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index.Rmd
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---
title: "BECCA"
output:
flexdashboard::flex_dashboard:
theme: cerulean
logo: undp48.png
orientation: rows
runtime: shiny
vertical_layout: fill
runtime: shiny
---
```{r Load Packages, include=FALSE}
require(flexdashboard)
require(tm)
require(shiny)
require(shinyBS)
require(quanteda)
require(RYandexTranslate)
require(stm)
require(wordcloud)
require(htmlwidgets)
require(stmBrowser)
require(devtools)
require(jsonlite)
require(SnowballC)
require(ggplot2)
require(cluster)
require(fpc)
require(skmeans)
require(knitr)
require(xlsx) # for reading original xls files
require(plotly) # for interactive map
require(likert) # for Likert plots
require(RColorBrewer) # for the Brewer color palette used in Triad plots
require(grid) # required by Triad and likert plots
require(gridExtra) # for arrangin the Triad plots
require(mixtools) # for computing ellipsoids from clustering
require(sp)
require(magrittr)
require(plotly)
require(data.table)
require(xtable)
devtools::install_github("timelyportfolio/stmBrowser@htmlwidget") # This needs to be installed once
```
```{r Translation Switch, include=FALSE, echo=TRUE}
## Switchboard
switch_t <- 0 # Translation
switch_c <- 1 # Corpus
save(list = c("switch_c","switch_t"), file = "switches.RData")
```
```{r Load Datasets, include = FALSE, echo = FALSE}
## File Structure - Pleeeeaeease tell me if you mess with this ~Charlie.
#/Working Dir
#/dynamic_stats_v3.R
#/factor_to_integer.R
#/sort_signifiers.R
#/namegetter.R
#/[dataset]_factornames.RData - {No longer needed, namegetter.R returns a function not a file.}.
#/clean_data
#/[dataset]_clean.RData
#/texts
#/[dataset]_translated_texts.RData
#/corpus/
#/[dataset]_corpus.RData
#/subsets
#/[dataset]_subsets.RData
knitr::opts_knit$set(root.dir = normalizePath('./'))
# print(opts_knit$get("root.dir"))
## Load All Datasets
wd <- getwd()
dataset <- c("kyrgyzstan", "moldova", "unicef", "serbia", "tajikistan", "yemen")
# print(dataset)
for (d in 1:length(dataset)){
load(paste0(wd,"/clean_data/",dataset[d],"_clean.RData"))
dataset <- c("kyrgyzstan", "moldova", "unicef", "serbia", "tajikistan", "yemen")
assign(paste0(dataset[d]),clean)
}
## Choose Dataset
dataset <- "moldova" ## Pick one!
load(paste0(wd,"/clean_data/",dataset,"_clean.RData"))
load(paste0(wd,"/clean_data/texts/",dataset,"_translated_texts.RData"))
load(paste0(wd,"/clean_data/subsets/",dataset,"_subsets.RData"))
save("dataset", file = "dataset.txt")
## Attach to search dir
search <- search()
if (!is.na(match("clean", search))) {detach(clean)}
if (!is.na(match("data", search))) {detach(data)}
search <- search()
if (is.na(match("clean", search))) {attach(clean)}
# knitr::opts_knit$set(root.dir = normalizePath('../'))
# print(opts_knit$get("root.dir"))
```
```{r Load All Corpuses and Texts}
## Load all Corpuses
load(paste0(wd,"/clean_data/corpus/moldova_corpus.RData"))
moldova_corpus <- corpus
load(paste0(wd,"/clean_data/corpus/kyrgyzstan_corpus.RData"))
kyrgyzstan_corpus <- corpus
load(paste0(wd,"/clean_data/corpus/serbia_corpus.RData"))
serbia_corpus <- corpus
load(paste0(wd,"/clean_data/corpus/tajikistan_corpus.RData"))
tajikistan_corpus <- corpus
load(paste0(wd,"/clean_data/corpus/yemen_corpus.RData"))
yemen_corpus <- corpus
load(paste0(wd,"/clean_data/corpus/unicef_corpus.RData"))
unicef_corpus <- corpus
# names(summary(yemen_corpus))
# names(summary(tajikistan_corpus))
load(paste0(wd,"/clean_data/texts/moldova_translated_texts.RData"))
moldova_texts_eng <- texts_eng
moldova_texts_org <- texts_org
moldova_titles_eng <- titles_eng
moldova_titles_org <- titles_org
load(paste0(wd,"/clean_data/texts/kyrgyzstan_translated_texts.RData"))
kyrgyzstan_texts_eng <- texts_eng
kyrgyzstan_texts_org <- texts_org
kyrgyzstan_titles_eng <- titles_eng
kyrgyzstan_titles_org <- titles_org
load(paste0(wd,"/clean_data/texts/serbia_translated_texts.RData"))
serbia_texts_eng <- texts_eng
serbia_texts_org <- texts_org
serbia_titles_eng <- titles_eng
serbia_titles_org <- titles_org
load(paste0(wd,"/clean_data/texts/tajikistan_translated_texts.RData"))
tajikistan_texts_eng <- texts_eng
tajikistan_texts_org <- texts_org
tajikistan_titles_eng <- titles_eng
tajikistan_titles_org <- titles_org
load(paste0(wd,"/clean_data/texts/yemen_translated_texts.RData"))
yemen_texts_eng <- texts_eng
yemen_texts_org <- texts_org
yemen_titles_eng <- titles_eng
yemen_titles_org <- titles_org
load(paste0(wd,"/clean_data/texts/unicef_translated_texts.RData"))
unicef_texts_eng <- texts_eng
unicef_texts_org <- texts_org
unicef_titles_eng <- titles_eng
unicef_titles_org <- titles_org
# sum(is.na(unicef_texts_eng))
```
```{r SetupCA, echo = FALSE, include = FALSE}
source("undp_setup.R") # Initial parameters
source("undp_read.R") # Read data bases and translate foreign words of new records
source("undp_preprocess.R") # Process the data bases
source("undp_myworldmap.R") # Produces the world map of feeling
source("undp_mylikert.R") # Produces Likert plots
source("undp_mytimeseries.R") # Produces time series plots
source("undp_mytimeseries2.R") # Produces time series plots of Positive/Negative feeling
source("undp_mytriad.R") # Produces Triad plots
```
World Map {data-orientation=columns}
=====================================
Map {.sidebar}
-------------------------------------
```{r}
selectInput("mapVars", label = "Select Index",
choices = c("Population", "GDP", "GDP Per Capita", "GDP Growth",
"Average Feeling (Positive/Neutral/Negative)"),
selected = "Population")
```
Row
-------------------------------------
```{r Prepare for maps, include = FALSE, echo = TRUE}
# READ IN DATA
formaps <- read.xlsx("ForMaps.xlsx", 1)
# WHAT COLUMNS DO WE HAVE?
formaps$GDP.Growth<-as.numeric(levels(formaps$GDP.Growth))[formaps$GDP.Growth]
formaps$GDP.per.capita<-as.numeric(levels(formaps$GDP.per.capita))[formaps$GDP.per.capita]
formaps$GDP<-as.numeric(levels(formaps$GDP))[formaps$GDP]
formaps$Population<-as.numeric(levels(formaps$Population))[formaps$Population]
## Log variables to make graphs more legible
formaps$GDP<-log(formaps$GDP)
formaps$GDP.per.capita<-log(formaps$GDP.per.capita)
formaps$Population<-log(formaps$Population)
```
```{r Population, include = FALSE, echo = TRUE}
## Population
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = TRUE,
projection = list(type = 'Mercator'),
showland = TRUE,
landcolor = toRGB("grey83"),
#subunitcolor = toRGB("white"),
countrycolor = toRGB("white"),
showlakes = TRUE,
lakecolor = toRGB("white"),
#showsubunits = TRUE,
showcountries = TRUE,
resolution = 50,
countrywidth = 0.5,
subunitwidth = 0.5,
showlakes=TRUE,
lakecolor = toRGB("white")
)
population <- (plot_ly(formaps, z = Population,
locations = Country.Code,
type = 'choropleth',
color = Population, colors = 'Blues', marker = list(line = l),
colorbar = list(tickprefix = '', title = ' log Population'),
inherit = FALSE, # don't pass arguments into the next trace
filename="r-docs/choropleth-with-country-labels") %>%
layout(title = 'Country Population',
geo = g) %>%
dplyr::arrange(dplyr::desc(GDP)))[seq(1, 10), ] %>%
add_trace(type="scattergeo",
mode="text")
```
```{r GDP, include = FALSE, echo = TRUE}
## GDP
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = TRUE,
projection = list(type = 'Mercator'),
showland = TRUE,
landcolor = toRGB("lightgrey"),
#subunitcolor = toRGB("white"),
countrycolor = toRGB("white"),
showlakes = TRUE,
lakecolor = toRGB("white"),
#showsubunits = TRUE,
showcountries = TRUE,
resolution = 50,
countrywidth = 0.5,
subunitwidth = 0.5,
showlakes=TRUE,
lakecolor = toRGB("white")
)
GDP <- (plot_ly(formaps, z = GDP,
locations = Country.Code,
type = 'choropleth',
color = GDP, colors = 'Blues', marker = list(line = l),
colorbar = list(tickprefix = '', title = 'log GDP'),
inherit = FALSE, # don't pass arguments into the next trace
filename="r-docs/choropleth-with-country-labels") %>%
layout(title = 'Country GDP',
geo = g) %>%
dplyr::arrange(dplyr::desc(GDP)))[seq(1, 10), ] %>%
add_trace(type="scattergeo",
mode="text")
```
```{r GDP per capita, include = FALSE, echo = TRUE}
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = TRUE,
projection = list(type = 'Mercator'),
showland = TRUE,
landcolor = toRGB("lightgrey"),
#subunitcolor = toRGB("white"),
countrycolor = toRGB("white"),
showlakes = TRUE,
lakecolor = toRGB("white"),
#showsubunits = TRUE,
showcountries = TRUE,
resolution = 50,
countrywidth = 0.5,
subunitwidth = 0.5,
showlakes=TRUE,
lakecolor = toRGB("white")
)
percapita <- (plot_ly(formaps, z = GDP.per.capita,
locations = Country.Code,
type = 'choropleth',
color = GDP.per.capita, colors = 'Blues', marker = list(line = l),
colorbar = list(tickprefix = '', title = 'log GDP per capita'),
inherit = FALSE, # don't pass arguments into the next trace
filename="r-docs/choropleth-with-country-labels") %>%
layout(title = 'GPD per capita',
geo = g) %>%
dplyr::arrange(dplyr::desc(GDP)))[seq(1, 10), ] %>%
add_trace(type="scattergeo",
mode="text")
```
```{r GDP growth, include = FALSE, echo = TRUE}
## Growth
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = TRUE,
projection = list(type = 'Mercator'),
showland = TRUE,
landcolor = toRGB("lightgrey"),
#subunitcolor = toRGB("white"),
countrycolor = toRGB("white"),
showlakes = TRUE,
lakecolor = toRGB("white"),
#showsubunits = TRUE,
showcountries = TRUE,
resolution = 50,
countrywidth = 0.5,
subunitwidth = 0.5,
showlakes=TRUE,
lakecolor = toRGB("white")
)
growth <- (plot_ly(formaps, z = GDP.Growth,
locations = Country.Code,
type = 'choropleth',
color = GDP.Growth, colors = 'Blues', marker = list(line = l),
colorbar = list(tickprefix = '%', title = 'GDP Growth'),
inherit = FALSE, # don't pass arguments into the next trace
filename="r-docs/choropleth-with-country-labels") %>%
layout(title = 'Annual GDP Growth (Percent)',
geo = g) %>%
dplyr::arrange(dplyr::desc(GDP.Growth)))[seq(1, 10), ] %>%
add_trace(type="scattergeo",
mode="text")
```
### World Map
```{r Reactive Map}
renderPlotly({
if (input$mapVars == "Population") {
population
}
else if (input$mapVars == "GDP") {
GDP
}
else if (input$mapVars == "GDP Per Capita") {
percapita
}
else if (input$mapVars == "GDP Growth") {
growth
} else if (input$mapVars == "Average Feeling (Positive/Neutral/Negative)") {
myworldmap(data,Country)
}
})
```
Descriptive Statistics
=====================================
```{r Descriptive Stats, eval=FALSE}
shinyAppFile("dynamic_stats_v4.R")
```
Feeling Over Time
=====================================
Feeling {.sidebar}
-------------------------------------
```{r Feeling Inputs}
selectInput("Countrys", label = "Select Country",
choices = c("Moldova", "Kyrgyzstan", "Serbia", "Tajikistan", "Yemen"), selected = "Moldova")
reactncountrys <- reactive({
switch(input$Countrys, "Yemen" = {ncountry<-1},
"Kyrgyzstan" = {ncountry<-2}, "Moldova" = {ncountry<-3},
"Tajikistan" = {ncountry<-4}, "Serbia" = {ncountry<-5})
})
sliderInput("Cut_days", label = "Window length (days)",
min = 3, max = 30, value = 7, step = 1)
```
Choose Stories (upper left graph):
```{r Feeling Checkbox Inputs}
checkboxInput("checkPos", "Positive Stories", T)
checkboxInput("checkNeu", "Neutral Stories", T)
checkboxInput("checkNeg", "Negative Stories", T)
```
Row
-------------------------------------
### Percentage of Positive, Neutral and Negative Stories Over Time
```{r Feeling Pos Neu Neg Over Time}
renderPlot({
ncountry <- reactncountrys()
Cut_days <- input$Cut_days
checkNeg <- input$checkNeg
checkPos <- input$checkPos
checkNeu <- input$checkNeu
mytimeseries2(ncountry,Cut_days,checkNeg,checkPos,checkNeu)
}) # End of renderPlot()
```
### Mean Trend
```{r Feeling Mean Trend}
renderPlot({
ncountry <- reactncountrys()
Cut_days <- input$Cut_days
mytimeseries(data,Country,ncountry,Cut_days)
}) # End of renderPlot()
```
Row
-------------------------------------
### Likert Plot
```{r Feeling Likert Plot}
renderPlot({
ncountry <- reactncountrys()
Cut_days <- input$Cut_days
mylikert(data,Country,ncountry,Cut_days)
}) # End of renderPlot
```
Frequent Terms
=====================================
Frequent Terms {.sidebar}
-------------------------------------
```{r Frequent Terms Inputs}
selectInput("FrequentTermsCountry", label = "Select Country",
choices = c("Moldova" = "moldova", "Kyrgyzstan UNDP" = "kyrgyzstan", "Kyrgyzstan Unicef" = "unicef", "Serbia" = "serbia", "Tajikistan" = "tajikistan", "Yemen" = "yemen"),
selected = "moldova")
```
> **FREQUENT TERMS**
> **Wordclouds**: visualization tool showing the most frequent words appearing in the data. The bigger the word, the more frequent it is.
> **Bar chart** : Another visualization tool for frequent terms. The most frequent words are displayed, and the frequency with which they appear on the texts is given by the y-axis value.
Column
-------------------------------------
### Word Cloud {data-height=650}
```{r Moldova Term Frequency Setup , echo=FALSE}
# moldova <- read.csv("Moldova_1.csv")
# moldovatext <- Corpus(VectorSource(moldova$"Your.experience"))
moldovatext <- Corpus(VectorSource(moldova_texts_eng))
moldovatext <- tm_map(moldovatext, removePunctuation)
for(j in seq(moldovatext)) { #######(start Comment) Is this part still necessary after file has been cleaned by Charlie's code?
moldovatext[[j]] <- gsub("/", " ", moldovatext[[j]])
moldovatext[[j]] <- gsub("@", " ", moldovatext[[j]])
moldovatext[[j]] <- gsub("\\|", " ", moldovatext[[j]])
}
moldovatext <- tm_map(moldovatext, removeNumbers)
moldovatext <- tm_map(moldovatext, tolower)
moldovatext <- tm_map(moldovatext, removeWords, stopwords("english"))
# library(SnowballC)
moldovatext <- tm_map(moldovatext, stemDocument)
moldovatext <- tm_map(moldovatext, stripWhitespace)
moldovatext <- tm_map(moldovatext, PlainTextDocument) ################# (end Comment) ##########################################
## Stage the data
moldovadtm <- DocumentTermMatrix(moldovatext)
moldovatdm <- TermDocumentMatrix(moldovatext)
moldovafreq <- colSums(as.matrix(moldovadtm))
moldovafreq<-sort(moldovafreq, decreasing = TRUE)
moldovafrequency<-head(moldovafreq, 20)
moldovawf <- data.frame(moldovaword=names(moldovafrequency), moldovafrequency=moldovafrequency)
moldovawf$moldovaword <- factor(moldovawf$moldovaword, levels = moldovawf$moldovaword[order(moldovawf$moldovafreq, decreasing = TRUE)])
```
```{r Tagikistan Term Frequency Setup , echo=FALSE}
# moldova <- read.csv("Moldova_1.csv")
tajikistantext <- Corpus(VectorSource(tajikistan_texts_eng))
tajikistantext <- tm_map(tajikistantext, removePunctuation)
for(j in seq(tajikistantext)) { #######(start Comment) Is this part still necessary after file has been cleaned by Charlie's code?
tajikistantext[[j]] <- gsub("/", " ", tajikistantext[[j]])
tajikistantext[[j]] <- gsub("@", " ", tajikistantext[[j]])
tajikistantext[[j]] <- gsub("\\|", " ", tajikistantext[[j]])
}
tajikistantext <- tm_map(tajikistantext, removeNumbers)
tajikistantext <- tm_map(tajikistantext, tolower)
tajikistantext <- tm_map(tajikistantext, removeWords, stopwords("english"))
tajikistantext <- tm_map(tajikistantext, stemDocument)
tajikistantext <- tm_map(tajikistantext, stripWhitespace)
tajikistantext <- tm_map(tajikistantext, PlainTextDocument) ################# (end Comment) ##########################################
## Stage the data
tajikistandtm <- DocumentTermMatrix(tajikistantext)
tajikistantdm <- TermDocumentMatrix(tajikistantext)
tajikistanfreq <- colSums(as.matrix(tajikistandtm))
tajikistanfreq<-sort(tajikistanfreq, decreasing = TRUE)
tajikistanfrequency<-head(tajikistanfreq, 20)
tajikistanwf <- data.frame(tajikistanword=names(tajikistanfrequency), tajikistanfrequency=tajikistanfrequency)
tajikistanwf$tajikistanword <- factor(tajikistanwf$tajikistanword, levels = tajikistanwf$tajikistanword[order(tajikistanwf$tajikistanfreq, decreasing = TRUE)])
```
```{r Kyrgyzstan Term Frequency Setup , echo=FALSE}
kyrgyzstantext <- Corpus(VectorSource(kyrgyzstan_texts_eng))
kyrgyzstantext <- tm_map(kyrgyzstantext, removePunctuation)
for(j in seq(kyrgyzstantext)) { #######(start Comment) Is this part still necessary after file has been cleaned by Charlie's code?
kyrgyzstantext[[j]] <- gsub("/", " ", kyrgyzstantext[[j]])
kyrgyzstantext[[j]] <- gsub("@", " ", kyrgyzstantext[[j]])
kyrgyzstantext[[j]] <- gsub("\\|", " ", kyrgyzstantext[[j]])
}
kyrgyzstantext <- tm_map(kyrgyzstantext, removeNumbers)
kyrgyzstantext <- tm_map(kyrgyzstantext, tolower)
kyrgyzstantext <- tm_map(kyrgyzstantext, removeWords, stopwords("english"))
kyrgyzstantext <- tm_map(kyrgyzstantext, stemDocument)
kyrgyzstantext <- tm_map(kyrgyzstantext, stripWhitespace)
kyrgyzstantext <- tm_map(kyrgyzstantext, PlainTextDocument) ################# (end Comment) ##########################################
## Stage the data
kyrgyzstandtm <- DocumentTermMatrix(kyrgyzstantext)
kyrgyzstantdm <- TermDocumentMatrix(kyrgyzstantext)
kyrgyzstanfreq <- colSums(as.matrix(kyrgyzstandtm))
kyrgyzstanfreq<-sort(kyrgyzstanfreq, decreasing = TRUE)
kyrgyzstanfrequency<-head(kyrgyzstanfreq, 20)
kyrgyzstanwf <- data.frame(kyrgyzstanword=names(kyrgyzstanfrequency), kyrgyzstanfrequency=kyrgyzstanfrequency)
kyrgyzstanwf$kyrgyzstanword <- factor(kyrgyzstanwf$kyrgyzstanword, levels = kyrgyzstanwf$kyrgyzstanword[order(kyrgyzstanwf$kyrgyzstanfreq, decreasing = TRUE)])
```
```{r Serbia Term Frequency Setup , echo=FALSE}
serbiatext <- Corpus(VectorSource(serbia_texts_eng))
serbiatext <- tm_map(serbiatext, removePunctuation)
for(j in seq(serbiatext)) { #######(start Comment) Is this part still necessary after file has been cleaned by Charlie's code?
serbiatext[[j]] <- gsub("/", " ", serbiatext[[j]])
serbiatext[[j]] <- gsub("@", " ", serbiatext[[j]])
serbiatext[[j]] <- gsub("\\|", " ", serbiatext[[j]])
}
serbiatext <- tm_map(serbiatext, removeNumbers)
serbiatext <- tm_map(serbiatext, tolower)
serbiatext <- tm_map(serbiatext, removeWords, stopwords("english"))
# library(SnowballC)
serbiatext <- tm_map(serbiatext, stemDocument)
serbiatext <- tm_map(serbiatext, stripWhitespace)
serbiatext <- tm_map(serbiatext, PlainTextDocument) ################# (end Comment) ##########################################
## Stage the data
serbiadtm <- DocumentTermMatrix(serbiatext)
serbiatdm <- TermDocumentMatrix(serbiatext)
serbiafreq <- colSums(as.matrix(serbiadtm))
serbiafreq<-sort(serbiafreq, decreasing = TRUE)
serbiafrequency<-head(serbiafreq, 20)
serbiawf <- data.frame(serbiaword=names(serbiafrequency), serbiafrequency=serbiafrequency)
serbiawf$serbiaword <- factor(serbiawf$serbiaword, levels = serbiawf$serbiaword[order(serbiawf$serbiafreq, decreasing = TRUE)])
```
```{r Yemen Term Frequency Setup , echo=FALSE}
yementext <- Corpus(VectorSource(yemen_texts_eng))
yementext <- tm_map(yementext, removePunctuation)
for(j in seq(yementext)) { #######(start Comment) Is this part still necessary after file has been cleaned by Charlie's code?
yementext[[j]] <- gsub("/", " ", yementext[[j]])
yementext[[j]] <- gsub("@", " ", yementext[[j]])
yementext[[j]] <- gsub("\\|", " ", yementext[[j]])
}
yementext <- tm_map(yementext, removeNumbers)
yementext <- tm_map(yementext, tolower)
yementext <- tm_map(yementext, removeWords, stopwords("english"))
yementext <- tm_map(yementext, stemDocument)
yementext <- tm_map(yementext, stripWhitespace)
yementext <- tm_map(yementext, PlainTextDocument) ################# (end Comment) ##########################################
## Stage the data
yemendtm <- DocumentTermMatrix(yementext)
yementdm <- TermDocumentMatrix(yementext)
yemenfreq <- colSums(as.matrix(yemendtm))
yemenfreq<-sort(yemenfreq, decreasing = TRUE)
yemenfrequency<-head(yemenfreq, 20)
yemenwf <- data.frame(yemenword=names(yemenfrequency), yemenfrequency=yemenfrequency)
yemenwf$yemenword <- factor(yemenwf$yemenword, levels = yemenwf$yemenword[order(yemenwf$yemenfreq, decreasing = TRUE)])
```
```{r Unicef Term Frequency Setup , echo=FALSE}
uniceftext <- Corpus(VectorSource(unicef_texts_eng))
uniceftext <- tm_map(uniceftext, removePunctuation)
for(j in seq(uniceftext)) { #######(start Comment) Is this part still necessary after file has been cleaned by Charlie's code?
uniceftext[[j]] <- gsub("/", " ", uniceftext[[j]])
uniceftext[[j]] <- gsub("@", " ", uniceftext[[j]])
uniceftext[[j]] <- gsub("\\|", " ", uniceftext[[j]])
}
uniceftext <- tm_map(uniceftext, removeNumbers)
uniceftext <- tm_map(uniceftext, tolower)
uniceftext <- tm_map(uniceftext, removeWords, stopwords("english"))
uniceftext <- tm_map(uniceftext, stemDocument)
uniceftext <- tm_map(uniceftext, stripWhitespace)
uniceftext <- tm_map(uniceftext, PlainTextDocument) ################# (end Comment) ##########################################
## Stage the data
unicefdtm <- DocumentTermMatrix(uniceftext)
uniceftdm <- TermDocumentMatrix(uniceftext)
uniceffreq <- colSums(as.matrix(unicefdtm))
uniceffreq<-sort(uniceffreq, decreasing = TRUE)
uniceffrequency<-head(uniceffreq, 20)
unicefwf <- data.frame(unicefword=names(uniceffrequency), uniceffrequency=uniceffrequency)
unicefwf$unicefword <- factor(unicefwf$unicefword, levels = unicefwf$unicefword[order(unicefwf$uniceffreq, decreasing = TRUE)])
```
```{r WordCloud}
renderPlot({
wordcloud(names((get(paste0(input$FrequentTermsCountry, "freq")))), (get(paste0(input$FrequentTermsCountry, "freq"))), max.words = 30, scale=c(5, .1), random.color = FALSE, colors=brewer.pal(5, "Blues"))
})
```
### Bar chart {data-height=650}
```{r Frequent Terms}
renderPlot({
ggplot(((get(paste0(input$FrequentTermsCountry,"wf")))), aes(((get(paste0(input$FrequentTermsCountry,"word")))),(get(paste0(input$FrequentTermsCountry ,"frequency"))))) + geom_bar(stat="identity") +
theme(axis.text.x=element_text(angle=45, hjust=1)) + ylab("Frequency") + xlab("Word")
})
```
Cluster Analysis
=====================================
Cluster Analysis {.sidebar}
-------------------------------------
```{r Cluster Analysis Inputs}
selectInput("ClusterAnalysisCountry", label = "Select Country",
choices = c("Moldova" = "moldova", "Kyrgyzstan UNDP" = "kyrgyzstan", "Kyrgyzstan Unicef" = "unicef", "Serbia" = "serbia",
"Tajikistan" = "tajikistan", "Yemen" = "yemen"),
selected = "moldova")
```
>**CLUSTER ANALYSIS**
> **Cluster dendogram**: Red line divides more frequent words in different clusters. Words in the same cluster are more likely to appear together in the same text.
> **Cluster analysis**: another visualisation for clusters. Each color represents a different cluster. Words in the same clusters are more likely to appear together in the text.
Column
------------------------------------
### Dendogram {data-height=650}
```{r Moldova Cluster Setup, include = FALSE}
moldovadtmss <- removeSparseTerms(moldovadtm, 0.95) # This makes a matrix that is only 15% empty space, maximum.
# inspect(moldovadtmss)
# library(cluster)
moldovad <- dist(t(moldovadtmss), method="maximum")
moldovafit <- hclust(d=moldovad, method="ward")
# moldovafit
plot(moldovafit, hang=-1)
plot.new()
plot(moldovafit, hang=-1)
moldovagroups <- cutree(moldovafit, k=3) # "k=" defines the number of clusters you are using
rect.hclust(moldovafit, k=3, border="red")
# library(fpc)
moldovad <- dist(t(moldovadtmss), method="maximum")
moldovakfit <- kmeans(moldovad, 3)
moldovadoo <- skmeans_xdist(t(moldovadtmss))
moldovakfitoo <- skmeans(moldovadoo,3)
```
```{r Tajikistan Cluster Setup, include = FALSE}
tajikistandtmss <- removeSparseTerms(tajikistandtm, 0.80) # This makes a matrix that is only 15% empty space, maximum.
tajikistand <- dist(t(tajikistandtmss), method="maximum")
tajikistanfit <- hclust(d=tajikistand, method="ward")
plot(tajikistanfit, hang=-1)
plot.new()
plot(tajikistanfit, hang=-1)
tajikistangroups <- cutree(tajikistanfit, k=3) # "k=" defines the number of clusters you are using
rect.hclust(tajikistanfit, k=3, border="red")
# library(fpc)
tajikistand <- dist(t(tajikistandtmss), method="maximum")
tajikistankfit <- kmeans(tajikistand, 3)
tajikistandoo <- skmeans_xdist(t(tajikistandtmss))
tajikistankfitoo <- skmeans(tajikistandoo,3)
```
```{r Serbia Cluster Setup, include = FALSE}
serbiadtmss <- removeSparseTerms(serbiadtm, 0.90) # This makes a matrix that is only 15% empty space, maximum.
serbiad <- dist(t(serbiadtmss), method="maximum")
serbiafit <- hclust(d=serbiad, method="ward")
plot(serbiafit, hang=-1)
plot.new()
plot(serbiafit, hang=-1)
serbiagroups <- cutree(serbiafit, k=3) # "k=" defines the number of clusters you are using
rect.hclust(serbiafit, k=3, border="red")
# library(fpc)
serbiad <- dist(t(serbiadtmss), method="maximum")
serbiakfit <- kmeans(serbiad, 3)
serbiadoo <- skmeans_xdist(t(serbiadtmss))
serbiakfitoo <- skmeans(serbiadoo,3)
```
```{r Kyrgyzstan Cluster Setup, include = FALSE}
kyrgyzstandtmss <- removeSparseTerms(kyrgyzstandtm, 0.91) # This makes a matrix that is only 15% empty space, maximum.
kyrgyzstand <- dist(t(kyrgyzstandtmss), method="maximum")
kyrgyzstanfit <- hclust(d=kyrgyzstand, method="ward")
plot(kyrgyzstanfit, hang=-1)
plot.new()
plot(kyrgyzstanfit, hang=-1)
kyrgyzstangroups <- cutree(kyrgyzstanfit, k=3) # "k=" defines the number of clusters you are using
rect.hclust(kyrgyzstanfit, k=3, border="red")
# library(fpc)
kyrgyzstand <- dist(t(kyrgyzstandtmss), method="maximum")
kyrgyzstankfit <- kmeans(kyrgyzstand, 3)
kyrgyzstandoo <- skmeans_xdist(t(kyrgyzstandtmss))
kyrgyzstankfitoo <- skmeans(kyrgyzstandoo,3)
```
```{r Yemen Cluster Setup, include = FALSE}
yemendtmss <- removeSparseTerms(yemendtm, 0.80) # This makes a matrix that is only 15% empty space, maximum.
yemend <- dist(t(yemendtmss), method="maximum")
yemenfit <- hclust(d=yemend, method="ward")
plot(yemenfit, hang=-1)
plot.new()
plot(yemenfit, hang=-1)
yemengroups <- cutree(yemenfit, k=3) # "k=" defines the number of clusters you are using
rect.hclust(yemenfit, k=3, border="red")
# library(fpc)
yemend <- dist(t(yemendtmss), method="maximum")
yemenkfit <- kmeans(yemend, 3)
yemendoo <- skmeans_xdist(t(yemendtmss))
yemenkfitoo <- skmeans(yemendoo,3)
```
```{r Unicef Cluster Setup, include = FALSE}
unicefdtmss <- removeSparseTerms(unicefdtm, 0.93) # This makes a matrix that is only 15% empty space, maximum.
unicefd <- dist(t(unicefdtmss), method="maximum")
uniceffit <- hclust(d=unicefd, method="ward")
plot(uniceffit, hang=-1)
plot.new()
plot(uniceffit, hang=-1)
unicefgroups <- cutree(uniceffit, k=3) # "k=" defines the number of clusters you are using
rect.hclust(uniceffit, k=3, border="red")
# library(fpc)
unicefd <- dist(t(unicefdtmss), method="maximum")
unicefkfit <- kmeans(unicefd, 3)
unicefdoo <- skmeans_xdist(t(unicefdtmss))
unicefkfitoo <- skmeans(unicefdoo,3)
```
```{r}
renderPlot({
plot((get(paste0(input$ClusterAnalysisCountry, "fit"))), hang=-1)
groups <- cutree((get(paste0(input$ClusterAnalysisCountry, "fit"))),k=3) # "k=" defines the number of clusters you are using
rect.hclust((get(paste0(input$ClusterAnalysisCountry, "fit"))), k=3, border="red")
})
```
### Cluster analysis {data-height=650}
```{r}
renderPlot({
# if (input$Country == "Moldova") {
# moldovad <- skmeans_xdist(t(moldovadtmss))
# moldovakfit <- skmeans(moldovad,3)
clusplot(as.matrix(get(paste0(input$ClusterAnalysisCountry, "doo"))), get(paste0(input$ClusterAnalysisCountry, "kfitoo"))$cluster, main = "CLUSTER ANALYSIS",
color=T, shade=T, labels=2, lines=0)
# }
})
```
Understanding Topics {data-navmenu="STM"}
=====================================
Understanding Topics {.sidebar}
-------------------------------------
```{r, echo=FALSE}
## Set Working Directory and create environment
stm.env <<- new.env()
stm.values <- reactiveValues()
if (length(ls(stm.env)) != 0) rm(ls(stm.env))
## Dependencies
require(shiny)
test_stm <<- 0 # Use test_model instead of running stm (for faster debugging).
## Choose Country
selectInput("STMCountry", label = "Select Country",
choices = c("Moldova","Kyrgyzstan UNDP","Kyrgyzstan Unicef", "Serbia","Tajikistan","Yemen"))
## Choose Language
radioButtons(inputId = "stm_lang",
label = "Select Language",
choices = c("Original" = "texts_org",
"English" = "texts_eng"))
## Choose K Topics
sliderInput("select_k", "Select number of topics", min = 5, max = 50, value = 25)
## Choose Prevalence Covariates
p("Choose Prevalence Covariates")
## Choose signifer type to drop-down
selectInput(inputId = "stm_sigs",
label = "Signifier Type",
choices = c("Select","Triads","Dyads","Stones","Questions","Descriptors"),
selected = "Select")
renderUI({
## Get dataset
stm_dataset <<- switch(input$STMCountry,
"Moldova" = moldova,
"Kyrgyzstan UNDP" = kyrgyzstan,
"Kyrgyzstan Unicef" = unicef,
"Serbia" = serbia,
"Tajikistan" = tajikistan,
"Yemen" = yemen)