STA 326 2.0 Programming and Data Analysis with R

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# STA 326 2.0 Programming and Data Analysis with R
## R Data Import and Export
### Dr Thiyanga Talagala
### Online distance learning/teaching materials during the COVID-19 outbreak.

---

# Data import with readr

## R package

`readr`: part of the core tidyverse.

```r
library(tidyverse)
```

```
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
```

```
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.2     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
```

```
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
```

## `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

## Syntax

```r
datasetname <- read_csv("include_file_path")
```

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

.full-width[.content-box-yellow[Switch to R]]

---

# If the file is saved inside the project folder

.full-width[.content-box-green[Demo: Go to google classroom and watch the video importdatacsv1.mov]]

# If the file is saved outside the project folder

.full-width[.content-box-green[Demo: Go to google classroom and watch the video importdatacsv2.mov]]

---

# 🛠 Importing csv file from a website

## Syntax

```r
datasetname <- read_csv("include url here")
```

## Example

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

```
Warning: Missing column names filled in: 'X43' [43]
```

```
Parsed with column specification:
cols(
  .default = col_double()
)
```

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

```r
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 to a File

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

- `write_csv()` is in the `readr` package.

## Syntax

```r
write_csv(name_of_the_data_set_you_want_to_save, "path_to_write_to")
```

## Example

```r
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") 
```

.full-width[.content-box-yellow[Switch to R]]

.full-width[.content-box-green[Demo: Go to google classroom and watch the video exportdatacsv.mov]]

---

# 🛠 Importing Excel .xlsx files

## Syntax

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

.full-width[.content-box-yellow[Switch to R]]

.full-width[.content-box-green[Demo: Go to google classroom and watch the video readxlsx.mov]]

---
# Importing SAS, SPSS and STATA files

## SAS

```r
read_sas("mtcars.sas7bdat")
write_sas(mtcars, "mtcars.sas7bdat")
```
## SPSS

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

```
## Stata
```r
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

.full-width[.content-box-blue[Working with feather, httr, jsonlite, rvest and xml2 is beyond the scope of the course.]]

---

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Slides available at: hellor.netlify.app