Bishwajit Ghose

    Add a header to begin generating the table of contents

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    Combining multiple plots in R

    #the library

    library(patchwork)

    #combine the plots

    p1 <- ggplot(iris) + geom_point(aes(Sepal.Width, Sepal.Length))
    p2 <- ggplot(iris) + geom_boxplot(aes(Sepal.Width, Sepal.Length, group = Species))
    p3 <- ggplot(iris) + geom_point(aes(Petal.Width, Petal.Length))
    p4 <- ggplot(iris) + geom_boxplot(aes(Petal.Width, Petal.Length, group = Species))
    p1 + p2 + p3 + p4

    Correlation plot in R

    #using the mtcars dataset

    #Plottinng coefs 

     
    library(corrplot) 
    cp = cor(mtcars) corrplot(cp, method = 'number', type = 'lower', diag = FALSE)

    #Plottinng both coefs and symbols

    corrplot.mixed(cp)

    Clustered bar chart in R

    #the library

    library(ggplot2)

    #making the plot

    ggplot(data=diamonds) + geom_bar(mapping = aes(x=cut, fill = color),
                     position="dodge")+ coord_flip()+
                     theme_light()

    Correlation plot in Stata

    #load a sample dataset

    sysuse auto, clear

    #create the correlation matrix:

    correlate price mpg trunk weight length turn foreign
    matrix m = r(C)

    #plot

    heatplot C, values(format(%9.3f)) lower nodiagonal legend(off) sch(burd4)

    Changing to polar shape

    Coord polar chart in R

    #making the plot

    ggplot(data=diamonds) + geom_bar(mapping = aes(x=cut, fill = color),
                     position="dodge")+ coord_flip()+
                     theme_light()

    Changing to polar shape

    p + coord_polar()

    Colouring indivudual bars in R

    Step 1: making the plot

    packages <- c(
      "tidyverse",
      "WDI",
      "forcats",'scales'
    )
    pacman::p_load(packages, character.only = TRUE, install = FALSE)
    
    
    df <- WDI(indicator = "SP.POP.TOTL", start = 2020, end = 2020)
    country_code <- as_tibble(WDI_data$country)
    
    df <- df %>%
      rename(Population = SP.POP.TOTL) %>%
      inner_join(country_code, by = "iso2c") %>%
      filter(region != "Aggregates") %>%
      top_n(20, Population) %>%
      mutate(country = fct_reorder(country.x, Population))
    
    
    
    p <- ggplot(df, aes(x = country, y = Population, fill = region)) +
      geom_bar(stat = "identity", alpha = .6, width = .4) 
    
    
    p

    Step 2: Add additional features to the chart

    p+ coord_flip(ylim = c(10000, 1500000000)) +
      geom_hline(yintercept = 0, alpha = .5) +
      xlab("") +
      ylab("20 most populous countries (2020)") + 
      theme_classic() +
      scale_fill_brewer(palette = "Set2", name = "Region") +
      theme(
        axis.line.y = element_blank(),
        axis.title.x = element_text(size = 12),
        axis.text.x = element_text(size = 10),
        axis.ticks.y = element_blank(),
        legend.text = element_text(size = 10) +  
        scale_y_continuous(labels = function(x) format(x, scientific = FALSE))
      )

    Linear Regression Plot in R

    Logistic Regression Plot in R