Data manipulation

Introduction to dplyr (package in the tidyverse)

Review

  • types of data collection
    • experimental vs observational
  • tidy data
  • variables
    • explanatory vs response variables
    • associated vs independent
    • type numeric vs categorical
  • summary statistics
  • target population
  • census vs sample
  • sample statistic vs population parameter
  • random sampling and random assignment

Review: experimental vs observational

design-experiment

design-observational

Source: Introduction to data: 2 - Types of Studies

Data transformation with dplyr

  • dplyr is a powerful R package for data manipulation.

  • one of the packages in the tidyverse

  • Provides a coherent set of verbs functions to help you resolve most data manipulation challenges

Using dplyr

  • expects tidy data

    • each variable in its own column

    • each observation in its own row

  • works with pipes |> (or %>% )

    • x |> f becomes f(x, y)
    • |> take something as input for next command
      • “then”

Data transformation with dplyr

  • first argument is a data frame

  • subsequent arguments describe what to do with the data frame

  • result is a new data frame

Key functions in dplyr

function() Action
glimpse() get a glimpse of your data
count() count the unique values of one or more variables
filter() picks rows based on their values
mutate() creates new variables (columns)
select() picks variables (columns)
summarize() reduces multiple values down to a single statistic
arrange() changes the order of the rows based on their values
group_by() create subsets of data to apply functions to

Outline

Functions we will cover today:

Load the packages and data

```{r}
library(tidyverse)
library(openintro)
```
  • cars93 is part of the openintro package
  • type ?cars93 for the help page

cars93 data frame is tidy

  • Each row is a car (observation)

  • Variables (columns) contain information on a car

Variables

  • type - levels large, midsize, and small

  • price

  • mpg_city

  • drive_train - levels 4WD, front, and rear

  • passengers

  • weight

Glimpse

  • fct refers to categories or levels of data

  • int and dbl refer to “integer” and “double” or numerical data

  • Which variables in cars93 are
    • numerical (int or dbl)
    • categorical or categorical (fct)
glimpse(cars93)
Rows: 54
Columns: 6
$ type        <fct> small, midsize, midsize, midsize, midsize, large, large, m…
$ price       <dbl> 15.9, 33.9, 37.7, 30.0, 15.7, 20.8, 23.7, 26.3, 34.7, 40.1…
$ mpg_city    <int> 25, 18, 19, 22, 22, 19, 16, 19, 16, 16, 21, 17, 20, 20, 29…
$ drive_train <fct> front, front, front, rear, front, front, rear, front, fron…
$ passengers  <int> 5, 5, 6, 4, 6, 6, 6, 5, 6, 5, 6, 6, 6, 6, 5, 5, 6, 5, 6, 4…
$ weight      <int> 2705, 3560, 3405, 3640, 2880, 3470, 4105, 3495, 3620, 3935…

Count the number of observations for each type 

```{r}
cars93  |> 
  count(type, sort=TRUE)  
```
type n
midsize 22
small 21
large 11
  • Which car type has the highest number of observations?

Your Turn - count

Which drive_train has the highest number of observations?

```{r}
cars93  |> 
  ???(???, ??? = TRUE) 

```

Filter

picks rows based on their values

Filter: comparison operators

  • == equality

  • > greater than

  • < less than

  • >= greater than or equal to

  • <= less than or equal to

  • != not equal to

  • between numeric variable in a specified range

  • near compare 2 numeric vectors. Set tolerance

Filter

Combine criteria using operators that make comparisons:

  • | or

  • & and ,

Filter - extract observations (rows)

```{r}
cars93  |> 
  filter(type == "midsize") 

```
type price mpg_city drive_train passengers weight
midsize 33.9 18 front 5 3560
midsize 37.7 19 front 6 3405
midsize 30.0 22 rear 4 3640
midsize 15.7 22 front 6 2880
midsize 26.3 19 front 5 3495
midsize 40.1 16 front 5 3935
midsize 15.9 21 front 6 3195
midsize 15.6 21 front 6 3080
midsize 20.2 21 front 5 3325
midsize 13.9 20 front 5 2885
midsize 47.9 17 rear 5 4000
midsize 28.0 18 front 5 3510
midsize 35.2 18 rear 4 3515
midsize 34.3 17 front 6 3695
midsize 61.9 19 rear 5 3525
midsize 14.9 19 rear 5 3610
midsize 26.1 18 front 5 3730
midsize 21.5 21 front 5 3200
midsize 16.3 23 front 5 2890
midsize 18.5 19 front 5 3450
midsize 18.2 22 front 5 3030
midsize 26.7 20 front 5 3245

Your Turn

Extract the cars that have front drive_train


```{r}
cars93 |> 
  filter(??? == ???)
```

Your Turn

Extract the cars that are type large AND have 4WD drive_train


```{r}
cars93 |> 
  filter(??? == ??? &  ??? == ???)
```

Your Turn

Extract the cars that are type large OR have 4WD drive_train


```{r}
??? |> 
  filter(??? == ??? |  ??? == ???)
```

Summary

Quiz

bus320-quarto.netlify.app