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plots7.R
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plots7.R
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#getting ready.
original_wd<-getwd()
setwd("C:/Google Drive/work/new-spirit/Artists")
Sys.setlocale("LC_ALL", "Hebrew")
library(xlsx)
library(dplyr)
library(tidyr)
library(ggplot2)
library(grid)
options(stringsAsFactors = F)
#reading working file and metainfo files:
dataset <- readRDS(file="./data-Artists/working_file")
headings<- read.xlsx("./data-Artists/QP_Nov_2016/headings.xlsx",sheetIndex = 1, encoding="UTF-8",header = T,as.data.frame = T)
factor_labels<-read.xlsx("./data-Artists/QP_Nov_2016/factor_labels.xlsx",sheetIndex = 1,encoding = "UTF-8",colClasses = rep("character",4))
#filtering the dataset accordign to the diff plots:
dataset<-dataset %>% filter(max_point %in% c(2,3,-99)) #delete very short responses
dataset<-dataset %>% filter(lived_in_past %in% c(2,3)) %>%
mutate_each(funs("is.na<-"),group:comments) %>%
bind_rows(filter(dataset,!lived_in_past %in% c(2,3) |is.na(lived_in_past))) #turning the data of the women who left the city to NA
#plotting questions with only 1's
#making a table with the correct number of n for each question
n_data<-dataset %>% select(privious_interaction,place,visit_us,activity1) %>%
gather(q_name,value) %>%
count(q_name,value) %>%
filter(!is.na(value)) %>%
filter((q_name=="place" & value!="1") |
(q_name=="activity1") |
(q_name=="privious_interaction" & value!="1") |
(q_name=="visit_us" & value=="1")) %>%
group_by(q_name) %>% summarise(total_n=sum(n))
#preperaing data,
sum_data<-dataset %>% select(c(activity1:which_project5, reasons_leaving1:reasons_leaving5,when_visit1:when_visit5)) %>%
mutate_all(funs(as.numeric(.))) %>%
gather(q_name,value) %>%
filter(!is.na(value)) %>%
group_by(q_name) %>%
summarise(sum_value=sum(na.omit(value))) %>% ungroup() %>%
mutate(total_n=ifelse(grepl("reasons",q_name),
n_data[match("place",n_data$q_name),2],
ifelse( grepl("activity",q_name),
n_data[match("privious_interaction",n_data$q_name),2],
ifelse( grepl("when",q_name),
n_data[match("visit_us",n_data$q_name),2],
ifelse(grepl("project",q_name),
n_data[match("activity1",n_data$q_name),2],
"could not find"))))) %>%
mutate(total_n=as.numeric(total_n)) %>%
mutate(pct=sum_value/total_n) %>%
left_join(headings[,3:4],by=c("q_name"="new_var_name")) %>%
mutate(label_with_n=paste(" (",sum_value,") ", "\n",new_label_hebrew,sep=""))%>%
select(q_name,label_with_n,sum_value,total_n,everything())
#adding n's to the labels
#prepearin facorial data for plotting
f_index<-c("place","lived_in_past","education",
"privious_interaction","basic_familarity","close_relations",
"prof_relations", "cross_group1","cross_group2","cross_group3","cross_group4",
"stay_in_town","org_activities","visit_us","make_project")
f_data<- dataset %>%
select(one_of(f_index)) %>%
mutate(place_new=ifelse(place==1,1,2)) %>%
gather(q_name,level) %>% filter(!is.na(level)) %>%
count(q_name,level) %>%
group_by(q_name) %>% mutate(total_n=sum(as.numeric(n),na.rm=T),
pct=n/total_n) %>% ungroup() %>%
left_join(factor_labels[,c(1,2:3)], by = c("q_name" = "new_var_name", "level" = "q_level")) %>%
mutate(label_with_n=paste("(",n,") ",q_label,sep="")) #adding n's to the labels
plots_index<-read.xlsx("./data-Artists/QP_Nov_2016/plots_index.xlsx",
sheetIndex = 1, encoding="UTF-8",header = T,as.data.frame = T)
source("C:/Users/nogka/Documents/R/FAN_n.bar.R")
for (i in seq_along(plots_index$plot_data)) {
g_names<-paste("g_",1:nrow(plots_index),sep="")
g_temp<-n.bar(data=get(plots_index[i,"plot_data"]),
x_val=plots_index[i,"x_values"],
y_val=plots_index[i,"y_values"],
filter_by = plots_index[i,"filter_by"],
xlab = plots_index[i,"x_lab"],
ylab = plots_index[i,"y_lab"],
is.pct = plots_index[i,"is.pct"],
fill_col=plots_index[i,"color_set"],
reorder_x=plots_index[i,"reorder"])
assign(g_names[i],g_temp)
print(eval(parse(text=g_names[i])))
}
#relations in the network / exp. for plotting few question with several levels each.
#adjusting the data:
f_data_m<- f_data %>% filter(q_name %in%
c("prof_relations","basic_familarity","close_relations"))%>%
left_join(headings[,c("new_var_name","new_label_hebrew")],
by = c("q_name" = "new_var_name")) %>%
mutate(label_with_n=paste("(",total_n,") ",new_label_hebrew,sep=""))
#plotting
f_1<-ggplot(f_data_m,aes(x=reorder(q_label,-as.numeric(level)),
y=pct, fill=reorder(q_label,-as.numeric(level)),
label=scales::percent(round(pct,2))))+
geom_bar(stat = "identity", width = 0.4,color="gray")+
xlab("עם כמה אמנים")+ ylab("")+
geom_text(size = 5,hjust=.4, position = position_stack(vjust = .5))+
scale_fill_brewer(direction = +1, guide=FALSE)+
scale_y_continuous(labels = scales::percent) +
facet_grid(label_with_n ~ .)
f_1<-set_scales_size(f_1)
print(f_1)
#plotting gender by group
#making data for the plot
gender_data<-dataset %>%
select(new_gender,shevet0=group) %>%
filter(!is.na(shevet0)) %>%
count(shevet0,new_gender) %>%
mutate(pct=n/sum(n)) %>%
group_by(shevet0) %>%
mutate(tot_shevet=sum(n)) %>% ungroup() %>%
mutate(label_with_n=paste("(",tot_shevet,") ",shevet0,sep=""))
#plotting
p_12<-ggplot(gender_data,aes(x=reorder(label_with_n,tot_shevet),y=n, fill=new_gender,label=n))+
geom_bar(stat="identity",color='gray', width = 0.5) +
geom_text(aes(y=n+1.4),size = 5, position = position_stack(vjust = .5))+
scale_y_continuous(name="מספר משיבים")+
scale_x_discrete(name="שבט") +
scale_fill_manual(name="מגדר",values=c("#EF8A62", "#67A9CF"))
p_12<-set_scales_size(p_12)
print(p_12)
#cross group interactions:
#preparing data:
cross_group<- dataset %>% filter(max_point>1) %>%
select(starts_with("cross_"),group) %>%
filter(!group %in% c("חרדי","ערבי",NA ))%>%
gather(q_name,level,-group) %>% filter(!is.na(level)) %>%
count(group,q_name,level) %>%
group_by(group,q_name) %>% mutate(total_n=sum(as.numeric(n),na.rm=T),
pct=n/total_n) %>% ungroup() %>%
left_join(factor_labels[,c(1,2:3)], by = c("q_name" = "new_var_name", "level" = "q_level")) %>%
left_join(headings[,3:4],by=c("q_name"="new_var_name")) %>%
mutate(label_with_n=paste("(",total_n,") ",group,sep="")) %>% #adding n's to the labels
arrange(group,q_name,level )
grp_lev<-unique(cross_group$group) #setting factor in the desired order
new_lev<-grp_lev[c(5,3,2,1,4)]
cross_group$group<-with(cross_group,factor(group,levels = new_lev))
#plotting:
p_13<-ggplot(cross_group,aes(x=q_label,y=n, fill=q_label,label=n))+
geom_bar(stat="identity",color='gray',width = 0.3) +
geom_text(aes(y=n+10),size = 4, vjust = 1)+
scale_y_continuous(name="מספר משיבים")+
scale_x_discrete(name="") +
scale_fill_manual(values=c("#EF8A62", "#67A9CF"),guide=F)+
facet_grid(group~new_label_hebrew)
p_13<-set_scales_size(p_13,sizey = 10)
print(p_13)
#plotting means:
means_data<-dataset %>%
select(networking1:networking3,art_scene1:art_scene4,city_general1:city_general7) %>%
mutate_all(funs(as.numeric)) %>%
gather("var_name","value") %>%
left_join(headings[,3:5],by=c("var_name"="new_var_name")) %>%
group_by(q_group) %>% mutate(group_mean=mean(value,na.rm=T)) %>%
group_by(q_group,new_label_hebrew,var_name,group_mean) %>%
summarise(mean=mean(value,na.rm = T),
n=sum(!is.na(value)),
low=mean(between(value,1,2),na.rm=T),
medium = mean(between(value,3,3),na.rm=T),
high = mean(between(value ,4,5),na.rm=T)) %>%
gather("sum_type","value",-one_of(c("var_name","n","new_label_hebrew","q_group","group_mean"))) %>%
mutate(label_with_n=paste("(",n,")",new_label_hebrew,sep=""))
source("C:/Users/nogka/Documents/R/FAN_n.bar.R")
for (j in seq_along(unique(means_data$q_group))) {
g_names<-paste("m_",1:length(unique(means_data$q_group)),sep="")
g_temp<-m.bar(filter_by = unique(means_data$q_group)[j])
assign(g_names[j],g_temp)
print(eval(parse(text=g_names[j])))
}
m_4<-ggplot(means_data[means_data$sum_type=="mean",],
aes(x=reorder(label_with_n, value),y=value, fill=q_group,
label=round(value,1)))+
geom_bar(stat = "identity", width = 0.4+length(unique(means_data[["label_with_n"]]))*0.02,
color="gray")+
coord_flip(ylim = c(1,5),expand = F) +
xlab("")+
ylab("Avg")+
geom_text(size = 5, position = position_stack(vjust = .95))+
scale_fill_manual(values = c('#8dd3c7','#ffffb3','#bebada'),
guide_legend(title = "קבוצת היגדים"))
m_4<-set_scales_size(m_4)
print(m_4)
means_data$sum_type <- factor(means_data$sum_type, levels = c("mean","high","medium","low"))
dist_data<-means_data %>% filter(sum_type %in% c("low","medium","high")) %>%
mutate(sum_type_heb=ifelse(sum_type=="low","נמוך"
,ifelse(sum_type=="medium", "בינוני",
"גבוה")))%>%
mutate(type_order=ifelse(sum_type=="low",3,
ifelse(sum_type=="medium",2,1)))
#dist by group1
for (j in seq_along(unique(dist_data$q_group))) {
g_names<-paste("d_",1:length(unique(means_data$q_group)),sep="")
ds<-filter(dist_data, q_group==unique(means_data$q_group)[j])
g_temp<-ggplot(ds, aes(x=label_with_n,y=value, fill=reorder(sum_type_heb, type_order),
label=scales::percent(round(value,2))))+
geom_bar(stat = "identity", width = 0.4)+
xlab("")+ylab("")+
geom_text(size = 5, position = position_stack(vjust = .5))+
scale_fill_manual(values = c("#1a9641","#fdae61","#d7191c"),
guide_legend(title = "קטגורית ציון"))+
scale_y_continuous(labels = scales::percent)+
scale_x_discrete(labels = function(x) lapply(
strwrap(x, width = 15, simplify = FALSE), paste, collapse="\n"))
g_temp<-set_scales_size(g_temp)
assign(g_names[j],g_temp)
#print(eval(parse(text=g_names[j])))
}
#dist chart, general.
dist_data$label_with_n=factor(dist_data$label_with_n,
levels=rev(levels(reorder(dist_data[dist_data$sum_type=="low",]$label_with_n,
dist_data[dist_data$sum_type=="low",]$value))))
d_4<-ggplot(dist_data, aes(x=label_with_n,y=value, fill=reorder(sum_type_heb, type_order),
label=scales::percent(round(value,2))))+
geom_bar(stat = "identity", width = 0.6)+
coord_flip() +xlab("")+ylab("")+
geom_text(size = 5, position = position_stack(vjust = .5))+
scale_fill_manual(values = c("#1a9641","#fdae61","#d7191c"),
guide_legend(title = "קטגורית ציון"))+
scale_y_continuous(labels = scales::percent)
d_4<-set_scales_size(d_4)
#reporting results to a pptx file:
filename <- "report9.pptx" # the document to produce
slides_index<-read.xlsx("./data-Artists/QP_Nov_2016/slides_index.xlsx",
sheetIndex = 1, encoding="UTF-8",header = T,as.data.frame = T)
library(ReporteRs)
ppt_report<-pptx(template = "temp_artists.pptx") #choosing tmplt
for (i in seq_along(slides_index$slide_num)){
ppt_report<-addSlide(ppt_report,
slide.layout = slide.layouts(ppt_report)[slides_index$slide_theme[i]])
if (!is.na(slides_index$slide_head[i])){
ppt_report <-addTitle(ppt_report, slides_index$slide_head[i])
}
ppt_report<-addPlot(ppt_report, function() print(get(slides_index$plot1[i])))
if (!is.na(slides_index$plot2[i])){
ppt_report <-addPlot(ppt_report, function() print(get(slides_index$plot2[i])))
}
print(c("done",i))
}
writeDoc(ppt_report,file = filename )