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Data Visualisation

Time Series Plots

library(nomisdata)
library(ggplot2)
library(dplyr)

# Fetch unemployment data over time
unemployment <- fetch_nomis(
  "NM_1_1",
  date = paste0("2020", c("01", "02", "03", "04", "05", "06")),
  geography = "2092957697",  # UK
  measures = 20201,           # Rate
  sex = 7
)

# Plot
unemployment |>
  mutate(DATE = as.Date(paste0(DATE, "-01"))) |>
  ggplot(aes(x = DATE, y = OBS_VALUE)) +
  geom_line(linewidth = 1, colour = "#0066cc") +
  geom_point(size = 2, colour = "#0066cc") +
  labs(
    title = "UK Unemployment Rate",
    subtitle = "Claimant Count Rate, 2020",
    x = NULL,
    y = "Rate (%)",
    caption = "Source: Nomis / ONS"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    panel.grid.minor = element_blank()
  )

Regional Comparisons

# Fetch data for all regions
regions <- fetch_nomis(
  "NM_1_1",
  time = "latest",
  geography = "TYPE480",  # Regions
  measures = 20201,       # Rate
  sex = 7
)

# Bar chart
regions |>
  arrange(desc(OBS_VALUE)) |>
  ggplot(aes(x = reorder(GEOGRAPHY_NAME, OBS_VALUE), y = OBS_VALUE)) +
  geom_col(fill = "#0066cc", alpha = 0.8) +
  coord_flip() +
  labs(
    title = "Unemployment Rate by Region",
    x = NULL,
    y = "Claimant Count Rate (%)",
    caption = "Source: Nomis / ONS"
  ) +
  theme_minimal()

Gender Comparisons

# Fetch data by sex
by_sex <- fetch_nomis(
  "NM_1_1",
  time = "latest",
  geography = "TYPE499",  # Countries
  measures = 20201,
  sex = c(5, 6)  # Male, Female
)

# Grouped bar chart
by_sex |>
  ggplot(aes(x = GEOGRAPHY_NAME, y = OBS_VALUE, fill = SEX_NAME)) +
  geom_col(position = "dodge", alpha = 0.8) +
  scale_fill_manual(values = c("#0066cc", "#cc0066")) +
  labs(
    title = "Unemployment Rate by Sex and Country",
    x = NULL,
    y = "Rate (%)",
    fill = "Sex",
    caption = "Source: Nomis / ONS"
  ) +
  theme_minimal() +
  theme(legend.position = "top")

Spatial Visualisation

library(sf)

# Fetch spatial data
spatial_data <- fetch_spatial(
  "NM_1_1",
  time = "latest",
  geography = "TYPE480",
  measures = 20201,
  sex = 7
)

# Map
ggplot(spatial_data) +
  geom_sf(aes(fill = OBS_VALUE), colour = "white", size = 0.3) +
  scale_fill_viridis_c(option = "plasma") +
  labs(
    title = "Unemployment Rate by Region",
    fill = "Rate (%)"
  ) +
  theme_void()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.