How about this?
Edit: Actually, I have noted that it looks nothing like the chart in your first post. You can sort that out.
library(tidyverse)
actuals <- tibble::tribble(
~workflow_code, ~expected_time, ~processed_time,
"Emirates", "11:00:00", "10:38:10 AM",
"American Airlines", "15:30:00", "3:25:11 PM",
"Qantas", "19:30:00", "3:25:20 PM",
"Virgin Australia", "15:30:00", "3:25:14 PM",
"Etihad", "19:30:00", "3:26:18 PM",
"Air Canada", "15:30:00", "3:28:10 PM",
"Lufthansa", "15:30:00", "3:28:12 PM",
"SriLankan Airlines", "19:30:00", "3:27:06 PM",
"Singapore Airlines", "19:30:00", "3:27:06 PM",
"Cathay Pacific", "15:30:00", "3:25:12 PM",
"Indian Airlines", "19:30:00", "3:25:12 PM",
"Air India", "15:30:00", "3:26:21 PM",
"Qatar Airways", "19:30:00", "3:25:15 PM",
"Philippine Airlines", "15:30:00", "3:27:09 PM",
"Royal Brunei Airlines", "19:30:00", "3:26:24 PM",
"Turkish Airlines", "15:30:00", "3:25:15 PM",
"Alaska Airlines", "19:30:00", "3:25:11 PM"
)
c1_data <- actuals %>% select(workflow_code, expected_time) # your melt didn't actually do anything
ggplot(c1_data, aes(y=expected_time, x=workflow_code)) + # missing 'k' in workflow
geom_bar(stat="identity", fill="white") +
geom_text(aes(label="ET"),vjust=0.3, size=3.5) +
coord_flip() # looks better flipped