Organize scale of x axis of time series graph - r

Question

Here I have data that looks like this:
# Data
df <- data.frame("Hospital" = c("Buge Hospital", "Buge Hospital", "Greta Hospital", "Greta Hospital",
"Makor Hospital", "Makor Hospital"),
"Period" = c("Jul-18","Aug-18", "Jul-19","Aug-19", "Jul-20","Aug-20"),
"Medical admissions" = c(12,56,0,40,5,56),
"Surgical admissions" = c(10,2,0,50,20,56),
"Inpatient admissions" = c(9,5,6,0,60,96))
Now this data has a column called period which is monthy data for different years, 2018,2019 and 2020
if I plot this data, here is how it looks
library(ggplot2
# Melt data into long format
df2 <- melt(data = df,
id.vars = c("Hospital","Period"),
measure.vars = names(df[3:5]))
# Stacked barplot
ggplot( df2, aes(x = Period, y = value, fill = variable, group = variable)) +
geom_bar(stat = "identity") +
theme(legend.position = "none") +
ggtitle(unique(df2$Hospital))+
scale_x_date(date_labels = %Y)+
labs(x = "Month", y = "Number of People", fill = "Type")
It plots well but the x axis is not organized in ascending order, I have tried to use scale_x_date function but still the plot is the same. What I want is months for the year 2018 to start, then followed with months for 2019 and 2020. I mean x axis to be organized in ascending order based on years like this
Aug-18, Jul-18, Aug-19,Jul-19, Aug-20,Jul-20.

Answer 1

To solve your issue, you need to convert your Period in a date format.
For example, you can use parse_date function from lubridate package:
library(lubridate)
library(tidyr)
library(dplyr)
df %>% mutate(Date = parse_date(as.character(Period), format = "%b-%y")) %>%
pivot_longer(cols = Medical.admissions:Inpatient.admissions, names_to = "Var", values_to = "Val")
# A tibble: 18 x 5
Hospital Period Date Var Val
<fct> <fct> <date> <chr> <dbl>
1 Buge Hospital Jul-18 2018-07-01 Medical.admissions 12
2 Buge Hospital Jul-18 2018-07-01 Surgical.admissions 10
3 Buge Hospital Jul-18 2018-07-01 Inpatient.admissions 9
4 Buge Hospital Aug-18 2018-08-01 Medical.admissions 56
5 Buge Hospital Aug-18 2018-08-01 Surgical.admissions 2
6 Buge Hospital Aug-18 2018-08-01 Inpatient.admissions 5
7 Greta Hospital Jul-19 2019-07-01 Medical.admissions 0
8 Greta Hospital Jul-19 2019-07-01 Surgical.admissions 0
9 Greta Hospital Jul-19 2019-07-01 Inpatient.admissions 6
10 Greta Hospital Aug-19 2019-08-01 Medical.admissions 40
11 Greta Hospital Aug-19 2019-08-01 Surgical.admissions 50
12 Greta Hospital Aug-19 2019-08-01 Inpatient.admissions 0
13 Makor Hospital Jul-20 2020-07-01 Medical.admissions 5
14 Makor Hospital Jul-20 2020-07-01 Surgical.admissions 20
15 Makor Hospital Jul-20 2020-07-01 Inpatient.admissions 60
16 Makor Hospital Aug-20 2020-08-01 Medical.admissions 56
17 Makor Hospital Aug-20 2020-08-01 Surgical.admissions 56
18 Makor Hospital Aug-20 2020-08-01 Inpatient.admissions 96
So, then, you can use scale_x_date to set appropriate labeling option on your x axis:
library(lubridate)
library(tidyr)
library(dplyr)
library(ggplot2)
df %>% mutate(Date = parse_date(as.character(Period), format = "%b-%y")) %>%
pivot_longer(cols = Medical.admissions:Inpatient.admissions, names_to = "Var", values_to = "Val") %>%
ggplot(aes(x = Date, y = Val, fill= Var, group = Var))+
geom_col()+
scale_x_date(date_breaks = "month", date_labels = "%b %Y")+
labs(x = "Month", y = "Number of People", fill = "Type")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Does it answer your question ?
EDIT: Using `lubridate v1.7.8
On lubridate version 1.7.8, parse_date does not exist anymore. You will have to replace it by parse_date_time as follow:
library(lubridate)
library(dplyr)
df %>% mutate(Date = ymd(parse_date_time2(as.character(Period), orders = "%b-%y"))) %>% ....