Basic Visualization Samples

Line Graphs

# Load the ggplot2 package
library(ggplot2)
# Load necessary library
library(lubridate)

# Set seed for reproducibility
set.seed(123)

# Create a sequence of dates from 2011 to 2019 by quarters
dates <- seq(as.Date("2011-01-01"), as.Date("2019-12-31"), by="quarter")

# Generate random wage growth and inflation rates
wage_growth <- runif(length(dates), min = 1, max = 5)
inflation <- runif(length(dates), min = 0.5, max = 4)

# Combine into a data frame
data <- data.frame(date = dates, wage_growth = wage_growth, inflation = inflation)

# Display the first few rows of the data
head(data)

        date wage_growth inflation
1 2011-01-01    2.150310 3.1546084
2 2011-04-01    4.153221 1.2574278
3 2011-07-01    2.635908 1.6136335
4 2011-10-01    4.532070 1.3106902
5 2012-01-01    4.761869 0.9998001
6 2012-04-01    1.182226 1.9509122

# Assuming your data frame is named 'data' and it has columns 'date', 'wage_growth', and 'inflation'
ggplot(data, aes(x=date)) + 
  geom_line(size = 0.8,aes(y=wage_growth, colour="Wage growth")) + 
  geom_line(size = 0.8,aes(y=inflation, colour="Inflation")) +
  #coord_cartesian(xlim = c(2011, 2020), ylim = c(1,5)) + 
  scale_x_date(breaks = seq(as.Date("2011-01-01"), as.Date("2020-01-01"), by="1 year"),date_labels = "%Y")+  # Correct approach for Date objects
  #scale_x_continuous(breaks = seq(2011, 2020, by = 1)) +  # Add the scale layer
  geom_hline(yintercept = c(1, 2, 3,4 ,5 ), color = "grey", linewidth = 0.5, linetype = "solid") +  # Add horizontal lines
  scale_colour_manual(values=c("Wage growth"="darkred", "Inflation"="#00CED1")) +
  labs(title="Wages continue to outstrip inflation",
       subtitle="Three-month average figures",
       caption="Source: ONS. Inflation = CPIH, Three-month average figures",
       x="",
       y="")+
      annotate("text", x = as.Date("2019-01-01"), y = 5, label = "Wage growth (1.7%)", colour = "darkred", hjust = 0.8, vjust = 0,fontface = "bold", family = "sans", size = 4) +
      annotate("text", x = as.Date("2019-01-01"), y = 0.8, label = "Inflation (1.4%)", colour = "#00CED1", hjust = 1.5, vjust = 1,fontface = "bold", family = "sans", size = 4) +
        
  theme_minimal() +
  
  theme(text = element_text(family = "Arial", colour = "black"),
        plot.title = element_text(size = 25, face = "bold"),
        plot.subtitle = element_text(size = 16),
        plot.caption = element_text(size = 10),
        axis.text.x = element_text(angle = 0, vjust = 0.5, size = 12,colour = "black"),
        axis.text.y = element_text(size = 15),
        axis.line.y = element_blank(),
        legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.line = element_line(colour = "black")) 

Bar Graph

# Load necessary library
library(dplyr)

# Create vectors for each column
industries <- c("Leisure and hospitality", "Wholesale and retail trade",
                "Education and health services", "Government workers",
                "Manufacturing", "Professional and business services",
                "Construction", "Other services", "Other industries")

unemployed_numbers <- c(4.86, 3.22, 2.55, 2.02, 1.99, 1.70, 1.53, 1.42, 3.21)  # in millions

unemployment_rates <- c(39, 17, 11, 9, 13, 10, 17, 23, 0)  # in percentage, placeholder for 'Other industries'

# Create a data frame
job_crisis_data <- data.frame(Industry = industries,
                              UnemployedNumbers = unemployed_numbers,
                              UnemploymentRate = unemployment_rates)
# Print the data frame
print(job_crisis_data)

                            Industry UnemployedNumbers UnemploymentRate
1            Leisure and hospitality              4.86               39
2         Wholesale and retail trade              3.22               17
3      Education and health services              2.55               11
4                 Government workers              2.02                9
5                      Manufacturing              1.99               13
6 Professional and business services              1.70               10
7                       Construction              1.53               17
8                     Other services              1.42               23
9                   Other industries              3.21                0

library(ggplot2)
job_crisis_data$UnemployedNumbers <- job_crisis_data$UnemployedNumbers

# Creating the plot
ggplot(data = job_crisis_data, aes(x = reorder(Industry, UnemployedNumbers), y = UnemployedNumbers)) + 
  geom_bar(stat = "identity", fill = "#7f0c0f") +
  geom_text(aes(label = paste0(UnemployedNumbers, " m")), 
            vjust = 0.5, 
            hjust = 1.2,
            color = "white",
            fontface = 'bold',
            size = 4) +
  scale_y_continuous(labels = scales::comma, 
                     breaks = scales::pretty_breaks(n = 10)) +
  theme_minimal() +
  labs(title = "The Industries Worst Affected\n by the COVID-19 Job Crisis",
       subtitle = "Number of unemployed persons aged 16 and over\n in the U.S. in April 2020, by industry",
       x = "",
       y = "") +
  theme(
        plot.title = element_text(size = 18, face = "bold"),
        plot.subtitle = element_text(size = 12),
        axis.text.x = element_blank(),
        axis.text.y = element_text(size = 11,colour = "black"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),) +
  coord_flip() # Flip the coordinates to make a horizontal bar chart