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R4DS Study Group - Week 1

Pierrette Lo 4/7/2020

This week’s assignment

  • Chapter 1 (Welcome - Introduction)
  • Chapter 2 (Explore - Introduction)
  • Chapter 3.1 - 3.2 (First Steps)

Notes

On typing code:

  • It’s good practice to type in code yourself, rather than just copying & pasting
  • Certain things will autofill, e.g. (), "", etc. while you’re typing
  • Tooltips will show when you start to type a function
  • Pop-up suggestions may also show – you can select using up/down arrow and Enter
  • You can insert line breaks in your code to make it more readable. Hit Enter to start a new line, and the prompt will change from > to +

RStudio “dark mode”:

  • Tools > Global options > Appearance

R vocabulary:

  • Dataframe = table; tibble = dataframe with special features
  • Objects and variables = nouns
  • Functions = verbs

How to read a Help page:

  • e.g. ?mpg
  • Title: short description
  • Description: more detail
  • Usage: what to type
  • Format (for datasets): what is in the dataset
  • Arguments (required vs default): what are the options you can use with the function
  • Details: more about how the function works
  • Value: what is the output of the function
  • Examples

Following along with Chapter 3 text

Load tidyverse (you’ll see several warning messages that can be ignored):

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.6.1

## -- Attaching packages -- tidyverse 1.3.0 --

## v ggplot2 3.2.1     v purrr   0.3.3
## v tibble  3.0.0     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0

## Warning: package 'ggplot2' was built under R version 3.6.1

## Warning: package 'tibble' was built under R version 3.6.3

## Warning: package 'tidyr' was built under R version 3.6.1

## Warning: package 'purrr' was built under R version 3.6.1

## Warning: package 'dplyr' was built under R version 3.6.1

## -- Conflicts ----- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Take a look at the built-in mpg dataset:

mpg
## # A tibble: 234 x 11
##    manufacturer model    displ  year   cyl trans   drv     cty   hwy fl    class
##    <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr> <chr>
##  1 audi         a4         1.8  1999     4 auto(l~ f        18    29 p     comp~
##  2 audi         a4         1.8  1999     4 manual~ f        21    29 p     comp~
##  3 audi         a4         2    2008     4 manual~ f        20    31 p     comp~
##  4 audi         a4         2    2008     4 auto(a~ f        21    30 p     comp~
##  5 audi         a4         2.8  1999     6 auto(l~ f        16    26 p     comp~
##  6 audi         a4         2.8  1999     6 manual~ f        18    26 p     comp~
##  7 audi         a4         3.1  2008     6 auto(a~ f        18    27 p     comp~
##  8 audi         a4 quat~   1.8  1999     4 manual~ 4        18    26 p     comp~
##  9 audi         a4 quat~   1.8  1999     4 auto(l~ 4        16    25 p     comp~
## 10 audi         a4 quat~   2    2008     4 manual~ 4        20    28 p     comp~
## # ... with 224 more rows

Look at the help page for mpg:

?mpg

Run the code to make a plot:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Chapter 3 exercises

See https://jrnold.github.io/r4ds-exercise-solutions/data-visualisation.html for more detailed solutions.

  1. Run ggplot(data = mpg). What do you see?
ggplot(data = mpg)

Just an empty plot - the other components haven’t been specified yet.

  1. How many rows are in mpg? How many columns?
mpg
## # A tibble: 234 x 11
##    manufacturer model    displ  year   cyl trans   drv     cty   hwy fl    class
##    <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr> <chr>
##  1 audi         a4         1.8  1999     4 auto(l~ f        18    29 p     comp~
##  2 audi         a4         1.8  1999     4 manual~ f        21    29 p     comp~
##  3 audi         a4         2    2008     4 manual~ f        20    31 p     comp~
##  4 audi         a4         2    2008     4 auto(a~ f        21    30 p     comp~
##  5 audi         a4         2.8  1999     6 auto(l~ f        16    26 p     comp~
##  6 audi         a4         2.8  1999     6 manual~ f        18    26 p     comp~
##  7 audi         a4         3.1  2008     6 auto(a~ f        18    27 p     comp~
##  8 audi         a4 quat~   1.8  1999     4 manual~ 4        18    26 p     comp~
##  9 audi         a4 quat~   1.8  1999     4 auto(l~ 4        16    25 p     comp~
## 10 audi         a4 quat~   2    2008     4 manual~ 4        20    28 p     comp~
## # ... with 224 more rows

Typing name of dataset - first line of output tells you number of rows x columns

Or you can try the below functions to summarize or count rows and columns

structure(mpg)
glimpse(mpg)
nrow(mpg)
ncol(mpg)
  1. What does the drv variable describe? Read the help for ?mpg to find out.
?mpg

drv = drive system of car

Categorical variable = finite number of categories/groups, not necessarily ordered

Continuous variable = infinite number of possible values (e.g. numbers, time)

  1. Make a scatterplot of hwy vs cyl.
ggplot(data = mpg) +
  geom_point(mapping = aes(x = hwy, y = cyl))

  1. What happens if you make a scatterplot of class vs drv? Why is the plot not useful?
ggplot(data = mpg) +
  geom_point(mapping = aes(x = class, y = drv))

Both class and drv are categorical - the scatterplot only tells you when the categories overlap, but not how many datapoints are in each.

See solutions manual for examples of better ways to visualize 2 categorical variables.