Importing Data

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<div class="footer-small">https://github.com/mjfrigaard/ph-lacounty-r/</div>

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# Importing Data
#.fancy[Getting Data into RStudio]

.large[by Martin Frigaard]

Written: October 03 2022

Updated: December 02 2022

.footer-large[.right[.fira[
 [Created using the "λέξις" theme](https://jhelvy.github.io/lexis/index.html#what-does-%CE%BB%CE%AD%CE%BE%CE%B9%CF%82-mean)
]]]

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# Materials

The slides are in the `slides.pdf` file

The materials for this training are in the `worksheets` folder:

```
worksheets
├── import.Rmd
├── export.Rmd
├── objects.Rmd
├── rmd-basic.Rmd
├── rmd-tables.Rmd
└── rmd-visualizations.Rmd
```

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# Outline

.leftcol[

#### 1. .red[Importing data]

#### 2. Common Data Objects

#### 3. R Markdown

]

.rightcol[

#### 4. R Markdown Data Visualizations

#### 5. R Markdown Tables

#### 6. Exporting Data

]

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# .large[Import Data]

.font90[.green[Open `import.Rmd` to follow along]]

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# Importing Data

#### Packages for importing data:

| File type                           | Package               |
|-------------------------------------|-----------------------|
| SAS (`.sas7bdat`)                   | `haven`               |
| Excel (`.xlsx`, `.xls`)             | `readxl`, `openxlsx`  |
| Plain Text (`.csv`, `.tsv`, `.txt`) | `readr`, `data.table` |

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# Importing Data (*Environment*)

#### The .blue[Environment] Pane

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# Importing Data (*Import Dataset*)

.leftcol[

#### Click .blue[Import Dataset]

#### Click .blue[From SAS]

]

.rightcol[

]

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# Importing Data (*Required Packages*)

#### If you see a prompt to install required packages, click .blue[Yes]

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# Importing Data (*Dialogue Box*)

.leftcol20[

.font80[**You will see the .blue[Import Statistical Data] Dialogue Box**]

.font80[**Click .blue[Browse] and navigate to the `data/medical.sas7bdat` file**]

]

.rightcol80[

]

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# Importing Data (*Dialogue Box*)

.leftcol20[

.font80[**You will see the path in .blue[File/URL]**]

.font80[**A preview of the data will appear in .blue[Data Preview] **]

]

.rightcol80[

]

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# Importing Data (*Dialogue Box*)

.font90[**You see we have additional .blue[Import Options]**]

.font90[**We also see a .blue[Code Preview]. Click on the small copy icon, then click .blue[Import]**]

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# Importing Data (*Data Viewer*)

.font90[**RStudio imports the data and opens it in the .blue[Data Viewer]**]

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# Importing Data (*Data Viewer*)

.font90[**We can also see `medical` has been added to our .blue[Environment] pane**]

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# Importing Data

### .font120[Is what we did reproducible?]

.font120[***.red[No, but it can be!]***]

.font120[Open `import.Rmd` from the `worksheets` folder]

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# Importing Data

.leftcol20[

In `Import.Rmd`

- Instructions inside `#` boxes won't run

- Fill in `author` and `date` (inside quotes)

]

.rightcol80[

]

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# Importing Data (from local)

We already have the code to import `medical.sas7bdat` from local

We need to adjust the file path to `../data/medical.sas7bdat`

.leftcol[

.code70[

```
. # importing with dialogue
└── data/
      └── medical.sas7bdat
```

]
]

.rightcol[

.code70[

```
. # importing from file
├── data/
│   └──  medical.sas7bdat
└── worksheets/
    └──  import.Rmd
```

]
]

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# Importing Data (download and import)

We can also download the file from a `url`

.code70[

```r
download.file(
*   url = "http://www.principlesofeconometrics.com/sas/medical.sas7bdat",
    )
```

]

And save this to a local `destfile`

.code70[

```r
download.file(
    url = "http://www.principlesofeconometrics.com/sas/medical.sas7bdat", 
*   destfile = "../data/downloads/medical.sas7bdat")
```

]

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# Importing Data (download and import)

.font120[Now we can import the file from our `downloads/` folder]

.leftcol35[

.code60[

```
. # importing from downloads folder
├── data/
│   ├── medical.sas7bdat
│   └── downloads/
│            └── medical.sas7bdat
└── worksheets/
    └── import.Rmd
```

]

.rightcol65[

.code70[

```r
medical <- read_sas("../data/downloads/medical.sas7bdat") 
```

]

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# Importing Data (parameters)

.font90[For a more permanent solution, we can use parameters in our R Markdown file to store file location (or other metadata)]

.code70[

```yaml
title: "May Report"
author: "Joe Smith"
date: "2022-11-30"
output: html_document

params:
  sas_data_url: !r file.path("http://www.principlesofeconometrics.com/sas/medical.sas7bdat")
  sas_data_dir: !r c("../data/sas/")
```

]

.code70[

```r
*download.file(url = params$sas_data_url,
    )
```
]

.code70[

```r
download.file(url = params$sas_data_url, 
*   destfile = params$sas_data_dir)
```

]

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# Importing Data (multiple files)

If we have a folder with multiple files, we can reduce duplicated code with iteration.

.leftcol[
.code80[

```
. # importing multiple files
├── data/sas/
│   ├── elemapi-2000.sas7bdat
│   ├── elemapi2-2000.sas7bdat
│   ├── hsb2.sas7bdat
│   └── nations.sas7bdat
└── worksheets/
    └── import.Rmd
```

]
]

.rightcol[

.code80[

```r
# create vector of files
sas_filenames <- list.files(
 path = "../data/sas", 
 full.names = TRUE)
all_sas_data <- sas_filenames |> 
 # give this vector names
 purrr::set_names() |> 
 # use read_sas() on all files
 purrr::map(.x = , .f = read_sas)
```

]

.font90[`all_sas_data` is a list of datasets]

]

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# Importing Data (multiple files)

.font90[Each named according to their path in `data/sas/`]

.code55[

```r
str(all_sas_data)
*# $ ../data/sas/elemapi-2000.sas7bdat : tibble [400 × 21] (S3: tbl_df/tbl/data.frame)
#   ..$ snum    : num [1:400] 906 889 887 876 888 ...
#   .. ..- attr(*, "label")= chr "school number"
#   ..$ dnum    : num [1:400] 41 41 41 41 41 98 98 108 108 108 ...
#   .. ..- attr(*, "label")= chr "district number"
#   .. [list output truncated]
*#  $ ../data/sas/elemapi2-2000.sas7bdat: tibble [400 × 22] (S3: tbl_df/tbl/data.frame)
#   ..$ snum    : num [1:400] 906 889 887 876 888 ...
#   .. ..- attr(*, "label")= chr "school number"
#   ..$ dnum    : num [1:400] 41 41 41 41 41 98 98 108 108 108 ...
#   .. ..- attr(*, "label")= chr "district number"
#   .. [list output truncated]
*#  $ ../data/sas/hsb2.sas7bdat         : tibble [200 × 11] (S3: tbl_df/tbl/data.frame)
#   ..$ id     : num [1:200] 3 5 16 35 8 19 6 1 4 22 ...
#   ..$ female : num [1:200] 0 0 0 1 1 1 1 1 1 0 ...
#   .. [list output truncated]
*#  $ ../data/sas/nations.sas7bdat      : tibble [109 × 15] (S3: tbl_df/tbl/data.frame)
#   ..$ country : chr [1:109] "Algeria" "Argentin" "Australi" "Austria" ...
#   .. ..- attr(*, "label")= chr "Country"
#   ..$ pop     : num [1:109] 21.9 30.5 15.8 7.6 100.6 ...
#   .. ..- attr(*, "label")= chr "1985 population in millions"
#   .. [list output truncated]
```

]