Import Data to R

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# Import Data to R

## Data Literacy in R

### Tatjana Kecojevic

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# Your Tool: .emphasis[download & install]

## First: [R](https://cran.r-project.org/)

## Then: [RStudio](https://rstudio.com/products/rstudio/download/)

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# .shadow-text[RStudio IDE]

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<div class="flex" style="margin: 0 1em;">
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    <h3> What package do I need? </h3>
    <img src="images/astro_boy_package.jpg" style="width: 100%;">
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    <h3> What is it? </h3>
    <h5> “In R, the fundamental unit of shareable code is the package." <a href="http://r-pkgs.had.co.nz/intro.html">Hadley Wickham, R packages</a></h5>
    <h5> When you run R you will automatically upload the <b><mark><strong>package:base</strong></mark></b>, which is the system library.</h5>
    <h5> You can install a package using the <b><mark><strong>install.packages()</strong></mark></b> function.</h5>
    <h5>Once installed, the package will appear in the list of available packages in your <b><mark><strong>Packages pane</strong></mark></b>.</h5>
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# R can import all types of data:

- (Tab, Blank space) Delimited Text
- CSV files
- Excel files
- JSON
- SAS
- STATA
- MiniTab
- SPSS…

#### the R base: Read tabular and csv data into R

- `df_txt <- read.table(file_name.txt, header = TRUE)`

- `df_txt <- read.csv(file_name.csv, stringsAsFactors = FALSE)`

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## **Using `readr::read_csv()`**

- quickly than base R `read.cdv()`
- it assumes characters are strings and not factors by default

## **Importing Excel files with `readxl::read_excel()`**

- Importing Excel data files is not straightforward as it might contain multiple sheets
- To access the data from an Excel sheet you can’t just copy and paste the URL for the file. You have to download the file first
- People like to make their Excel spreadsheets look ‘pretty’ with ‘fancy’ formatting, which could create difficulty when reading them in R
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# .shadow-text[Accessing ecdc's covid-19 data]

## .emphasis[<https://www.ecdc.europa.eu/en/publications-data>]
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## Read .csv File

```r
*ecdc_csv <- readr::read_csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv")

```

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## Check the Dimention of Your Data

```r
ecdc_csv <- readr::read_csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv") 
*dim(ecdc_csv)

```

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## Get A Glimpse Of Your Data

```r
ecdc_csv <- readr::read_csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv") 
dim(ecdc_csv)
*dplyr::glimpse(ecdc_csv)

```

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.footnote[
💡 If you don't have `dplyr` installed yet run: `install.packages("dplyr")`
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## Filter Your Data with dplyr

```r
ecdc_csv <- readr::read_csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv") 
dim(ecdc_csv)
dplyr::glimpse(ecdc_csv) 
# find your country
# ------- SERBIA ---------
*library(dplyr)
*covid_sr <- ecdc_csv %>%
* filter(countryterritoryCode == "SRB")

```

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## Get A Glimpse Of Your Filtered Data

```r
ecdc_csv <- readr::read_csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv") 
dim(ecdc_csv)
dplyr::glimpse(ecdc_csv) 
# find your country
# ------- SERBIA ---------
library(dplyr) 
covid_sr <- ecdc_csv %>% 
  filter(countryterritoryCode == "SRB") 
*glimpse(covid_sr)
```

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# .shadow-text[Importing Excel files with readxl]

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## Working with URLs and HTTP

```r
*library(httr)
*url <- "https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-2020-04-20.xlsx"
*GET(url, write_disk(tmf <- tempfile(fileext = ".xlsx")))

```

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## Read the Data from an Excel file

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## Display The Structure of the Data

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## Filter: Match the Column With Given Values

```r
*library(httr)
url <- "https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-2020-04-20.xlsx" 
GET(url, write_disk(tmf <- tempfile(fileext = ".xlsx"))) 
covid_ecdc <- readxl::read_excel(tmf)
str(covid_ecdc)
# select exYU countries
*covid_yu <- covid_ecdc %>%
* filter(countriesAndTerritories %in% c("Bosnia_and_Herzegovina",
*                                       "Croatia", "Montenegro",
*                                       "North_Macedonia", "Serbia",
*                                       "Slovenia"))
```

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## Get A Glimpse Of Your Filtered Data

```r
library(httr) 
url <- "https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-2020-04-20.xlsx" 
GET(url, write_disk(tmf <- tempfile(fileext = ".xlsx"))) 
covid_ecdc <- readxl::read_excel(tmf)
str(covid_ecdc)
# select exYU countries
covid_yu <- covid_ecdc %>% 
  filter(countriesAndTerritories %in% c("Bosnia_and_Herzegovina", 
                                        "Croatia", "Montenegro", 
                                        "North_Macedonia", "Serbia", 
                                        "Slovenia"))
*glimpse(covid_yu)
```

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# .shadow-text[To learn more visit: <https://dataliteracy.rbind.io>]

[<i class="fab  fa-twitter "></i> @Tatjana_Kec](https://twitter.com/Tatjana_Kec)

[<i class="fab  fa-github "></i> @TanjaKec](https://github.com/TanjaKec)