Microprocessor Trend Data

Data - Log

Info

# **Last data update**: 01 aoû 2026, 21:01. **Last compile**: 15 aoû 2026, 15:37

Info

• Microprocessor Trend Data html

variable

`microprocessor-trend-data` |>
  group_by(variable) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

variable	Nobs
cores	91
frequency	102
specint	76
transistors	100
watts	102

Transistors

Linear

plot_linear <- `microprocessor-trend-data` |>
  filter(variable == "transistors") |>
  transmute(V1 = as.numeric(V1),
            V2 = V2) |>
  filter(!is.na(V1), !is.na(V2)) |>
  ggplot() + geom_line(aes(x = V1, y = V2)) +
  scale_y_continuous(breaks = 10^7*seq(0, 10, 1)) +
  theme_minimal()  +
  ylab("# of transistors per microprocessor") + xlab("")

plot_linear

Log

plot_log <- plot_linear +
  scale_y_log10(breaks = 10^(seq(1, 10, 1)))

plot_log

Both

ggarrange(plot_linear + ggtitle("Moore's Law\nLinear Scale"),
          plot_log + ggtitle("\nLog Scale") + ylab(""))

Watts

Both

plot1 <- `microprocessor-trend-data` |>
  filter(variable == "watts") |>
  transmute(V1 = as.numeric(V1),
            V2 = V2) |>
  filter(!is.na(V1), !is.na(V2)) |>
  ggplot() + geom_line(aes(x = V1, y = V2)) +
  scale_y_continuous(breaks = seq(0, 1000, 50)) +
  theme_minimal() + ggtitle("Linear Scale") +
  ylab("Watts") + xlab("")


plot2 <- plot1 +
  scale_y_log10(breaks = c(1, 2, 5, 8, 10, 20, 50, 80, 100, 200, 500)) +
  ggtitle("Log Scale")

ggarrange(plot1, plot2)

Linear

plot1

Log

plot2

Cores

Both

plot1 <- `microprocessor-trend-data` |>
  filter(variable == "cores") |>
  transmute(V1 = as.numeric(V1),
            V2 = V2) |>
  filter(!is.na(V1), !is.na(V2)) |>
  ggplot() + geom_line(aes(x = V1, y = V2)) +
  scale_y_continuous(breaks = seq(0, 1000, 50)) +
  theme_minimal() + ggtitle("Linear Scale") +
  ylab("Cores") + xlab("")


plot2 <- plot1 +
  scale_y_log10(breaks = c(1, 2, 5, 8, 10, 20, 50, 80, 100, 200, 500)) +
  ggtitle("Log Scale")

ggarrange(plot1, plot2)

Linear

plot1

Log

plot2