io38-tidylog.Rmd

---
title: "R Notebook"
output:
  html_document:
    df_print: paged
---

## Examples with tidylog

Install the package if not already installed,
then load libraries

```{r setup}
### devtools::install_github("elbersb/tidylog")
library("dplyr")
library("tidylog", warn.conflicts = FALSE)
library(tidyverse)
library(broom)
library(scales)
```

The tidylog package will give you feedback about basic dplyr operations.
It provides simple feedback for the most common functions, such as filter, mutate, select, full_join, and group_by.

Let's see an example with a pipe.

```{r pipe_example, warning=TRUE, collapse=TRUE}
summary <- mtcars %>%
    select(mpg, cyl, hp, am) %>%
    filter(mpg > 15) %>%
    mutate(mpg_round = round(mpg)) %>%
    group_by(cyl, mpg_round, am) %>%
    tally() %>%
    filter(n >= 1)
summary
```

Examples with filter and distinct

```{r filter_distinct}
a <- filter(mtcars, mpg > 20)

b <- filter(mtcars, mpg > 100)
 
c <- filter(mtcars, mpg > 0)
 
d <- filter_at(mtcars, vars(starts_with("d")), any_vars((. %% 2) == 0))
 
e <- distinct(mtcars)
 
```

Examples with mutate and transmute

```{r}
a <- mutate(mtcars, new_var = 1)
 
b <- mutate(mtcars, new_var = runif(n()))
 
c <- mutate(mtcars, new_var = NA)
 
d <- mutate_at(mtcars, vars(mpg, gear, drat), round)
 
e <- mutate(mtcars, am_factor = as.factor(am))
 
f <- mutate(mtcars, am = as.factor(am))
 
g <- mutate(mtcars, am = ifelse(am == 1, NA, am))
 
h <- mutate(mtcars, am = recode(am, `0` = "zero", `1` = NA_character_))
 

i <- transmute(mtcars, mpg = mpg * 2, gear = gear + 1, new_var = vs + am)
 
```

Examples with select

```{r select}
a <- select(mtcars, mpg, wt)
 
b <- select(mtcars, matches("a"))
 
c <- select_if(mtcars, is.character)
```

Examples with joins

```{r joins}
a <- left_join(band_members, band_instruments, by = "name")
 
b <- full_join(band_members, band_instruments, by = "name")
 
c <- anti_join(band_members, band_instruments, by = "name")

a
b
c

```

Examples with summarize

```{r}
a <- mtcars %>%
    group_by(cyl, carb) %>%
    summarize(total_weight = sum(wt))
a

b <- iris %>%
    group_by(Species) %>%
    summarize_if(is.numeric, list(min = min, max =max))
b
```

How to turn tidylogging off and back on when needed

```{r tidylog_off}
options("tidylog.display" = list())  # turn off
a <- filter(mtcars, mpg > 20)
a
```

Now back on

```{r tidylog_on}
options("tidylog.display" = NULL)    # turn on
a <- filter(mtcars, mpg > 20)
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

```{r chisq}
mtcars %>% 
  group_by(cyl) %>% 
  count(am) %>% 
  spread(am, n) %>% 
  column_to_rownames('cyl') %>% 
  chisq.test() %>% 
  tidy() 
```

```{r ttest}
mt <- mtcars %>% 
  group_by(cyl) %>% 
  filter(cyl <7) %>% 
  t.test(mpg ~ cyl, data = .) %>% 
  tidy() 
```

The p value for this t test is `r mt$p.value[1] %>% pvalue(accuracy = 0.0001, decimal.mark = ".", add_p = FALSE)`


```{r anova}
mtcars %>% 
  aov(mpg ~ cyl, data = .) %>% 
  tidy() ->
result
```


The result of the ANOVA test is F(`r result$df[1]`, `r result$df[2]`) = `r result$statistic[1] %>% round(2)`, with p `r result$p.value[1] %>% pvalue(., accuracy = 0.0001, decimal.mark = ".", add_p = FALSE)`