Tag Archives: women

Women in Orchestras

I believe in the truth of fairy-tales more than I believe in the truth in the newspaper (Lotte Reiniger)

In my opinion, this graph is a visual demonstration that we live in a male chauvinist world.

Orchestras2

In this experiment I download the members of ten top orchestras of the world with the amazing rvest package. After cleaning texts, I obtain the gender of names with genderizeR package as I did here. Since I only take into account names genderized with high probability, these numbers cannot be exact. Apart of this, the plot speaks by itself.

setwd("YOUR WORKING DIRECTORY HERE")
library(rvest)
library(dplyr)
library(genderizeR)
read_html("http://www.berliner-philharmoniker.de/en/orchestra/") %>%
html_nodes(".name") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("[\r,\n]"," ", .) %>%
gsub("\\s+", " ", .) %>%
paste(collapse=" ") %>%
findGivenNames() -> berliner
saveRDS(berliner, file="berliner.RDS")
read_html("https://www.concertgebouworkest.nl/en/musicians") %>%
html_nodes(".u-padding--b2") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("\\s+", " ", .) %>%
paste(collapse=" ") %>%
findGivenNames() -> rco
saveRDS(rco, file="rco.RDS")
read_html("http://www.philharmonia.spb.ru/en/about/orchestra/zkrasof/contents/") %>%
html_nodes(".td") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("[\r,\n]"," ", .) %>%
gsub("\\s+", " ", .) %>%
.[23] %>%
findGivenNames() -> spb
saveRDS(spb, file="spb.RDS")
read_html("http://ocne.mcu.es/conoce-a-la-ocne/orquesta-nacional-de-espana/componentes/") %>%
html_nodes(".col-main") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("[\r,\n]"," ", .) %>%
gsub("\\s+", " ", .) %>%
gsub("([[:lower:]])([[:upper:]][[:lower:]])", "\\1 \\2", .) %>%
findGivenNames() -> one
saveRDS(one, file="one.RDS")
read_html("http://www.gewandhausorchester.de/en/orchester/") %>%
html_nodes("#content") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("[\r,\n]"," ", .) %>%
gsub("\\s+", " ", .) %>%
findGivenNames() -> leipzig
saveRDS(leipzig, file="leipzig.RDS")
read_html("http://www.wienerphilharmoniker.at/orchestra/members") %>%
html_nodes(".ModSuiteMembersC") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("[\r,\n,\t,*]"," ", .) %>%
gsub("\\s+", " ", .) %>%
gsub("([[:lower:]])([[:upper:]][[:lower:]])", "\\1 \\2", .) %>%
paste(collapse=" ") %>%
.[-18] %>%
findGivenNames() -> wiener
saveRDS(wiener, file="wiener.RDS")
read_html("http://www.laphil.com/philpedia/orchestra-roster") %>%
html_nodes(".view-content") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("\\s+", " ", .) %>%
gsub("(?%
.[1] %>%
findGivenNames() -> laphil
saveRDS(laphil, file="laphil.RDS")
read_html("http://nyphil.org/about-us/meet/musicians-of-the-orchestra") %>%
html_nodes(".resp-tab-content-active") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("[\r,\n]"," ", .) %>%
gsub("\\s+", " ", .) %>%
gsub("(?%
findGivenNames() -> nyphil
saveRDS(nyphil, file="nyphil.RDS")
urls=c("http://lso.co.uk/orchestra/players/strings.html",
"http://lso.co.uk/orchestra/players/woodwind.html",
"http://lso.co.uk/orchestra/players/brass.html",
"http://lso.co.uk/orchestra/players/percussion-harps-and-keyboards.html")
sapply(urls, function(x)
{
read_html(x) %>%
html_nodes(".clearfix") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("[\r,\n,\t,*]"," ", .) %>%
gsub("\\s+", " ", .)
}) %>% paste(., collapse=" ") %>%
findGivenNames() -> lso
saveRDS(lso, file="lso.RDS")
read_html("http://www.osm.ca/en/discover-osm/orchestra/musicians-osm") %>%
html_nodes("#content-column") %>%
html_text(trim=TRUE) %>%
iconv("UTF-8") %>%
gsub("[\r,\n]"," ", .) %>%
gsub("\\s+", " ", .) %>%
findGivenNames() -> osm
saveRDS(osm, file="osm.RDS")
rbind(c("berliner", "Berliner Philharmoniker"),
c("rco", "Royal Concertgebouw Amsterdam"),
c("spb", "St. Petersburg Philharmonic Orchestra"),
c("one", "Orquesta Nacional de España"),
c("leipzig", "Gewandhaus Orchester Leipzig"),
c("wiener", "Wiener Philarmoniker"),
c("laphil", "The Los Angeles Philarmonic"),
c("nyphil", "New York Philarmonic"),
c("lso", "London Symphony Orchestra"),
c("osm", "Orchestre Symphonique de Montreal")) %>% as.data.frame()-> Orchestras
colnames(Orchestras)=c("Id", "Orchestra")
list.files(getwd(),pattern = ".RDS") %>%
lapply(function(x)
readRDS(x) %>% as.data.frame(stringsAsFactors = FALSE) %>% cbind(Id=gsub(".RDS", "", x))
) %>% do.call("rbind", .) -> all
all %>% mutate(probability=as.numeric(probability)) %>%
filter(probability > 0.9 & count > 15) %>%
filter(!name %in% c("viola", "tuba", "harp")) %>%
group_by(Id, gender) %>%
summarize(Total=n())->all
all %>% filter(gender=="female") %>% mutate(females=Total) %>% select(Id, females) -> females
all %>% group_by(Id) %>% summarise(Total=sum(Total)) -> total
inner_join(total, females, by = "Id") %>% mutate(po_females=females/Total) %>%
inner_join(Orchestras, by="Id")-> df
library(ggplot2)
library(scales)
opts=theme(legend.position="none",
plot.background = element_rect(fill="gray85"),
panel.background = element_rect(fill="gray85"),
panel.grid.major.y=element_blank(),
panel.grid.major.x=element_line(colour="white", size=2),
panel.grid.minor=element_blank(),
axis.title = element_blank(),
axis.line.y = element_line(size = 2, color="black"),
axis.text = element_text(colour="black", size=18),
axis.ticks=element_blank(),
plot.title = element_text(size = 35, face="bold", margin=margin(10,0,10,0), hjust=0))
ggplot(df, aes(reorder(Orchestra, po_females), po_females)) +
geom_bar(stat="identity", fill="darkviolet", width=.5)+
scale_y_continuous(labels = percent, expand = c(0, 0), limits=c(0,.52))+
geom_text(aes(label=sprintf("%1.0f%%", 100*po_females)), hjust=-0.05, size=6)+
ggtitle(expression(atop(bold("Women in Orchestras"), atop("% of women among members", "")))) +
coord_flip()+opts

The World We Live In #4: Marriage Ages

It is time for women to stop being politely angry (Leymah Gbowee, Nobel Prize Peace Winner)

Sometimes very simple plots give insight into we live in a world of differences. This plot shows the mean age at marriage for men and women across countries:

Marriage Ages

Being a woman in some countries of this world must be a hard experience:

#Singulate mean age at marriage: http://data.un.org/Data.aspx?d=GenderStat&f=inID%3a20
#Population: http://data.un.org/Data.aspx?d=SOWC&f=inID%3a105
require("sqldf")
require("ggplot2")
setwd("YOUR WORKING DIRECTORY HERE")
mar=read.csv("UNdata_Export_20150309_171525152.csv", nrows=321, header=T, row.names=NULL)
pop=read.csv("UNdata_Export_20150309_172046384.csv", nrows=999, header=T, row.names=NULL)
colnames(mar)[1]="Country"
colnames(pop)[1]="Country"
data=sqldf("SELECT
  a.Country,
  a.Value as Pop,
  b.Value as Female,
  c.Value as Male
FROM
  pop a INNER JOIN mar b
  ON (a.Country=b.Country AND b.Subgroup='Female') INNER JOIN mar c
  ON (a.Country=c.Country AND c.Subgroup='Male')
WHERE a.Subgroup = 'Total'")
opts=theme(
  panel.background = element_rect(fill="gray98"),
  panel.border = element_rect(colour="black", fill=NA),
  axis.line = element_line(size = 0.5, colour = "black"),
  axis.ticks = element_line(colour="black"),
  panel.grid.major = element_line(colour="gray75", linetype = 2),
  panel.grid.minor = element_blank(),
  axis.text = element_text(colour="gray25", size=15),
  axis.title = element_text(size=18, colour="gray10"),
  legend.key = element_blank(),
  legend.position = "none",
  legend.background = element_blank(),
  plot.title = element_text(size = 40, colour="gray10"))
ggplot(data, aes(x=Female, y=Male, size=log(Pop), label=Country), guide=FALSE)+
  geom_point(colour="white", fill="chartreuse3", shape=21, alpha=.55)+
  scale_size_continuous(range=c(2,36))+
  scale_x_continuous(limits=c(16,36), breaks=seq(16, 36, by = 2), expand = c(0, 0))+
  scale_y_continuous(limits=c(16,36), breaks=seq(16, 36, by = 2), expand = c(0, 0))+
  geom_abline(intercept = 0, slope = 1, colour = "gray10", linetype=2)+
  labs(title="The World We Live In #4: Marriage Ages",
       x="Females mean age at marriage",
       y="Males mean age at marriage")+
  geom_text(data=subset(data, abs(Female-Male)>7), size=5.5, colour="gray25", hjust=0, vjust=0)+
  geom_text(data=subset(data, Female>=32|Female<=18), size=5.5, colour="gray25", hjust=0, vjust=0)+
  geom_text(aes(24, 17), colour="gray25", hjust=0, label="Source: United Nations (size of bubble depending on population)", size=5)+opts