rstatsZH - Data Science mit R

class: title-slide

# rstatsZH - Data Science mit R
## Mit mehreren Dataframes arbeiten / Funktionen schreiben

.bottom[

<p>Lars Schöbitz</p><p>2023-10-19</p>
]

---
# Rückblick - Woche 6

- Erweiterte Vektoren
  - Faktoren
  - Datumswerte
- Daten importieren
  - `read_csv()`
- Tidy Data Konzept
- Daten drehen (pivoting) mit `{tidyr}`
  - `pivot_longer()`
  - `pivot_wider()`

---
background-image: url(img/questions.jpg)
background-size: contain
background-position: right
class: middle

# .large[Hausaufgabe 6]

---
# Hausaufgabe 6 - Datenschutz

1. Unterordner für Daten erstellen
2. Daten in diesem Ordner speichern

---
# Hausaufgabe 6 - Datenschutz

1. `.gitignore` Datei öffnen
2. Neue Zeile hinzufügen mit Text `/data`

---
# Ziele für diese Woche

Am Ende dieser Woche könnt ihr:

- Mehrere Dataframes zusammenfügen
- Tabellen mit verschiedenen Packages darstellen 
- **Bonusmaterial:** Eine eigene Funktion schreiben
- Mit den im Kurs erlernten Fähigkeiten selbstständig weiter arbeiten

---
class: middle, inverse

# .large[Mit mehreren Dataframes arbeiten]

---
class: middle

# .hand[Wir...]

.huge[.green[haben]] .hand[mehrere Dataframes]

.huge[.pink[wollen]] .hand[diese zusammenbringen]

---
## Data: Women in science

Informationen zu 10 Frauen in der Wissenschaft welche die Welt verändert haben

.small[
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> name </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Ada Lovelace </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Marie Curie </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Janaki Ammal </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Chien-Shiung Wu </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Katherine Johnson </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Rosalind Franklin </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Vera Rubin </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Gladys West </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Flossie Wong-Staal </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Jennifer Doudna </td>
  </tr>
</tbody>
</table>
]

.footnote[
Quelle: [Discover Magazine](https://www.discovermagazine.com/the-sciences/meet-10-women-in-science-who-changed-the-world)
]

???
Daten kommen aus dem Discover Magazine. Daten aus dem Artikel herausgeholt.

---
## Inputs - Drei Dataframes

.panelset[

.panel[.panel-name[professions]

```
# A tibble: 10 × 2
   name               profession                        
   <chr>              <chr>                             
 1 Ada Lovelace       Mathematician                     
 2 Marie Curie        Physicist and Chemist             
 3 Janaki Ammal       Botanist                          
 4 Chien-Shiung Wu    Physicist                         
 5 Katherine Johnson  Mathematician                     
 6 Rosalind Franklin  Chemist                           
 7 Vera Rubin         Astronomer                        
 8 Gladys West        Mathematician                     
 9 Flossie Wong-Staal Virologist and Molecular Biologist
10 Jennifer Doudna    Biochemist                        
```
]

.panel[.panel-name[dates]

```
# A tibble: 8 × 3
  name               birth_year death_year
  <chr>                   <dbl>      <dbl>
1 Janaki Ammal             1897       1984
2 Chien-Shiung Wu          1912       1997
3 Katherine Johnson        1918       2020
4 Rosalind Franklin        1920       1958
5 Vera Rubin               1928       2016
6 Gladys West              1930         NA
7 Flossie Wong-Staal       1947         NA
8 Jennifer Doudna          1964         NA
```
]

.panel[.panel-name[works]

```
# A tibble: 9 × 2
  name               known_for                                   
  <chr>              <chr>                                       
1 Ada Lovelace       first computer algorithm                    
2 Marie Curie        theory of radioactivity,  discovery of elem…
3 Janaki Ammal       hybrid species, biodiversity protection     
4 Chien-Shiung Wu    confim and refine theory of radioactive bet…
5 Katherine Johnson  calculations of orbital mechanics critical …
6 Vera Rubin         existence of dark matter                    
7 Gladys West        mathematical modeling of the shape of the E…
8 Flossie Wong-Staal first scientist to clone HIV and create a m…
9 Jennifer Doudna    one of the primary developers of CRISPR, a …
```
]

]

???
.small[
**Professions**
 
Hier sehen wir die berufsbezeichungen der 10 Wissenschatfler_innen welche in dem Artikelk auftauchen. So haben wir:

- Namen: Ada lovelace
- Profession (Berufsbezeichnung): Sie war Mathematikerin

**Dates**

Wir haben einen weiteren Dataframe mit dem Namen "dates" in welchem wir nur Info zu 8 der Wissenschaftlerinnen haben.

Wir hätten zwar die zwei fehlenden nachschlagen können, aber in diesem Beispiel soll es darum gehen zu zeigen wie die Funktionen sich bei nicht vollständigen Daten verhalten.

Wir sehen zwei NAs. Dies heisst nicht dass wir das Todesjahr nicht wissen, sondern dass diese Wissenschaftlerinenn noch leben.

**Works**

Einen dritten Dataframe in welchem wir sehen wofür die Wissenschaftlerinnen bekannt waren. Zum Beispiel for Ada Lovelace wir sehen den ersten Computer algorhyhtmus.

] 
---
## Gewünschter Output

```
# A tibble: 10 × 5
   name               profession  birth_year death_year known_for
   <chr>              <chr>            <dbl>      <dbl> <chr>    
 1 Ada Lovelace       Mathematic…         NA         NA first co…
 2 Marie Curie        Physicist …         NA         NA theory o…
 3 Janaki Ammal       Botanist          1897       1984 hybrid s…
 4 Chien-Shiung Wu    Physicist         1912       1997 confim a…
 5 Katherine Johnson  Mathematic…       1918       2020 calculat…
 6 Rosalind Franklin  Chemist           1920       1958 <NA>     
 7 Vera Rubin         Astronomer        1928       2016 existenc…
 8 Gladys West        Mathematic…       1930         NA mathemat…
 9 Flossie Wong-Staal Virologist…       1947         NA first sc…
10 Jennifer Doudna    Biochemist        1964         NA one of t…
```

???

Hier sehen wir unseren gewünschten Output. Das Ziel nachdem die drei Dataframes zusammengefügt wurden:

- name und profession aus dem ersten Dataframe
- name, birth_year und death_year aus dem zweiten Dataframe
- name und known_for aus dem dritten Dataframe

---
## Inputs als Erinnerung

.pull-left[

```r
names(professions)
```

```
[1] "name"       "profession"
```

```r
names(dates)
```

```
[1] "name"       "birth_year" "death_year"
```

```r
names(works)
```

```
[1] "name"      "known_for"
```
]

.pull-right[

```r
nrow(professions)
```

```
[1] 10
```

```r
nrow(dates)
```

```
[1] 8
```

```r
nrow(works)
```

```
[1] 9
```
]

???

- Name ist hier sozusagen der Schlüssel
- Professions hat die komplette Info während bei den anderen etwas fehlt

---
class: middle, inverse

# .large[Dataframes zusammenfügen]

---

## Dataframes zusammenfügen

```r
abcd_join(x, y)
```

- `left_join()`: alle Reihen aus x
- `right_join()`: alle Reihen aus y
- `full_join()`: alle Reihen aus x und y
- ...
 
---
## Beispiel

Für die nächsten Folien

.pull-left[

```
# A tibble: 3 × 2
     id var_x
  <dbl> <chr>
1     1 x1   
2     2 x2   
3     3 x3   
```
]
.pull-right[

```
# A tibble: 3 × 2
     id var_y
  <dbl> <chr>
1     1 y1   
2     2 y2   
3     4 y4   
```
]

---

## `left_join()`

.pull-left[
<img src="img/left-join.gif" width="80%" style="background-color: #FDF6E3" style="display: block; margin: auto;" />
]
.pull-right[

```r
left_join(tib_x, tib_y)
```

```
# A tibble: 3 × 3
     id var_x var_y
  <dbl> <chr> <chr>
1     1 x1    y1   
2     2 x2    y2   
3     3 x3    <NA> 
```
]

---

## `left_join()`

```r
professions %>%
* left_join(dates)
```

```
# A tibble: 10 × 4
   name               profession            birth_year death_year
   <chr>              <chr>                      <dbl>      <dbl>
 1 Ada Lovelace       Mathematician                 NA         NA
 2 Marie Curie        Physicist and Chemist         NA         NA
 3 Janaki Ammal       Botanist                    1897       1984
 4 Chien-Shiung Wu    Physicist                   1912       1997
 5 Katherine Johnson  Mathematician               1918       2020
 6 Rosalind Franklin  Chemist                     1920       1958
 7 Vera Rubin         Astronomer                  1928       2016
 8 Gladys West        Mathematician               1930         NA
 9 Flossie Wong-Staal Virologist and Molec…       1947         NA
10 Jennifer Doudna    Biochemist                  1964         NA
```

---

## `right_join()`

.pull-left[
<img src="img/right-join.gif" width="80%" style="background-color: #FDF6E3" style="display: block; margin: auto;" />
]
.pull-right[

```r
right_join(tib_x, tib_y)
```

```
# A tibble: 3 × 3
     id var_x var_y
  <dbl> <chr> <chr>
1     1 x1    y1   
2     2 x2    y2   
3     4 <NA>  y4   
```
]

---

## `right_join()`

```r
professions %>%
* right_join(dates)
```

```
# A tibble: 8 × 4
  name               profession             birth_year death_year
  <chr>              <chr>                       <dbl>      <dbl>
1 Janaki Ammal       Botanist                     1897       1984
2 Chien-Shiung Wu    Physicist                    1912       1997
3 Katherine Johnson  Mathematician                1918       2020
4 Rosalind Franklin  Chemist                      1920       1958
5 Vera Rubin         Astronomer                   1928       2016
6 Gladys West        Mathematician                1930         NA
7 Flossie Wong-Staal Virologist and Molecu…       1947         NA
8 Jennifer Doudna    Biochemist                   1964         NA
```

---

## `full_join()`

.pull-left[
<img src="img/full-join.gif" width="80%" style="background-color: #FDF6E3" style="display: block; margin: auto;" />
]
.pull-right[

```r
full_join(tib_x, tib_y)
```

```
# A tibble: 4 × 3
     id var_x var_y
  <dbl> <chr> <chr>
1     1 x1    y1   
2     2 x2    y2   
3     3 x3    <NA> 
4     4 <NA>  y4   
```
]

---

## `full_join()`

```r
dates %>%
* full_join(works)
```

```
# A tibble: 10 × 4
   name               birth_year death_year known_for            
   <chr>                   <dbl>      <dbl> <chr>                
 1 Janaki Ammal             1897       1984 hybrid species, biod…
 2 Chien-Shiung Wu          1912       1997 confim and refine th…
 3 Katherine Johnson        1918       2020 calculations of orbi…
 4 Rosalind Franklin        1920       1958 <NA>                 
 5 Vera Rubin               1928       2016 existence of dark ma…
 6 Gladys West              1930         NA mathematical modelin…
 7 Flossie Wong-Staal       1947         NA first scientist to c…
 8 Jennifer Doudna          1964         NA one of the primary d…
 9 Ada Lovelace               NA         NA first computer algor…
10 Marie Curie                NA         NA theory of radioactiv…
```

???

---

## Alles in einer Code Sequenz

```r
professions %>%
  left_join(dates) %>%
  left_join(works)
```

---

## Fehlende Daten ergänzen

```r
professions %>% 
  left_join(dates) %>% 
  mutate(birth_year = case_when( 
    name == "Ada Lovelace" ~ 1815, # looked up dates on Wikipedia
    name == "Marie Curie" ~ 1867,
    TRUE ~ birth_year
  ))
```

```
# A tibble: 10 × 4
  name              profession            birth_year death_year
  <chr>             <chr>                      <dbl>      <dbl>
1 Ada Lovelace      Mathematician               1815         NA
2 Marie Curie       Physicist and Chemist       1867         NA
3 Janaki Ammal      Botanist                    1897       1984
4 Chien-Shiung Wu   Physicist                   1912       1997
5 Katherine Johnson Mathematician               1918       2020
6 Rosalind Franklin Chemist                     1920       1958
7 Vera Rubin        Astronomer                  1928       2016
# ℹ 3 more rows
```

---
.note[

# Praktikum 10 - Daten zusammenfügen

## 2er Teams

1. **GitHub**: Öffne die GitHub Organisation für den Kurs: https://github.com/rstatszh-k007
2. **GitHub**: Nutze die Suche um das Praktikum 10 (prak-10) mit deinem Benutzernamen zu finden
3. **GitHub**: Klicke auf den grünen Button (Code) und kopiere die URL in die Zwischenablage
3. **Posit Cloud**: Öffne deinen Arbeitsbereich für den Kurs in der Posit Cloud
4. **Posit Cloud / Projects**: Klicke auf "New Project from GitHub Repository" 
5. **RStudio IDE**: Finde den Datei Manager und den Git Reiter
6. **Zoom Chat**: Schreibt wenn ihr soweit seid

]

---
## Inputs - Drei Dataframes

.panelset[

.panel[.panel-name[einkaeufe]

```
# A tibble: 9 × 4
  kunden_id produkt_name einkauf einheit
  <chr>     <chr>          <dbl> <chr>  
1 k1        Chips            2   anzahl 
2 k1        Milch            3   anzahl 
3 k1        Avocado          1   anzahl 
4 k2        Pfirsich         2.5 kg     
5 k2        Birne            0.5 kg     
6 k2        Apfel            2   kg     
7 k2        Tomate           1.5 kg     
8 k2        Pfirsich         1   kg     
9 k2        Milch            4   anzahl 
```
]

.panel[.panel-name[preise]

```
# A tibble: 10 × 2
   produkt_name    preis
   <chr>           <dbl>
 1 Toilettenpapier  14.3
 2 Chips             3.8
 3 Blaubeeren        7.8
 4 Milch             2.2
 5 Avocado           3.2
 6 Banane            1.9
 7 Birne             2.6
 8 Apfel             4.1
 9 Tomate            2.7
10 Pfirsich          6.5
```
]

.panel[.panel-name[kundenprofile]

```
# A tibble: 5 × 10
  kunden_id email     phone vorname nachname wohnort kanton   plz
  <chr>     <chr>     <chr> <chr>   <chr>    <chr>   <chr>  <dbl>
1 k1        edwin.du… 075 … Edwin   Dumont   Domles… Zug     8099
2 k2        leonora.… 076 … Leonora Garcia   Hornus… Glarus  6894
3 k3        lydia.bl… 079 … Lydia   Blancha… Albula… Zug     5790
4 k4        maeva.mo… 076 … Maeva   Moreau   Lostal… Zürich  8113
5 k5        frank.le… 077 … Frank   Legrand  Stein … Bern    4282
# ℹ 2 more variables: geburtstag <date>, alter <dbl>
```
]

]

---
## Gewünschter Output

<div id="pzveloiefp" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
<style>#pzveloiefp table {
  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}

#pzveloiefp thead, #pzveloiefp tbody, #pzveloiefp tfoot, #pzveloiefp tr, #pzveloiefp td, #pzveloiefp th {
  border-style: none;
}

#pzveloiefp p {
  margin: 0;
  padding: 0;
}

#pzveloiefp .gt_table {
  display: table;
  border-collapse: collapse;
  line-height: normal;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#pzveloiefp .gt_caption {
  padding-top: 4px;
  padding-bottom: 4px;
}

#pzveloiefp .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#pzveloiefp .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 3px;
  padding-bottom: 5px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#pzveloiefp .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#pzveloiefp .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#pzveloiefp .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#pzveloiefp .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#pzveloiefp .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#pzveloiefp .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#pzveloiefp .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#pzveloiefp .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 5px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#pzveloiefp .gt_spanner_row {
  border-bottom-style: hidden;
}

#pzveloiefp .gt_group_heading {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  text-align: left;
}

#pzveloiefp .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#pzveloiefp .gt_from_md > :first-child {
  margin-top: 0;
}

#pzveloiefp .gt_from_md > :last-child {
  margin-bottom: 0;
}

#pzveloiefp .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#pzveloiefp .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
}

#pzveloiefp .gt_stub_row_group {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
  vertical-align: top;
}

#pzveloiefp .gt_row_group_first td {
  border-top-width: 2px;
}

#pzveloiefp .gt_row_group_first th {
  border-top-width: 2px;
}

#pzveloiefp .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#pzveloiefp .gt_first_summary_row {
  border-top-style: solid;
  border-top-color: #D3D3D3;
}

#pzveloiefp .gt_first_summary_row.thick {
  border-top-width: 2px;
}

#pzveloiefp .gt_last_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#pzveloiefp .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
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  padding-bottom: 8px;
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  <thead>
    
    <tr class="gt_col_headings">
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="vorname">vorname</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="nachname">nachname</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="summe">summe</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="email">email</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="vorname" class="gt_row gt_left">Edwin</td>
<td headers="nachname" class="gt_row gt_left">Dumont</td>
<td headers="summe" class="gt_row gt_right">9.2</td>
<td headers="email" class="gt_row gt_left">edwin.dumont@example.com</td></tr>
    <tr><td headers="vorname" class="gt_row gt_left">Leonora</td>
<td headers="nachname" class="gt_row gt_left">Garcia</td>
<td headers="summe" class="gt_row gt_right">24.6</td>
<td headers="email" class="gt_row gt_left">leonora.garcia@example.com</td></tr>
  </tbody>
  
  
</table>
</div>

---
## Schritt 1 - Daten zusammenfügen

```r
einkaeufe_preise <- einkaeufe %>% 
  left_join(preise)

einkaeufe_preise
```

```
# A tibble: 9 × 5
  kunden_id produkt_name einkauf einheit preis
  <chr>     <chr>          <dbl> <chr>   <dbl>
1 k1        Chips            2   anzahl    3.8
2 k1        Milch            3   anzahl    2.2
3 k1        Avocado          1   anzahl    3.2
4 k2        Pfirsich         2.5 kg        6.5
5 k2        Birne            0.5 kg        2.6
6 k2        Apfel            2   kg        4.1
7 k2        Tomate           1.5 kg        2.7
8 k2        Pfirsich         1   kg        6.5
9 k2        Milch            4   anzahl    2.2
```

---
## Schritt 2 - Neue Variable erstellen

```r
einkaeufe_kosten <- einkaeufe_preise %>% 
  mutate(kosten = einkauf * preis)

einkaeufe_kosten
```

```
# A tibble: 9 × 6
  kunden_id produkt_name einkauf einheit preis kosten
  <chr>     <chr>          <dbl> <chr>   <dbl>  <dbl>
1 k1        Chips            2   anzahl    3.8   7.6 
2 k1        Milch            3   anzahl    2.2   6.6 
3 k1        Avocado          1   anzahl    3.2   3.2 
4 k2        Pfirsich         2.5 kg        6.5  16.2 
5 k2        Birne            0.5 kg        2.6   1.3 
6 k2        Apfel            2   kg        4.1   8.2 
7 k2        Tomate           1.5 kg        2.7   4.05
8 k2        Pfirsich         1   kg        6.5   6.5 
9 k2        Milch            4   anzahl    2.2   8.8 
```

---
## Schritt 3 - Daten zusammefassen

```r
einkaeufe_kosten_sum <- einkaeufe_kosten %>% 
  group_by(kunden_id) %>% 
  summarise(
    summe = sum(kosten)
  )

einkaeufe_kosten_sum
```

```
# A tibble: 2 × 2
  kunden_id summe
  <chr>     <dbl>
1 k1          9.2
2 k2         24.6
```

---
## Schritt 4 - Daten zusammenfügen + eingrenzen

```r
kunden_tab <- einkaeufe_kosten_sum %>% 
  left_join(kundenprofile) %>% 
  select(ends_with("name"), summe, email)

kunden_tab
```

```
# A tibble: 2 × 4
  vorname nachname summe email                     
  <chr>   <chr>    <dbl> <chr>                     
1 Edwin   Dumont     9.2 edwin.dumont@example.com  
2 Leonora Garcia    24.6 leonora.garcia@example.com
```

---
## Schritt 5 - Daten als Tabelle darstellen

```r
kunden_tab %>% 
  gt()
```

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      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="summe">summe</th>
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</table>
</div>

---
## Als eine Code Sequenz

```r
einkaeufe %>%
  left_join(preise) %>%
  mutate(kosten = einkauf * preis) %>% 
  group_by(kunden_id) %>% 
  summarise(
    summe = sum(kosten)
  ) %>% 
  left_join(kundenprofile) %>% 
  select(ends_with("name"), summe, email) %>% 
  gt()
```

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  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#xdduhtxcdz .gt_caption {
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  padding-bottom: 4px;
}

#xdduhtxcdz .gt_title {
  color: #333333;
  font-size: 125%;
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  border-bottom-width: 0;
}

#xdduhtxcdz .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
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}

#xdduhtxcdz .gt_heading {
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}

#xdduhtxcdz .gt_bottom_border {
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  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#xdduhtxcdz .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#xdduhtxcdz .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#xdduhtxcdz .gt_column_spanner_outer {
  color: #333333;
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  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#xdduhtxcdz .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#xdduhtxcdz .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#xdduhtxcdz .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 5px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#xdduhtxcdz .gt_spanner_row {
  border-bottom-style: hidden;
}

#xdduhtxcdz .gt_group_heading {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  text-align: left;
}

#xdduhtxcdz .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#xdduhtxcdz .gt_from_md > :first-child {
  margin-top: 0;
}

#xdduhtxcdz .gt_from_md > :last-child {
  margin-bottom: 0;
}

#xdduhtxcdz .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#xdduhtxcdz .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
}

#xdduhtxcdz .gt_stub_row_group {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
  vertical-align: top;
}

#xdduhtxcdz .gt_row_group_first td {
  border-top-width: 2px;
}

#xdduhtxcdz .gt_row_group_first th {
  border-top-width: 2px;
}

#xdduhtxcdz .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#xdduhtxcdz .gt_first_summary_row {
  border-top-style: solid;
  border-top-color: #D3D3D3;
}

#xdduhtxcdz .gt_first_summary_row.thick {
  border-top-width: 2px;
}

#xdduhtxcdz .gt_last_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#xdduhtxcdz .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#xdduhtxcdz .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#xdduhtxcdz .gt_last_grand_summary_row_top {
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  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: double;
  border-bottom-width: 6px;
  border-bottom-color: #D3D3D3;
}

#xdduhtxcdz .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#xdduhtxcdz .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#xdduhtxcdz .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#xdduhtxcdz .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#xdduhtxcdz .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#xdduhtxcdz .gt_sourcenote {
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#xdduhtxcdz .gt_left {
  text-align: left;
}

#xdduhtxcdz .gt_center {
  text-align: center;
}

#xdduhtxcdz .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#xdduhtxcdz .gt_font_normal {
  font-weight: normal;
}

#xdduhtxcdz .gt_font_bold {
  font-weight: bold;
}

#xdduhtxcdz .gt_font_italic {
  font-style: italic;
}

#xdduhtxcdz .gt_super {
  font-size: 65%;
}

#xdduhtxcdz .gt_footnote_marks {
  font-size: 75%;
  vertical-align: 0.4em;
  position: initial;
}

#xdduhtxcdz .gt_asterisk {
  font-size: 100%;
  vertical-align: 0;
}

#xdduhtxcdz .gt_indent_1 {
  text-indent: 5px;
}

#xdduhtxcdz .gt_indent_2 {
  text-indent: 10px;
}

#xdduhtxcdz .gt_indent_3 {
  text-indent: 15px;
}

#xdduhtxcdz .gt_indent_4 {
  text-indent: 20px;
}

#xdduhtxcdz .gt_indent_5 {
  text-indent: 25px;
}
</style>
<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
  <thead>
    
    <tr class="gt_col_headings">
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="vorname">vorname</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="nachname">nachname</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="summe">summe</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="email">email</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td headers="vorname" class="gt_row gt_left">Edwin</td>
<td headers="nachname" class="gt_row gt_left">Dumont</td>
<td headers="summe" class="gt_row gt_right">17.4</td>
<td headers="email" class="gt_row gt_left">edwin.dumont@example.com</td></tr>
    <tr><td headers="vorname" class="gt_row gt_left">Leonora</td>
<td headers="nachname" class="gt_row gt_left">Garcia</td>
<td headers="summe" class="gt_row gt_right">45.1</td>
<td headers="email" class="gt_row gt_left">leonora.garcia@example.com</td></tr>
  </tbody>
  
  
</table>
</div>

---
class: middle, inverse

# .large[Funktionen]

---
class: middle, inverse

# .large[Funktionen - Bonusmaterial]

---
.note[

# Praktikum 11 - Funktionen

## Live Coding

1. **GitHub**: Öffne die GitHub Organisation für den Kurs: https://github.com/rstatszh-k007
2. **GitHub**: Nutze die Suche um das Praktikum 11 (prak-11) mit deinem Benutzernamen zu finden
3. **GitHub**: Klicke auf den grünen Button (Code) und kopiere die URL in die Zwischenablage
3. **Posit Cloud**: Öffne deinen Arbeitsbereich für den Kurs in der Posit Cloud
4. **Posit Cloud / Projects**: Klicke auf "New Project from GitHub Repository" 
5. **RStudio IDE**: Finde den Datei Manager und den Git Reiter
6. **Zoom Chat**: Schreibt wenn ihr soweit seid

]

---
class: middle, inverse

# .large[The End]

---
class: middle, inverse

# .large[The End - Noch nicht ganz]

---
# Was habt ihr gelernt?

- Anwendung von Tidyverse Packages zum 
  - Importieren,
  - Aufräumen (Tidying),
  - Transformieren, 
  - Visualisieren, und
  - Kommunizieren von Daten.
- Kollaboration und Versionsverwaltung mit Git/GitHub
- Datenprojekte reproduzierbar publizieren mit GitHub
- Das Konzept von Tidy Data

---
class: middle, inverse

# .large[Wie geht's weiter?]

---
# Raus aus der RStudio Cloud

1. Installationen: https://github.com/rstatsZH/kochbuch/tree/main/01-Installation
2. Einmalig: Tidyverse Packages installieren
3. Danach: Tidyverse Packages laden
4. Packages ausserhalb des Tidyverse installieren und laden (e.g. `janitor`)

```r
# Einmalig in Konsole ausführen
install.packages("tidyverse")

# In jedem Skript
library(tidyverse)
```

---
class: middle

# Weiterführende Ressourcen - Üben + Vertiefen

https://rstatszh.github.io/website/posts/2021-04-30-woche07/

---
# Projektarbeit - Unterstützung bis Anfang Mai 2023

**Hausaufgabe 6**

1. GitHub Repository erstellen und RStudio Projekt aufgleisen (Hausaufgabe 6)
2. Daten für das Projekt identifizieren

**Wie es weiter geht: Bericht mit R Markdown schreiben**

1. Daten importieren  
2. Daten (visuell) erkunden
3. Daten ggf. transformieren und dann erneut (visuell) erkunden
4. Fragen an den Datensatz formulieren
5. Versuchen zu Antworten zu kommen und dokumentieren
6. Immer wieder, git add, commit, push

---
class: middle, inverse

# .big[Reflexion]

---
# Reflexion

## 5 min Nachdenken + Notizen

.question[

1. Was sind die drei nützlichsten Dinge die du gelernt hast?
2. Welches Thema war besonders schwer zu folgen? 
3. Was hat dir gefehlt?

]

--
<img src="img/kommentar.png" width="100%" style="display: block; margin: auto;" />

---
class: middle, inverse

# .big[Feedback]

---
class: left
background-image: url(img/tor.jpg)
background-position: right
background-size: contain

# Ziele erreicht?

Bitte ausfüllen:

[https://forms.gle/fEmF9899WEnCETdL8](https://forms.gle/fEmF9899WEnCETdL8)

.pull-left[

.footnote[Photo by: [Virgil Cayasa](https://unsplash.com/@virgilcayasa)]

]

---
# Wie es für mich weiter geht

1. **Beratung:** Projektbezogener Support, Code Review, Coaching
2. **rstatsZH Kursleitung:** Info über den Kurs verbreiten
3. **Kurse zu vertiefenden Themen:** Entwicklung von 4-Stunden Workshops

Contact: [Lars@Lse.de](mailto:lars@lse.de)

---
# 🌻 Danke

Für die Aufmerksamkeit!

Für die R packages [{xaringan}](https://github.com/yihui/xaringan) und [{xaringanthemer}](https://github.com/gadenbuie/xaringanthemer) mit welchen die Folien geschrieben wurden.

Eine PDF Version der Folien kann hier heruntergeladen werden: https://github.com/rstatsZH/website/raw/master/slides/e1_d07-data-join/e1_d07-data-join.pdf

<hr/>

Für [Data Science in a Box](https://datasciencebox.org/) und [Remaster the Tidyverse](https://github.com/rstudio-education/remaster-the-tidyverse), von welchen ich Materialien für diesen Kurs nutze und welche genau wie diese Folien mit [Creative Commons Attribution Share Alike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) lizensiert sind.