How the authors in my feed are divided along the lines of gender, race, and whether they are an ECR or not

Demographics of the feed

Gender

#Create a string called "BlogName" with all the names of the different blogs in it
BlogName<-c("Brown", "Coyne", "Allen", "Neurobonkers", "Sakaluk", "Heino", "Kruschke", "Giner-Sorolla", "Magnusson", "Zwaan", "CogTales", "Campbell", "Vanderkerckhove", "Mayo", "Funder", "Schonbrodt", "Fried", "Coyne", "Yarkoni", "Neuroskeptic", "JEPS", "Morey", "PsychBrief", "DataColada", "Innes-Ker", "Schwarzkopf", "PIG-E", "Rousselet", "Gelman", "Bishop", "Srivastava", "Vazire", "Etz", "Bastian", "Zee", "Schimmack", "Hilgard", "Rouder", "Lakens")
#Create a vector called "BlogGender" with a string of numbers to represent either female, male, or N/a
BlogGender<-c(2,2,2,2,2,2,2,2,2,2,1,1,2,2,1,2,2,2,2,2,2,3,2,1,2,2,2,1,2,2,2,2,1,2,1,2,1,2,2,2,2,2)
#Turn BlogGender into a factor where 1 is labelled Female, 2 male, and 3 N/a
BlogGender<-factor(BlogGender, levels= c(1:3), labels =c("Female","Male", "N/a"))
#Create a data frame of the variable BlogName by the variable BlogGender
Blogs<-data.frame(Name=BlogName, Gender=BlogGender)
#Because I'm a peasant and can't work out how to create a graph straight from the data frame I created (though #I'm pretty sure I can't in its current form and don't know how to transform it into something that can be #mapped to a graph) I created one vector and one string with the number of male and female blog authors after #counting them up
Gender<-c("Female", "Male")
Frequency<-c(7,34)
#Data frame of the vector and string
Blogsdata<-data.frame(Gender,Frequency)
#Graph object of the data frame with gender as the x axis and frequency as the y, coloured according to the variable Gender
Gender_Graph<-ggplot(Blogsdata, aes(Gender, Frequency, fill=Gender))
#Put bars on my graph object and give it a title
Gender_Graph+geom_bar(stat="identity")+ ggtitle("Number of female blog authors compared to male blog authors")

Ethnicity
BlogName<-c("Brown", "Coyne", "Allen", "Neurobonkers", "Sakaluk", "Heino", "Kruschke", "Giner-Sorolla", "Magnusson", "Zwaan", "CogTales", "Campbell", "Vanderkerckhove", "Mayo", "Funder", "Schonbrodt", "Fried", "Coyne", "Yarkoni", "Neuroskeptic", "JEPS", "Morey", "PsychBrief", "DataColada", "Innes-Ker", "Schwarzkopf", "PIG-E", "Rousselet", "Gelman", "Bishop", "Srivastava", "Vazire", "Etz", "Bastian", "Zee", "Schimmack", "Hilgard", "Rouder", "Lakens")
Ethnlist<-c(1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1)
BlogEthn<-factor(Ethnlist, levels= c(1:3), labels =c("White","Non-white", "N/a"))
Ethn<-c("White", "Non-white")
Frequency<-c(39, 2)
Ethndata<-data.frame(Ethn,Frequency)
EthnGraph<-ggplot(Ethndata, aes(Ethn, Frequency, fill=Ethn))
EthnGraph+geom_bar(stat="identity")+ ggtitle("Number of non-white blog authors compared to white blog authors")

ECR

BlogName<-c("Brown", "Coyne", "Allen", "Neurobonkers", "Sakaluk", "Heino", "Kruschke", "Giner-Sorolla", "Magnusson", "Zwaan", "CogTales", "Campbell", "Vanderkerckhove", "Mayo", "Funder", "Schonbrodt", "Fried", "Coyne", "Yarkoni", "Neuroskeptic", "JEPS", "Morey", "PsychBrief", "DataColada", "Innes-Ker", "Schwarzkopf", "PIG-E", "Rousselet", "Gelman", "Bishop", "Srivastava", "Vazire", "Etz", "Bastian", "Zee", "Schimmack", "Hilgard", "Rouder", "Lakens")
ECRlist<-c(1,2,2,3,1,1,2,2,1,2,1,1,2,2,2,2,2,2,2,2,2,3,2,1,2,2,2,2,2,2,2,2,2,2,2,1,2,1,2,2,2,2)
BlogECR<-factor(ECRlist, levels= c(1:3), labels =c("Yes","No", "N/a"))
ECR<-c("Yes", "No")
Frequency<-c(9, 31)
ECRdata<-data.frame(ECR,Frequency)
ECRGraph<-ggplot(ECRdata, aes(ECR, Frequency, fill=ECR))
ECRGraph+geom_bar(stat="identity")+ ggtitle("Number of non-ECR blog authors compared to ECR blog authors")