######################################################## # Sedef Bicer, University of Zurich # DDJ FS_2018 # Text-Analysen # Graph for speeches (Parlamentsreden) ######################################################### ##### combine the subset of all speeches of all the parties and create the plot # which shows the appearance of the islam related terms in time #clear the workspace rm(list=ls(all=TRUE)) # clear working space ## Loading the data setwd("/Users/**/Datenjournalismus_FS18") # define working directory ### Analyse Speeches #SVP load("svp1_speech.Rda") load("svp2_speech.Rda") load("svp3_speech.Rda") load("svp4_speech.Rda") #SP load("sp1_speech.Rda") load("sp2_speech.Rda") load("sp3_speech.Rda") load("sp4_speech.Rda") #CVP load("cvp1_speech.Rda") load("cvp2_speech.Rda") load("cvp3_speech.Rda") load("cvp4_speech.Rda") #FDP load("fdp1_speech.Rda") load("fdp2_speech.Rda") load("fdp3_speech.Rda") load("fdp4_speech.Rda") #GPS load("gps1_speech.Rda") load("gps2_speech.Rda") load("gps3_speech.Rda") load("gps4_speech.Rda") # combine the dataframes to one dataframe df_sum_date_speeches <- rbind(df_sum_date_svp1, df_sum_date_svp2, df_sum_date_svp3, df_sum_date_svp4, df_sum_date_sp1, df_sum_date_sp2, df_sum_date_sp3, df_sum_date_sp4, df_sum_date_cvp1, df_sum_date_cvp2, df_sum_date_cvp3, df_sum_date_cvp4, df_sum_date_fdp1, df_sum_date_fdp2, df_sum_date_fdp3, df_sum_date_fdp4, df_sum_date_gps1, df_sum_date_gps2, df_sum_date_gps3, df_sum_date_gps4) ##plot graph ggplot(df_sum_date_speeches, aes(dateString, partei, colour=partei)) + geom_point(aes(size=nUsed), alpha = 0.38) + xlab(label='Datum') + ylab(label = "Partei") + theme_bw() + scale_colour_manual(values = c("SVP" = "darkgreen", "CVP" = "orange", "GPS" = "green", "SP" = "red", "FDP" = "blue")) + scale_size(range = c(2, 15)) + ggtitle("Nennung von Begriffen mit Islambezug in Parlamentsreden") + theme(plot.title = element_text(size=18, face="bold")) + labs(colour="Partei", size="Anzahl Nennungen") ##Zahlen von Interesse #Anzahl Nennungen over all sum(df_sum_date_speeches$nUsed) #Anzahl Reden in welchem der Islam genannt wurde sum(df_sum_date_speeches$Islammeldung) summary(df_sum_date_speeches) ##### combine the subset of all medienmitteilungen of all the parties and create the plot # which shows the appearance of the islam related terms in time ##loading data #SVP load("svp1_medien.Rda") #SP load("sp1_medien.Rda") #CVP load("cvp1_medien.Rda") #FDP load("fdp1_medien.Rda") #GPS load("gps1_medien.Rda") # combine the dataframes to one dataframe df_sum_date <- rbind(df_sum_date_svp, df_sum_date_cvp, df_sum_date_gps, df_sum_date_sps, df_sum_date_fdp) #combine data.frame of different data.frames df_sum_date$partei[df_sum_date$partei=="SPS"] <- "SP" ## plot graph ggplot(df_sum_date, aes(dateString, partei, colour=partei)) + geom_point(aes(size=nUsed), alpha = 0.38) + xlab(label='Datum') + ylab(label = "Partei") + theme_bw() + scale_colour_manual(values = c("SVP" = "darkgreen", "CVP" = "orange", "GPS" = "green", "SP" = "red", "FDP" = "blue")) + scale_size(range = c(3, 10)) + ggtitle("Nennung von Begriffen mit Islambezug in Partei-Medienmitteilungen") + theme(plot.title = element_text(size=18, face="bold")) + labs(colour="Partei", size="Anzahl Nennungen")