library(readxl)
library(tidyverse)
library(dplyr)
library(sysfonts)
library(ggplot2)
library(ggimage)
library(ggtext)
library(maps)
library(geosphere)
library(BBmisc)
library(randomNames)
library(networkD3)
font_paths("/Users/theotimebourgeois/Library/Fonts/AvertaPE-Black.otf")
font_add(family = "AvertaPE-Black.otf",
regular = "AvertaPE-Black.otf")
font_paths("/Users/theotimebourgeois/Library/Fonts/AvertaPE-Regular.otf")
font_add(family = "AvertaPE-Regular.otf",
regular = "AvertaPE-Regular.otf")
font <- "AvertaPE-Regular"
purple <- c("#2C2C54")
pink <- c("#A40E4C")
blue <- c("#2E86AB")
yellow <- c("#FF9C00")
lila <- c("#E3DFFF")
brown <- c("#C3979F")
grey <- c("#F2F2F2")
white <- c("#FFFFFF")
colfunc <- colorRampPalette(c(pink, blue, purple, yellow))
colfunc2 <- colorRampPalette(c(pink,yellow,lila,purple))
colfunc3 <- colorRampPalette(c(pink,yellow,purple))
FormDB <- read_excel("/Users/theotimebourgeois/Downloads/Mobile payment methods, use according to your network? (réponses).xlsx", sheet = "Réponses au formulaire 1")
SourceText <- "Source: ‟Mobile payment methods, use according to your network?”\nForm performed by Théotime Bourgeois"
People <- nrow(FormDB)
FormDB[,6:29][ FormDB[,6:29] == "5 (Strongly agree)" ] <- "5"
FormDB[,6:29][ FormDB[,6:29] == "1 (Strongly disagree)" ] <- "1"
FormDB[,6:29] <- FormDB[,6:29] %>% mutate_if(is.character,as.numeric)
FormDB$LastName <-
FormDB$`Your last name (Your family name will be anonymized: it is only useful to identify your family group):` %>% toupper
FormDB <- FormDB %>% select(-`Your last name (Your family name will be anonymized: it is only useful to identify your family group):`)
FormDB$`Name of your company:` <- FormDB$`Name of your company:` %>% toupper
FormDB$`Approximate start date of your studies:` <- FormDB$`Approximate start date of your studies:` %>% format("%Y")
FormDB$`Approximate end date of your studies:` <- FormDB$`Approximate end date of your studies:` %>% format("%Y")
FormDB <- FormDB %>%
mutate(ID=1,
ID=cumsum(ID),
ID=paste("Person",ID))
Gender <- FormDB$`Your gender:` %>% table() %>% prop.table() %>% as.data.frame()
FormDB$Link[FormDB$ID=="Person 2"] <- "Person 1"
FormDB$Link[FormDB$ID=="Person 21"] <- "Person 9"
FormDB$Link[FormDB$ID=="Person 24"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 26"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 28"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 29"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 27"] <- "Person 1"
FormDB$Link[FormDB$ID=="Person 27"] <- "Person 1"
FormDB$Link[FormDB$ID=="Person 30"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 31"] <- "Person 8"
FormDB$Link[FormDB$ID=="Person 32"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 33"] <- "Person 1"
FormDB$Link[FormDB$ID=="Person 37"] <- "Person 38"
FormDB$Link[FormDB$ID=="Person 39"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 34"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 35"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 36"] <- "Person 7"
FormDB$Link[FormDB$ID=="Person 41"] <- "Person 2"
FormDB$Link[FormDB$ID=="Person 45"] <- "Person 43"
FormDB$Link[FormDB$ID=="Person 50"] <- "Person 7"
FormDB$Name <- randomNames(nrow(FormDB), which.names="first")
UTAUT_List <- FormDB[6:29] %>% colnames() %>% as.data.frame()
UTAUT_List <- strsplit(UTAUT_List$.,"\\[") %>% as.data.frame() %>% t()
rownames(UTAUT_List) <- 1:nrow(UTAUT_List)
PurposeOT <- FormDB$`I make online transactions to : (check all that apply to you)` %>%
strsplit(", ") %>%
unlist() %>%
table() %>%
as.data.frame() %>%
`colnames<-`(c("Purpose","Freq")) %>%
arrange(desc(Freq)) %>%
mutate(
Purpose = sapply(Purpose %>% str_split("\\("), `[`, 1),
Purpose = factor(Purpose, levels=rev(Purpose)))
PurposeOT <- PurposeOT %>% mutate(
ID = 1:nrow(PurposeOT),
label = paste0(Purpose,": ",Freq))
PurposeGraph <- ggplot(PurposeOT, aes(x = ID, y = Freq, fill = Purpose)) +
geom_segment(aes(x = 1:nrow(PurposeOT), xend = 1:nrow(PurposeOT), y = People, yend = 0),col = grey)+
geom_bar(width = 0.9, stat="identity")+
geom_segment(aes(x = 0, xend = nrow(PurposeOT)+1, y = 0, yend = 0))+
coord_polar(theta = "y")+
scale_fill_manual(values=colfunc(nrow(PurposeOT)))+
xlim(c(-5,1+nrow(PurposeOT))) +
ylim(c(0,People)) +
geom_text(hjust = 1.05, size = 3, family = font,
aes(x = ID, y = 0, label = label)) +
geom_richtext(size = 4.5, col = purple,family = "AvertaPE-Black", fill = NA, label.color = NA,
aes(x = -5, y = 0,
label=paste0("On a sample of <br><span style='font-size:25pt; color:",
pink,"'> **",People," people**</span>")))+
labs(title="Main reasons for using these applications",
y=NULL,x=NULL, fill="Skills",
caption=SourceText)+
theme_minimal() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(size = 15, family = "AvertaPE-Black", color = purple),
plot.caption = element_text(size = 8, color = blue),
axis.line = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank())
ggsave("Purpose.png", PurposeGraph)
## Saving 7 x 5 in image
PurposeGraph
HowOT <- FormDB$`What is the reason for this?` %>%
strsplit(", ") %>%
unlist() %>%
table() %>%
as.data.frame() %>%
`colnames<-`(c("How","Freq")) %>%
arrange(desc(Freq)) %>%
mutate(
How = sapply(How %>% str_split("\\("), `[`, 1),
How = factor(How, levels=rev(How)))
HowOT$How <- HowOT$How %>% droplevels() %>% as.character()
HowOT$How[HowOT$How %>% str_detect("institution")] <- "Existing user institution"
HowOT$How[HowOT$How %>% str_detect("knew")] <- "Option already known"
HowOT$How[HowOT$How %>% str_detect("payment")] <- "Time of payment"
HowOT$How[HowOT$How %>% str_detect("friend")] <- "Conversion by a friend"
HowOT$How[HowOT$How %>% str_detect("need")] <- "Meet a need"
HowOT$How[HowOT$Freq <= 2] <- "Other"
HowOT <- HowOT %>%
group_by(How) %>%
summarise(Freq = sum(Freq))
HowOT <- HowOT %>%
arrange(desc(Freq)) %>%
mutate(ID = 1:nrow(HowOT),
label = paste0(How,": ",Freq),
How = factor(How, levels=rev(How)))
HowGraph <- ggplot(HowOT, aes(x = ID, y = Freq, fill = How)) +
geom_segment(aes(x = 1:nrow(HowOT), xend = 1:nrow(HowOT), y = max(Freq)+1, yend = 0),col = grey)+
geom_bar(width = 0.9, stat="identity")+
geom_segment(aes(x = 0, xend = nrow(HowOT)+1, y = 0, yend = 0))+
coord_polar(theta = "y")+
scale_fill_manual(values=colfunc(nrow(HowOT)))+
xlim(c(-5,1+nrow(HowOT))) +
ylim(c(0,max(HowOT$Freq)+1)) +
geom_text(hjust = 1.05, size = 3, family = font,
aes(x = ID, y = 0, label = label)) +
geom_richtext(size = 4.5, col = purple,family = "AvertaPE-Black", fill = NA, label.color = NA,
aes(x = -5, y = 0,
label =paste0("On a sample of <br><span style='font-size:25pt; color:",
pink,"'> **",People," people**</span>")))+
labs(title="Conversion mostly done thanks to users",
subtitle = "Institutions as majority converters",
y=NULL,x=NULL, fill="Skills",
caption=SourceText)+
theme_minimal() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(size = 15, family = "AvertaPE-Black", color = purple),
plot.caption = element_text(size = 8, color = blue),
axis.line = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.ticks = element_blank())
ggsave("How.png", HowGraph)
HowGraph
Market <- FormDB$`What mobile applications/payment methods do you use to transfer money to a friend?` %>%
strsplit(", ") %>%
unlist() %>%
table() %>%
as.data.frame() %>%
`colnames<-`(c("Market","Freq")) %>%
droplevels.data.frame()
Market$Image[Market$Market %>% str_detect("Lydia")] <- "Lydia.png"
Market$Image[Market$Market %>% str_detect("RIB")] <- "RIB.png"
Market$Image[Market$Market %>% str_detect("Check")] <- "Check.png"
Market$Image[Market$Market %>% str_detect("Lyf Pay")] <- "Lyf pay.png"
Market$Image[Market$Market %>% str_detect("Pumpkin")] <- "Pumpkin.png"
Market$Image[Market$Market %>% str_detect("Paylib")] <- "PayLib.png"
Market$Image[Market$Market %>% str_detect("PayPal")] <- "PayPal.png"
Market <- Market %>%
arrange(desc(Freq)) %>%
head(7) %>%
mutate(Market = factor(Market, levels=rev(Market)))
size <- 10
Market_graph <- ggplot(Market, aes(y = factor(Market)))+
geom_segment(aes(x = 0, xend = Freq-1.9, yend = Market), size = size, col = purple)+
geom_point(aes(x = Freq-1.9), size = size*0.95, col = yellow)+
geom_image(aes(x = Freq, image=Image,),
asp = 1.8,
size=.07)+
geom_text(aes(x=Freq-1.9, label = Freq), col = white, family = "AvertaPE-Black")+
labs(title="New payment methods: a complement to traditional methods",
subtitle="Use of payment methods in respondents' daily lives",
y=NULL, x="Count",
caption = SourceText)+
scale_y_discrete(labels = function(y) str_wrap(y, width = 12))+
theme(text=element_text(size=12, family="AvertaPE-Regular"),
panel.background = element_blank(),
legend.position = "right",
legend.background = element_blank(),
axis.line = element_line(colour = purple),
plot.title = element_text(size = 15, family = "AvertaPE-Black", color = purple),
plot.caption = element_text(size = 10, color = blue))
ggsave("Market.png", Market_graph)
Market_graph
FormDB$Age <- (Sys.Date() %>% format("%Y") %>% as.numeric())-FormDB$`Your year of birth:`
AppAge <- FormDB %>%
select(`What mobile applications/payment methods do you use to transfer money to a friend?`,Age) %>%
`colnames<-`(c("App","Age")) %>%
separate(col=App,sep=", ",c("1","2","3","4","5"))
## Warning: Expected 5 pieces. Missing pieces filled with `NA` in 50 rows [1, 2, 3,
## 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
AppAge <- rbind(
AppAge %>% select(1,6) %>% `colnames<-`(c("App","Age")),
AppAge %>% select(2,6) %>% `colnames<-`(c("App","Age")),
AppAge %>% select(3,6) %>% `colnames<-`(c("App","Age")),
AppAge %>% select(4,6) %>% `colnames<-`(c("App","Age")),
AppAge %>% select(5,6) %>% `colnames<-`(c("App","Age"))) %>%
group_by(App) %>%
summarise(Age=mean(Age) %>% round(0)) %>%
`colnames<-`(c("Market","Age"))
AppAge <- merge(Market,AppAge, by="Market") %>%
arrange(desc(Age)) %>%
mutate(Market = factor(Market, levels=rev(Market)))
AppAge_graph <- ggplot(AppAge, aes(y = factor(Market)))+
geom_segment(aes(x = 0, xend = Age-3, yend = Market), size = size, col = purple)+
geom_point(aes(x = Age-3), size = size*0.95, col = yellow)+
geom_image(aes(x = Age, image=Image,),
asp = 1,
size=.07)+
geom_text(aes(x=Age-3, label = Age), col = white, family = "AvertaPE-Black")+
labs(title="The older the technology, the higher the average age of users",
subtitle="New technologies appeal to young people",
y=NULL, x="Age",
caption = SourceText)+
scale_y_discrete(labels = function(y) str_wrap(y, width = 12))+
theme(text=element_text(size=12, family="AvertaPE-Regular"),
panel.background = element_blank(),
legend.position = "right",
legend.background = element_blank(),
axis.line = element_line(colour = purple),
plot.title = element_text(size = 15, family = "AvertaPE-Black", color = purple),
plot.caption = element_text(size = 10, color = blue))
ggsave("AppAge.png", AppAge_graph)
## Saving 7 x 5 in image
AppAge_graph
ZIPCodeDB <- read_csv("/Users/theotimebourgeois/Documents/Cours/BSB/MASTER2/Thesis/R New Thesis/cities.csv")
#https://www.data.gouv.fr/en/datasets/regions-departements-villes-et-villages-de-france-et-doutre-mer/
ZIPDB <- FormDB %>%
select(`Postal code of your company:`,`Postal code of your recently attended school:`,`Your postal code:`) %>%
as.data.frame() %>%
`colnames<-`(c("Pro","School","Perso"))
ZIPCode <- ZIPCodeDB %>%
select(zip_code, gps_lat, gps_lng,name) %>%
mutate(ID=1, ID=ave(ID, zip_code, FUN=cumsum)) %>%
filter(ID==1) %>%
select(-ID)
ZIPCode <- rbind(ZIPCode,
c("LU-2182",49.58317971148099,6.122436170578234, "Luxembourg"),
c("BE-5000",50.4669,4.8675, "Namur"),
c("BE-6840",49.8407,5.4353, "Neufchâteau"),
c("NZ-0632",-36.74421756663356,174.69668408595604, "Auckland"),
c("CH-1010",46.5377,6.6553, "Lausanne"),
c("LU-1137",49.58453333444523,6.138253219045353, "Howald"),
c("CH-1295",46.3072,6.1811, "Lausanne")) %>% unique()
ZIPPoints <- rbind(ZIPDB[1] %>% `colnames<-`(c("Zip")),
ZIPDB[2] %>% `colnames<-`(c("Zip")),
ZIPDB[3] %>% `colnames<-`(c("Zip"))) %>%
table() %>%
as.data.frame() %>%
`colnames<-`(c("zip_code","Freq"))
ZIPPoints$zip_code <- ZIPPoints$zip_code %>% droplevels() %>% as.character()
ZIPToAdd <- ZIPPoints$zip_code[!ZIPPoints$zip_code %in% ZIPCode$zip_code]
ZIPPoints <- ZIPPoints %>% merge(y = ZIPCode, by = "zip_code")
ZIPCode <- ZIPCode %>% select(zip_code, gps_lat, gps_lng)
ZIPCode <- ZIPCode[ZIPCode$zip_code %in% ZIPPoints$zip_code,]
PersoPro <- ZIPDB %>%
select("Pro","Perso") %>%
`colnames<-`(c("zip_code","Perso")) %>%
merge(y = ZIPCode, by = "zip_code") %>%
`colnames<-`(c("Pro","zip_code","Pro_lat","Pro_lng")) %>%
merge(y = ZIPCode, by = "zip_code") %>%
`colnames<-`(c("Perso","Pro","Pro_lat","Pro_lng","Perso_lat","Perso_lng")) %>%
table() %>%
as.data.frame() %>%
filter(Freq>0)
SchoolPerso <- ZIPDB %>% select("Perso","School") %>% `colnames<-`(c("zip_code","School")) %>%
merge(y = ZIPCode, by = "zip_code") %>%
`colnames<-`(c("Perso","zip_code","Perso_lat","Perso_lng")) %>%
merge(y = ZIPCode, by = "zip_code") %>%
`colnames<-`(c("School","Perso","Perso_lat","Perso_lng","School_lat","School_lng")) %>%
table() %>%
as.data.frame() %>%
filter(Freq>0)
SchoolPerso$School_lng <- SchoolPerso$School_lng %>% droplevels() %>% as.character() %>% as.numeric()
SchoolPerso$School_lat <- SchoolPerso$School_lat %>% droplevels() %>% as.character() %>% as.numeric()
SchoolPerso$Perso_lat <- SchoolPerso$Perso_lat %>% droplevels() %>% as.character() %>% as.numeric()
SchoolPerso$Perso_lng <- SchoolPerso$Perso_lng %>% droplevels() %>% as.character() %>% as.numeric()
PersoPro$Pro_lng <- PersoPro$Pro_lng %>% droplevels() %>% as.character() %>% as.numeric()
PersoPro$Pro_lat <- PersoPro$Pro_lat %>% droplevels() %>% as.character() %>% as.numeric()
PersoPro$Perso_lat <- PersoPro$Perso_lat %>% droplevels() %>% as.character() %>% as.numeric()
PersoPro$Perso_lng <- PersoPro$Perso_lng %>% droplevels() %>% as.character() %>% as.numeric()
PersoPro$Width <- PersoPro$Freq %>% normalize(method = "range",range = c(1, 5))
SchoolPerso$Width <- SchoolPerso$Freq %>% normalize(method = "range",range = c(1, 5))
ZIPPoints$Width <- ZIPPoints$Freq %>% normalize(method = "range",range = c(0.5, 1.5))
map('world',
col="#f2f2f2", fill=TRUE, bg="white", lwd=0.05,
mar=rep(0,4),border=0, ylim=c(40,55), xlim=c(-10,20)
)
for (i in 1:nrow(SchoolPerso)){
inter <- gcIntermediate(c(SchoolPerso$School_lng[i],SchoolPerso$School_lat[i]),
c(SchoolPerso$Perso_lng[i],SchoolPerso$Perso_lat[i]),
n=50, addStartEnd=TRUE, breakAtDateLine=F)
lines(inter, col=yellow, lwd=SchoolPerso$Width[i])
}
for (i in 1:nrow(PersoPro)){
inter <- gcIntermediate(c(PersoPro$Pro_lng[i],PersoPro$Pro_lat[i]),
c(PersoPro$Perso_lng[i],PersoPro$Perso_lat[i]),
n=50, addStartEnd=TRUE, breakAtDateLine=F)
lines(inter, col=pink, lwd=PersoPro$Width[i])
}
points(x=ZIPPoints$gps_lng, y=ZIPPoints$gps_lat, col=purple, cex=ZIPPoints$Width, pch=20)
ZIPPoints <- ZIPPoints %>%
mutate(zip_code = substring(zip_code,1,2)) %>%
group_by(zip_code) %>%
summarise(Freq=sum(Freq),
name=first(name),
gps_lat=first(gps_lat),
gps_lng=first(gps_lng)) %>%
mutate(Width = normalize(Freq, method = "range",range = c(2, 9)))
ZIPPointsCol <- ZIPPoints$Freq %>% unique() %>% sort() %>% as.data.frame() %>% `colnames<-`(c("Freq"))
ZIPPointsCol$Color <- colfunc3(nrow(ZIPPointsCol))
ZIPPoints <- merge(x = ZIPPoints, y = ZIPPointsCol, by = "Freq")
map('world',
col="#f2f2f2", fill=TRUE, bg=pink, lwd=0.05,
mar=rep(0,4),border=0, ylim=c(40,55), xlim=c(-15,21)
)
points(x=ZIPPoints$gps_lng, y=ZIPPoints$gps_lat, col=ZIPPoints$Color, cex=ZIPPoints$Width, pch=20)
BirthYear <- FormDB$`Your year of birth:` %>% table() %>% as.data.frame()
Year <- (BirthYear$. %>% droplevels() %>% as.character() %>% as.numeric() %>% min()):(BirthYear$. %>% droplevels() %>% as.character() %>% as.numeric() %>% max()) %>% as.data.frame()
Year <- Year[!Year$. %in% BirthYear$.,] %>% as.data.frame() %>% mutate(Freq=NA)
BirthYear$. <- BirthYear$. %>% droplevels() %>% as.character() %>% as.numeric()
BirthYear <- rbind(BirthYear,Year) %>% arrange(.)
BirthYear$Year <- ((BirthYear$.*2) %>% round(-1))/2
BirthYear <- BirthYear %>% group_by(Year) %>% summarise(Freq = sum(Freq, na.rm=T))
Age_graph <- ggplot(BirthYear, aes(x = Year, y = Freq))+
geom_bar(stat = "summary", fun = "mean", fill=pink)+
geom_smooth(col = yellow, method = lm, formula = y ~ splines::bs(x, 2), se = FALSE)+
geom_text(aes(label = Freq),
size=3,
hjust=0.5,
vjust=-1,
check_overlap = T)+
ylim(0,max(BirthYear$Freq)+4)+
labs(title="Distribution of respondents' years of birth",
subtitle=paste0("1998 corresponds to the year of birth of the majority of the students of my promotion"),
y="Frequency", x="Year",
caption = SourceText)+
theme(text=element_text(size=12, family="AvertaPE-Regular"),
panel.background = element_blank(),
legend.position = "right",
legend.background = element_blank(),
axis.line = element_line(colour = purple),
plot.title = element_text(size = 15, family = "AvertaPE-Black", color = purple),
plot.caption = element_text(size = 8, color = blue))
ggsave("Age.png", Age_graph)
Age_graph
MatrixName <- matrix(data=NA, nrow = nrow(FormDB), ncol = nrow(FormDB)) %>%
as.data.frame() %>%
`colnames<-`(FormDB$ID) %>%
`rownames<-`(FormDB$ID)
for (col in 1:ncol(MatrixName)){
for (row in 1:nrow(MatrixName)){
MatrixName[row,col] <-
FormDB$LastName[FormDB$ID==rownames(MatrixName[row,])]==FormDB$LastName[FormDB$ID==colnames(MatrixName)[col]]
}
}
MatrixLastName <- MatrixName %>% mutate(from=rownames(MatrixName))
MatrixLastName <- MatrixLastName[,c(ncol(MatrixLastName),1:(ncol(MatrixLastName)-1))] %>%
as.data.frame() %>%
gather(key="to", value="value", -1) %>%
mutate(to = gsub("\\.", " ",to)) %>%
na.omit() %>%
filter(value==TRUE)
for (col in 1:ncol(MatrixName)){
for (row in 1:nrow(MatrixName)){
MatrixName[row,col] <-
FormDB$`Name of your company:`[FormDB$ID==rownames(MatrixName[row,])]==FormDB$`Name of your company:`[FormDB$ID==colnames(MatrixName)[col]]
}
}
MatrixPro <- MatrixName %>% mutate(from=rownames(MatrixName))
MatrixPro <- MatrixPro[,c(ncol(MatrixPro),1:(ncol(MatrixPro)-1))] %>%
as.data.frame() %>%
gather(key="to", value="value", -1) %>%
mutate(to = gsub("\\.", " ",to)) %>%
na.omit() %>%
filter(value==TRUE)
for (col in 1:ncol(MatrixName)){
for (row in 1:nrow(MatrixName)){
MatrixName[row,col] <-
FormDB$`Name of your recently attended school:`[FormDB$ID==rownames(MatrixName[row,])]==FormDB$`Name of your recently attended school:`[FormDB$ID==colnames(MatrixName)[col]]
}
}
MatrixSchool <- MatrixName %>% mutate(from=rownames(MatrixName))
MatrixSchool <- MatrixSchool[,c(ncol(MatrixSchool),1:(ncol(MatrixSchool)-1))] %>%
as.data.frame() %>%
gather(key="to", value="value", -1) %>%
mutate(to = gsub("\\.", " ",to)) %>%
na.omit() %>%
filter(value==TRUE, from!=to)
MatrixLastName$value <- "Family"
MatrixSchool$value <- "School"
MatrixPro$value <- "Pro"
MatrixPerso <- FormDB %>%
select(ID, Link) %>%
filter(!is.na(Link)) %>%
`colnames<-`(c("from","to")) %>%
mutate(value = "Perso")
Top_Network <- rbind(MatrixLastName,MatrixSchool,MatrixPro,MatrixPerso)
Top_Network <- Top_Network[Top_Network$from!=Top_Network$to,]
Top_Network_Top <- Top_Network %>% select(from,to) %>% unique()
Top_Network_Top <- c(Top_Network_Top$from,Top_Network_Top$to) %>% table() %>% as.data.frame() %>% arrange(desc(Freq))
p <- simpleNetwork(Top_Network %>% select(from,to), height="100px", width="100px",
Source = 1, # column number of source
Target = 2, # column number of target
linkDistance = 10, # distance between node. Increase this value to have more space between nodes
charge = -900, # numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value)
fontSize = 14, # size of the node names
fontFamily = font, # font og node names
linkColour = purple, # colour of edges, MUST be a common colour for the whole graph
nodeColour = pink, # colour of nodes, MUST be a common colour for the whole graph
opacity = 1, # opacity of nodes. 0=transparent. 1=no transparency
zoom = T # Can you zoom on the figure?
)
p
library(igraph)
## Warning: package 'igraph' was built under R version 4.1.2
##
## Attaching package: 'igraph'
## The following object is masked from 'package:BBmisc':
##
## normalize
## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union
## The following objects are masked from 'package:purrr':
##
## compose, simplify
## The following object is masked from 'package:tidyr':
##
## crossing
## The following object is masked from 'package:tibble':
##
## as_data_frame
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
links <- Top_Network %>% select(from,to)
network <-
graph.data.frame(links,
vertices = unique(FormDB$ID),
directed = TRUE)
plot(
as.undirected(network),
layout = layout.kamada.kawai(network),
vertex.size = 3,
vertex.label = FormDB$ID,
vertex.label.color = purple,
vertex.label.dist = 1,
edge.arrow.size = 0.2,
vertex.color = pink
)
UTAUTAnalysis <- FormDB[6:29] %>% t() %>% as.data.frame()
rownames(UTAUTAnalysis) <- 1:nrow(UTAUTAnalysis)
UTAUT_List <- UTAUT_List %>% as.data.frame() %>% mutate(`1`=1, `2`=2, `3`=3, `4`=4, `5`=5)
for (i in 1:nrow(UTAUT_List)){
UTAUT_List$Mean[i] <- UTAUTAnalysis[i,] %>% unlist() %>% mean()
for (j in 1:5){
UTAUT_List[i,j+2] <- (UTAUTAnalysis[i,] %>%
unlist() %>%
table() %>%
as.data.frame() %>%
`colnames<-`(c("Value","Freq")) %>%
filter(Value==j))[2] %>% as.numeric()
}
}
colnames(UTAUT_List)[1:2] <- c("Category","Question")
UTAUT_List$Category <- UTAUT_List$Category %>% str_squish()
UTAUT_List$Question <- UTAUT_List$Question %>% str_remove_all("\\]")
UTAUT_Group <- UTAUT_List %>%
group_by(Category) %>%
summarise(Mean = mean(Mean),
`1`=sum(`1`,na.rm=T),
`2`=sum(`2`,na.rm=T),
`3`=sum(`3`,na.rm=T),
`4`=sum(`4`,na.rm=T),
`5`=sum(`5`,na.rm=T))
UTAUT_Radar <- UTAUT_Group %>% select(1,2) %>% t()
UTAUT_Radar[1,] <- c("Behavior","Effort","Facilitating","Enjoyment","Performance","Social","Trust")
colnames(UTAUT_Radar) <- UTAUT_Radar[1,] %>% str_replace(" / ","/") %>% str_replace(" ","\n")
UTAUT_Radar <- UTAUT_Radar[2,] %>% t() %>% as.data.frame()
UTAUT_Radar[1,] <- ((UTAUT_Radar[1,] %>% as.numeric())/5) %>% round(2)
UTAUT_Radar[2,] <- (c(3.224,2.535,3.348,3.264,3.1,3.325,3.163)/5) %>% round(2)
UTAUT_Radar[,1:7] <- sapply(UTAUT_Radar[,1:7], as.numeric)
UTAUT_Radar <- cbind(c("Thesis Results","Triasesiarta Nur Results"),UTAUT_Radar[1:7])
colnames(UTAUT_Radar)[1] <- "Name"
library(ggradar)
Skill_radar_graph <- UTAUT_Radar %>%
ggradar(font.radar = font,
grid.label.size = 4, # Affects the grid annotations (0%, 50%, etc.)
axis.label.size = 4,
group.point.size = 5, # Simply the size of the point
group.colours = c(yellow, pink))+
labs(title = paste("Comparison of the results obtained by the UTAUT Model"),
subtitle = "between my study and the literature review",
caption = SourceText)+
theme(text=element_text(size=12, family="AvertaPE-Regular"),
legend.position = c(-0.1,-0.025),
legend.justification = "left",
legend.text = element_text(size = 10, family = font),
legend.key = element_rect(fill = NA, color = NA),
panel.background = element_blank(),
legend.background = element_blank(),
plot.title = element_text(size = 15, family = "AvertaPE-Black", color = purple),
plot.caption = element_text(size = 8, color = blue))
ggsave("Comparison radar.png", Skill_radar_graph)
Skill_radar_graph
UTAUT_Group2 <- rbind(UTAUT_Group %>% select(1,3) %>% `colnames<-`(c("Category","Freq")) %>% mutate(Rate = 1),
UTAUT_Group %>% select(1,4) %>% `colnames<-`(c("Category","Freq")) %>% mutate(Rate = 2),
UTAUT_Group %>% select(1,5) %>% `colnames<-`(c("Category","Freq")) %>% mutate(Rate = 3),
UTAUT_Group %>% select(1,6) %>% `colnames<-`(c("Category","Freq")) %>% mutate(Rate = 4),
UTAUT_Group %>% select(1,7) %>% `colnames<-`(c("Category","Freq")) %>% mutate(Rate = 5)) %>%
mutate(Rate = as.character(Rate))
library(ggridges)
UTAUT_Group_graph <- ggplot(UTAUT_Group2, aes(x = Category, y = Freq, fill = Rate, group = Rate)) +
geom_col(position = "dodge")+
coord_flip()+
scale_fill_manual(values=colfunc2(5))+
labs(title="Model UTAUT analysis on a scale of 1 to 5",
subtitle=paste0("1998 corresponds to the year of birth of the majority of the students of my promotion"),
y="Rate Frequency", x=element_blank(),
caption = SourceText)+
theme(text=element_text(size=12, family="AvertaPE-Regular"),
panel.background = element_blank(),
legend.position = "right",
legend.background = element_blank(),
axis.line = element_line(colour = purple),
plot.title = element_text(size = 15, family = "AvertaPE-Black", color = purple),
plot.caption = element_text(size = 8, color = blue))
ggsave("UTAUT Overview.png", UTAUT_Group_graph)
UTAUT_Group_graph
UTAUT_List$Category <- UTAUT_List$Category %>% str_replace_all(" / "," and ")
for (i in 1:nrow(UTAUT_List)){
UTAUT_List2 <- UTAUT_List %>%
filter(Category==UTAUT_List$Category[i])
UTAUT_graph <- rbind(
UTAUT_List2 %>% select(1,2,3) %>% `colnames<-`(c("Category","Question","Freq")) %>% mutate(Rate = "1"),
UTAUT_List2 %>% select(1,2,4) %>% `colnames<-`(c("Category","Question","Freq")) %>% mutate(Rate = "2"),
UTAUT_List2 %>% select(1,2,5) %>% `colnames<-`(c("Category","Question","Freq")) %>% mutate(Rate = "3"),
UTAUT_List2 %>% select(1,2,6) %>% `colnames<-`(c("Category","Question","Freq")) %>% mutate(Rate = "4"),
UTAUT_List2 %>% select(1,2,7) %>% `colnames<-`(c("Category","Question","Freq")) %>% mutate(Rate = "5")) %>%
ggplot(aes(x = Question, y = Freq, fill = Rate, group = Rate)) +
geom_col(position = "dodge")+
scale_x_discrete(labels = function(y) str_wrap(y, width = 20))+
coord_flip()+
scale_fill_manual(values=colfunc2(5))+
labs(title="Model UTAUT analysis on a scale of 1 to 5",
subtitle=paste0(UTAUT_List2[1,1]),
y="Rate Frequency", x=element_blank(),
caption = SourceText)+
theme(text=element_text(size=12, family="AvertaPE-Regular"),
panel.background = element_blank(),
legend.position = "right",
legend.background = element_blank(),
axis.line = element_line(colour = purple),
plot.title = element_text(size = 15, family = "AvertaPE-Black", color = purple),
plot.caption = element_text(size = 8, color = blue))
ggsave(paste0(UTAUT_List$Category[i],".png"), UTAUT_graph)
}
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