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Data Science Workflow: Import

Data import with readr

readr: part of the core tidyverse.

library(tidyverse)

readr data import functions

• read_csv: reads comma-delimited files.

• read_csv2: reads semicolon-separated files

• read_tsv: reads tab-delimited files

🛠 Import data from a .csv file (local machine)

Syntax

datasetname <- read_csv("include_file_path")

When you run read_csv, it prints out the names and type of each column.

Switch to R

If the file is saved inside the project folder: part 1

If the file is saved outside the project folder: part 2

🛠 Importing csv file from a website

Syntax

datasetname <- read_csv("include url here")

Example

url <- "https://thiyanga.netlify.app/project/datasets/foodlabel.csv"
foodlabel <- read_csv(url)

head(foodlabel, 1)

# A tibble: 1 x 80
  Gender   Age Education Employment Income Housesize children marital fshopper
   <dbl> <dbl>     <dbl>      <dbl>  <dbl>     <dbl>    <dbl>   <dbl>    <dbl>
1      1    22         5          4      3         5        2       0        0
# … with 71 more variables: mplanner <dbl>, place <dbl>, FA <dbl>,
#   Diabetes <dbl>, Metabolic cyndrents <dbl>, Other <dbl>, specific <dbl>,
#   job1 <dbl>, job2 <dbl>, Exercise <dbl>, Health <dbl>, taste <dbl>,
#   easy <dbl>, familiarity <dbl>, friends <dbl>, Useful <dbl>, Easiness <dbl>,
#   Sufficient <dbl>, Trusfulness <dbl>, Clear <dbl>, attractive pack <dbl>,
#   hc/nutriclaims <dbl>, graphical <dbl>, Free/prize <dbl>, source <dbl>,
#   netquan <dbl>, low in fat <dbl>, low in cho <dbl>, sodium <dbl>,
#   e labels <dbl>, place2 <dbl>, fa2 <dbl>, Health_1 <dbl>, X43 <dbl>,
#   f1 <dbl>, f2 <dbl>, f3 <dbl>, f4 <dbl>, f5 <dbl>, f6 <dbl>, f7 <dbl>,
#   f8 <dbl>, f9 <dbl>, f10 <dbl>, f11 <dbl>, f12 <dbl>, f13 <dbl>, f14 <dbl>,
#   f15 <dbl>, f16 <dbl>, f17 <dbl>, f18 <dbl>, i1 <dbl>, i2 <dbl>, i3 <dbl>,
#   i4 <dbl>, i5 <dbl>, i6 <dbl>, i7 <dbl>, i8 <dbl>, i9 <dbl>, i10 <dbl>,
#   i11 <dbl>, i12 <dbl>, i13 <dbl>, i14 <dbl>, i15 <dbl>, i16 <dbl>,
#   i17 <dbl>, i18 <dbl>, cluster <dbl>

read.csv and read_csv

• read.csv is in base R.

• read_csv is in tidyverse.

• read.csv() performs a similar job to read_csv().

• read_csv() works well with other parts of the tidyverse.

• read_csv() is faster than read.csv().

• read_csv() will always read variables containing text as character variable. In contrast, the base R function read.csv() will, by default, convert any character variable to a factor.

🛠 Writing data to a .csv file

• We can save tibble (or dataframe) to a csv file, using write_csv().

• write_csv() is in the readr package.

Syntax

write_csv(name_of_the_data_set_you_want_to_save, "path_to_write_to")

Example

data(iris)
# This will save inside your project folder
write_csv(iris, "iris.csv") 
# This will save inside the data folder which is inside your project folder
write_csv(iris, "data/iris.csv")

Swtich to R

🛠 Importing data from .xlsx files

Syntax

library(readxl)
mydata <- read_xlsx("file_path")

Switch to R

Importing SAS, SPSS and STATA files

SAS

read_sas("mtcars.sas7bdat")
write_sas(mtcars, "mtcars.sas7bdat")

SPSS

read_sav("mtcars.sav")
write_sav(mtcars, "mtcars.sav")

Stata

read_dta("mtcars.dta")
write_dta(mtcars, "mtcars.dta")

Importing other types of data

• feather: for sharing with Python and other languages

• httr: for web apis

• jsonlite: for JSON

• rvest: for web scraping

• xml2: for XML

Working with feather, httr, jsonlite, rvest and xml2 is beyond the scope of the course.