summarise_all: Summarise and mutate multiple columns.

Description Usage Arguments Value See Also Examples

View source: R/colwise-mutate.R

Description

These verbs are scoped variants of summarise(), mutate() and transmute(). They apply operations on a selection of variables.

Usage

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summarise_all(.tbl, .funs, ...)

summarise_if(.tbl, .predicate, .funs, ...)

summarise_at(.tbl, .vars, .funs, ..., .cols = NULL)

summarize_all(.tbl, .funs, ...)

summarize_if(.tbl, .predicate, .funs, ...)

summarize_at(.tbl, .vars, .funs, ..., .cols = NULL)

mutate_all(.tbl, .funs, ...)

mutate_if(.tbl, .predicate, .funs, ...)

mutate_at(.tbl, .vars, .funs, ..., .cols = NULL)

transmute_all(.tbl, .funs, ...)

transmute_if(.tbl, .predicate, .funs, ...)

transmute_at(.tbl, .vars, .funs, ..., .cols = NULL)

Arguments

.tbl

A tbl object.

.funs

List of function calls generated by funs(), or a character vector of function names, or simply a function.

Bare formulas are passed to rlang::as_function() to create purrr-style lambda functions. Note that these lambda prevent hybrid evaluation from happening and it is thus more efficient to supply functions like mean() directly rather than in a lambda-formula.

...

Additional arguments for the function calls in .funs. These are evaluated only once, with tidy dots support.

.predicate

A predicate function to be applied to the columns or a logical vector. The variables for which .predicate is or returns TRUE are selected. This argument is passed to rlang::as_function() and thus supports quosure-style lambda functions and strings representing function names.

.vars

A list of columns generated by vars(), a character vector of column names, a numeric vector of column positions, or NULL.

.cols

This argument has been renamed to .vars to fit dplyr's terminology and is deprecated.

Value

A data frame. By default, the newly created columns have the shortest names needed to uniquely identify the output. To force inclusion of a name, even when not needed, name the input (see examples for details).

See Also

vars(), funs()

Examples

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# The scoped variants of summarise() and mutate() make it easy to
# apply the same transformation to multiple variables:

iris %>%
  group_by(Species) %>%
  summarise_all(mean)

# There are three variants.
# * _all affects every variable
# * _at affects variables selected with a character vector or vars()
# * _if affects variables selected with a predicate function:

# The _at() variants directly support strings:
starwars %>% summarise_at(c("height", "mass"), mean, na.rm = TRUE)

# You can also supply selection helpers to _at() functions but you have
# to quote them with vars():
iris %>% mutate_at(vars(matches("Sepal")), log)
starwars %>% summarise_at(vars(height:mass), mean, na.rm = TRUE)

# The _if() variants apply a predicate function (a function that
# returns TRUE or FALSE) to determine the relevant subset of
# columns. Here we apply mean() to the numeric columns:
starwars %>% summarise_if(is.numeric, mean, na.rm = TRUE)

# mutate_if() is particularly useful for transforming variables from
# one type to another
iris %>% as_tibble() %>% mutate_if(is.factor, as.character)
iris %>% as_tibble() %>% mutate_if(is.double, as.integer)

# ---------------------------------------------------------------------------
# If you want apply multiple transformations, use funs()
by_species <- iris %>% group_by(Species)

by_species %>% summarise_all(funs(min, max))
# Note that output variable name now includes the function name, in order to
# keep things distinct.

# You can express more complex inline transformations using .
by_species %>% mutate_all(funs(. / 2.54))

# Function names will be included if .funs has names or multiple inputs
by_species %>% mutate_all(funs(inches = . / 2.54))
by_species %>% summarise_all(funs(med = median))
by_species %>% summarise_all(funs(Q3 = quantile), probs = 0.75)
by_species %>% summarise_all(c("min", "max"))

Example output

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

# A tibble: 3 x 5
     Species Sepal.Length Sepal.Width Petal.Length Petal.Width
      <fctr>        <dbl>       <dbl>        <dbl>       <dbl>
1     setosa        5.006       3.428        1.462       0.246
2 versicolor        5.936       2.770        4.260       1.326
3  virginica        6.588       2.974        5.552       2.026
# A tibble: 1 x 2
   height     mass
    <dbl>    <dbl>
1 174.358 97.31186
    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1       1.629241   1.2527630          1.4         0.2     setosa
2       1.589235   1.0986123          1.4         0.2     setosa
3       1.547563   1.1631508          1.3         0.2     setosa
4       1.526056   1.1314021          1.5         0.2     setosa
5       1.609438   1.2809338          1.4         0.2     setosa
6       1.686399   1.3609766          1.7         0.4     setosa
7       1.526056   1.2237754          1.4         0.3     setosa
8       1.609438   1.2237754          1.5         0.2     setosa
9       1.481605   1.0647107          1.4         0.2     setosa
10      1.589235   1.1314021          1.5         0.1     setosa
11      1.686399   1.3083328          1.5         0.2     setosa
12      1.568616   1.2237754          1.6         0.2     setosa
13      1.568616   1.0986123          1.4         0.1     setosa
14      1.458615   1.0986123          1.1         0.1     setosa
15      1.757858   1.3862944          1.2         0.2     setosa
16      1.740466   1.4816045          1.5         0.4     setosa
17      1.686399   1.3609766          1.3         0.4     setosa
18      1.629241   1.2527630          1.4         0.3     setosa
19      1.740466   1.3350011          1.7         0.3     setosa
20      1.629241   1.3350011          1.5         0.3     setosa
21      1.686399   1.2237754          1.7         0.2     setosa
22      1.629241   1.3083328          1.5         0.4     setosa
23      1.526056   1.2809338          1.0         0.2     setosa
24      1.629241   1.1939225          1.7         0.5     setosa
25      1.568616   1.2237754          1.9         0.2     setosa
26      1.609438   1.0986123          1.6         0.2     setosa
27      1.609438   1.2237754          1.6         0.4     setosa
28      1.648659   1.2527630          1.5         0.2     setosa
29      1.648659   1.2237754          1.4         0.2     setosa
30      1.547563   1.1631508          1.6         0.2     setosa
31      1.568616   1.1314021          1.6         0.2     setosa
32      1.686399   1.2237754          1.5         0.4     setosa
33      1.648659   1.4109870          1.5         0.1     setosa
34      1.704748   1.4350845          1.4         0.2     setosa
35      1.589235   1.1314021          1.5         0.2     setosa
36      1.609438   1.1631508          1.2         0.2     setosa
37      1.704748   1.2527630          1.3         0.2     setosa
38      1.589235   1.2809338          1.4         0.1     setosa
39      1.481605   1.0986123          1.3         0.2     setosa
40      1.629241   1.2237754          1.5         0.2     setosa
41      1.609438   1.2527630          1.3         0.3     setosa
42      1.504077   0.8329091          1.3         0.3     setosa
43      1.481605   1.1631508          1.3         0.2     setosa
44      1.609438   1.2527630          1.6         0.6     setosa
45      1.629241   1.3350011          1.9         0.4     setosa
46      1.568616   1.0986123          1.4         0.3     setosa
47      1.629241   1.3350011          1.6         0.2     setosa
48      1.526056   1.1631508          1.4         0.2     setosa
49      1.667707   1.3083328          1.5         0.2     setosa
50      1.609438   1.1939225          1.4         0.2     setosa
51      1.945910   1.1631508          4.7         1.4 versicolor
52      1.856298   1.1631508          4.5         1.5 versicolor
53      1.931521   1.1314021          4.9         1.5 versicolor
54      1.704748   0.8329091          4.0         1.3 versicolor
55      1.871802   1.0296194          4.6         1.5 versicolor
56      1.740466   1.0296194          4.5         1.3 versicolor
57      1.840550   1.1939225          4.7         1.6 versicolor
58      1.589235   0.8754687          3.3         1.0 versicolor
59      1.887070   1.0647107          4.6         1.3 versicolor
60      1.648659   0.9932518          3.9         1.4 versicolor
61      1.609438   0.6931472          3.5         1.0 versicolor
62      1.774952   1.0986123          4.2         1.5 versicolor
63      1.791759   0.7884574          4.0         1.0 versicolor
64      1.808289   1.0647107          4.7         1.4 versicolor
65      1.722767   1.0647107          3.6         1.3 versicolor
66      1.902108   1.1314021          4.4         1.4 versicolor
67      1.722767   1.0986123          4.5         1.5 versicolor
68      1.757858   0.9932518          4.1         1.0 versicolor
69      1.824549   0.7884574          4.5         1.5 versicolor
70      1.722767   0.9162907          3.9         1.1 versicolor
71      1.774952   1.1631508          4.8         1.8 versicolor
72      1.808289   1.0296194          4.0         1.3 versicolor
73      1.840550   0.9162907          4.9         1.5 versicolor
74      1.808289   1.0296194          4.7         1.2 versicolor
75      1.856298   1.0647107          4.3         1.3 versicolor
76      1.887070   1.0986123          4.4         1.4 versicolor
77      1.916923   1.0296194          4.8         1.4 versicolor
78      1.902108   1.0986123          5.0         1.7 versicolor
79      1.791759   1.0647107          4.5         1.5 versicolor
80      1.740466   0.9555114          3.5         1.0 versicolor
81      1.704748   0.8754687          3.8         1.1 versicolor
82      1.704748   0.8754687          3.7         1.0 versicolor
83      1.757858   0.9932518          3.9         1.2 versicolor
84      1.791759   0.9932518          5.1         1.6 versicolor
85      1.686399   1.0986123          4.5         1.5 versicolor
86      1.791759   1.2237754          4.5         1.6 versicolor
87      1.902108   1.1314021          4.7         1.5 versicolor
88      1.840550   0.8329091          4.4         1.3 versicolor
89      1.722767   1.0986123          4.1         1.3 versicolor
90      1.704748   0.9162907          4.0         1.3 versicolor
91      1.704748   0.9555114          4.4         1.2 versicolor
92      1.808289   1.0986123          4.6         1.4 versicolor
93      1.757858   0.9555114          4.0         1.2 versicolor
94      1.609438   0.8329091          3.3         1.0 versicolor
95      1.722767   0.9932518          4.2         1.3 versicolor
96      1.740466   1.0986123          4.2         1.2 versicolor
97      1.740466   1.0647107          4.2         1.3 versicolor
98      1.824549   1.0647107          4.3         1.3 versicolor
99      1.629241   0.9162907          3.0         1.1 versicolor
100     1.740466   1.0296194          4.1         1.3 versicolor
101     1.840550   1.1939225          6.0         2.5  virginica
102     1.757858   0.9932518          5.1         1.9  virginica
103     1.960095   1.0986123          5.9         2.1  virginica
104     1.840550   1.0647107          5.6         1.8  virginica
105     1.871802   1.0986123          5.8         2.2  virginica
106     2.028148   1.0986123          6.6         2.1  virginica
107     1.589235   0.9162907          4.5         1.7  virginica
108     1.987874   1.0647107          6.3         1.8  virginica
109     1.902108   0.9162907          5.8         1.8  virginica
110     1.974081   1.2809338          6.1         2.5  virginica
111     1.871802   1.1631508          5.1         2.0  virginica
112     1.856298   0.9932518          5.3         1.9  virginica
113     1.916923   1.0986123          5.5         2.1  virginica
114     1.740466   0.9162907          5.0         2.0  virginica
115     1.757858   1.0296194          5.1         2.4  virginica
116     1.856298   1.1631508          5.3         2.3  virginica
117     1.871802   1.0986123          5.5         1.8  virginica
118     2.041220   1.3350011          6.7         2.2  virginica
119     2.041220   0.9555114          6.9         2.3  virginica
120     1.791759   0.7884574          5.0         1.5  virginica
121     1.931521   1.1631508          5.7         2.3  virginica
122     1.722767   1.0296194          4.9         2.0  virginica
123     2.041220   1.0296194          6.7         2.0  virginica
124     1.840550   0.9932518          4.9         1.8  virginica
125     1.902108   1.1939225          5.7         2.1  virginica
126     1.974081   1.1631508          6.0         1.8  virginica
127     1.824549   1.0296194          4.8         1.8  virginica
128     1.808289   1.0986123          4.9         1.8  virginica
129     1.856298   1.0296194          5.6         2.1  virginica
130     1.974081   1.0986123          5.8         1.6  virginica
131     2.001480   1.0296194          6.1         1.9  virginica
132     2.066863   1.3350011          6.4         2.0  virginica
133     1.856298   1.0296194          5.6         2.2  virginica
134     1.840550   1.0296194          5.1         1.5  virginica
135     1.808289   0.9555114          5.6         1.4  virginica
136     2.041220   1.0986123          6.1         2.3  virginica
137     1.840550   1.2237754          5.6         2.4  virginica
138     1.856298   1.1314021          5.5         1.8  virginica
139     1.791759   1.0986123          4.8         1.8  virginica
140     1.931521   1.1314021          5.4         2.1  virginica
141     1.902108   1.1314021          5.6         2.4  virginica
142     1.931521   1.1314021          5.1         2.3  virginica
143     1.757858   0.9932518          5.1         1.9  virginica
144     1.916923   1.1631508          5.9         2.3  virginica
145     1.902108   1.1939225          5.7         2.5  virginica
146     1.902108   1.0986123          5.2         2.3  virginica
147     1.840550   0.9162907          5.0         1.9  virginica
148     1.871802   1.0986123          5.2         2.0  virginica
149     1.824549   1.2237754          5.4         2.3  virginica
150     1.774952   1.0986123          5.1         1.8  virginica
# A tibble: 1 x 2
   height     mass
    <dbl>    <dbl>
1 174.358 97.31186
# A tibble: 1 x 3
   height     mass birth_year
    <dbl>    <dbl>      <dbl>
1 174.358 97.31186   87.56512
# 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         3.0          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          5.0         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          5.0         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
# A tibble: 150 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <int>       <int>        <int>       <int>  <fctr>
 1            5           3            1           0  setosa
 2            4           3            1           0  setosa
 3            4           3            1           0  setosa
 4            4           3            1           0  setosa
 5            5           3            1           0  setosa
 6            5           3            1           0  setosa
 7            4           3            1           0  setosa
 8            5           3            1           0  setosa
 9            4           2            1           0  setosa
10            4           3            1           0  setosa
# ... with 140 more rows
# A tibble: 3 x 9
     Species Sepal.Length_min Sepal.Width_min Petal.Length_min Petal.Width_min
      <fctr>            <dbl>           <dbl>            <dbl>           <dbl>
1     setosa              4.3             2.3              1.0             0.1
2 versicolor              4.9             2.0              3.0             1.0
3  virginica              4.9             2.2              4.5             1.4
# ... with 4 more variables: Sepal.Length_max <dbl>, Sepal.Width_max <dbl>,
#   Petal.Length_max <dbl>, Petal.Width_max <dbl>
# A tibble: 150 x 5
# Groups:   Species [3]
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl>  <fctr>
 1     2.007874    1.377953    0.5511811  0.07874016  setosa
 2     1.929134    1.181102    0.5511811  0.07874016  setosa
 3     1.850394    1.259843    0.5118110  0.07874016  setosa
 4     1.811024    1.220472    0.5905512  0.07874016  setosa
 5     1.968504    1.417323    0.5511811  0.07874016  setosa
 6     2.125984    1.535433    0.6692913  0.15748031  setosa
 7     1.811024    1.338583    0.5511811  0.11811024  setosa
 8     1.968504    1.338583    0.5905512  0.07874016  setosa
 9     1.732283    1.141732    0.5511811  0.07874016  setosa
10     1.929134    1.220472    0.5905512  0.03937008  setosa
# ... with 140 more rows
# A tibble: 150 x 9
# Groups:   Species [3]
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl>  <fctr>
 1          5.1         3.5          1.4         0.2  setosa
 2          4.9         3.0          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          5.0         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          5.0         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, and 4 more variables: Sepal.Length_inches <dbl>,
#   Sepal.Width_inches <dbl>, Petal.Length_inches <dbl>,
#   Petal.Width_inches <dbl>
# A tibble: 3 x 5
     Species Sepal.Length_med Sepal.Width_med Petal.Length_med Petal.Width_med
      <fctr>            <dbl>           <dbl>            <dbl>           <dbl>
1     setosa              5.0             3.4             1.50             0.2
2 versicolor              5.9             2.8             4.35             1.3
3  virginica              6.5             3.0             5.55             2.0
# A tibble: 3 x 5
     Species Sepal.Length_Q3 Sepal.Width_Q3 Petal.Length_Q3 Petal.Width_Q3
      <fctr>           <dbl>          <dbl>           <dbl>          <dbl>
1     setosa             5.2          3.675           1.575            0.3
2 versicolor             6.3          3.000           4.600            1.5
3  virginica             6.9          3.175           5.875            2.3
# A tibble: 3 x 9
     Species Sepal.Length_min Sepal.Width_min Petal.Length_min Petal.Width_min
      <fctr>            <dbl>           <dbl>            <dbl>           <dbl>
1     setosa              4.3             2.3              1.0             0.1
2 versicolor              4.9             2.0              3.0             1.0
3  virginica              4.9             2.2              4.5             1.4
# ... with 4 more variables: Sepal.Length_max <dbl>, Sepal.Width_max <dbl>,
#   Petal.Length_max <dbl>, Petal.Width_max <dbl>

dplyr documentation built on May 19, 2018, 9:04 a.m.