library(tidyverse)
library(ggtext)
library(patchwork)
library(ggcorrplot)
library(extrafont)
theme_update(rect = element_rect(color = NA,
fill = "#a9f51d"),
line = element_blank(),
text = element_text(color = "black"))
################# MANIPULATION ############################
data<- read.csv(file="cancer data for MOOC 1.csv", header = TRUE, sep = ',')
sum(is.na(data))
data[is.na(data)]<-0
data$fruitveg<- data$fruit + data$veg
names(data)<-c("patient_id","Age","Gender","BMI","Smoking","Exercise",
"Fruit","Veg","Cancer","Fruit & Veg")
corr_df<-data%>%select(-1)
corr<-cor(corr_df)
corr2<-cor(corr_df[c(-6,-7)])
####################### PLOTTING ######################
correlation <- ggcorrplot(corr,method="circle")+
ggtitle(label="Cancer and contributing factors",
subtitle="Causation is multifactorial, and provide examples of modification of the risk factors through prevention, \nunderstanding of population cancer aetiology interaction effects between risk factors and joint factors.\n'Evaluating intrinsic and non-intrinsic cancer risk factors' 2018\n(Wu, S., Zhu, W., Thompson, P. et al.)")+
theme(plot.title = element_text(family="Courier New",size=14,face="bold",color="#460046"),
plot.subtitle = element_text(family="Courier New",size=11),
axis.text = element_text(size=11,family="Courier New",face="bold"),
legend.title = element_text(size=8,family="Courier New"),
legend.text = element_text(size=8,family="Courier New"))
#plot.margin = margin(5,5,5,5))
correlation2<-ggcorrplot(corr2,hc.order = TRUE, type = "lower",
outline.col = "white",lab = TRUE)+
theme(legend.position = "none",
axis.text = element_text(size=11,family="Courier New",face="bold"))
main_plot <-correlation + correlation2
################### WIGGETTING ############################
labels <-
tibble(
labels = c(
"<img src='exercise-png-Transparent-Images.png'
+ width='100' /><br><b style='color:#00947E'>Exercise</b><br><i style='color:#00947E'>13%</i></b>",
"<img src='fruitveg.png'
+ width='90' /><br><b style='color:#FF5180'>Fruit & Veg</b><br><i style='color:#FF5180'>-25%</i></b>",
"<img src='obesity.png'
+ width='90' /><br><b style='color:#FF5180'>BMI</b><br><i style='color:#FF5180'>-12%</i></b>",
"<img src='gender.png'
+ width='90' /><br><b style='color:#FF5180'>Gender</b><br><i style='color:#FF5180'>20%</i></b>",
"<img src='smoking.png'
+ width='90' /><br><b style='color:#FF5180'>Smoking</b><br><i style='color:#FF5180'>30%</i></b>"
),
x = 1:5,
y = rep(1, 5)
)
legend <-
ggplot(labels, aes(x, y)) +
geom_richtext(aes(label = labels),
fill = NA,
color = NA,
vjust = 0) +
annotate("text", x = 3.5, y = 1.018,
label = "Correlated Risk factors",
size = 15,
fontface = "bold",
family = "Courier New") +
scale_x_continuous(limits = c(0.6, 6.1)) +
scale_y_continuous(limits = c(1, 1.02)) +
theme_void() +
theme(plot.background = element_rect(fill = "#87cf80"))
########################### CAPTIONING #####################################
caption <-
ggplot(data.frame(x = 1:2, y = 1:10)) +
labs(x = NULL, y = NULL,
caption = "Viz @fgazzelloni| Source: Imperial College data Mooc | Correlation Day 13")+
theme(line = element_blank(),
plot.caption = element_text(size=8, family="Courier New",color="#460046",face="bold"),
panel.background = element_rect(fill = "transparent"),
plot.background = element_rect(fill = "transparent",color = "transparent"),
panel.border = element_rect(color = "transparent"),
axis.text = element_blank())
###################### SAVING ############################
final <- legend + main_plot + caption + plot_layout(ncol = 1,heights = c(0.6, 1, 0))
ragg::agg_png(here::here("day13_correlation", "Correlation_day13.png"),
res = 320, width = 14, height = 8, units = "in")
final
dev.off()correlation day 13
data for Stats for pub health program created by imperial college
Load libraries