Data

R for Data Science
Basel R Bootcamp
     

February 2019

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Data

In this session you will get to know...

R's 3 main data types

a little more about functions

R's Import/Export functions

from cloudtweaks.com

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3 Object types for data

R has 3 main data objects...

list - R's multi-purpose container

Can carry any data, incl. lists
Often used for function outputs

data_frame - R's spreadsheet

Specific type of list
Typical data format
For multi-variable data sets

vectors - R's data container

Actually carries the data
Contain data of 1 of many types

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`list`

1 - Can carry any data, incl. lists, data_frames, vectors, etc.

2 - Are often used for function outputs

3 - Have named elements.

4 - Elements can be inspected via names() or str().

5 - Elements are (typically) selected by $.

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`list`: Select element using `$`

# regression
reg_model <- lm(height ~ sex + age,
               data = baselers)
reg_results <- summary(reg_model)
# get element names
names(reg_results)

## [1] "call"         "terms"       
## [3] "residuals"    "coefficients"
## [5] "aliased"      "sigma"       
## [7] "df"           "r.squared"

# select element using $
reg_results$coefficients

##               Estimate  t value
## (Intercept) 164.171266 499.5339
## sexmale      13.993699  66.4724
## age          -0.003753  -0.5819

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`data_frame`

1 - Are lists containing vectors of equal length representing the variables.

2 - Contain vectors of different types: numeric, character, etc.

3 - Have named elements.

4 - Elements can be inspected via names(), str(), print(), View(), or skimr::skim().

5 - Elements are (typically) selected by $.

6 - Come in different flavors: data.frame(), data.table(), tibble().

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Inspect content

# inspect baselers via print
baselers

## # A tibble: 10,000 x 20
##      id sex     age height weight income
##   <dbl> <chr> <dbl>  <dbl>  <dbl>  <dbl>
## 1     1 male     44   174.  113.    6300
## 2     2 male     65   180.   75.2  10900
## 3     3 fema…    31   168.   55.5   5100
## 4     4 male     27   209    93.8   4200
## 5     5 male     24   177.   NA     4000
##   education confession children
##   <chr>     <chr>         <dbl>
## 1 SEK_III   catholic          2
## 2 obligato… confessio…        2
## 3 SEK_III   <NA>              2
## 4 SEK_III   catholic          2
## 5 SEK_III   catholic          1
## # … with 9,995 more rows, and 11 more
## #   variables

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Inspect content

# View dataframe in a new window
View(baselers)

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Select via `$`

# Access age column from baselers
baselers$age

##  [1] 44 65 31 27 24 63 71 41 43 31 42 31
## [13] 38 49 39 54 78 62 88 74

# Access education column from baselers
baselers$education

##  [1] "SEK_III"          
##  [2] "obligatory_school"
##  [3] "SEK_III"          
##  [4] "SEK_III"          
##  [5] "SEK_III"          
##  [6] "SEK_III"          
##  [7] "SEK_III"          
##  [8] "SEK_III"          
##  [9] "apprenticeship"   
## [10] "SEK_II"

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Change/Add via `$`

# Divide income by 1000
baselers$income <- baselers$income / 1000
# inspect baselers
baselers

## # A tibble: 10,000 x 20
##      id sex     age height weight income
##   <dbl> <chr> <dbl>  <dbl>  <dbl>  <dbl>
## 1     1 male     44   174.  113.     6.3
## 2     2 male     65   180.   75.2   10.9
## 3     3 fema…    31   168.   55.5    5.1
## 4     4 male     27   209    93.8    4.2
## 5     5 male     24   177.   NA      4  
##   education confession children
##   <chr>     <chr>         <dbl>
## 1 SEK_III   catholic          2
## 2 obligato… confessio…        2
## 3 SEK_III   <NA>              2
## 4 SEK_III   catholic          2
## 5 SEK_III   catholic          1
## # … with 9,995 more rows, and 11 more
## #   variables

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`vector`

1 - R's basic and, in a way, only data container.

2 - Can contain only a single type of data and missing values.

3 - Data types

numeric - All numbers
character - All characters (e.g., names)
logical - TRUE or FALSE
...
NA - missing values

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Select/Change/(Add) via `[ ]`

# extract vector containing age
age <- baselers$age
age

## [1] 44 65 31 27 24 63 71 41 43

# select value
age[2]

## [1] 65

# change value
age[2] <- 100
age

## [1]  44 100  31  27  24  63  71  41  43

Find more info on indexing here.

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Data types: `numeric`

numeric vectors are used to store numbers and only numbers.

baselers$age

## [1] 44 65 31 27 24 63 71 41 43

# evaluate class
class(baselers$age)

## [1] "numeric"

# is age numeric?
is.numeric(baselers$age)

## [1] TRUE

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Data types: `character`

character vectors are used to store data represented by letters and symbols, and all other data.

You can always recognise character vectors by quotation marks " "

baselers$sex

## [1] "male"   "male"   "female" "male"  
## [5] "male"   "male"   "male"   "female"

baselers$education

## [1] "SEK_III"          
## [2] "obligatory_school"
## [3] "SEK_III"          
## [4] "SEK_III"

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Data types: `character`

character vectors are used to store data represented by letters and symbols, and all other data.

You can always recognise character vectors by quotation marks " "

baselers$age

## [1] 44 65 31 27 24 63 71 41

# convert age to character
as.character(baselers$age)

## [1] "44" "65" "31" "27" "24" "63" "71"
## [8] "41" "43"

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Data types: `logical`

logical vector are used to slice data aka to select elements or rows. logical are typically created from other vectors via logical comparisons.

# which sex values are male?
baselers$sex == "male"

## [1]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
## [7]  TRUE FALSE

# which ages are less than 30?
baselers$age < 30

## [1] FALSE FALSE FALSE  TRUE  TRUE FALSE
## [7] FALSE FALSE FALSE

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Data types: `logical`

logical vector are used to slice data aka to select elements or rows. logical are typically created from other vectors via logical comparisons.

Logical operators

== - is equal to

<, > - smaller/greater than

<=, >= - smaller/greater than or equal

&, && - logical AND

|, || - logical OR

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Data I/O

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Raw (structured) Data

delim-separated data .csv, .txt, etc.

markup data .xml, .xls, .html, (.json), etc.

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Delim-separated data

1 - Most typical file format.

2 - Requires delimiter to separate entries.

delim-separated data .csv, .txt, etc.

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`readr`

readr is a tidyverse package that provides convenient functions to read in flat (non-nested) data files into data frames (tibbles to be precise):

# Importing data from a file
data <- read_csv(file, ...)   # comma-delimited
data <- read_csv2(file, ...)  # semicolon-delimeted
data <- read_delim(file, ...) # arbitrary-delimited
# Writing a data frame to a file
write_csv(data_object, path, ...)    # comma-delimited
write_delim(data_object, path, ...)  # arbitrary-delimited

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Finding the file path

1 - Identify the file path using the auto-complete.

2 - Initiate auto-complete and browse through the folder structure by placing the cursor between two quotation marks and using the tab key.

3 - Auto-complete begins with the project folder - place your data inside your project folder!

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Identifying the delimiter

1 - Find the file on your hard drive. Should be in your data folder inside your project.

2 - Open the file in RStudio (right-click on the file in the Files pane) a text viewer, e.g., TextEdit (Mac), TextWrangler (Mac), WordPad (Windows).

baselers.csv

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Identifying the delimiter

1 - Find the file on your hard drive. Should be in your data folder inside your project.

2 - Open the file in RStudio (right-click on the file in the Files pane) a text viewer, e.g., TextEdit (Mac), TextWranger (Mac), WordPad (Windows).

# Read with explicit column names
baselers <-read_delim(file = ".../baselers.csv",
                      delim = c(","))

baselers.csv

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Handling headers

1 - readr- functions typically expect the column names in the first line.

2 - If no column names are available, use the col_names-argument to provide them.

# Read with explicit column names
baselers <- read_csv(file = ".../baselers.csv",
                     col_names = c("id",
                                  "age",
                                   ...))

baselers.csv

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Handling data types

Reading in data, readr infers the type of data for each column.

# Read baselers
read_csv(file = "1_Data/baselers.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   sex = col_character(),
##   education = col_character(),
##   confession = col_character(),
##   fasnacht = col_character(),
##   eyecor = col_character()
## )

## See spec(...) for full column specifications.

## # A tibble: 10,000 x 20
##      id sex     age height weight income
##   <dbl> <chr> <dbl>  <dbl>  <dbl>  <dbl>
## 1     1 male     44   174.  113.    6300
## 2     2 male     65   180.   75.2  10900
## 3     3 fema…    31   168.   55.5   5100
## 4     4 male     27   209    93.8   4200
## 5     5 male     24   177.   NA     4000
##   education confession children
##   <chr>     <chr>         <dbl>
## 1 SEK_III   catholic          2
## 2 obligato… confessio…        2
## 3 SEK_III   <NA>              2
## 4 SEK_III   catholic          2
## 5 SEK_III   catholic          1
## # … with 9,995 more rows, and 11 more
## #   variables

baselers.csv

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Handling data types

Incorrect data types can be fixed. Typically this involves:

1 - removing character elements from otherwise numeric variables.
2 - Setting explicit NA strings using the na-argument.
3 - Re-running type_convert.

# Read baselers
baseslers <- read_csv(file = ".../baselers.csv",
                      na = c('NA'))
# Try to fix incorrect data types
baselers <- type_convert(baselers)

baselers.csv

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Other data

R provides read and write functions for practically all data file formats. See rio.

`readr`

# read fixed width files (can be fast)
data <- read_fwf(file, ...)
# read Apache style log files
data <- read_log(file, ...)

`haven`

# read SAS's .sas7bat and sas7bcat files
data <- read_sas(file, ...)
# read SPSS's .sav files
data <- read_sav(file, ...)
# etc

`readxl`

# read Excel's .xls and xlsx files
data <- read_excel(file, ...)

Other

# Read Matlab .mat files
data <- R.matlab::readMat(file, ...)
# Read and wrangle .xml and .html
data <- XML::xmlParseParse(file, ...)
# from package jsonlite: read .json files
data <- jsonlite::read_json(file, ...)

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Practical

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Data

R for Data Science Basel R Bootcamp

February 2019

Data

3 Object types for data

list

list: Select element using $

data_frame

Inspect content

Inspect content

Select via $

Change/Add via $

vector

Select/Change/(Add) via [ ]

Data types: numeric

Data types: character

Data types: character

Data types: logical

Data types: logical

Raw (structured) Data

Delim-separated data

readr

Finding the file path

Identifying the delimiter

Identifying the delimiter

Handling headers

Handling data types

Handling data types

Other data

readr

haven

readxl

Other

Practical

Data

Help

R for Data Science
Basel R Bootcamp
     

`list`

`list`: Select element using `$`

`data_frame`

Select via `$`

Change/Add via `$`

`vector`

Select/Change/(Add) via `[ ]`

Data types: `numeric`

Data types: `character`

Data types: `character`

Data types: `logical`

Data types: `logical`

`readr`

`readr`

`haven`

`readxl`