Recap II

# Recap II
### Introduction to R <a href='https://therbootcamp.github.io'> Bern R Bootcamp </a> <a href='https://dwulff.github.io/Intro2R_Unibe/'> </a>  <a href='https://therbootcamp.github.io'> </a>  <a href='mailto:therbootcamp@gmail.com'> </a>  <a href='https://www.linkedin.com/company/basel-r-bootcamp/'> </a>
### June 2020

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 <img src="https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/by-sa.png" height=14 style="vertical-align: middle"/>
 
 <a href="https://therbootcamp.github.io/">
 
 
 www.therbootcamp.com
 
 
 </a>
 <a href="https://therbootcamp.github.io/">
 
 Introduction to R | June 2020
 
 </a>
 
 </div>
 
 
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# Statistics I

#### <high>Descriptive statistics</high> with <mono>dplyr</mono>

```r
# Group-summarise idiom
baselers %>%
  group_by(sex, eyecor) %>%
  summarise(
    N = n(),
    age_mean = mean(age),
    height_median = median(height),
    children_max = max(children)
  )
```

#### <high>Simple hypothesis tests</high> with <mono>stats</mono>

```r
# Simple hypothesis test
t.test(baselers$happiness,
       baselers$fitness,
       var.equal = TRUE)
```

]

.pull-right45[
 

<img src="image/null_hypothesis.png" height=430px> 
from <a href="https://xkcd.com/892/">xkcd.com</a>

]

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# <mono>summarise()</mono>

Use `summarise()` to create new columns of <high>summary statistics</high>.

The result of `summarise()` is always be a tibble.

Functions used in `summerise()` <high>must return a single value</high>.

```r
data %>%
  summarise(
    NAME = SUMMARY_FUN(A),
    NAME = SUMMARY_FUN(B),
    ...
  )
```

]

```r
# Calculate summary statistics
baselers %>%
  summarise(
    N = n(),
    age_mean = mean(age),
    height_median = median(height),
    children_max = max(children)
  )
```

```
# A tibble: 1 x 4
 N age_mean height_median children_max
 <int> <dbl> <dbl> <dbl>
1 10000 44.6 171. 6
```

]

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# <mono>group_by()</mono> + <mono>summarise()</mono>

Use `group_by()` to <high>group data</high> according to one or more columns.

Then, use `summarise()` to <high>calculate summary statistics</high> for each group.

You can include <high>one or more</high> grouping variables.

```r
data %>%
  group_by(A, B, ...) %>%
  summarise(
    NAME = SUMMARY_FUN(A),
    NAME = SUMMARY_FUN(B),
    ...
  )
```

]

```r
baselers %>%
  group_by(sex) %>%
  summarise(
    N = n(),
    age_mean = mean(age),
    income_median = median(income, na.rm = TRUE)
  )
```

]

---

# <mono>group_by()</mono> + <mono>summarise()</mono>

Use `group_by()` to <high>group data</high> according to one or more columns.

Then, use `summarise()` to <high>calculate summary statistics</high> for each group.

You can include <high>one or more</high> grouping variables.

```r
data %>%
  group_by(A, B, ...) %>%
  summarise(
    NAME = SUMMARY_FUN(A),
    NAME = SUMMARY_FUN(B),
    ...
  )
```

]

```r
baselers %>%
  group_by(sex) %>%
  summarise(
    N = n(),
    age_mean = mean(age),
    income_median = median(income, na.rm = TRUE)
  )
```

```
# A tibble: 2 x 4
 sex N age_mean income_median
 <chr> <int> <dbl> <dbl>
1 female 5000 45.4 7300
2 male 5000 43.8 7100
```

]

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# Decision tree

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# Plotting

```r
ggplot(data = mpg, 
       mapping = aes(x = XXX, 
                     y = XXX)) +
  geom_XXX() + 
  geom_XXX() +
  labs(x = "XXX",
       y = "XXX",
       title = "XXX",
       caption = "XXX") + 
  theme_bw()
```

]

]

---

# Plotting

```r
ggplot(data = mpg, 
       mapping = aes(x = cty, 
                     y = hwy)) +
  geom_point() + 
  geom_smooth() +
  labs(x = "Miles per gallon in city",
       y = "Miles per gallon on highway",
       title = "MPG data",
       caption = "Source: mpg data tidyverse.") + 
  theme_bw()
```

]

]

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<h1><a href="https://dwulff.github.io/Intro2R_Unibe/1_Data/BernRBootcamp_Day3.zip">Downloads</a></h1>

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<hfont><high>Questions?</high></hfont> 
 <a href = "https://therbootcamp.github.io/Intro2DataScience_2018Oct/">Link to schedule</a>

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