Friday, January 10, 2020

Working with R language Day 01


  1. List of built-in data sets

    data()

  2. The R Datasets Package

    help(package = "datasets")
  3. List the data sets in all *available* packages

    data(package = .packages(all.available = TRUE))
  4. List data sets including in ggplot2 package

    data(package = "ggplot2")
  5. Describe data set "movies" which is included in the "psych" package

    library(psych)
    describe(movies)
  6. Draw a pie chart using different colours schemes

    library(ggplot2)
    par(mfrow =  c(2,2)) # plot 4 pie charts in one diagram
    pie(rep(1,8), col = FALSE, main = "Blank Pie") # no colours shown
    pie(rep(1,8), main = "Default Colours")
    pie(c(1,2,3,4,5,6,7,8), col = heat.colors(7), main = "Heat Colours")
    pie(rep(1,8), col = rainbow(8), main = "Rainbow Colours")
  7. EDA

    library(ggplot2)
    library(ggplot2movies)
    library(psych)

    data("movies") # using movies data
    dim(movies)
    summary(movies)
    str(movies)
    head(movies)
    describe(movies)
    movies$rating #list values of rating column (variable)
  8. Plot a histogram of ratings and length for a random sample of 1000 movies

    library(ggplot2)
    library(ggplot2movies)
    library(ggpubr) # for ggrange()

    movies_1000_rows <- movies[sample(nrow(movies),1000),]

    rating_htg <- qplot(rating, data = movies_1000_rows, geom = "histogram", main = "Movie Ratings")

    lenth_htg <- qplot(length, data = movies_1000_rows, geom = "histogram", main = "Movie Length")

    # Display two histograms in the same figure

    ggarrange(rating_htg, lenth_htg, labels = c("A", "B"), ncol = 2, nrow = 1)
  9. Working with Data frames

    movies$unused_column <- NULL #remove column unused_column

    medianRating <- median(movies$rating)
    movies$median_rating_col <- ifelse(movies$rating > medianRating, "Larger", "Smaller")

    OR to replacing rating column with "Larger" or "Smaller" values

    movies$rating <- with(movies, ifelse(rating > medianRating, "Larger", "Smaller")
  10. # Summarise a data frame by groups in R using dplyr package

    library(ggplot2movies)
    library(dplyr) # for group_by(), select(), summarise()

    # List number of movies produced by each year

    mymovies <- movies %>%
    select(year, length, title) %>%
    group_by(year) %>%
    summarise(avg_length=mean(length), number_of_titles=n())
  11. Using apply function

    apply(movies, 2, mean)
  12. #Find all functions that has apply in the end
    apropos(".apply")





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