library(knitr)

## Warning: package 'knitr' was built under R version 3.6.2

hook_output = knit_hooks$get('output')
knit_hooks$set(output = function(x, options) {
  # this hook is used only when the linewidth option is not NULL
  if (!is.null(n <- options$linewidth)) {
    x = knitr:::split_lines(x)
    # any lines wider than n should be wrapped
    if (any(nchar(x) > n)) x = strwrap(x, width = n)
    x = paste(x, collapse = '\n')
  }
  hook_output(x, options)
})

knitr::opts_chunk$set(collapse = FALSE, tidy = FALSE, linewidth = 75)

1 Data frame

1.1 Acknowledgement

This note is largely based on Applied Statistics with R. https://daviddalpiaz.github.io/appliedstats/

1.2 Introduction

A data frame is the most common way that we store and interact with data in this course.

example_data = data.frame(x = c(1, 3, 5, 7, 9, 1, 3, 5, 7, 9),
                          y = c(rep("Hello", 9), "Goodbye"),
                          z = rep(c(TRUE, FALSE), 5))

A data frame is a list of vectors.
- Each vector must contain the same data type
- The difference vectors can store different data types.

example_data

write.csv save (or export) the dataframe in .csv format.

1.3 Load csv file

We can also import data from various file types in into R, as well as use data stored in packages.
Read csv file into R.
- read.csv() function as default
- read_csv() function from the readr package. This is faster for larger data.

# install.packages("readr") 
#library(readr)
#example_data_from_csv = read_csv("example-data.csv")
example_data_from_csv = read.csv("example-data.csv")

Note: This particular line of code assumes that the file example_data.csv exists in your current working directory.
The current working directory is the folder that you are working with. To see this, you type

getwd()

## [1]
"C:/Users/Yuta/Dropbox/Teaching/2020_1_3_4_Applied_Metrics/Note_Github/02_RIntro"

If you want to set the working directory, use setwd() function

setwd(dir = "directory path" )

1.4 Examine dataframe

Inside the ggplot2 package is a dataset called mpg. By loading the package using the library() function, we can now access mpg.

library(ggplot2)

Three things we would generally like to do with data:
- Look at the raw data.
- Understand the data. (Where did it come from? What are the variables? Etc.)
- Visualize the data.
To look at the data, we have two useful commands: head() and str()

head(mpg, n = 10)

The function str() will display the “structure” of the data frame.
- It will display the number of observations and variables, list the variables, give the type of each variable, and show some elements of each variable.
- This information can also be found in the “Environment” window in RStudio.

str(mpg)

## Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 11 variables:
## $ manufacturer: chr "audi" "audi" "audi" "audi" ...
## $ model : chr "a4" "a4" "a4" "a4" ...
## $ displ : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr "f" "f" "f" "f" ...
## $ cty : int 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr "p" "p" "p" "p" ...
## $ class : chr "compact" "compact" "compact" "compact" ...

names() function to obtain names of the variables in the dataset

names(mpg)

## [1] "manufacturer" "model" "displ" "year" "cyl"
## [6] "trans" "drv" "cty" "hwy" "fl"
## [11] "class"

To access one of the variables as a vector, we use the $ operator.

mpg$year

## [1] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 2008 1999 1999
2008 2008
## [16] 1999 2008 2008 2008 2008 2008 1999 2008 1999 1999 2008 2008 2008
2008 2008
## [31] 1999 1999 1999 2008 1999 2008 2008 1999 1999 1999 1999 2008 2008
2008 1999
## [46] 1999 2008 2008 2008 2008 1999 1999 2008 2008 2008 1999 1999 1999
2008 2008
## [61] 2008 1999 2008 1999 2008 2008 2008 2008 2008 2008 1999 1999 2008
1999 1999
## [76] 1999 2008 1999 1999 1999 2008 2008 1999 1999 1999 1999 1999 2008
1999 2008
## [91] 1999 1999 2008 2008 1999 1999 2008 2008 2008 1999 1999 1999 1999
1999 2008
## [106] 2008 2008 2008 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008
2008 2008
## [121] 2008 2008 2008 2008 1999 1999 2008 2008 2008 2008 1999 2008 2008
1999 1999
## [136] 1999 2008 1999 2008 2008 1999 1999 1999 2008 2008 2008 2008 1999
1999 2008
## [151] 1999 1999 2008 2008 1999 1999 1999 2008 2008 1999 1999 2008 2008
2008 2008
## [166] 1999 1999 1999 1999 2008 2008 2008 2008 1999 1999 1999 1999 2008
2008 1999
## [181] 1999 2008 2008 1999 1999 2008 1999 1999 2008 2008 1999 1999 2008
1999 1999
## [196] 1999 2008 2008 1999 2008 1999 1999 2008 1999 1999 2008 2008 1999
1999 2008
## [211] 2008 1999 1999 1999 1999 2008 2008 2008 2008 1999 1999 1999 1999
1999 1999
## [226] 2008 2008 1999 1999 2008 2008 1999 1999 2008

mpg$hwy

## [1] 29 29 31 30 26 26 27 26 25 28 27 25 25 25 25 24 25 23 20 15 20 17
17 26 23
## [26] 26 25 24 19 14 15 17 27 30 26 29 26 24 24 22 22 24 24 17 22 21 23
23 19 18
## [51] 17 17 19 19 12 17 15 17 17 12 17 16 18 15 16 12 17 17 16 12 15 16
17 15 17
## [76] 17 18 17 19 17 19 19 17 17 17 16 16 17 15 17 26 25 26 24 21 22 23
22 20 33
## [101] 32 32 29 32 34 36 36 29 26 27 30 31 26 26 28 26 29 28 27 24 24 24
22 19 20
## [126] 17 12 19 18 14 15 18 18 15 17 16 18 17 19 19 17 29 27 31 32 27 26
26 25 25
## [151] 17 17 20 18 26 26 27 28 25 25 24 27 25 26 23 26 26 26 26 25 27 25
27 20 20
## [176] 19 17 20 17 29 27 31 31 26 26 28 27 29 31 31 26 26 27 30 33 35 37
35 15 18
## [201] 20 20 22 17 19 18 20 29 26 29 29 24 44 29 26 29 29 29 29 23 24 44
41 29 26
## [226] 28 29 29 29 28 29 26 26 26

We can use the dim(), nrow() and ncol() functions to obtain information about the dimension of the data frame.

dim(mpg)

## [1] 234  11

nrow(mpg)

## [1] 234

ncol(mpg)

## [1] 11

1.5 Subsetting data

Subsetting data frames can work much like subsetting matrices using square brackets, [,].
Here, we find fuel efficient vehicles earning over 35 miles per gallon and only display manufacturer, model and year.

mpg[mpg$hwy > 35, c("manufacturer", "model", "year")]

An alternative would be to use the subset() function, which has a much more readable syntax.

subset(mpg, 
       subset = hwy > 35, 
       select = c("manufacturer", "model", "year"))

Lastly, we could use the filter and select functions from the dplyr package which introduces the %>% operator from the magrittr package.

library(dplyr)
mpg %>% 
filter(hwy > 35) %>% 
select(manufacturer, model, year)

I will give you an assignment about dplyr package in the DataCamp as a makeup lecture.

R Basics 2 -Data-

Instructor: Yuta Toyama

Last updated: 2020-03-31

1 Data frame

1.1 Acknowledgement

1.2 Introduction

1.3 Load csv file

1.4 Examine dataframe

1.5 Subsetting data