4 May 2017

lazy evaluation

the tidyverse makes your life easy by evaluating columns in df/tibble context.

R base, must refers to mtcars

plot(mtcars$wt, mtcars$mpg)

ggplot2 does the job

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

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non standard evaluation is hard

evaluation in base, problem

wt is unknow in the global environment

wt
Error in eval(expr, envir, enclos): object 'wt' not found

evaluation in base, solution

combine substitute and eval in df context

eval(substitute(wt), mtcars)
 [1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440
[12] 4.070 3.730 3.780 5.250 5.424 5.345 2.200 1.615 1.835 2.465 3.520
[23] 3.435 3.840 3.845 1.935 2.140 1.513 3.170 2.770 3.570 2.780

evaluation in tidyverse

mtcars %>%
  select(wt) %>%
  head()
                     wt
Mazda RX4         2.620
Mazda RX4 Wag     2.875
Datsun 710        2.320
Hornet 4 Drive    3.215
Hornet Sportabout 3.440
Valiant           3.460

Drawback

  • Make your life easy when performing interactive analysis
  • Hard when doing programming, as soon as you need iteration and hence function
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ggplot2

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The issue

ggplot2

When dealing with global variables, fine. But local ones in function…

in global env

mtcars %>%
  ggplot(aes(x = mpg)) +
  geom_histogram(bins = 25)

in function

mtcars_dens <- function(df, col) {
  df %>%
    ggplot(aes(x = col)) +
    geom_histogram(bins = 25)
}
mtcars_dens(df = mtcars, col = "mpg")
Error: StatBin requires a continuous x variable the x variable is discrete. Perhaps you want stat="count"?

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Solution: aes_string()

only for ggplot2

mtcars_dens <- function(df, col) {
  df %>%
    ggplot(aes_string(x = col)) +
    geom_histogram(bins = 25)
}
mtcars_dens(df = mtcars, col = "mpg")

mtcars_dens(df = mtcars, col = "hp")

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dplyr < 0.6

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before upcoming 0.6

in global env

mtcars %>%
  filter(hp > 250)
   mpg cyl disp  hp drat   wt qsec vs am gear carb
1 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4
2 15.0   8  301 335 3.54 3.57 14.6  0  1    5    8

in function

mtcars_filter <- function(df, col) {
  df %>%
    filter(col > 250)
}
mtcars_filter(df = mtcars, col = "hp")
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
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Solution: underscore versions

dplyr < 0.6

mtcars_filter <- function(df, col) {
  filter_call <- lazyeval::interp(~ var > 250, var = as.name(col))
  df %>%
    filter_(.dots = filter_call)
}
mtcars_filter(df = mtcars, col = "hp")
   mpg cyl disp  hp drat   wt qsec vs am gear carb
1 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4
2 15.0   8  301 335 3.54 3.57 14.6  0  1    5    8

Obscure

Ugly, complex, hard but mandatory

Proceed anyway

With dynamic threshold

mtcars_filter <- function(df, col, threshold) {
  filter_call <- lazyeval::interp(~ var > thr,
                                  var = as.name(col),
                                  thr = quote(threshold))
  df %>%
    filter_(.dots = filter_call)
}
mtcars_filter(df = mtcars, col = "hp", threshold = 265)
  mpg cyl disp  hp drat   wt qsec vs am gear carb
1  15   8  301 335 3.54 3.57 14.6  0  1    5    8
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dplyr >= 0.6

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tidyeval

dplyr 0.6, upcoming!

described in this vignette

classic quoting

variables as strings

fun1 <- function(name) {
  paste0("name is, ", name)
}
fun1("stringent")
[1] "name is, stringent"

introducing glue

fun2 <- function(name) {
  glue::glue("name is {name}")
}
fun2("stringent")
name is stringent
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Solution with tidyeval

currently discussed

We can pass on hp as a promise (it is unknow in Global Env), evaluation is delayed and enquo() uses some dark magic

mtcars_filter <- function(df, column) {

  col <- enquo(column)
  message(col)
  df %>%
    select(!! col)
}
mtcars_filter(df = mtcars, column = hp)
~hp
                     hp
Mazda RX4           110
Mazda RX4 Wag       110
Datsun 710           93
Hornet 4 Drive      110
Hornet Sportabout   175
Valiant             105
Duster 360          245
Merc 240D            62
Merc 230             95
Merc 280            123
Merc 280C           123
Merc 450SE          180
Merc 450SL          180
Merc 450SLC         180
Cadillac Fleetwood  205
Lincoln Continental 215
Chrysler Imperial   230
Fiat 128             66
Honda Civic          52
Toyota Corolla       65
Toyota Corona        97
Dodge Challenger    150
AMC Javelin         150
Camaro Z28          245
Pontiac Firebird    175
Fiat X1-9            66
Porsche 914-2        91
Lotus Europa        113
Ford Pantera L      264
Ferrari Dino        175
Maserati Bora       335
Volvo 142E          109
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Expressions

quoinside function

mtcars_filter <- function(df, column, threshold) {

  var <- quo(hp  > !! threshold)
  message(var)
  df %>%
    filter(!! var)
}
mtcars_filter(df = mtcars, hp, threshold = 300)
~hp > 300
  mpg cyl disp  hp drat   wt qsec vs am gear carb
1  15   8  301 335 3.54 3.57 14.6  0  1    5    8

quoins function' call

mtcars_filter2 <- function(df, expr) {
  var <- enquo(expr)
  message(var)
  df %>%
    filter(!! var)
}
mtcars_filter2(df = mtcars, cyl > 6)
~cyl > 6
    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
2  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
3  16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
4  17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
5  15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
6  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
7  10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
8  14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
9  15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
10 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
11 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
12 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
13 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
14 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
mtcars_filter2(df = mtcars, hp > 250)
~hp > 250
   mpg cyl disp  hp drat   wt qsec vs am gear carb
1 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4
2 15.0   8  301 335 3.54 3.57 14.6  0  1    5    8
mtcars_filter2(df = mtcars, am == 0)
~am == 0
    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
1  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
2  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
3  18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
4  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
5  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
6  22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
7  19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
8  17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
9  16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
10 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
11 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
12 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
13 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
14 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
15 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
16 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
17 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
18 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
19 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
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different output / input variables

  • to turn a quosure into a name that could be pasted
  • Must uses a specific assignment :=
my_mutate <- function(df, out_name, expr) {
  expr <- enquo(expr)
  out <- enquo(out_name)
  g_name <- paste0("mean_", quo_name(out))

  summarise(df, 
    !!g_name := mean(!!expr)
  )
}

my_mutate(mtcars, out_name = gear_type, am)
  mean_gear_type
1        0.40625
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Pass arguments as promises, not strings

I can categorically say if you're pasting strings to program with dplyr, there is always better way. Hadley

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