Loading necessary packages and configuring system fonts for visualization.
###Libraries#####
library(tidyverse)
library(extrafont)
font_import(prompt = FALSE)
loadfonts(device = "win")
####Fall_Related####
library(readxl)
library(readxl)
EPI <- read_excel("C:/Users/Acer/Desktop/EPID_SCI/EPI.xlsx",
sheet = "Dx")
view(EPI)
FF <- EPI %>% filter(Cause %in% c("Fall on the ground", "Fall from height",
"Fall while carrying heavy weight"))
cause_year_counts <- FF %>%
group_by(Year, Cause) %>%
summarise(Count = n()) %>%
ungroup()
cause_year_counts <- cause_year_counts %>%
group_by(Year) %>%
mutate(Percentage = (Count / sum(Count)) * 100) %>%
ungroup()
view(cause_year_count)
FALL<-ggplot(cause_year_counts, aes(x = Year, y = Percentage, color = Cause, group = Cause)) +
geom_line(aes(linetype = Cause)) +
geom_point(aes(shape = Cause), size = 4) +
labs( x = "Year", y = "Percentage (%)") +
theme_classic()+
scale_x_continuous(breaks = c(2011, 2021))+
scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100))+
theme(
text = element_text(family = "Times New Roman"),
plot.title = element_text(family = "Times New Roman", size = 16),
axis.text = element_text(family = "Times New Roman", size = 12),
legend.text = element_text(family = "Times New Roman", size = 12),
legend.title = element_text(family = "Times New Roman", size = 12)
)
ggsave("C:\\Users\\Acer\\Desktop\\EPID_SCI\\Fall History.png",plot = FALL, width = 10.18, height = 3.81, units = "in", dpi = 300)
###Non-Traumatic ######
library(readxl)
library(readxl)
EPI_N_T <- read_excel("C:/Users/Acer/Desktop/EPID_SCI/EPI.xlsx",
sheet = "NTT")
N_T <- EPI_N_T %>% filter(Cause %in% c("Transvers myelitis",
"Spinal tumour",
"Pott's disease",
"Cervical myelopathy",
"Spina bifida",
"Neurofibromatosis",
"Idiopathic"
))
cause_year_counts_N_T <- N_T %>%
group_by(Year, Cause) %>%
summarise(Count = n()) %>%
ungroup()
cause_year_counts_P_N_T <- cause_year_counts_N_T %>%
group_by(Year) %>%
mutate(Percentage = (Count / sum(Count)) * 100) %>%
ungroup()
FACET_PLOTT<-ggplot(cause_year_counts_P_N_T, aes(x = Year, y = Percentage, color = Cause, group = Cause)) +
geom_point(aes(shape = Cause), size= 4, alpha= 2)+
geom_line() +
labs(title = "Non-traumatic Causes", x = "Year", y = "Percentage (%)") +
theme_minimal()+
scale_shape_manual(values = c("Transvers myelitis"= 15,
"Spinal tumour" = 16,
"Pott's disease"=15,
"Cervical myelopathy"=19,
"Spina bifida"=17,
"Neurofibromatosis"=17,
"Idiopathic"=15))+
scale_x_continuous(breaks = c(2011,2013,2015,2017,2019,2021))+
scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100))+
theme(panel.spacing = unit(1, "cm"),
text = element_text(family = "Times New Roman", size = 16),
plot.title = element_text(family = "Times New Roman", hjust=0.5,size = 16),
axis.text = element_text(family = "Times New Roman", size = 12),
legend.text = element_blank(),
legend.title = element_blank(),
legend.position = "none"
)+
facet_wrap(~Cause)
ggsave("C:\\Users\\Acer\\Desktop\\EPID_SCI\\Non_traumatic_Causes.jpg",plot = FACET_PLOTT, width = 10.18, height = 9.81, units = "in", dpi = 900)
####Traumatic causes######
library(tidyverse)
library(readxl)
EPI_T <- read_excel("C:/Users/Acer/Desktop/EPID_SCI/EPI.xlsx",
sheet = "DX_T")
cause_year_counts_T_T <- EPI_T %>%
group_by(Year, Cause) %>%
summarise(Count = n()) %>%
ungroup()
cause_year_counts_T_T_P<- cause_year_counts_T_T %>%
group_by(Year) %>%
mutate(Percentage = (Count / sum(Count)) * 100) %>%
ungroup()
F_T<-ggplot(cause_year_counts_T_T_P, aes(x = Year, y = Percentage, color = Cause, group = Cause)) +
geom_point(aes(shape = Cause), size=2.5, alpha= 2)+
geom_line() +
labs(title = "Traumatic Causes", x = "Year", y = "Percentage (%)") +
theme_minimal()+
scale_shape_manual(values = c("Fall related"= 15,
"Diving into shallow water" = 16,
"Physical assualt"=15,
"Post surgical complications"=19,
"RTA"=17,
"Stab injury"=15,
"Sports injury"=19,
"Gunshot injury"=15,
"Bull attack"=19))+
scale_x_continuous(breaks = c(2011,2013,2015,2017,2019,2021))+
scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100))+
theme(panel.spacing = unit(1, "cm"),
text = element_text(family = "Times New Roman", size = 16),
plot.title = element_text(family = "Times New Roman", hjust=0.5,size = 16),
axis.text = element_text(family = "Times New Roman", size = 12),
legend.text = element_blank(),
legend.title = element_blank(),
legend.position = "none"
)+
facet_wrap(~Cause)
ggsave("C:\\Users\\Acer\\Desktop\\EPID_SCI\\Traumatic_Causes.png",plot = F_T, width = 8.18, height = 8.81, units = "in", dpi = 900)
###Pott's disease~Male\Female########
library(readxl)
library(readxl)
EPI_PPT <- read_excel("C:/Users/Acer/Desktop/EPID_SCI/EPI.xlsx",
sheet = "PPTT")
cause_year_counts_POT<- EPI_PPT %>%
group_by(Y_DX, Gender) %>%
summarise(Count = n()) %>%
ungroup()
cause_year_counts_POT_F<- cause_year_counts_POT %>%
group_by(Y_DX) %>%
mutate(Percentage = (Count / sum(Count)) * 100) %>%
ungroup()
PP_T<-ggplot(cause_year_counts_POT_F, aes(x = Y_DX, y = Percentage, color = Gender, group = Gender)) +
geom_point(aes(shape = Gender), size=4.5, alpha= 2)+
geom_line() +
labs(title = "Incidence of Pott's Disease", x = "Year", y = "Percentage (%)") +
theme_minimal()+
scale_shape_manual(values = c("Male"= 15,
"Female" = 17))+
scale_x_continuous(breaks = c(2011,2013,2015,2017,2019,2021))+
scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100))+
theme(panel.spacing = unit(1, "cm"),
text = element_text(family = "Times New Roman", size = 18),
plot.title = element_blank(),
axis.text = element_text(family = "Times New Roman", size = 16),
legend.text = element_blank(),
legend.title = element_blank(),
legend.position = "none"
)+
facet_wrap(~Gender)
ggsave("C:\\Users\\Acer\\Desktop\\EPID_SCI\\POTT.png",plot = PP_T, width = 10.18, height = 3.81, units = "in", dpi = 900)
####Yearly_Div#####
library(tidyverse)
library(readxl)
Yearly <- read_excel("C:/Users/Acer/Desktop/EPID_SCI/EPI.xlsx",
sheet = "YY_I", na = "**")
Y_C <- Yearly %>%
drop_na(Division, `Year of Discharge`) %>%
group_by(Division, `Year of Discharge`) %>%
summarise(Count = n()) %>%
ungroup()
Y_F <- Y_C %>%
group_by(`Year of Discharge`) %>%
mutate(Percentage = round((Count / sum(Count)) * 100, 2)) %>%
ungroup()
#####RTA#####
library(readxl)
EPI_R <- read_excel("C:/Users/Acer/Desktop/EPID_SCI/EPI.xlsx",
sheet = "RTA")
cause_year_counts_R <- EPI_R %>%
group_by(Year, Outcome) %>%
summarise(Count = n()) %>%
ungroup()
cause_year_counts_R <- cause_year_counts_R %>%
group_by(Year) %>%
mutate(Percentage = (Count / sum(Count)) * 100) %>%
ungroup()
RTA_GG<-ggplot(cause_year_counts_R, aes(x = Year, y = Percentage, color = Outcome, group = Outcome)) +
geom_line(aes(linetype = Outcome)) +
geom_point(aes(shape = Outcome), size = 4) +
labs( x = "Year", y = "Percentage (%)") +
theme_classic()+
scale_x_continuous(breaks = c(2011,2013,2015,2017,2019,2021))+
scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100))+
scale_color_manual(values = c("Paraplegic" = "blue", "Tetraplegic" = "red")) +
scale_shape_manual(values = c("Paraplegic" = 15, "Tetraplegic" =17)) +
scale_linetype_manual(values = c("Paraplegic" = "dashed", "Tetraplegic" = "solid")) +
theme(
text = element_text(family = "Times New Roman",size = 16),
plot.title = element_text(family = "Times New Roman", size = 16),
axis.text = element_text(family = "Times New Roman", size = 12),
legend.text = element_text(family = "Times New Roman", size = 12),
legend.title = element_text(family = "Times New Roman", size = 12)
)
ggsave("C:\\Users\\Acer\\Desktop\\EPID_SCI\\RTA.png",plot = RTA_GG, width = 10.18, height = 3.81, units = "in", dpi = 900)