Tidyverse

Visualization and Data Structure

Tidy data refers to data arranged to make data processing, analysis, and visualization simpler. Remember that in a tidy data set we should consider:

• Each variable must have its column.
• Each observation must have its row.
• Each value must have its cell.

Video

Slides

Exercises

Exercise 1

Let’s say we want to organize the data anscombe. Below I show how this data looks like:

anscombe

1. Organize this data set to obtain tidy data. Remember here we have two response variables been measured four times.

ex1 <- anscombe %>%
  

2. Filter the data set to get replications 2 and 4, and summarise it to get the maximum, minimum, and mean values.

ex2 %>%
  filter() %>%
  summarise(
    
  )

Exercise 2

Often you do not need the entire data set, but just part of it.

1. Here, you should make the data mtcars tidy before making any selection.

(dataEx3 <- readRDS("./data/dataEx3.rds"))

As you can see, some columns are not variable names but values. Create two new variables calling mpg (for observations) and gear (with column values).

dataEx3 <- dataEx3 %>%
  pivot_longer(
    
  )

2. Select the columns mpg, hp, gear, and carb, and then make a plot using ggplot2 where mpg is the response variable, and hp is the co-variate in the x-axis. Also include different shapes and colours for gear, and facets for carb.

dataEx3 %>%
  select() %>%
  ggplot() %>%
  geom_point() %>%
  facet_wrap() %>%
  theme_bw()

Exercise 3

The following data represents song rankings for Billboard top 100 in the year 2000. The rank of the song is displayed in each week after it entered.

billboard

A slightly more complex case where columns have a common prefix and missing missings are structural, so should be dropped. So, make this data tidy.

billboard %>%
  

Data Structure

Exercise 1

1. Make this data tidy by including tmin and tmax as variable. Remember that here type is carrying to variables names rather than factors.

(dataEx2 <- as.tibble(readRDS("./data/dataEx2.RDS")))

dataEx2 <- dataEx2 %>%
  pivot_wider()

Now, build a new variable called tdiff, which is the difference between tmax and tmin. Moreover, display a ggplot2 graph that shows tdiff over time.

dataEx2 %>%
  

Exercise 2

Our cattle data data is already in a tidy format.

(cbp <- readRDS("./data/animal_sim.RDS"))

For this exercise, complete the following tasks with that data set:

1. Calculate the average phenotype per year by sex and herd using the summarise() function in the dplyr package.
2. Add two columns to cattle data using the mutate() function:
   1. Column 1: Phenotype should be rescaled to have a mean of zero and a standard deviation of one. You can call this new variable as PhenoStd.
   2. Column 2: Rank the PhenoStd using the function min_rank().
   3. The output data frame should have only PhenoStd > 0.

cbp %>%
  summarise(
    
  ) %>%

Exercise 3

Excerpt of the Gapminder data on life expectancy, GDP per capita, and population by country. This data has 142 countries observed from the year 1952 to 2007 in increments of 5 years. The response variable observed was the life expectancy at birth (in years), population size, and Per capita gross domestic product (GDP).

Per capita gross domestic product (GDP) measures a country’s economic response per person and is calculated by dividing its GDP by its population. It is a global measure for gauging the prosperity of nations as we can analyze the worth of a country based on its economic growth. Thus, countries that have the highest per capita GDP tend to be more developed.

gapminder

Questions:

1. What are the ten highest gpdPercap values?

gapminder %>%

2. Find both the median life expectancy (lifeExp) and the median and maximum GDP per capita (gdpPercap) in 1957, 1982, and 2007, by country and continent. Call them medianLifeExp, medianGdpPercap, and maxGdpPercap, respectively.

dat <- gapminder %>%

3. Use a scatter plot to compare the median GDP and median life expectancy. Use the variables continent and year to produce this plot.

dat %>%