd=3) |
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--- | |
title: "How does your BMI measure up?" | |
output: flexdashboard::flex_dashboard | |
runtime: shiny | |
--- | |
Inputs {.sidebar} | |
------------------------------------- | |
```{r} |
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pvaluesPain = c(0.02, 0.06, 0.75, 0.38, 0.3, 0.07, 0.22, 0.27) | |
adjustedPvaluesPain = p.adjust(pvaluesPain,method="bonferroni") | |
adjustedPvaluesPain | |
sum(adjustedPvaluesPain < 0.05) | |
pvaluesNoPain = c(0.82, 0.98, 0.65, 0.39, 0.88, 0.95, 0.29, 0.64, 0.81) | |
adjustedPvaluesNoPain = p.adjust(pvaluesNoPain,method="bonferroni") | |
adjustedPvaluesNoPain | |
sum(adjustedPvaluesNoPain < 0.05) |
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## Load libraries | |
library(XML) | |
library(dplyr) | |
library(RCurl) | |
## Get the results for a specific term | |
scrape_term = function(search_term,npages){ | |
base_url = "http://scholar.google.com/scholar?" | |
search_string = paste0("q=",paste0(strsplit(search_term," ")[[1]],collapse="+")) | |
dat = data.frame(NA,nrow=10*npages,ncol=3) |
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library(hextri) | |
library(tidyr) | |
noco = readRDS("/cloud/project/postpi/noco.rds") | |
truth = readRDS("/cloud/project/postpi/truth.rds") | |
par = readRDS("/cloud/project/postpi/par_postpi.rds") | |
nonpar = readRDS("/cloud/project/postpi/nonpar_postpi.rds") | |
nbins = 20 |
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### Let's simulate some data very simply | |
## Load libraries | |
library(ggplot2) | |
library(dplyr) | |
library(gam) | |
library(patchwork) | |
# Set parameters | |
n_sample = 100 |
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--- | |
title: "Tabset Column" | |
runtime: shiny | |
output: flexdashboard::flex_dashboard | |
--- | |
```{r setup, include=FALSE} | |
library(flexdashboard) |
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#library(ggnet) | |
#library(RkittleBrewer) | |
#library(network) | |
plot_net = function(nodes_per_layer, | |
layer_shape, | |
connection_type, | |
stride_length, | |
layer_color, | |
node_values, |
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plot_net = function(nodes_per_layer, layer_shape, connection_type, stride_length){ | |
total_nodes = sum(nodes_per_layer) | |
adjacency = matrix(0,nrow=total_nodes,ncol=total_nodes) | |
for(i in seq_along(connection_type)){ | |
source_nodes = 1:nodes_per_layer[i] + (i-1 > 0)*sum(nodes_per_layer[1:(i-1)]) | |
sink_nodes = 1:nodes_per_layer[(i+1)] + (i > 0)*sum(nodes_per_layer[1:i]) | |
nsource = length(source_nodes) |
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library(png) | |
library(ggforce) | |
library(animation) | |
library(tweenr) | |
jhu_png = readPNG("jhudsl.png") | |
x = rep(1:510,each=555) | |
y = rep(1:555,510) | |
val = jhu_png[,,3] | |
val = round(val) |
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