Some text
suppressWarnings(suppressMessages(library("dplyr", quietly = T)))
suppressWarnings(suppressMessages(library("readxl", quietly = T)))
suppressWarnings(suppressMessages(library("tidyverse", quietly = T)))
suppressWarnings(suppressMessages(library("ggplot2", quietly = T)))
## to tidy up our table
suppressWarnings(suppressMessages(library("tidyr", quietly = T)))
## combining plots
suppressWarnings(suppressMessages(library("patchwork", quietly = T)))
##suppressMessages(library(dplyr))
# Read Excel file
ds <- read_xlsx(path = "C:/LocalFiles/RProjects/Test/data/JCH_data.xlsx", sheet="Sheet2")
names(ds)## [1] "ANSPs"
## [2] "Financial gate-to-gate cost-effectiveness"
## [3] "Unit cost of en-route ATFM delays"
## [4] "Unit cost of airport ATFM delays"
## [5] "Economic cost-effectiveness"
## [6] "Economic cost-effectiveness (1/4)"
## [7] "Economic cost-effectiveness (3/4))"
# check if there are na values somewhere
colSums(is.na(ds))## ANSPs
## 1
## Financial gate-to-gate cost-effectiveness
## 1
## Unit cost of en-route ATFM delays
## 1
## Unit cost of airport ATFM delays
## 1
## Economic cost-effectiveness
## 1
## Economic cost-effectiveness (1/4)
## 2
## Economic cost-effectiveness (3/4))
## 2
Another chunk
#Changing the names of columns
names(ds)[1] <- "ANSP_NAME"
names(ds)[2] <- "FIN_CEF"
names(ds)[3] <- "UC_ER_DLY"
names(ds)[4] <- "UC_APT_DLY"
names(ds)[5] <- "CEF"
names(ds)[6] <- "CEF1_4"
names(ds)[7] <- "CEF3_4"
#Select a range of rows
#ds[1:38,] -> ds
ds <- ds %>%
filter(!is.na(ANSP_NAME), ANSP_NAME != "Total European System")
#Coerce into a facto... it wasn´t necessary at the end
ds %>% mutate(ANSP2 = as.factor(ANSP_NAME))## # A tibble: 38 x 8
## ANSP_NAME FIN_CEF UC_ER_DLY UC_APT_DLY CEF CEF1_4 CEF3_4 ANSP2
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
## 1 skeyes 856. 279. 54.8 1190. 340. 526. skeyes
## 2 LVNL 681. 13.3 347. 1042. 340. 526. LVNL
## 3 Austro Control 525. 364. 32.0 920. 340. 526. Austro Contr~
## 4 HungaroControl 336. 535. 0.592 871. 340. 526. HungaroContr~
## 5 DFS 527. 247. 20.9 795. 340. 526. DFS
## 6 Skyguide 675. 38.4 79.2 793. 340. 526. Skyguide
## 7 UkSATSE 679. 0 1.76 681. 340. 526. UkSATSE
## 8 DSNA 448. 156. 14.3 618. 340. 526. DSNA
## 9 LPS 530. 34.6 0 564. 340. 526. LPS
## 10 Albcontrol 532. 0.128 0 533. 340. 526. Albcontrol
## # ... with 28 more rows
#Tidy data
tmp <- ds %>% select (1:4) %>%
pivot_longer(cols = c("FIN_CEF","UC_ER_DLY","UC_APT_DLY"), names_to = "TYPE", values_to ="value")
# Change "Continental" by "Cont"
tmp <- tmp %>% mutate(across(ANSP_NAME,~str_replace_all(ANSP_NAME,"Continental","Cont.")))Working now with tidy data
# We plot now the tidy data
# But first we create a table with the labels for the graph... I tried first with an additional column but better with a separate table
# tmp <- tmp %>% group_by(ANSP_NAME) %>% mutate(graph_label = sum(value))
mylabels <- tmp %>% group_by(ANSP_NAME) %>% summarise(graph_label=sum(value))
# turns out the sorting below is not needed
# mylabels <- mylabels %>% arrange(-graph_label)
# I need the quartiles too for the lines
mylabels <- mylabels %>% mutate(q25=unname(quantile(graph_label, probs=0.25)), q75=unname(quantile(graph_label, probs=0.75)))
vis1 <- tmp %>%
ggplot()+
# note the refactoring of the TYPE to change the ordering in the stacked bar
aes(x=reorder(ANSP_NAME, -value), y = value, fill = factor(TYPE,levels=c("UC_ER_DLY", "UC_APT_DLY", "FIN_CEF")))+
geom_col()+
# Add labels to the bars
geom_text(data=mylabels, size=3, aes(x=ANSP_NAME, y=graph_label, label = round(graph_label, digits=0), angle = 90, hjust=-0.2), inherit.aes = FALSE)+
# resetting the y axis limit so the label is not out of range
coord_cartesian(ylim=c(0,max(mylabels$graph_label)+200))+
# adding the quartiles
geom_line(data=mylabels, aes(x=ANSP_NAME, y=q25, group="OSCAR"), linetype = "dashed", color="#333399", size=1, alpha = 0.5, inherit.aes = FALSE)+
geom_line(data=mylabels, aes(x=ANSP_NAME, y=q75, group="OSCAR"), linetype = "dashed", color="#333399", size=1, alpha = 0.5, inherit.aes = FALSE)+
# This line changes the colour of the bars and the labels of the legend
scale_fill_manual(values = c("#eee600", "#ff0000", "#9999ff"), labels = c("Unit cost of en-route ATFM delays", "Unit cost of aiport ATFM delays", "Financial G2G cost-effectiveness"))+
##geom_text(size=3, aes(label = round(Total, digits = 2)), angle = 90, vjust = +0.5, hjust = +1)+
theme_minimal()+
theme(axis.text.x = element_text(size=8, angle=90, hjust=1, vjust=0.2))+
# Customize legend
theme(legend.position="top", legend.title = element_blank(), legend.key.size = unit(0.3,"cm"))+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
labs(x=NULL, y="€ per composite flight-hour")
## vis1
##vis1 + vis1
##vis1 / vis1Creating the inset graph
largest5 <- tmp %>% filter(ANSP_NAME %in% c("DFS", "DSNA", "ENAV", "ENAIRE", "NATS (Cont.)"))
vis2 <- largest5 %>%
ggplot()+
aes(x=ANSP_NAME, y = value, fill = factor(TYPE,levels=c("UC_ER_DLY", "UC_APT_DLY", "FIN_CEF")))+
geom_col()+
scale_fill_manual(values = c("#eee600", "#ff0000", "#9999ff"))+
theme_minimal()+
theme(axis.text.x = element_text(size=7, angle=90, hjust=1, vjust=0.2))+
theme(legend.position="none", panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
labs(x=NULL, y=NULL)
vis1 + inset_element(vis2, left = 0.6, bottom = 0.3, right = 1, top = 1)
This was the first attempt, kept here in case we need it
# plot graph
ds %>%
ggplot()+
aes (x=reorder(ANSP_NAME,-FIN_CEF), y=FIN_CEF)+
geom_col(fill="#2e86c1")+
geom_line(aes(x=ANSP_NAME, y=CEF1_4, group="OSCAR"))+
geom_line(aes(x=ANSP_NAME, y=CEF3_4, group="JC")) +
theme_minimal()+
theme(axis.text.x=element_text(size=8, angle=90,hjust=1,vjust=0.2))+
labs(title="Economic Cost-Effectiveness", subtitle="done by cool guys", caption="this is my caption", x=NULL, y="Financial Cost-Efficiency (€)")