ukbb_asian_plotting.Rmd

---
title: "UKBB plotting Asian populations"
author: 
  - name: Alex Diaz-Papkovich
affiliation:
  - &cruk Quantitative Life Sciences, McGill University,  Montreal, Canada
date: '`r format(Sys.Date(), "%Y-%B-%d")`'
output:
  html_notebook:
  df_print: paged
code_folding: show
toc: yes
toc_float: 
  collapsed: false
smooth_scroll: false
---

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

## Import libraries

```{r}
library(tidyverse)
library(ggplot2)
```

## Import the files
```{r}

data_path <- "/Volumes/Stockage/alex/ukbb_other/data/ukbb_asian_data.csv"
ukbb_asian_data <- read.csv(data_path)

```

## Recreate the original image from the jupyter notebook
```{r}

out_dir <- "/Volumes/Stockage/alex/ukbb_images/ggplot_for_export"
fname <- "Asthma_FullAsian_PC_QC_new_UMAP_PC5_NC2_NN15_MD05_201931173256"
h <- 10
w <- 6.49
s <- 0.5
a <- 0.6

# Colour palette from other projection
#original_pal <- c("#fcc5c0","#3f007d","#f768a1","#e377c2","#49006a","#ae017e")

# New colour palette to better distinguish groups
colour_pal <- c("#fcc5c0","#c7c7c7","#ff7f0e","#e377c2","#49006a","#2ca02c")

p_asian <- ggplot(data = ukbb_asian_data, aes(x=x_coords, y=y_coords, colour=eth_txt)) +
  geom_point(size=s, alpha=a) +
  scale_color_manual(values=colour_pal) +
  theme(
        axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank(),
        axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),
        panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.border = element_blank(), panel.background = element_blank(),
        legend.position="bottom", legend.title=element_blank()
        ) +
  guides(colour = guide_legend(override.aes = list(size=3)))
#+
#  guides(colour=FALSE)

p_asian

ggsave(paste(paste(out_dir,fname,sep="/"),".jpeg", sep=""), height=h, width=w, units="in")
```

# Tweak the dataset so that we can use ethnicity x COB
```{r}
ukbb_asian_data$eth_COB <- paste(ukbb_asian_data$eth_txt, "born in", ukbb_asian_data$COB)

eth_cob_counts <- as.data.frame(table(ukbb_asian_data$eth_COB))
eth_cob_counts[order(-eth_cob_counts$Freq),][1:10,]
```

We only want the top few.

```{r}
ukbb_asian_data$eth_COB_2 <- ifelse(ukbb_asian_data$eth_COB=="Indian born in Kenya",
                                    "Indian born in Kenya", "Other")
```

# Now do some more plotting with this new variable
```{r}
out_dir <- "/Volumes/Stockage/alex/ukbb_images/ggplot_for_export"
fname <- "Asthma_FullAsian_PC_QC_new_UMAP_PC5_NC2_NN15_MD05_201931173256_ethcob"
h <- 10
w <- 6.49
s <- 0.5
a <- 0.4

# Colour palette from other projection
colour_pal <- c("#49006a","#e8e8e8")

p_asian <- ggplot(data = ukbb_asian_data, aes(x=x_coords, y=y_coords, colour=eth_COB_2)) +
  geom_point(size=s, alpha=a) +
  scale_color_manual(values=colour_pal) +
  theme(axis.title.y=element_blank(),
      axis.text.y=element_blank(),
      axis.ticks.y=element_blank(),
      axis.title.x=element_blank(),
      axis.text.x=element_blank(),
      axis.ticks.x=element_blank(),
      panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank()) +
  guides(colour=FALSE) +
  annotate("text", x=5, y=-25,label="Indians born in Kenya",colour=colour_pal[1])

p_asian

ggsave(paste(paste(out_dir,fname,sep="/"),".jpeg", sep=""), height=h, width=w, units="in")
```