Intro 2 R

.title[
# Intro 2 R
]
.author[
### <a href='http://schultem.pages.imucb.ch/cavd_hs2024/'> Communicating and Visualizing data </a>
]
.date[
### HS 2024
]

---

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 <a href="https://therbootcamp.github.io/">
 
 
 www.therbootcamp.com
 
 
 </a>
 <a href="https://therbootcamp.github.io/">
 
 Communicating and Visualizing data | HS 2024
 
 </a>
 
 </div>

---

<!---
.pull-left45[

# What is "Wrangling"?

<ul>
 <li class="m1"><high>Transform</high>
 
 <ul class="level">
 <li>Change column names</li>
 <li>Create new variables</li>
 </ul></li>
 <li class="m2"><high>Organize</high>
 
 <ul class="level">
 <li>Sort rows</li>
 <li>Join data sets</li>
 <li>Transpose data</li>
 </ul></li>
 <li class="m3"><high>Aggregate</high>
 
 <ul class="level">
 <li>Build groups</li>
 <li>Calculate statistics</li>
 </ul></li>
</ul>

]

]

--->

# The R ecosphere

---

# Base R primer

<ul>
 <li class="m1">Assignments </li>
 <ul class="level">
 <li>Object <mono><-</mono> function</li>
 <li><mono><-</mono> creates objects</li>
 </ul> 
 <li class="m2">Functions</li>
 <ul class="level">
 <li>Everything that happens is a function call</li>
 <li>Functions have (default) arguments</li>
 <li>Beware object/argument classes</li>
 <li>Functions "live" in packages</li>
 </ul> 
</ul>

]

]

---

# Object <mono><-</mono> function

<ul>
 <li class="m1">Assignments </li>
 <ul class="level">
 <li><high>Object <mono><-</mono> function</high></li>
 <li><mono><-</mono> creates objects</li>
 </ul> 
 <li class="m2">Functions</li>
 <ul class="level">
 <li>Everything that happens is a function call</li>
 <li>Functions have (default) arguments</li>
 <li>Beware object/argument classes</li>
 <li>Functions "live" in packages</li>
 </ul> 
</ul>

]

]

---

# <mono><-</mono> creates objects

<ul>
 <li class="m1">Assignments </li>
 <ul class="level">
 <li>Object <mono><-</mono> function</li>
 <li><high><mono><-</mono> creates objects</high></li>
 </ul> 
 <li class="m2">Functions</li>
 <ul class="level">
 <li>Everything that happens is a function call</li>
 <li>Functions have (default) arguments</li>
 <li>Beware object/argument classes</li>
 <li>Functions "live" in packages</li>
 </ul> 
</ul>

]

``` r
# an object named score
score <- c(8, 4, 6, 3, 7, 3)

# print the object (aka show its contents)
print(score)
```

```
## [1] 8 4 6 3 7 3
```

``` r
# print the object (aka show its contents)
score
```

```
## [1] 8 4 6 3 7 3
```

]

---

# <mono><-</mono> creates objects

]

``` r
# an object named score
score <- c(8, 4, 6, 3, 7, 3)

# mean of score
mean(score)
```

```
## [1] 5.167
```

]

---

# Functions

.pull-left4[
<ul>
 <li class="m1">Assignments </li>
 <ul class="level">
 <li>Object <mono><-</mono> function</li>
 <li><mono><-</mono> creates objects</li>
 </ul> 
 <li class="m2">Functions</li>
 <ul class="level">
 <li><high>Everything that happens is a function call</high></li>
 <li>Functions have (default) arguments</li>
 <li>Beware object/argument classes</li>
 <li>Functions "live" in packages</li>
 </ul> 
</ul>
]

---

# Functions

``` r
# an object named score
score <- c(8, 4, 6, 3, 7, 3)

# print the object (aka show its contents)
score
```

```
## [1] 8 4 6 3 7 3
```

``` r
# mean of score
mean(score)
```

```
## [1] 5.167
```
]

---

# Arguments

.pull-left4[
<ul>
 <li class="m1">Assignments </li>
 <ul class="level">
 <li>Object <mono><-</mono> function</li>
 <li><mono><-</mono> creates objects</li>
 </ul> 
 <li class="m2">Functions</li>
 <ul class="level">
 <li>Everything that happens is a function call</li>
 <li><high>Functions have (default) arguments</high></li>
 <li>Beware object/argument classes</li>
 <li>Functions "live" in packages</li>
 </ul> 
</ul>
]

``` r
# no argument
mean()
```

```
## Error in mean.default(): argument "x" is missing, with no default
```

``` r
# one (necessary) argument
mean(score)
```

```
## [1] 5.167
```

``` r
# one (necessary) argument with name
mean(x = score)
```

```
## [1] 5.167
```

``` r
# NA added to vector score
mean(x = c(score, NA))
```

```
## [1] NA
```
]

---

# Arguments

.pull-left4[
<ul>
 <li class="m1">Assignments </li>
 <ul class="level">
 <li>Object <mono><-</mono> function</li>
 <li><mono><-</mono> creates objects</li>
 </ul> 
 <li class="m2">Functions</li>
 <ul class="level">
 <li>Everything that happens is a function call</li>
 <li><high>Functions have (default) arguments</high></li>
 <li>Beware object/argument classes</li>
 <li>Functions "live" in packages</li>
 </ul> 
</ul>
]

``` r
# no argument
mean()
```

```
## Error in mean.default(): argument "x" is missing, with no default
```

``` r
# one (necessary) argument
mean(score)
```

```
## [1] 5.167
```

``` r
# one (necessary) argument with name
mean(x = score)
```

```
## [1] 5.167
```

``` r
# change default in additional argument
mean(x = c(score, NA), na.rm = TRUE)
```

```
## [1] 5.167
```
]

---

# Classes

.pull-left4[
<ul>
 <li class="m1">Assignments </li>
 <ul class="level">
 <li>Object <mono><-</mono> function</li>
 <li><mono><-</mono> creates objects</li>
 </ul> 
 <li class="m2">Functions</li>
 <ul class="level">
 <li>Everything that happens is a function call</li>
 <li>Functions have (default) arguments</li>
 <li><high>Beware object/argument classes</high></li>
 <li>Functions "live" in packages</li>
 </ul> 
</ul>
]

]

---

# Packages

.pull-left4[
<ul>
 <li class="m1">Assignments </li>
 <ul class="level">
 <li>Object <mono><-</mono> function</li>
 <li><mono><-</mono> creates objects</li>
 </ul> 
 <li class="m2">Functions</li>
 <ul class="level">
 <li>Everything that happens is a function call</li>
 <li>Functions have (default) arguments</li>
 <li>Beware object/argument classes</li>
 <li><high>Functions "live" in packages</high></li>
 </ul> 
</ul>
]

<high>Install</high> package <high>once</high> using `install.packages()`

``` r
install.packages("tidyverse")
```
<br2>
<high>Load</high> existing package <high>every time</high> using `library()`

``` r
library(tidyverse)
```

<img src="image/tidyverse_load.png" height="180">
]

---

# The R ecosphere

---

# Tidyverse

<ul>
 <li class="m1">The tidyverse is...</li> 
 <ul class="level">
 <li>A collection of user-friendly <high>packages</high> for analyzing <high>tidy data</high></li> 
 <li>An <high>ecosystem</high> for analytics and data science with common design principles</li> 
 <li>A <high>dialect</high> of the R language</li>
 </ul>
</ul>

]

---

# Tidyverse

]

---

# <mono>%>% |></mono>

<ul>
 <li class="m1">The <high>novel pipe operator</high> from the <a href="https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html"><mono>magrittr</mono></a> package makes chaining commands easy.</li>
</ul>

]

``` r
# Numeric vector
score <- c(8, 4, 6, 3, 7, 3)
score
```

```
## [1] 8 4 6 3 7 3
```

``` r
# Mean: Base-R-style
mean(score)
```

```
## [1] 5.167
```

``` r
# Mean: dplyr-style
score %>%  
  mean()  
```

```
## [1] 5.167
```

]

---

# <mono>%>% |></mono>

]

]

---

# <mono>readr</mono>

<ul>
 <li class="m1">Benefits over <mono>read.csv</mono>:</li>
 <ul class="level">
 <li>Better type inference</li>
 <li>Avoids <mono>factors</mono></li>
 <li>Produces <highm>tibble</highm></li>
 </ul></li>
</ul>

]

]

---

# <mono>readr</mono>

]

``` r
# Read in taxation
basel <- read_csv("1_Data/taxation.csv")

basel
```

```
## # A tibble: 357 × 10
## year quarter quarter_no N
## <dbl> <chr> <dbl> <dbl>
## 1 2001 Altstadt … 1 1673
## 2 2001 Vorstädte 2 3204
## 3 2001 Am Ring 3 6579
## 4 2001 Breite 4 5433
## 5 2001 St. Alban 5 6179
## # ℹ 352 more rows
## # ℹ 6 more variables:
## # income_mean <dbl>,
## # income_median <dbl>,
## # income_gini <dbl>,
## # wealth_mean <dbl>,
## # wealth_median <dbl>, …
```

]

---

# <mono>tibble</mono>

<ul>
 <li class="m1">Benefits over <mono>data.frame</mono>:</li>
 <ul class="level">
 <li><high>Better print</high>: More informative and cleaner</li>
 <li>More consistent subsetting</li>
 </ul></li>
</ul>

]

``` r
# Read in taxation
basel <- read_csv("1_Data/taxation.csv")

basel
```

]

---

# <mono>dplyr</mono>

<ul>
 <li class="m1">Benefits over Base R:</li>
 <ul class="level">
 <li><high>No more brackets</high></li>
 <li><high>Data masking</high></li>
 <li>Tidy selection</li>
 <li>Intuitively named functions</li>
 </ul></li>
</ul>

]

<table cellspacing="0" cellpadding="0" class="clean_table" width="100%">
<col width="42%">
<col width="58%">
<tr>
<td>Key verbs</td>
<td>Purpose</td>
</tr>
<tr>
<td style="padding-top:20px">Transformation</td>
<td></td>
</tr>
<tr>
<td><mono>rename()</mono></td>
<td>Rename column names</td>
</tr>
<tr>
<td><mono>mutate()</mono></td>
<td>Create/change columns</td>
</tr>
<td style="padding-top:20px">Organization</td>
<td></td>
</tr>
<tr>
<td><mono>arrange()</mono></td>
<td>Sort</td>
</tr>
<tr>
<td><mono>select()</mono></td>
<td>Select variables</td>
</tr>
<tr>
<td><mono>slice()</mono>, <mono>filter()</mono></td>
<td>Select rows</td>
</tr>
<tr>
<td><mono>left_join()</mono>, <mono>inner_join()</mono>, etc.</td>
<td>Join data sets</td>
</tr>
<td style="padding-top:20px">Aggregation</td>
<td></td>
</tr>
<tr>
<td><mono>summarize()</mono></td>
<td>Calculate statistics</td>
</tr>
<tr>
<td><mono>group()</mono></td>
<td>Summarize group-wise</td>
</tr>
</table>

]

---

# `select()`

``` r
# Select two columns
TIBBLE %>% 
  select(VAR1, VAR2)

# Select everything but 
TIBBLE %>% 
  select(-VAR1)
```

]

``` r
basel %>%
  
  # Select columns
  select(year, quarter, income_mean)
```

```
## # A tibble: 357 × 3
## year quarter income_mean
## <dbl> <chr> <dbl>
## 1 2001 Altstadt Gross… 87776
## 2 2001 Vorstädte 84109
## 3 2001 Am Ring 62582
## 4 2001 Breite 52039
## 5 2001 St. Alban 89956
## 6 2001 Gundeldingen 51229
## 7 2001 Bruderholz 96124
## 8 2001 Bachletten 70348
## # ℹ 349 more rows
```

]

<!---

# `slice()`

``` r
# Slice using :
TIBBLE %>%
  slice(INDEX_START:INDEX_STOP)

# Slice using vector  
TIBBLE %>%
  slice(c(INDEX1, INDEX2, ...))
```

]

``` r
basel %>%
    select(year, quarter, income_mean) %>%

# Select rows 20 to 30
  slice(20:30)
```

```
## # A tibble: 11 × 3
## year quarter income_mean
## <dbl> <chr> <dbl>
## 1 2001 Riehen 84857
## 2 2001 Bettingen 83803
## 3 2002 Altstadt Gross… 89525
## 4 2002 Vorstädte 86350
## 5 2002 Am Ring 64797
## 6 2002 Breite 52483
## 7 2002 St. Alban 85906
## 8 2002 Gundeldingen 52035
## # ℹ 3 more rows
```

]

--->

---

# `filter()`

``` r
# Filter using logical comparisons
TIBBLE %>%
 filter(VAR1 == VAL1,
 VAR2 > VAL2,
 VAR3 < VAL3,
 VAR4 == VAL4 | VAR5 < VAL5)
```
]

``` r
basel %>%
    select(year, quarter, income_mean) %>%

# Select rows rows where year is 2017
  filter(year == 2017)
```

```
## # A tibble: 21 × 3
## year quarter income_mean
## <dbl> <chr> <dbl>
## 1 2017 Altstadt Gross… 97111
## 2 2017 Vorstädte 103714
## 3 2017 Am Ring 78761
## 4 2017 Breite 56888
## 5 2017 St. Alban 102457
## 6 2017 Gundeldingen 56544
## 7 2017 Bruderholz 105973
## 8 2017 Bachletten 81580
## # ℹ 13 more rows
```

]

---

# `arrange()`

``` r
# Sort ascending
TIBBLE %>%
  arrange(VAR1, VAR2)

# Sort descending w/ desc()
TIBBLE %>%
  arrange(desc(VAR1), VAR2)
```

]

``` r
basel %>%
  select(year, quarter, income_mean) %>%
  filter(year == 2017) %>% 
  
  # Sort by income
  arrange(income_mean)
```

```
## # A tibble: 21 × 3
## year quarter income_mean
## <dbl> <chr> <dbl>
## 1 2017 Klybeck 41569
## 2 2017 Kleinhüningen 45664
## 3 2017 Clara 50680
## 4 2017 Matthäus 50786
## 5 2017 Iselin 51600
## 6 2017 St. Johann 52890
## 7 2017 Rosental 54543
## 8 2017 Gundeldingen 56544
## # ℹ 13 more rows
```

]

---

# `summarize()`

``` r
# Create new summary variables
TIBBLE %>%
  summarise(
    NAME1 = SUMMARY_FUN(VAR1),
    NAME2 = SUMMARY_FUN(VAR2)
  )
```

]

``` r
basel %>%
  filter(year == 2017) %>% 
 
  # Calculate averages in 2017
  summarize(
    income = mean(income_mean),
    wealth = mean(wealth_mean))
```

```
## # A tibble: 1 × 2
## income wealth
## <dbl> <dbl>
## 1 72388. 560333.
```

]

<!---

# `summarise_if()`

``` r
# Create new summary variables
TIBBLE %>%
  summarise_if(
    CONDITION,
    SUMMARY_FUN
  )
```

]

``` r
basel %>% 
  
  # Calculate averages in 2017
  summarize_if(is.numeric,
               mean)
```

```
## # A tibble: 1 × 9
## year quarter_no N
## <dbl> <dbl> <dbl>
## 1 2009 11.4 5381.
## # ℹ 6 more variables:
## # income_mean <dbl>,
## # income_median <dbl>,
## # income_gini <dbl>,
## # wealth_mean <dbl>,
## # wealth_median <dbl>,
## # wealth_gini <dbl>
```

]

--->

---

# `group_by()`

``` r
# Create grouped summary variables
TIBBLE %>%
  group_by(GRUPPEN_VAR) %>%
  summarise(
    NAME1 = SUMMARY_FUN(VAR1),
    NAME2 = SUMMARY_FUN(VAR2)
  )
```

]

``` r
basel %>%
  
  # Calculate averages for all years
  group_by(year) %>% 
  summarize(
    income = mean(income_mean),
    wealth = mean(wealth_mean))
```

```
## # A tibble: 17 × 3
## year income wealth
## <dbl> <dbl> <dbl>
## 1 2001 63027. 347770.
## 2 2002 63555. 367401.
## 3 2003 63083. 373278.
## 4 2004 62298. 353968.
## 5 2005 63133. 441864.
## 6 2006 64148. 465242.
## 7 2007 66594 435270.
## 8 2008 66463. 401131.
## # ℹ 9 more rows
```

]

---

# `group_by()`

``` r
# Create grouped summary variables
TIBBLE %>%
  group_by(GRUPPEN_VAR) %>%
  summarise(
    NAME1 = SUMMARY_FUN(VAR1),
    NAME2 = SUMMARY_FUN(VAR2)
  )
```

]

``` r
basel %>%
  
  # Calculate averages for all years
  group_by(year) %>% 
  summarize(
    income = mean(income_mean),
    wealth = mean(wealth_mean)) %>% 
  arrange(income) 
```

```
## # A tibble: 17 × 3
## year income wealth
## <dbl> <dbl> <dbl>
## 1 2004 62298. 353968.
## 2 2001 63027. 347770.
## 3 2003 63083. 373278.
## 4 2005 63133. 441864.
## 5 2002 63555. 367401.
## 6 2006 64148. 465242.
## 7 2011 66050. 398102.
## 8 2008 66463. 401131.
## # ℹ 9 more rows
```

]

---

# `*_join()`

``` r
# Join two tibbles
TIBBLE1 %>%
  left_join(TIBBLE2, 
            by = c("KEY1" = "KEY2"))
```

]

``` r
basel %>%
  group_by(year) %>% 
  summarize(
    income = mean(income_mean),
    wealth = mean(wealth_mean)) %>% 
  
  # join back to basel
  right_join(basel)
```

```
## # A tibble: 357 × 12
## year income wealth quarter 
## <dbl> <dbl> <dbl> <chr> 
## 1 2001 63027. 347770. Altstadt Gr…
## 2 2001 63027. 347770. Vorstädte 
## 3 2001 63027. 347770. Am Ring 
## 4 2001 63027. 347770. Breite 
## 5 2001 63027. 347770. St. Alban 
## 6 2001 63027. 347770. Gundeldingen
## 7 2001 63027. 347770. Bruderholz 
## 8 2001 63027. 347770. Bachletten 
## # ℹ 349 more rows
## # ℹ 8 more variables:
## # quarter_no <dbl>, N <dbl>,
## # income_mean <dbl>,
## # income_median <dbl>,
## # income_gini <dbl>,
## # wealth_mean <dbl>, …
```
]

---

# <mono>pivot_longer()</mono>

<ul>
 <li class="m1">Convert from <high>wide</high> to <high>long</high></li>
</ul>

]

<img src="image/tidyr-spread-gather-loop.gif" height=420px> 
adapted from <a href="https://github.com/gadenbuie/tidyexplain">tidyexplain</a>

]

---

# `pivot_longer()`

``` r
# wide to long
TIBBLE %>% 
  pivot_longer(cols = VARS,
               names_to = NAME1,
               values_to = NAME2)
```

]

``` r
# wide to long
basel %>% 
 select(year, quarter, 
        income_mean, wealth_mean) %>% 
 pivot_longer(cols = c(income_mean, wealth_mean),
              names_to = 'name',
              values_to = 'value')
```

```
## # A tibble: 714 × 4
## year quarter name value
## <dbl> <chr> <chr> <dbl>
## 1 2001 Altstadt Gros… inco… 8.78e4
## 2 2001 Altstadt Gros… weal… 1.01e6
## 3 2001 Vorstädte inco… 8.41e4
## 4 2001 Vorstädte weal… 1.12e6
## 5 2001 Am Ring inco… 6.26e4
## 6 2001 Am Ring weal… 3.01e5
## 7 2001 Breite inco… 5.20e4
## 8 2001 Breite weal… 1.05e5
## # ℹ 706 more rows
```
]

---

# The R ecosphere

---

# RStudio

<ul>
 <li class="m1">RStudio is...</li> 
 <ul class="level">
 <li>A high-productivity <high>data science IDE.</high></li> 
 <li>The <high>company</high> behind the tidyverse.</li> 
 <li>Designed for R tidyverse with <high>good integration</high> for Python, Markdown, or C++.</li> 
 </ul>
</ul>

]

---

# RStudio

]

<img src="image/editor_edit.png">
 
Shortcut for <high>send Code to Console</high>: <br2>&#8984;/ctrl + &#9166; <br2><br2>
Shortcut for <high>run Chunk again</high>: <br2> &#8984;/ctrl + &#8997; + p

]

---

# RStudio

]

]

---

# RStudio

]

]

---

<h1><a href="">Practical</a></h1>