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helper_functions.R
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helper_functions.R
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# Helper functions to generalize charts and tables for LaTeX
#
figure_sparkline <- function(couName,table){
cou <- .getCountryCode(couName)
## Examples like Edward Tufte's sparklines:
#table <- "combo1"
data <- Entrepr_data %>%
filter(CountryCode==cou, Subsection2==table, !is.na(Observation)) %>%
mutate(Period = ifelse(is.na(Period),as.character(as.numeric(thisYear)-1),Period))
if (table == "figureFin2"){
data <- filter(data,Observation > 0)
dataLast <- filter(data, Period == max(Period,na.rm=TRUE))
dataLast$Observation <- ifelse(dataLast$Observation>1000000,dataLast$Observation/1000000,dataLast$Observation)
} else {
data <- filter(data,!is.na(Observation))
dataLast <- filter(data, Period == max(Period,na.rm=TRUE))
}
# data
dataPoint <- format(dataLast$Observation, digits=2, decimal.mark=".",
big.mark=",",small.mark=".", small.interval=3)
# period
dataPeriod <- dataLast$Period
dataWorld <- filter(Entrepr_data, Subsection2==table)
dataWorld <- filter(dataWorld,!is.na(Observation))
dataWorld <- dataWorld %>%
group_by(iso2) %>%
mutate(Period = max(Period,na.rm=TRUE)) %>%
distinct(Period, .keep_all = TRUE) %>%
as.data.frame()
# dataWorld <- merge(dataWorld,countries[,c("CountryCodeISO2","CountryAlternat")],by.x="iso2c",by.y="CountryCodeISO2",all.x = TRUE)
# dataWorld <- filter(dataWorld, !(CountryAlternat==""))
dataWorld <- arrange(dataWorld, desc(Observation))
# rank in the world
rank <- which(dataWorld$CountryCode == cou)
rankedTotal <- nrow(dataWorld)
# Ad-hoc shorten some indicatores and units names:
if (table == "figure1"){
indicator <- "Tech Startups"
unit <- "number per million pop"
}
if (table == "figure2"){
indicator <- "Doing Business"
unit <- "1=most business-friendly regulat."
}
if (table == "figure3"){
indicator <- "Broadband Internet"
unit <- "Subscriptions per 100 pop."
}
if (table == "figure4"){
indicator <- "Scientists, Engineers"
unit <- "Availability 1-7, 7=best"
}
if (table == "figure5"){
indicator <- "Tertiary Education"
unit <- "Enrollments in percent of pop."
}
if (table == "figure6"){
indicator <- "Venture Capital"
unit <- "Availability 1-7, 7=best"
}
if (table == "figureFin1"){
indicator <- "FDI, net inflows"
unit <- "BoP, current US$, as % GDP"
}
if (table == "figureFin2"){
indicator <- "Investment in Telecoms w/ Private Part."
unit <- "Millions, $US"
}
if (table == "figureFin3"){
indicator <- "Market Capitaliz. of Listed Companies"
unit <- "% of GDP"
}
if (nrow(data)>0){
minPeriod <- min(data$Period, na.rm=TRUE)
maxPeriod <- max(data$Period, na.rm=TRUE)
# sparkline
spark <- data %>%
arrange(Period) %>%
select(Observation)
# text
#indicator <- dataLast$IndicatorShort
#unit <- dataLast$Unit
# impute NAs and standardize so all sparklines are scaled
spark[is.na(spark),1] <- mean(spark[,1],na.rm = TRUE) #impute NAs to the mean of the column
if (sum(spark[,1],na.rm = TRUE)==0){
spark[,1] <- 0
#x[1,i] <- -10
spark[nrow(spark),1] <- 10
}
# Print the combo -----------------------------------------------
par(family = 'serif',mfrow=c(5,1), #sets number of rows in space to number of cols in data frame x
mar=c(0,2,0,2), #sets margin size for the figures
oma=c(0,1,0,1)) #sets outer margin
# print indicator name
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1.1,indicator, col="#22a6f5", cex=10)
graphics::text(1.5, 0.7,paste0(unit, " (",dataPeriod,")"), col="#818181", cex=5)
# print data point and rank
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 0.95,dataPoint, col="#22a6f5", cex=18)
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1.1,paste0("(Rank: ",rank,"/",rankedTotal,")"), col="grey", cex=7)
# plot sparkline
par(family = 'serif',#sets number of rows in space to number of cols in data frame x
mar=c(0,5,0,5))#sets margin size for the figures
#oma=c(0,4,0,4)) #sets outer margin
if (sum(spark[1:(nrow(spark)-1),1])==0){ # paint in white empty rows
plot(spark[,1], #use col data, not rows from data frame x
col="white",lwd=4, #color the line and adjust width
axes=F,ylab="",xlab="",main="",type="l"); #suppress axes lines, set as line plot
axis(2,yaxp=c(min(spark[,1],na.rm = TRUE),max(spark[,1],na.rm = TRUE),2),col="white",tcl=0,labels=FALSE) #y-axis: put a 2nd white axis line over the 1st y-axis to make it invisible
ymin<-min(spark[,1],na.rm = TRUE); tmin<-which.min(spark[,1]);ymax<-max(spark[,1], na.rm = TRUE);tmax<-which.max(spark[,1]);
points(x=c(tmin,tmax),y=c(ymin,ymax),pch=19,col=c("white","white"),cex=7) # add coloured points at max and min#
} else {
plot(spark[,1], #use col data, not rows from data frame x
col="darkgrey",lwd=10, #color the line and adjust width
axes=F,ylab="",xlab="",main="",type="l"); #suppress axes lines, set as line plot
axis(2,yaxp=c(min(spark[,1],na.rm = TRUE),max(spark[,1],na.rm = TRUE),2),col="white",tcl=0,labels=FALSE) #y-axis: put a 2nd white axis line over the 1st y-axis to make it invisible
ymin<-min(spark[,1],na.rm = TRUE); tmin<-which.min(spark[,1]);ymax<-max(spark[,1], na.rm = TRUE);tmax<-which.max(spark[,1]); #
points(x=c(tmin,tmax),y=c(ymin,ymax),pch=19,col=c("red","green"),cex=7) # add coloured points at max and min
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
if (minPeriod==maxPeriod){
graphics::text(1.5, 1.2,minPeriod, col="grey", cex=5)
} else{
graphics::text(1.05, 1.2,minPeriod, col="grey", cex=5)
graphics::text(1.95, 1.2,maxPeriod, col="grey", cex=5)
}
}
} else {
# Print the combo -----------------------------------------------
par(family = 'serif',mfrow=c(5,1), #sets number of rows in space to number of cols in data frame x
mar=c(0,2,0,2), #sets margin size for the figures
oma=c(0,1,0,1)) #sets outer margin
# print indicator name
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1.1,indicator, col="#22a6f5", cex=10)
graphics::text(1.5, 0.7,unit, col="#818181", cex=5)
# print data point and rank
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 0.95,"No data available", col="grey", cex=10)
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1.1,paste0("(Rank: /",rankedTotal,")"), col="grey", cex=7)
# plot sparkline
par(family = 'serif',#sets number of rows in space to number of cols in data frame x
mar=c(0,5,0,5))#sets margin size for the figures
#oma=c(0,4,0,4)) #sets outer margin
}
}
## ---- table_time ----
table_time <- function(couName,section, table){
cou <- .getCountryCode(couName)
#table <- "table1"
tableKeys <- unique(filter(Entrepr_data, Section == section, Subsection==table)[,c("Key","IndicatorShort")])
data <- filter(Entrepr_data, CountryCode==cou, Section == section, Subsection==table)
data <- merge(tableKeys,select(data,-IndicatorShort),by="Key",all.x=TRUE)
# keep the latest period (excluding projections further than 2 years)
data <- mutate(data, Period = ifelse(is.na(Period),max(as.numeric(Period),na.rm=TRUE),Period))
data <- filter(data, Period <= (as.numeric(thisYear) + 1))
#keep only periods of interest in data
data <- filter(data, Period > (as.numeric(thisYear) - 7))
# Scale Observations
data <- mutate(data, ObsScaled = ifelse(grepl("current US",Unit),Observation/1000000000,Observation),
Unit = ifelse(grepl("current US",Unit),"USD billions",Unit),
IndicatorShort = paste0(IndicatorShort, ", ",Unit))
data <- arrange(data, Key)
data <- select(data, Key, IndicatorShort, Period, ObsScaled)
# restrict to 2 decimal places
data$ObsScaled <- round(data$ObsScaled,2)
data[is.na(data)] <- "..."
# format numbers
data$ObsScaled <- format(data$ObsScaled, digits=2, decimal.mark=".",
big.mark=",",small.mark=".", small.interval=3)
# for (i in 1:nrow(data)){
#
# data$IndicatorShort[i] <- ifelse(!is.na(merge(data,TCMN_indic[TCMN_indic$Subsection==table,], by="Key")$Note[i]),
# paste0(data$IndicatorShort[i]," \\large{[", merge(data,TCMN_indic[TCMN_indic$Subsection==table,], by="Key")$Note[i],"]}"),
# data$IndicatorShort[i])
# }
# escape reserved characters
data$IndicatorShort <- gsub("%", "\\%", data$IndicatorShort, fixed=TRUE)
data$IndicatorShort <- gsub("&", "\\&", data$IndicatorShort, fixed=TRUE)
# final table format
data <- spread(data, Period, ObsScaled)
data <- data[,-1] #drop the Key column
# remove columns with all NAs
data <- data[,!(colSums(data == "... ")==nrow(data))]
# dummy columns in to keep the pdf layout fixed to 6 columns
if (ncol(data)<=5){
for (j in (ncol(data)+1):7){
data[,j] <- "---"
names(data)[j] <- as.character(as.numeric(thisYear)-6+j)
}
}
# I have to add a dummy column so the alignment works (align)
data$dummy <- rep("",nrow(data))
# modify column names
names(data) <- c("",names(data)[2:(ncol(data)-1)],"")
# substitute NAs for "---" em-dash
data[is.na(data)] <- "---"
rowsSelect <- seq(1,nrow(data)-1,2)
col <- rep("\\rowcolor[gray]{0.95}", length(rowsSelect))
data.table <- xtable(data, digits=rep(1,ncol(data)+1)) #control decimals
align(data.table) <- c('l','>{\\raggedright}p{6in}','r',rep('>{\\raggedleft}p{0.8in}',ncol(data.table)-3),'l')
print(data.table, include.rownames=FALSE,include.colnames=TRUE, floating=FALSE,
size="\\Large",add.to.row = list(pos = as.list(rowsSelect), command = col),
booktabs = FALSE, table.placement="", hline.after = c(0) ,latex.environments = "center",
sanitize.text.function = function(x){x}) # include sanitize to control formats
}
## ---- line_chart ----
line_chart <- function(couName, section, table){
cou <- .getCountryCode(couName)
couRegion <- as.character(countries[countries$iso3==cou,]$region) # obtain the region for the selected country
data <- filter(Entrepr_data, region==couRegion, Section == section, Subsection == table, !(is.na(Observation))) #select country, region and world
#data <- merge(data, countries[,c("CountryCodeISO3","Country")],by.x = "CountryCode", by.y="CountryCodeISO3")
#data$Country <- gsub("(ES) ","",data$Country,fixed=TRUE)
income <- filter(Entrepr_data, region==couRegion & Section=="aux_income")
income <- income %>%
group_by(CountryCode) %>%
filter(!is.na(Observation), Period < thisYear, !(CountryCode==cou)) %>%
filter(Period == max(Period,na.rm=TRUE))
topNeighbors <- head(arrange(as.data.frame(income), desc(Observation)),15)$CountryCode
data <- filter(data, CountryCode %in% c(cou,topNeighbors)) %>%
arrange(CountryCode,Period)
# order lines in chart and hide elements in legend
if (nrow(filter(data,CountryCode==cou))>0){
order_legend <- c(couName,as.character(unique(data[data$CountryCode %in% topNeighbors,]$Country)))
country_order <- factor(order_legend, levels = c(couName,order_legend[2:length(order_legend)]))
my_order <- data.frame(Country = country_order, order = seq(1,length(order_legend),1))
data <- merge(data,my_order, by="Country") %>%
filter(order < 6) %>% # keep 5 countries
arrange(order,Period)
ggplot(data, aes(x=Period, y=Observation)) +
geom_line(stat="identity",aes(group=factor(order), colour=factor(order), size=factor(order), alpha=factor(order))) +
theme(legend.key=element_blank(),
legend.title=element_blank(),
legend.position="top",
legend.text = element_text(family="Times", size = 10, colour = "#818181"),
panel.border = element_blank(),
panel.background = element_blank(),plot.title = element_text(family="Times", lineheight=.5),
axis.line = element_line(size=0.1, colour = "lightgrey"),
axis.text.x = element_text(family="Times", color="#818181",hjust = 1),
axis.text.y = element_text(family="Times", color="#818181")) +
labs(x="",y=""#,title="Goods Export and Import volume growth, 2012-2015"
) +
scale_color_manual(labels = order_legend, values = c("orange","brown","lightblue","lightgreen","pink")) +
scale_alpha_manual(labels = order_legend,values = c(1, rep(0.6,4))) +
scale_size_manual(labels = order_legend,values = c(2, rep(1,4))) +
scale_x_discrete(breaks = unique(arrange(data,Period)$Period)[seq(1,length(unique(data$Period)),4)])
} else {
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1,"Data not available", col="lightgrey", cex=1.5)
}
}
## ---- table_time_avg ----
table_time_avg <- function(couName,section,table){
cou <- .getCountryCode(couName)
#table <- "table1"
tableKeys <- unique(filter(Entrepr_data, Section == section, Subsection==table)[,c("Key","IndicatorShort")])
data <- filter(Entrepr_data, CountryCode==cou, Section == section, Subsection==table)
data <- merge(tableKeys,select(data,-IndicatorShort),by="Key",all.x=TRUE)
if (sum(data$Observation,na.rm=TRUE)==0){ # in case this country has no data
data$Observation <- 0
data$Period <- as.numeric(thisYear)-1
# To create table's reference points in the LaTeX output
data_initial <- data
for (per in (as.numeric(thisYear)-7):(as.numeric(thisYear)-2)){
data_plus <- mutate(data_initial,Period = per)
data <- bind_rows(data, data_plus)
}
data$Period <- as.character(data$Period)
}
# keep the latest period (excluding projections further than 2 years)
data <- mutate(data, Period = ifelse(is.na(Period),max(as.numeric(Period),na.rm=TRUE),Period)) %>%
filter(Period <= (as.numeric(thisYear))) %>%
# remove NAs rows
# calculate average for 1st column
mutate(Unit = ifelse(grepl("Active population",Unit),"% of TEA",Unit),
IndicatorShort = paste0(IndicatorShort,", ",Unit))
data$IndicatorShort <- gsub("Entrepreneurial","Entrepr.", data$IndicatorShort)
data$IndicatorShort <- gsub("auditors","audit.", data$IndicatorShort)
data_avg <- data %>%
group_by(Key) %>%
filter(Period < (as.numeric(thisYear)-5) & Period > (as.numeric(thisYear)-15)) %>%
mutate(historical_avg = mean(Observation,na.rm=TRUE))
# add average as one of the time periods
min_year <- min(data_avg$Period,na.rm=TRUE)
max_year <- max(data_avg$Period,na.rm=TRUE)
data_avg <- mutate(data_avg, Period = paste("Avg ",min_year,"-",max_year,sep=""),
Observation = historical_avg, ObsScaled = historical_avg)
data_avg <- data_avg[!duplicated(data_avg$Key),] # remove duplicates
data_avg <- select(data_avg, -historical_avg, -ObsScaled) # remove some variables
#keep only periods of interest in data
data <- mutate(data, Period = ifelse(Period==thisYear & is.na(CountryCode),as.numeric(thisYear)-1,Period)) %>%
filter(Period > (as.numeric(thisYear) - 7) & Period < (as.numeric(thisYear)))
data <- bind_rows(data, data_avg) %>% # add rows to data
#data <- as.data.frame(data)
# Scale Observations
mutate(ObsScaled = Observation) %>%
arrange(Key) %>%
select(Key, IndicatorShort, Period, ObsScaled)
# restrict to 2 decimal places
data$ObsScaled <- round(data$ObsScaled,2)
# format numbers
data$ObsScaled <- format(data$ObsScaled, digits=2, decimal.mark=".",
big.mark=",",small.mark=".", small.interval=3)
# escape reserved characters
data$IndicatorShort <- gsub("%", "\\%", data$IndicatorShort, fixed=TRUE)
data$IndicatorShort <- gsub("&", "\\&", data$IndicatorShort, fixed=TRUE)
data <- distinct(data, Key,Period, .keep_all = TRUE)
# final table format
data <- spread(data, Period, ObsScaled)
data <- data[,-1] #drop the Key column
if (ncol(data)>2){
data <- data[,c(1,ncol(data),2:(ncol(data)-1))] # reorder columns
# rid of characters in numeric columns
data[,ncol(data)] <- gsub("NA", "---", data[,ncol(data)], fixed=TRUE)
}
# dummy columns in to keep the pdf layout
if (ncol(data)<=6){
for (j in (ncol(data)+1):7){
data[,j] <- "---"
names(data)[j] <- as.character(as.numeric(thisYear)-6+j)
}
}
# I have to add a dummy column so the alignment works (align)
data$dummy <- rep("",nrow(data))
# modify column names
names(data) <- c("",names(data)[2:(ncol(data)-1)],"")
# substitute NAs for "---" em-dash
data[is.na(data)] <- "---"
#if (round(nrow(data)/2,0)>nrow(data)/2){ # odd number
rowsSelect <- seq(1,nrow(data)-1,2)
#} else{ # even
# rowsSelect <- seq(1,nrow(data)-1,2)
#}
if (section %in% c("Culture","Supports")){
col <- rep("\\rowcolor{white}", length(rowsSelect))
} else {
col <- rep("\\rowcolor[gray]{0.95}", length(rowsSelect))
}
data.table <- xtable(data, digits=rep(1,ncol(data)+1)) #control decimals
align(data.table) <- c('l','>{\\raggedright}p{6in}','r',rep('>{\\raggedleft}p{0.8in}',ncol(data.table)-3),'l')
print(data.table, include.rownames=FALSE,include.colnames=TRUE, floating=FALSE,
size="\\Large",add.to.row = list(pos = as.list(rowsSelect), command = col),
booktabs = FALSE, table.placement="", hline.after = c(0) ,latex.environments = "center",
sanitize.text.function = function(x){x}) # include sanitize to control formats
}
## ---- sparklines ----
sparklines <- function(couName,section,table){
cou <- .getCountryCode(couName)
#table <- "table1"
tableKeys <- unique(filter(Entrepr_data, Section == section, Subsection==table)[,c("Key","IndicatorShort")])
data <- filter(Entrepr_data, CountryCode==cou, Section == section, Subsection==table)
data <- merge(tableKeys,select(data,-IndicatorShort),by="Key",all.x=TRUE)
if (sum(data$Observation,na.rm=TRUE)==0){ # in case this country has no data
data$Observation <- 0
data$Period <- as.numeric(thisYear)-1
# To create table's reference points in the LaTeX output
data_initial <- data
for (per in (as.numeric(thisYear)-7):(as.numeric(thisYear)-2)){
data_plus <- mutate(data_initial,Period = per)
data <- bind_rows(data, data_plus)
}
data$Period <- as.character(data$Period)
}
if (nrow(data)>0){
data <- merge(tableKeys,select(data,-IndicatorShort),by="Key",all.x=TRUE)
data <- mutate(data, Period = ifelse(is.na(Period),max(as.numeric(Period),na.rm=TRUE),Period)) %>%
filter(Period <= (as.numeric(thisYear))) %>%
mutate(Period = ifelse(Period < 1900, 1900, Period))
# keep the latest period (excluding projections further than 2 years)
data <- data %>%
mutate(Period = ifelse(Period==thisYear & is.na(CountryCode),as.numeric(thisYear)-1,Period)) %>%
filter(Period > (as.numeric(thisYear) - 7) & Period < (as.numeric(thisYear))) %>%
mutate(Period = ifelse(is.na(Period),max(as.numeric(Period),na.rm=TRUE),Period)) %>%
filter(Period <= (as.numeric(thisYear) - 1), Period > (as.numeric(thisYear) - 15)) %>%
select(Key, Period, Observation) %>%
arrange(Key, Period) %>%
distinct(Key,Period, .keep_all = TRUE)
minPeriod <- min(data$Period,na.rm = TRUE)
maxPeriod <- max(data$Period,na.rm = TRUE)
x <- spread(data, Key, Observation)
x <- x[,-1] # don't need Period column anymore
# impute NAs and standardize so all sparklines are scales
for (i in 1:ncol(x)){ # setup for statement to loop over all elements in a list or vector
x[is.na(x[,i]),i] <- mean(x[,i],na.rm = TRUE) #impute NAs to the mean of the column
if (sum(x[,i],na.rm = TRUE)==0){
x[,i] <- 0
#x[1,i] <- -10
x[nrow(x),i] <- 10
}
}
#x <- scale(x) # standardize x
par(family = 'serif',mfrow=c(ncol(x)+3,1), #sets number of rows in space to number of cols in data frame x
mar=c(1,0,0,0), #sets margin size for the figures
oma=c(1,2,1,1)) #sets outer margin
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.05, 1,minPeriod, col="darkgrey", cex=3)
graphics::text(1.95, 1,maxPeriod, col="darkgrey", cex=3)
for (i in 1:ncol(x)){ # setup for statement to loop over all elements in a list or vector
if (sum(x[1:(nrow(x)-1),i])==0){ # paint in white empty rows
plot(x[,i], #use col data, not rows from data frame x
col="lightgrey",lwd=4, #color the line and adjust width
axes=F,ylab="",xlab="",main="",type="l"); #suppress axes lines, set as line plot
axis(2,yaxp=c(min(x[,i],na.rm = TRUE),max(x[,i],na.rm = TRUE),2),col="lightgrey",tcl=0,labels=FALSE) #y-axis: put a 2nd white axis line over the 1st y-axis to make it invisible
ymin<-min(x[,i],na.rm = TRUE); tmin<-which.min(x[,i]);ymax<-max(x[,i], na.rm = TRUE);tmax<-which.max(x[,i]);
points(x=c(tmin,tmax),y=c(ymin,ymax),pch=19,col=c("lightgrey","lightgrey"),cex=5) # add coloured points at max and min#
} else {
plot(x[,i], #use col data, not rows from data frame x
col="darkgrey",lwd=4, #color the line and adjust width
axes=F,ylab="",xlab="",main="",type="l"); #suppress axes lines, set as line plot
axis(2,yaxp=c(min(x[,i],na.rm = TRUE),max(x[,i],na.rm = TRUE),2),col="lightgrey",tcl=0,labels=FALSE) #y-axis: put a 2nd white axis line over the 1st y-axis to make it invisible
ymin<-min(x[,i],na.rm = TRUE); tmin<-which.min(x[,i]);ymax<-max(x[,i], na.rm = TRUE);tmax<-which.max(x[,i]); #
points(x=c(tmin,tmax),y=c(ymin,ymax),pch=19,col=c("red","green"),cex=5) # add coloured points at max and min
}
}
} else {
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1,"Data not available", col="lightgrey", cex=1)
}
}
## ---- bar_chart ----
bar_chart <- function(couName,section,table,paste_unit){
cou <- .getCountryCode(couName)
data <- filter(Entrepr_data, CountryCode==cou, Section==section, Subsection %in% table)
data <- data %>%
filter(!(is.na(Observation))) %>%
distinct(Key,Period,.keep_all=TRUE)
maxPeriod <- filter(data, Period == max(Period,na.rm=TRUE))$Period[1]
#couRegion <- as.character(countries[countries$CountryCodeISO3==cou,]$RegionCodeES) # obtain the region for the selected country
#data <- filter(Entrepr_data, CountryCode %in% c(cou,couRegion, "RWe"), Category=="Policy", Subsection=="bar1") #select country, region and world
# country, Region, World descriptors
#country <- as.character(countries[countries$CountryCodeISO3==cou,]$Country)
#region <- as.character(countries[countries$CountryCodeISO3==cou,]$Region)
#world <- "All Countries"
if (nrow(data)>0){
# order the factors
#data$IndicatorShort = factor(as.character(data$IndicatorShort),
# levels = data$IndicatorShort[order(data$Observation)])
if (!any(is.na(data$Period))){
data <- data %>%
group_by(Key) %>%
filter(Period == max(Period))
}
require(stringr) # to wrap label text
if (!paste_unit){ # should unit be included in indicator name
data <- mutate(data, Unit = ifelse(grepl("0-100",Unit),"100=full ownership allowed",Unit))
data <- mutate(data, IndicatorShort = str_wrap(paste0(IndicatorShort,", ",Unit," (",Period,")"), width = 30))
} else {
data <- mutate(data, IndicatorShort = str_wrap(paste0(IndicatorShort," (",Period,")"), width = 30))
}
if (section == "Markets"){
data_grey <- data.frame(IndicatorShort=data$IndicatorShort,Observation=rep(100,length(table)))
#data <- mutate(data, id = seq(1,nrow(data),1))
ggplot(NULL,aes(x=IndicatorShort,y=Observation)) +
geom_bar(data=data_grey,color="#f1f3f3",fill = "#f1f3f3",stat="identity") +
geom_bar(data=data,color="#22A6F5",fill="#22A6F5",stat="identity") +
geom_text(data=data, aes(label=round(Observation,1),y=ifelse(Observation<21,Observation + max(Observation)*.1,Observation - max(Observation)*.1)),
size=12,color=ifelse(data$Observation<21,"#22a6f5","white")) +
coord_flip()+
theme(legend.key=element_blank(),
legend.title=element_blank(),
legend.position='none',
panel.border = element_blank(),
panel.background = element_blank(),plot.title = element_text(family="Times", lineheight=.5),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(family="Times", size = 20),
axis.text = element_text(family="Times", color = "#818181")) +
labs(x="",y=""#,title="Top 5 constraints according to 2013 Enterprise Survey (in percent)"
)
} else {
ggplot(NULL,aes(x=IndicatorShort,y=Observation)) +
geom_bar(data=data,color="#22A6F5",fill="#22A6F5",stat="identity") +
geom_text(data=data, aes(label=round(Observation,1),y=ifelse(Observation<14,Observation + max(Observation)*.1,Observation - max(Observation)*.1)),
size=6,color=ifelse(data$Observation<14,"#22a6f5","white")) +
coord_flip()+
theme(legend.key=element_blank(),
legend.title=element_blank(),
legend.position='none',
panel.border = element_blank(),
panel.background = element_blank(),plot.title = element_text(family="Times", lineheight=.5),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(family="Times", size = 15)) +
labs(x="",y=""#,title="Top 5 constraints according to 2013 Enterprise Survey (in percent)"
)
}
} else {
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1,"Data not available", col="lightgrey", cex=1.5)
}
}
## ---- number_chart ----
number_chart <- function(couName,section,table,str_wrap_size){
cou <- .getCountryCode(couName)
data <- filter(Entrepr_data, CountryCode==cou, Section==section, Subsection %in% table)
data <- data %>%
filter(!(is.na(Observation))) %>%
distinct(Key,Period,.keep_all=TRUE)
# data
#dataPoints <- format(data$Observation, digits=2, decimal.mark=".",
# big.mark=",",small.mark=".", small.interval=3)
# period
#dataPeriods <- data$Period
dataWorld <- filter(Entrepr_data, Section==section, Subsection %in% table)
dataWorld <- filter(dataWorld,!is.na(Observation))
dataWorld <- dataWorld %>%
group_by(Country,Key) %>%
mutate(Period = max(Period,na.rm=TRUE)) %>%
distinct(Key, Period, .keep_all = TRUE) %>%
as.data.frame()
#dataWorld <- merge(dataWorld,countries[,c("CountryCodeISO2","CountryAlternat")],by.x="iso2c",by.y="CountryCodeISO2",all.x = TRUE)
#dataWorld <- filter(dataWorld, !(CountryAlternat==""))
dataWorld <- dataWorld %>%
group_by(Key) %>%
mutate(Observation = round(Observation,1)) %>%
arrange(desc(Observation)) %>%
as.data.frame()
if (nrow(data)>0){
require(stringr) # to wrap label text
# Print the combo -----------------------------------------------
par(family = 'serif',mfrow=c(length(unique(dataWorld$Key)),2), #sets number of rows in space to number of cols in data frame x
mar=c(0,2,0,2), #sets margin size for the figures
oma=c(0,1,0,1)) #sets outer margin
i <- 1
rankedTotal <- c()
rank <- c()
for (ind in unique(dataWorld$Key)){
thisKey <- filter(dataWorld, Key == ind)
thisKey <- mutate(thisKey, Unit = ifelse(grepl("0-100",Unit),"100=full ownership allowed",Unit))
thisKey <- mutate(thisKey, IndicatorShort = str_wrap(paste0(IndicatorShort), width = str_wrap_size))
rankedTotal[i] <- nrow(thisKey)
if (nrow(filter(thisKey, CountryCode == cou))>0){# country has data for this indicator
rank[i] <- which(thisKey$CountryCode == cou)
# print indicator name
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1, 1.1,thisKey$IndicatorShort[1], col="#22a6f5", cex=3, adj=0)
graphics::text(1, 0.75,paste0(thisKey$Unit[1], " (",thisKey$Period[1],")"), col="#818181", cex=2, adj = 0)
# print data point and rank
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.17, 1,filter(thisKey,CountryCode==cou)$Observation , col="#22a6f5", cex=8)
graphics::text(1.42, 0.95,paste0("(Rank: ",rank[i],"/",rankedTotal[i],")"), col="grey", cex=3, adj=0)
} else { # no data for this indicator
# print indicator name
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1, 1.1,thisKey$IndicatorShort[1], col="#22a6f5", cex=3, adj=0)
graphics::text(1, 0.75,paste0(thisKey$Unit[1], " (",thisKey$Period[1],")"), col="#818181", cex=2, adj = 0)
# print data point and rank
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.17, 1," " , col="#22a6f5", cex=8)
graphics::text(1.42, 0.95,paste0("(Rank: /",rankedTotal[i],")"), col="grey", cex=3, adj=0)
}
i <- i + 1
}
} else {
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1,"Data not available", col="lightgrey", cex=1.5)
}
}
## ---- bar_facewrap_chart ----
bar_facewrap_chart <- function(couName, section, table){
cou <- .getCountryCode(couName) # This chart needs to query neighbouring countries also
couRegion <- countries[countries$iso3==cou,]$region # obtain the region for the selected country
neighbors <- countries[countries$region==couRegion,]$iso3 # retrieve all countries in that region
neighbors <- as.character(neighbors[!(neighbors==cou)]) # exclude the selected country
# hardcode for now
#neighbors <- c("DZA","JOR","MAR","EGY","TUN")
data <- Entrepr_data %>%
filter(CountryCode %in% c(cou,neighbors), Section==section, Subsection==table) %>%
mutate(Period = ifelse(is.na(Period),as.character(as.numeric(thisYear)-1),Period))
if (nrow(filter(data, CountryCode==cou))>0){
#data <- merge(data, countries[,c("Country","CountryCodeISO3")], by.x="CountryCode", by.y="CountryCodeISO3") # add country name
data <- data %>%
filter(!is.na(Observation)) %>%
group_by(Key,Country) %>%
filter(Period == max(Period,na.rm=TRUE)) %>%
distinct(Key,CountryCode, .keep_all = TRUE)
# select top 4 countries from the neighborhood based on their income level
income <- filter(Entrepr_data, CountryCode %in% neighbors & Section=="aux_income")
income <- income %>%
group_by(CountryCode) %>%
filter(!is.na(Observation), Period < thisYear) %>%
filter(Period == max(Period,na.rm=TRUE))
topNeighbors <- head(arrange(as.data.frame(income), desc(Observation)),15)$CountryCode
data <- filter(data, CountryCode %in% c(cou,topNeighbors))
data$IndicatorShort <- gsub(" Index","",data$IndicatorShort)
#data <- group_by(Key,Country) %>%
# filter(Period == max(Period))
order_legend <- c(couName,as.character(unique(data[data$CountryCode %in% topNeighbors,]$Country)))
country_order <- factor(order_legend, levels = c(couName,order_legend[2:length(order_legend)]))
my_order <- data.frame(Country = country_order, order = seq(1,length(order_legend),1))
data <- merge(data,my_order, by="Country") %>%
arrange(order)
#filter(order < 10) #keep 5 countries to compare
# order the factors
# data$Country = factor(as.character(data$Country),
# levels = c(as.character(unique(data[data$CountryCode %in% topNeighbors,]$Country)),
# unique(as.character(data[data$CountryCode==cou,]$Country))))
# order_legend <- c(couName,as.character(unique(data[data$CountryCode %in% topNeighbors,]$Country)))
#
require(stringr) # to wrap label text
#data <- mutate(data, Country = str_wrap(Country, width = 15))
if (section == "Policy"){
#maxPeriod_thisCou <- filter(data, CountryCode==cou)$Period[1]
data <- data %>%
group_by(Key) %>%
filter(Period == max(Period,na.rm=TRUE)) %>%
mutate(IndicatorShort = str_wrap(paste0(IndicatorShort," (",Period,")"), width = 20)) %>%
filter(order < 6) %>%
as.data.frame()
ggplot(data, aes(x=reorder(Country,order),y=Observation,fill=reorder(Country,order),alpha=reorder(Country,order))) +
geom_bar(position="dodge",stat="identity") +
#coord_flip()+
facet_wrap(~IndicatorShort,scales="free_y") +
theme(strip.text.x = element_text(family="Times", size = 12, colour = "white"),
strip.background = element_rect(colour = "#22A6F5", fill = "#22A6F5"),
legend.key=element_blank(),
legend.title=element_blank(),
legend.text = element_text(family="Times", size = 10, colour = "#818181"),
panel.border = element_blank(),
panel.background = element_blank(),plot.title = element_text(family="Times", lineheight=.5),
#axis.ticks.y = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(family="Times", color="#818181")
) +
labs(x="",y="")+#,title="World Governance Indicators")+
scale_fill_manual(breaks = order_legend,values = c("orange","brown","lightblue","lightgreen","pink")) +
scale_alpha_manual(labels = order_legend,values = c(1, rep(0.4,4)),guide=FALSE)
} else{
maxPeriod_thisCou <- filter(data, CountryCode==cou)$Period[1]
data <- data %>%
group_by(Key) %>%
filter(Period == max(Period,na.rm=TRUE)) %>%
mutate(IndicatorShort = str_wrap(paste0(IndicatorShort," (",Period,")"), width = 20)) %>%
filter(order < 6) %>%
as.data.frame()
ggplot(data, aes(x=reorder(Country,order),y=Observation,fill=reorder(Country,order),alpha=reorder(Country,order))) +
geom_bar(position="dodge",stat="identity") +
coord_flip() +
facet_wrap(~IndicatorShort) +
theme(strip.text.x = element_text(family="Times", size = 12, colour = "white"),
strip.background = element_rect(colour = "#22A6F5", fill = "#22A6F5"),
legend.key=element_blank(),
legend.title=element_blank(),
legend.text = element_text(family="Times", size = 10, colour = "#818181"),
panel.border = element_blank(),
panel.background = element_blank(),plot.title = element_text(family="Times", lineheight=.5),
#axis.ticks.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(family="Times", color="#818181")) +
labs(x="",y="")+#,title="World Governance Indicators")+
scale_fill_manual(breaks=order_legend,values = c("orange","brown","lightblue","lightgreen","pink")) +
scale_alpha_manual(labels = order_legend,values = c(1, rep(0.4,4)),guide=FALSE)
}
} else {
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1,"Data not available", col="lightgrey", cex=1.5)
}
}
## ---- radar_chart ----
radar_chart <- function(couName,section,table){
cou <- .getCountryCode(couName) # This chart needs to query neighbouring countries also
couRegion <- countries[countries$iso3==cou,]$region # obtain the region for the selected country
neighbors <- countries[countries$region==couRegion,]$iso3 # retrieve all countries in that region
neighbors <- as.character(neighbors[!(neighbors==cou)]) # exclude the selected country
# country and Region descriptors
country <- as.character(countries[countries$iso3==cou,]$Country)
region <- as.character(countries[countries$iso3==cou,]$region)
#country <- paste0(country," (Rank: ",round(filter(TCMN_data, CountryCode==cou, Key=="P00b")$Observation,1),")")
#region <- paste0(region," (Avg Rank: ",round(filter(TCMN_data, CountryCode==couRegion, Key=="P00b")$Observation,1),")")
# filter the data
data <- filter(Entrepr_data, CountryCode %in% c(cou,neighbors), Section == section, Subsection==table)
if (nrow(filter(data, CountryCode==cou))>0){
# calculate the average for the region
data <- data %>%
filter(!is.na(Observation)) %>%
group_by(Key) %>%
mutate(Observation = ifelse(Observation > 0 & Observation < 1, Observation*100,Observation)) %>%
filter(Period == max(Period)) %>%
mutate(regionAvg = mean(Observation, na.rm=TRUE)) %>%
filter(CountryCode==cou) %>%
as.data.frame()
# I must add the max and min columns to make it work:
max<-100
min <-1
data <- as.data.frame(data)
data <- cbind(data,max,min)
# order labels ad-hoc:
order <- c(1,3,4,6,2,5)
data <- cbind(data,order)
data <- arrange(data,order) %>%
select(IndicatorShort, max, min, Observation, regionAvg)
# transpose the data for radarchart to read
dataTrans <- as.data.frame(t(data[,2:ncol(data)]))
layout(matrix(c(1,2),ncol=1), heights =c(4,1))
# col.axis="red",col.lab=c("red","red"),col.main="red",col.sub="red",family="serif")
par(mar=c(0,1,3,1),family="serif")
radarchart(dataTrans, axistype=1, centerzero = FALSE,seg=4, caxislabels=c(" ","","50%","","100%"),
plty=c(1,1),plwd=c(6,3),pcol=c("orange","lightblue"),pdensity=c(0, 0),
cglwd=2,axislabcol="lightgrey", vlabels=data$IndicatorShort, cex.main=1,cex=2.5)
#title="WEF Competitiveness Indicators, stage of development (1-7)",
par(family = 'serif',mar=c(0,1,1,1))
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
legend(1,1.5, legend=c(couName,region), seg.len=0.5, pch=19, inset=50,
bty="n" ,lwd=3, x.intersp=0.5, horiz=TRUE, col=c("orange","lightblue"))
} else {
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1,"Data not available", col="lightgrey", cex=1.5)
}
}
## ---- combo_percent ----
combo_percent <- function(couName,section,table){
cou <- .getCountryCode(couName)
data <- filter(Entrepr_data, CountryCode==cou, Section == section, Subsection==table)
if (is.na(data$Period)) data$Period <- as.character(as.numeric(thisYear) - 1)
if (nrow(filter(data, CountryCode==cou))>0){
data <- filter(data, Period==max(Period))
data <- select(data, IndicatorShort, Observation)
pickColor <- ifelse(data$Observation > 50,"darkgreen","red")
pickMirror <- ifelse(data$Observation > 50,FALSE,TRUE)
data <- rbind(data, c(" ",0)) # add "Other" category
data$Observation <- round(as.numeric(data$Observation),2)
data$color <- c(pickColor,"lightgrey") # add the color
data[data$IndicatorShort==" ",]$Observation <- 100 - sum(data$Observation)
# format numbers
data$Observation <- format(data$Observation, digits=0, decimal.mark=".",
big.mark=",",small.mark=".", small.interval=3)
data$Observation <- as.numeric(data$Observation)
data <- data %>%
mutate(ObsLabel = paste0(Observation,"%")) %>%
arrange(desc(IndicatorShort))
data$ObsLabel[2] <- ""
treemap(data,
index=c("IndicatorShort","ObsLabel"),
vSize="Observation",
fontsize.labels=c(24, 64),
align.labels=list(c("left", "top"), c("center","center")),
lowerbound.cex.labels=0.5,
vColor="color",
type="color",
mirror.x = pickMirror,
title="")
} else {
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1,"Data not available", col="lightgrey", cex=1.5)
}
}
## ---- combo_rate ----
combo_rate <- function(couName){
cou <- .getCountryCode(couName)
data <- filter(Entrepr_data, CountryCode==cou, Section == "Human capital", Subsection=="combo1")
if (is.na(data$Period)) data$Period <- as.character(as.numeric(thisYear) - 1)
if (nrow(filter(data, CountryCode==cou))>0){
data <- filter(data, Period==max(Period))
data <- select(data, IndicatorShort, Observation)
pickColor <- ifelse(data$Observation >= 3.5,"green","red")
pickMirror <- ifelse(data$Observation >= 3.5,FALSE,TRUE)
data <- rbind(data, c(" ",0)) # add "Other" category
data$Observation <- round(as.numeric(data$Observation),2)
data$color <- c(pickColor,"lightgrey") # add the color
data[data$IndicatorShort==" ",]$Observation <- 7 - sum(data$Observation)
# format numbers
data$Observation <- format(data$Observation, digits=2, decimal.mark=".",
big.mark=",",small.mark=".", small.interval=3)
data$Observation <- as.numeric(data$Observation)
data <- data %>%
mutate(ObsLabel = as.character(Observation)) %>%
arrange(desc(IndicatorShort))
data$ObsLabel[2] <- ""
treemap(data,
index=c("IndicatorShort","ObsLabel"),
vSize="Observation",
fontsize.labels=c(24, 64),
align.labels=list(c("left", "top"), c("center","center")),
lowerbound.cex.labels=0.5,
vColor="color",
type="color",
mirror.x = pickMirror,
title="")
} else {
plot(c(1,1),type="n", frame.plot = FALSE, axes=FALSE, ann=FALSE)
graphics::text(1.5, 1,"Data not available", col="lightgrey", cex=1.5)
}
}
## ---- table_region ----
table_region <- function(couName,section,table){
cou <- .getCountryCode(couName)
data <- filter(Entrepr_data, CountryCode==cou, Section == section, Subsection==table) #select country, region and world
if (nrow(data[data$CountryCode==cou,])>0){
couRegion <- as.character(countries[countries$CountryCodeISO3==cou,]$RegionCodeES) # obtain the region for the selected country
data <- filter(Entrepr_data, CountryCode %in% c(cou,couRegion, "RWe"), Section == section, Subsection==table) #select country, region and world
# country, Region, World descriptors
country <- as.character(countries[countries$CountryCodeISO3==cou,]$Country)
region <- as.character(countries[countries$CountryCodeISO3==cou,]$Region)
world <- "All Countries"
neighbors <- data.frame(CountryCode=c(cou,couRegion,"RWe"),colName=c(country,region,world), stringsAsFactors = FALSE)
# remove NAs rows
#data <- filter(data, !is.na(Observation))
# keep the latest period (excluding projections further than 2 years)
data <- data %>%
group_by(Key,CountryCode) %>%
mutate(Period = max(Period,na.rm = TRUE))
# Scale Observations
data <- mutate(data, ObsScaled = Observation)
data <- arrange(data, Key)
#data <- select(data, Key, IndicatorShort, Period, ObsScaled)
# restrict to 2 decimal places
data$ObsScaled <- round(data$ObsScaled,2)
# format numbers
data$ObsScaled <- format(data$ObsScaled, digits=2, decimal.mark=".",
big.mark=",",small.mark=".", small.interval=3)
data <- distinct(data, Key, .keep_all = TRUE)
#data <- filter(data, Period <= (as.numeric(thisYear) + 1))
# prepare for table
data <- merge(data, neighbors, by="CountryCode")
data <- mutate(data, Unit = ifelse(grepl("population",Unit),"per 100 pop.",Unit),
IndicatorShort = paste0(IndicatorShort," (",Unit,")"))
data <- select(data, IndicatorShort, Observation, colName)
data$Observation <- format(data$Observation, digits=2, decimal.mark=".",
big.mark=",",small.mark=".", small.interval=3)
data <- spread(data, colName, Observation)
if (ncol(data)==4){
data <- data[,c(1,4,3,2)]
}
names(data)[1] <-""
#names(data) <- str_wrap(names(data),width=12)
# I have to add a dummy column so the alignment works (align)
data$dummy <- rep("",nrow(data))
names(data)[ncol(data)] <-""
# make sure there are always 3 rows on the table to avoid black stripes on following table
if (nrow(data)<3){
for (r in 1:(3-nrow(data))){
data <- rbind(data,c("",rep("",ncol(data))))
}
}
# substitute NAs for "---" em-dash
data[is.na(data)] <- "---"
if (nrow(data)>1){
rowsSelect <- seq(1,nrow(data)-1,2)
} else{
rowsSelect <- c(1)
}
col <- rep("\\rowcolor[gray]{0.95}", length(rowsSelect))
data.table <- xtable(data)
align(data.table) <- c('l','l',rep('>{\\raggedleft}p{1.2in}',(ncol(data)-2)),'l')