1 dplyr

1.1 case_when()

dplyr::case_when goes really well with geom_text (or ggrepel) for when you want to programmatically label a few of the most interesting features in your plots! And the criteria can be as complex as you want.

set.seed(10)
mydata <- 
  data.frame(
    x = 1:10,
    y = rnorm(10),
    name = c("Apple", "Banana", "Kiwi", "Orange", "Watermelon", "Grapes", 
             "Pear", "Cantelope", "Tomato", "Satsuma"),
    stringsAsFactors = FALSE
  ) %>% 
  mutate(
    name_poor = case_when(
      y < 0 ~ name,
      TRUE ~ ""
    )
  )

ggplot(mydata, aes(x = x, y = y)) + 
  geom_point(size = 5) + 
  geom_text_repel(aes(label = name_poor), point.padding = 2)

(#fig:case_when)Selective labelling

Credit: @rensa_co

1.2 mutate_at()

mutate_at() takes a range of consecutive columns, with var(col1:coln) if you want to batch convert a bunch of columns:

iris %>% 
  mutate_at(vars(Sepal.Length:Petal.Width), as.character) %>% 
  as_tibble()

## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##    <chr>        <chr>       <chr>        <chr>       <fct>  
##  1 5.1          3.5         1.4          0.2         setosa 
##  2 4.9          3           1.4          0.2         setosa 
##  3 4.7          3.2         1.3          0.2         setosa 
##  4 4.6          3.1         1.5          0.2         setosa 
##  5 5            3.6         1.4          0.2         setosa 
##  6 5.4          3.9         1.7          0.4         setosa 
##  7 4.6          3.4         1.4          0.3         setosa 
##  8 5            3.4         1.5          0.2         setosa 
##  9 4.4          2.9         1.4          0.2         setosa 
## 10 4.9          3.1         1.5          0.1         setosa 
## # ... with 140 more rows

Credit: @vsbuffalo

1.3 mutate_if()

Convert all factor columns to characters.

iris %>% 
  mutate_if(is.factor, as.character) %>% 
  as_tibble()

## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # ... with 140 more rows

Credit: @GojThomson

1.4 summarise_at()

dplyr::summarise_at() applies multiple summary functions to multiple variables!

mtcars %>% 
  group_by(cyl, am) %>% 
  summarize_at(vars(disp, hp, mpg), funs(mean, sd))

## # A tibble: 6 x 8
## # Groups:   cyl [?]
##     cyl    am disp_mean hp_mean mpg_mean disp_sd hp_sd mpg_sd
##   <dbl> <dbl>     <dbl>   <dbl>    <dbl>   <dbl> <dbl>  <dbl>
## 1     4     0     136.     84.7     22.9   14.0  19.7   1.45 
## 2     4     1      93.6    81.9     28.1   20.5  22.7   4.48 
## 3     6     0     205.    115.      19.1   44.7   9.18  1.63 
## 4     6     1     155     132.      20.6    8.66 37.5   0.751
## 5     8     0     358.    194.      15.0   71.8  33.4   2.77 
## 6     8     1     326     300.      15.4   35.4  50.2   0.566

Credit: @thomas_mock