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Initial commit Chip dataset
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FCTanner committed Aug 26, 2022
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126 changes: 126 additions & 0 deletions 2022/2022-08-23 CHIP dataset/2022-08-23 CHIP dataset.Rmd
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---
title: "2022-08-23 CHIP dataset"
author: Florian Tanner
date: "`r format(Sys.time())`"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


```{r}
rm(list = ls())
library(tidyverse)
library(ggtext)
library(ggrepel)
library(png)
library(patchwork)
library(showtext)
sysfonts::font_add_google("Roboto Condensed")
showtext::showtext_auto()
showtext::showtext_opts(dpi = 300)
```

```{r}
chips_full <- read_csv("chip_dataset.csv") |>
janitor::clean_names()
nvidia_logo <- grid::rasterGrob(readPNG("nvidia_logo_crop.png"))
```

```{r}
data <- chips_full |>
filter(type == "GPU",
vendor == "NVIDIA",
str_detect(product, pattern = c("RTX | GTX|GT")),
release_date != "NaT") |>
mutate(year = substr(release_date, 1, 4),
product_line = case_when(str_detect(product, "GT") &
str_detect(product, "GTX", negate = T) &
str_detect(product, "GTS", negate = T)
~ "GT",
str_detect(product, "GTX") ~ "GTX",
str_detect(product, "RTX ") ~ "RTX",
TRUE ~ "other"),
release_date = as.Date(release_date),
product_label = str_remove(product, "NVIDIA "),
product_label = str_remove(product_label, "GeForce "))
```


```{r}
theme_nvidia <-
theme_bw(base_size = 30, base_family = "Roboto Condensed") +
theme(panel.background = element_rect(fill = "#76b900", color = "#76b900"),
panel.grid = element_blank(),
plot.background = element_rect(fill = "#76b900", color = "#76b900"),
axis.title.x = element_blank(),
axis.title.y = element_text(family = "Roboto Condensed"),
plot.title = element_blank(),
legend.position = "none")
nvidia_scheme <- c("#F5F5F5", "#B3CAE7", "#7598C1")
```

```{r}
nvidia_title <- "GPU performance<br><br><span style = 'color: #F5F5F5;font-weight:bold;'>GT</span>, <span style = 'color: #B3CAE7;font-weight:bold;'>GTX</span> and<br><span style = 'color: #7598C1;font-weight:bold;'>RTX</span> series"
```



```{r}
p <- data |>
filter(product_line != "other") |>
ggplot(aes(x = release_date, y = fp32_gflops, color = product_line )) +
geom_point(color = "black", size = 3) +
geom_point(size = 2.5) +
ggrepel::geom_label_repel(aes(label=product_label, fill = product_line), color= "black",
family = "Roboto Condensed") +
scale_color_manual(values = nvidia_scheme) +
scale_fill_manual(values = nvidia_scheme) +
scale_x_date(limits = as.Date(c("2006-01-01", "2022-01-01"))) +
scale_y_log10() +
geom_richtext(aes(x = as.Date("2006-01-01"), y = 20000, label = nvidia_title),
fill = NA, label.color = NA, size = 30, hjust = 0, color = "black",
family = "Roboto Condensed", fontface= "bold") +
labs(y = "FP32 GFLOPS, log scale", caption= "Data: Sun, Yifan, et al. Summarizing CPU and GPU design trends with product data. arXiv preprint arXiv:1911.11313 (2019). | Graphic: @TannerFlorian")
p <- p +
inset_element(nvidia_logo, left = 0.7,
bottom = 0.001,
right = 0.99,
top = 0.3) & theme_nvidia
```


```{r}
ggsave(plot = p, filename = "large.png", units = "cm", width = 60, height = 60, limitsize = F, device = "png")
```


```{r}
sessionInfo()
```


















178 changes: 178 additions & 0 deletions 2022/2022-08-23 CHIP dataset/2022-08-23-CHIP-dataset.md
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2022-08-23 CHIP dataset
================
Florian Tanner
2022-08-26 14:23:30

``` r
rm(list = ls())

library(tidyverse)
```

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()

``` r
library(ggtext)
library(ggrepel)
library(png)
library(patchwork)
library(showtext)
```

## Loading required package: sysfonts
## Loading required package: showtextdb

``` r
sysfonts::font_add_google("Roboto Condensed")
showtext::showtext_auto()
showtext::showtext_opts(dpi = 300)
```

``` r
chips_full <- read_csv("chip_dataset.csv") |>
janitor::clean_names()
```

## New names:
## Rows: 4854 Columns: 14
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (5): Product, Type, Release Date, Foundry, Vendor dbl (9): ...1, Process Size
## (nm), TDP (W), Die Size (mm^2), Transistors (mil...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`

``` r
nvidia_logo <- grid::rasterGrob(readPNG("nvidia_logo_crop.png"))
```

``` r
data <- chips_full |>
filter(type == "GPU",
vendor == "NVIDIA",
str_detect(product, pattern = c("RTX | GTX|GT")),
release_date != "NaT") |>
mutate(year = substr(release_date, 1, 4),
product_line = case_when(str_detect(product, "GT") &
str_detect(product, "GTX", negate = T) &
str_detect(product, "GTS", negate = T)
~ "GT",
str_detect(product, "GTX") ~ "GTX",
str_detect(product, "RTX ") ~ "RTX",
TRUE ~ "other"),
release_date = as.Date(release_date),
product_label = str_remove(product, "NVIDIA "),
product_label = str_remove(product_label, "GeForce "))
```

``` r
theme_nvidia <-
theme_bw(base_size = 30, base_family = "Roboto Condensed") +
theme(panel.background = element_rect(fill = "#76b900", color = "#76b900"),
panel.grid = element_blank(),
plot.background = element_rect(fill = "#76b900", color = "#76b900"),
axis.title.x = element_blank(),
axis.title.y = element_text(family = "Roboto Condensed"),
plot.title = element_blank(),
legend.position = "none")

nvidia_scheme <- c("#F5F5F5", "#B3CAE7", "#7598C1")
```

``` r
nvidia_title <- "GPU performance<br><br><span style = 'color: #F5F5F5;font-weight:bold;'>GT</span>, <span style = 'color: #B3CAE7;font-weight:bold;'>GTX</span> and<br><span style = 'color: #7598C1;font-weight:bold;'>RTX</span> series"
```

``` r
p <- data |>
filter(product_line != "other") |>
ggplot(aes(x = release_date, y = fp32_gflops, color = product_line )) +
geom_point(color = "black", size = 3) +
geom_point(size = 2.5) +
ggrepel::geom_label_repel(aes(label=product_label, fill = product_line), color= "black",
family = "Roboto Condensed") +
scale_color_manual(values = nvidia_scheme) +
scale_fill_manual(values = nvidia_scheme) +
scale_x_date(limits = as.Date(c("2006-01-01", "2022-01-01"))) +
scale_y_log10() +
geom_richtext(aes(x = as.Date("2006-01-01"), y = 20000, label = nvidia_title),
fill = NA, label.color = NA, size = 30, hjust = 0, color = "black",
family = "Roboto Condensed", fontface= "bold") +
labs(y = "FP32 GFLOPS, log scale", caption= "Data: Sun, Yifan, et al. Summarizing CPU and GPU design trends with product data. arXiv preprint arXiv:1911.11313 (2019). | Graphic: @TannerFlorian")



p <- p +
inset_element(nvidia_logo, left = 0.7,
bottom = 0.001,
right = 0.99,
top = 0.3) & theme_nvidia
```

``` r
ggsave(plot = p, filename = "large.png", units = "cm", width = 60, height = 60, limitsize = F, device = "png")
```

## Warning: Removed 28 rows containing missing values (geom_point).
## Removed 28 rows containing missing values (geom_point).

## Warning: Removed 28 rows containing missing values (geom_label_repel).

## Warning: ggrepel: 67 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

``` r
sessionInfo()
```

## R version 4.2.1 (2022-06-23 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
## [3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] showtext_0.9-5 showtextdb_3.0 sysfonts_0.8.8 patchwork_1.1.1
## [5] png_0.1-7 ggrepel_0.9.1 ggtext_0.1.1 forcats_0.5.1
## [9] stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4 readr_2.1.2
## [13] tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.9 lubridate_1.8.0 assertthat_0.2.1
## [4] digest_0.6.29 utf8_1.2.2 R6_2.5.1
## [7] cellranger_1.1.0 backports_1.4.1 reprex_2.0.1
## [10] evaluate_0.16 httr_1.4.3 pillar_1.8.0
## [13] rlang_1.0.4 curl_4.3.2 googlesheets4_1.0.0
## [16] readxl_1.4.0 rstudioapi_0.13 rmarkdown_2.14
## [19] googledrive_2.0.0 bit_4.0.4 munsell_0.5.0
## [22] gridtext_0.1.4 broom_1.0.0 janitor_2.1.0
## [25] compiler_4.2.1 modelr_0.1.8 xfun_0.32
## [28] pkgconfig_2.0.3 htmltools_0.5.3 tidyselect_1.1.2
## [31] fansi_1.0.3 crayon_1.5.1 tzdb_0.3.0
## [34] dbplyr_2.2.1 withr_2.5.0 grid_4.2.1
## [37] jsonlite_1.8.0 gtable_0.3.0 lifecycle_1.0.1
## [40] DBI_1.1.3 magrittr_2.0.3 scales_1.2.0
## [43] vroom_1.5.7 cli_3.3.0 stringi_1.7.8
## [46] farver_2.1.1 fs_1.5.2 snakecase_0.11.0
## [49] xml2_1.3.3 ellipsis_0.3.2 generics_0.1.3
## [52] vctrs_0.4.1 tools_4.2.1 bit64_4.0.5
## [55] glue_1.6.2 markdown_1.1 hms_1.1.1
## [58] parallel_4.2.1 fastmap_1.1.0 yaml_2.3.5
## [61] colorspace_2.0-3 gargle_1.2.0 rvest_1.0.2
## [64] knitr_1.39 haven_2.5.0
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