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https://github.com/robnewman/data-studios-datasources

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Host: GitHub
URL: https://github.com/robnewman/data-studios-datasources
Owner: robnewman
License: mit
Created: 2024-02-26T19:53:06.000Z (9 months ago)
Default Branch: main
Last Pushed: 2024-07-12T09:13:58.000Z (4 months ago)
Last Synced: 2024-07-12T12:27:26.481Z (4 months ago)
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Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

        # data-studios-datasources

## RStudio

### Example 1 - RNASeq

Note that the CSV file paths may be different for you.

```R

# Adapted from source: https://combine-australia.github.io/RNAseq-R/09-applying-rnaseq-solutions.html

install.packages("BiocManager")

BiocManager::install(c("limma"))

BiocManager::install(c("edgeR"))

BiocManager::install(c("org.Dm.eg.db"))

BiocManager::install(c("gplots"))

BiocManager::install(c("RColorBrewer"))

library(limma)

library(edgeR)

library(gplots)

library(RColorBrewer)

library(org.Dm.eg.db)

counts <- read.delim(file="/workspace/data/datastudios-demo-rstudio/input/2024-05-13/counts_Drosophila.txt")

targets <- read.delim(file="/workspace/data/datastudios-demo-rstudio/input/2024-05-13/SampleInfo_Drosophila.txt")

head(counts)

targets

table(targets$Group)

mycpm <- cpm(counts)

plot(counts[,1],mycpm[,1],xlim=c(0,20),ylim=c(0,5))

abline(v=10,col=2)

abline(h=2,col=4)

thresh <- mycpm > 2

keep <- rowSums(thresh) >= 3

table(keep)

counts.keep <- counts[keep,]

dim(counts.keep)

y <- DGEList(counts.keep)

barplot(y$samples$lib.size)

par(mfrow=c(1,1))

# Get log2 counts per million

logcpm <- cpm(y$counts,log=TRUE)

# Check distributions of samples using boxplots

boxplot(logcpm, xlab="", ylab="Log2 counts per million",las=2,outline=FALSE)

# Let's add a blue horizontal line that corresponds to the median logCPM

abline(h=median(logcpm),col="blue")

title("Boxplots of logCPMs (unnormalised)")

par(mfrow=c(1,2),oma=c(2,0,0,0))

group.col <- c("red","blue")[targets$Group]

boxplot(logcpm, xlab="", ylab="Log2 counts per million",las=2,col=group.col,

        pars=list(cex.lab=0.8,cex.axis=0.8))

abline(h=median(logcpm),col="blue")

title("Boxplots of logCPMs\n(coloured by groups)",cex.main=0.8)

lib.col <- c("light pink","light green")[targets$Library]

boxplot(logcpm, xlab="", ylab="Log2 counts per million",las=2, col=lib.col,

        pars=list(cex.lab=0.8,cex.axis=0.8))

abline(h=median(logcpm),col="blue")

title("Boxplots of logCPMs\n(coloured by library prep)",cex.main=0.8)

par(mfrow=c(1,2))

plotMDS(y,col=group.col)

legend("topright",legend=levels(targets$Group),fill=c("red","blue"))

plotMDS(y,col=lib.col)

legend("topleft",legend=levels(targets$Library),fill=c("light pink","light green"))

logcounts <- cpm(y,log=TRUE)

var_genes <- apply(logcounts, 1, var)

select_var <- names(sort(var_genes, decreasing=TRUE))[1:500]

highly_variable_lcpm <- logcounts[select_var,]

dim(highly_variable_lcpm)

mypalette <- brewer.pal(11,"RdYlBu")

morecols <- colorRampPalette(mypalette)

heatmap.2(highly_variable_lcpm,col=rev(morecols(50)),trace="none", main="Top 500 most variable genes across samples",ColSideColors=group.col,scale="row",margins=c(10,5))

```

### Example 2 - Shiny

```R

install.packages("dplyr")

install.packages("gapminder")

install.packages("ggplot2")

install.packages("shiny")

library(shiny)

library(dplyr)

library(ggplot2)

library(gapminder)

# Specify the application port

options(shiny.host = "0.0.0.0")

options(shiny.port = 8180)

ui <- fluidPage(

  sidebarLayout(

    sidebarPanel(

      tags$h4("Gapminder Dashboard"),

      tags$hr(),

      selectInput(inputId = "inContinent", label = "Continent", choices = unique(gapminder$continent), selected = "Europe")

    ),

    mainPanel(

      plotOutput(outputId = "outChartLifeExp"),

      plotOutput(outputId = "outChartGDP")

    )

  )

)

server <- function(input, output, session) {

  # Filter data and store as reactive value

  data <- reactive({

    gapminder %>%

      filter(continent == input$inContinent) %>%

      group_by(year) %>%

      summarise(

        AvgLifeExp = round(mean(lifeExp)),

        AvgGdpPercap = round(mean(gdpPercap), digits = 2)

      )

  })

  # Common properties for charts

  chart_theme <- ggplot2::theme(

    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),

    axis.title.x = element_text(size = 15),

    axis.title.y = element_text(size = 15),

    axis.text.x = element_text(size = 12),

    axis.text.y = element_text(size = 12)

  )

  # Render Life Exp chart

  output$outChartLifeExp <- renderPlot({

    ggplot(data(), aes(x = year, y = AvgLifeExp)) +

      geom_col(fill = "#0099f9") +

      geom_text(aes(label = AvgLifeExp), vjust = 2, size = 6, color = "#ffffff") +

      labs(title = paste("Average life expectancy in", input$inContinent)) +

      theme_classic() +

      chart_theme

  })

  # Render GDP chart

  output$outChartGDP <- renderPlot({

    ggplot(data(), aes(x = year, y = AvgGdpPercap)) +

      geom_line(color = "#f96000", size = 2) +

      geom_point(color = "#f96000", size = 5) +

      geom_label(

        aes(label = AvgGdpPercap),

        nudge_x = 0.25,

        nudge_y = 0.25

      ) +

      labs(title = paste("Average GDP per capita in", input$inContinent)) +

      theme_classic() +

      chart_theme

  })

}

shinyApp(ui = ui, server = server)

```

### Example 3 - Volcano Plot using Shiny

Note that the CSV file path may be different for you.

```R

install.packages("shiny")

install.packages("plotly")

install.packages("tidyverse")

library(shiny)

library(plotly)

library(tidyverse)

ui <- fluidPage(

  titlePanel("Volcano Plotly"),

  fluidRow(

    column(

      width = 7,

      plotlyOutput("volcanoPlot", height = "500px")

    ),

    column(

      width = 5,

      dataTableOutput("selectedProbesTable")

    )

  )

)

csv_file = "/workspace/data/datastudios-demo-rstudio/input/2024-05-20_volcano-examples/NKI-DE-results.csv"

server <- function(input, output) {

  differentialExpressionResults <-

    read.csv(csv_file, stringsAsFactors = FALSE) %>%

    mutate(

      probe.type = factor(ifelse(grepl("^Contig", probe), "EST", "mRNA")),

      minusLog10Pvalue = -log10(adj.P.Val),

      tooltip = ifelse(is.na(HUGO.gene.symbol), probe, paste(HUGO.gene.symbol, " (", probe, ")", sep = ""))

    ) %>%

    sample_n(1000)

  output$volcanoPlot <- renderPlotly({

    plot <- differentialExpressionResults %>%

      ggplot(aes(x = logFC,

                 y = minusLog10Pvalue,

                 colour = probe.type,

                 text = tooltip,

                 key = row.names(differentialExpressionResults))) +

      geom_point() +

      xlab("log fold change") +

      ylab("-log10(P-value)")

    plot %>%

      ggplotly(tooltip = "tooltip") %>%

      layout(dragmode = "select")

  })

  output$selectedProbesTable <- renderDataTable({

    eventData <- event_data("plotly_selected")

    selectedData <- differentialExpressionResults %>% slice(0)

    if (!is.null(eventData)) selectedData <- differentialExpressionResults[eventData$key,]

    selectedData %>%

      transmute(

        probe,

        gene = HUGO.gene.symbol,

        `log fold change` = signif(logFC, digits = 2),

        `p-value` = signif(adj.P.Val, digits = 2)

      )

  },

    options = list(dom = "tip", pageLength = 10, searching = FALSE)

  )

}

shinyApp(ui, server, options = list(height = 600))

```

### Example 4 - Load JSON lib, change working directory on startup, read CSV to data frame, export data frame to JSON file

Note that the CSV file path may be different for you.

```R

install.packages("RJSONIO")

library(RJSONIO)

setwd("/workspace/data")

df <- read.csv("input/2024-01-16/polling_places.csv")

exportJson <- toJSON(df)

write(exportJson, "output/output.json")

```

## JupyterLab

### Example 1 - Install additonal packages

```python

!pip install pandas[pyarrow] jupytext scipy jupyterlab-git qgrid seaborn nb_black

```

### Example 2 - Read CSV to data frame, export data frame to JSON file

Note that the CSV file path may be different for you.

```python

import pandas as pd

df = pd.read_csv('2024-01-16/polling_places.csv', low_memory=False)

df

df.to_json('output.json')

```

## VSCode

TBD