Reading Data into R with readr

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Foreword

• Output options: the ‘tango’ syntax and the ‘readable’ theme.
• Snippets and results.

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Importing data with readr

Reading a .csv file

Your first task will be to master the use of the read_csv() function. There are many arguments available, but the only required argument is file, a path to a CSV file on your computer (or the web).

One big advantage that read_csv() has over read.csv() is that it doesn’t convert strings into factors by default.

read_csv() recognizes 8 different data types (integer, logical, etc.) and leaves anything else as characters. That means you don’t have to set stringsAsFactors = FALSE every time you import a CSV file with character strings!

#install.packages('readr')
library(readr)

getwd()

## [1] "D:/.../Rprojects/Data Wrangling"

setwd("D:/.../Rprojects/Data Wrangling")

Import .csv (only ‘,’).

# Import chickwts.csv: cwts
cwts <- read_csv('chickwts.csv')

# View the head of cwts
head(cwts)

## # A tibble: 6 × 2
##   weight      feed
##    <int>     <chr>
## 1    179 horsebean
## 2    160 horsebean
## 3    136 horsebean
## 4    227 horsebean
## 5    217 horsebean
## 6    168 horsebean

Reading a (.txt) .tsv file

Skipping columns with col_skip().

Code only:

• Setting the column type.

cols(
  weight = col_integer(),
  feed = col_character()
)

• Setting the column names.

col_names = c('name', 'state', 'phone')

• Removing NA.

na = c('NA', 'null')

In practice.

# Import data
salaries <- read_tsv('Salaries.txt', col_names = FALSE, col_types = cols(
  X2 = col_skip(),
  X3 = col_skip(), 
  X4 = col_skip()
))

# View first six rows of salaries
head(salaries)

## # A tibble: 6 × 3
##          X1    X5     X6
##       <chr> <chr>  <int>
## 1      Prof  Male 139750
## 2      Prof  Male 173200
## 3  AsstProf  Male  79750
## 4      Prof  Male 115000
## 5      Prof  Male 141500
## 6 AssocProf  Male  97000

Reading a European .csv

In most of Europe, commas (rather than periods) are used as decimal points.

# Import data with read_csv2(): trees
trees <- read_csv2('trees.csv')

# View dimensions and head of trees
dim(trees)

## [1] 9 3

head(trees)

## # A tibble: 6 × 3
##   Girth Height Volume
##   <dbl>  <int>  <dbl>
## 1    83     70    103
## 2    86     65    103
## 3    88     63    102
## 4   105     72    164
## 5   107     81    188
## 6   108     83    197

Read a fixed-width file

Files containing columns of data that are separated by whitespace and all line up on one side.

Code only:

# Import names.txt: names
names <- read_table('names.txt', col_names = c('name', 'state', 'phone'), na = c('NA', 'null'))

Reading a text file

Import ordinary text files.

# vector of character strings. 
# Import as a character vector, one item per line: tweets
tweets <- read_lines('tweets.txt')
tweets

## [1] "carrots  can be eat by most people"                                                                                          
## [2] "On predisents day we honor the big US man himself: Aberham Liclon.   Tall, skinny, dry, and cruncy - he was america's carrot"
## [3] "knock knoc who is there? yup: carosot   ( joke )"                                                                            
## [4] "it is 2016 time for a carot emoji   please!"                                                                                 
## [5] "when life give you lemnos ,  have a carrot"                                                                                  
## [6] "If you squent your eyes real hard a football  look like  a dry brown carrot   Honestly"

# returns a length 1 vector of the entire file, with line breaks represented as \n
# Import as a length 1 vector: tweets_all
tweets_all <- read_file('tweets.txt')
tweets_all

## [1] "carrots  can be eat by most people\r\nOn predisents day we honor the big US man himself: Aberham Liclon.   Tall, skinny, dry, and cruncy - he was america's carrot\r\nknock knoc who is there? yup: carosot   ( joke )\r\nit is 2016 time for a carot emoji   please!\r\nwhen life give you lemnos ,  have a carrot\r\nIf you squent your eyes real hard a football  look like  a dry brown carrot   Honestly"

Writing .csv and .tsv files

Code only:

# Save cwts as chickwts.csv
write_csv(cwts, "chickwts.csv")

# Append cwts2 to chickwts.csv
write_csv(cwts2, "chickwts.csv", append = TRUE)

Writing .rds files

If the R object you’re working with has metadata associated with it, saving to a CSV will cause that information to be lost.

Exports an entire R object (metadata and all).

Code only:

# Save trees as trees.rds
write_rds(trees, 'trees.rds')

# Import trees.rds: trees2
trees2 <- read_rds('trees.rds')

# Check whether trees and trees2 are the same
identical(trees, trees2)

Parsing Data with readr

Coercing columns to different data types

readr functions are quite good at guessing the correct data type for each column in a dataset. Of course, they aren’t perfect, so sometimes you will need to change the type of a column after importing.

Code only:

# Convert all columns to double
trees2 <- type_convert(trees, col_types = cols(Girth = 'd', Height = 'd', Volume = 'd'))

Coercing character columns into factors

readr import functions is that they don’t automatically convert strings into factors like read.csv does.

Code only:

# Parse the title column
salaries$title <- parse_factor(salaries$title, levels = c('Prof', 'AsstProf', 'AssocProf'))

# Parse the gender column
salaries$gender <- parse_factor(salaries$gender, levels = c('Male', 'Female'))

Creating Date objects

The readr import functions can automatically recognize dates in standard ISO 8601 format (YYYY-MM-DD) and parse columns accordingly. If you want to import a dataset with dates in other formats, you can use parse_date.

Code only:

# Change type of date column
weather$date <- parse_date(weather$date, format = '%m/%d/%Y')

Parsing number formats

The readr importing functions can sometimes run into trouble parsing a column as numbers when it contains non-numeric symbols in addition to numerals.

Code only:

# Parse amount column as a number
debt$amount <- parse_number(debt$amount)

Viewing metadata before importing

In some cases, it may be easier to get an idea of how readr plans to parse a dataset before you actually import it. When you see the planned column specification, you might decide to change the type of one or more columns, for example.

• spec_csv for .csv and .tsv files.
• spec_delim for .txt files (among others).

# Specifications of chickwts
spec_csv('chickwts.csv')

## cols(
##   weight = col_integer(),
##   feed = col_character()
## )