How to extend the 'summary' function to include sd, kurtosis and skew?

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up vote
6
down vote

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R's summary function works really well on a dataframe, giving, for example:

> summary(fred)
 sum.count count sum value 
 Min. : 1.000 Min. : 1.0 Min. : 1 Min. : 0.00 
 1st Qu.: 1.000 1st Qu.: 6.0 1st Qu.: 7 1st Qu.:35.82 
 Median : 1.067 Median : 9.0 Median : 10 Median :42.17 
 Mean : 1.238 Mean : 497.1 Mean : 6120 Mean :43.44 
 3rd Qu.: 1.200 3rd Qu.: 35.0 3rd Qu.: 40 3rd Qu.:51.31 
 Max. :40.687 Max. :64425.0 Max. :2621278 Max. :75.95

What I'd like to do is modify the function so it also gives, after 'Mean', an entry for the standard deviation, the kurtosis and the skew.

What's the best way to do this? I've researched this a bit, and adding a function with a method doesn't work for me:

> summary.class <- function(x)

 return(sd(x))

The above is just ignored. I suppose that I need to understand how to define all classes to return.

r std summary skew kurtosis

share|improve this question

edited 2 hours ago

Tung

5,79321634

asked 5 hours ago

Peter Brooks

1358

• 
  
  
  

  
  

  
  
  
  
  
  

  
  summary.data.frame <- function(...) tt <- base::summary.data.frame(...); <code to modify tt>; return(tt)
  – Ben Bolker
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Possible duplicate of R extended summary numerical values including kurtosis, skew, etc?
  – Tung
  2 hours ago
  

  
  
  
  

add a comment | 

up vote
6
down vote

favorite

R's summary function works really well on a dataframe, giving, for example:

> summary(fred)
 sum.count count sum value 
 Min. : 1.000 Min. : 1.0 Min. : 1 Min. : 0.00 
 1st Qu.: 1.000 1st Qu.: 6.0 1st Qu.: 7 1st Qu.:35.82 
 Median : 1.067 Median : 9.0 Median : 10 Median :42.17 
 Mean : 1.238 Mean : 497.1 Mean : 6120 Mean :43.44 
 3rd Qu.: 1.200 3rd Qu.: 35.0 3rd Qu.: 40 3rd Qu.:51.31 
 Max. :40.687 Max. :64425.0 Max. :2621278 Max. :75.95

What I'd like to do is modify the function so it also gives, after 'Mean', an entry for the standard deviation, the kurtosis and the skew.

What's the best way to do this? I've researched this a bit, and adding a function with a method doesn't work for me:

> summary.class <- function(x)

 return(sd(x))

The above is just ignored. I suppose that I need to understand how to define all classes to return.

r std summary skew kurtosis

share|improve this question

edited 2 hours ago

Tung

5,79321634

asked 5 hours ago

Peter Brooks

1358

• 
  
  
  

  
  

  
  
  
  
  
  

  
  summary.data.frame <- function(...) tt <- base::summary.data.frame(...); <code to modify tt>; return(tt)
  – Ben Bolker
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Possible duplicate of R extended summary numerical values including kurtosis, skew, etc?
  – Tung
  2 hours ago
  

  
  
  
  

add a comment | 

up vote
6
down vote

favorite

up vote
6
down vote

favorite

R's summary function works really well on a dataframe, giving, for example:

> summary(fred)
 sum.count count sum value 
 Min. : 1.000 Min. : 1.0 Min. : 1 Min. : 0.00 
 1st Qu.: 1.000 1st Qu.: 6.0 1st Qu.: 7 1st Qu.:35.82 
 Median : 1.067 Median : 9.0 Median : 10 Median :42.17 
 Mean : 1.238 Mean : 497.1 Mean : 6120 Mean :43.44 
 3rd Qu.: 1.200 3rd Qu.: 35.0 3rd Qu.: 40 3rd Qu.:51.31 
 Max. :40.687 Max. :64425.0 Max. :2621278 Max. :75.95

What I'd like to do is modify the function so it also gives, after 'Mean', an entry for the standard deviation, the kurtosis and the skew.

What's the best way to do this? I've researched this a bit, and adding a function with a method doesn't work for me:

> summary.class <- function(x)

 return(sd(x))

The above is just ignored. I suppose that I need to understand how to define all classes to return.

r std summary skew kurtosis

share|improve this question

edited 2 hours ago

Tung

5,79321634

asked 5 hours ago

Peter Brooks

1358

R's summary function works really well on a dataframe, giving, for example:

> summary(fred)
 sum.count count sum value 
 Min. : 1.000 Min. : 1.0 Min. : 1 Min. : 0.00 
 1st Qu.: 1.000 1st Qu.: 6.0 1st Qu.: 7 1st Qu.:35.82 
 Median : 1.067 Median : 9.0 Median : 10 Median :42.17 
 Mean : 1.238 Mean : 497.1 Mean : 6120 Mean :43.44 
 3rd Qu.: 1.200 3rd Qu.: 35.0 3rd Qu.: 40 3rd Qu.:51.31 
 Max. :40.687 Max. :64425.0 Max. :2621278 Max. :75.95

What I'd like to do is modify the function so it also gives, after 'Mean', an entry for the standard deviation, the kurtosis and the skew.

What's the best way to do this? I've researched this a bit, and adding a function with a method doesn't work for me:

> summary.class <- function(x)

 return(sd(x))

The above is just ignored. I suppose that I need to understand how to define all classes to return.

r std summary skew kurtosis

r std summary skew kurtosis

share|improve this question

edited 2 hours ago

Tung

5,79321634

asked 5 hours ago

Peter Brooks

1358

share|improve this question

edited 2 hours ago

Tung

5,79321634

asked 5 hours ago

Peter Brooks

1358

share|improve this question

share|improve this question

edited 2 hours ago

Tung

5,79321634

edited 2 hours ago

Tung

5,79321634

edited 2 hours ago

Tung

5,79321634

5,79321634

asked 5 hours ago

Peter Brooks

1358

asked 5 hours ago

Peter Brooks

1358

asked 5 hours ago

Peter Brooks

1358

1358

• 
  
  
  

  
  

  
  
  
  
  
  

  
  summary.data.frame <- function(...) tt <- base::summary.data.frame(...); <code to modify tt>; return(tt)
  – Ben Bolker
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Possible duplicate of R extended summary numerical values including kurtosis, skew, etc?
  – Tung
  2 hours ago
  

  
  
  
  

add a comment | 

• 
  
  
  

  
  

  
  
  
  
  
  

  
  summary.data.frame <- function(...) tt <- base::summary.data.frame(...); <code to modify tt>; return(tt)
  – Ben Bolker
  4 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  Possible duplicate of R extended summary numerical values including kurtosis, skew, etc?
  – Tung
  2 hours ago
  

  
  
  
  

summary.data.frame <- function(...) tt <- base::summary.data.frame(...); <code to modify tt>; return(tt)
– Ben Bolker
4 hours ago

summary.data.frame <- function(...) tt <- base::summary.data.frame(...); <code to modify tt>; return(tt)
– Ben Bolker
4 hours ago

Possible duplicate of R extended summary numerical values including kurtosis, skew, etc?
– Tung
2 hours ago

Possible duplicate of R extended summary numerical values including kurtosis, skew, etc?
– Tung
2 hours ago

add a comment | 

2 Answers
2

active

oldest

votes

up vote
7
down vote



accepted

How about using already existing solutions from the psych package?

my.dat <- cbind(norm = rnorm(100), pois = rpois(n = 100, 10))

library(psych)
describe(my.dat)
# vars n mean sd median trimmed mad min max range skew kurtosis se
# norm 1 100 -0.02 0.98 -0.09 -0.06 0.86 -3.25 2.81 6.06 0.13 0.74 0.10
# pois 2 100 9.91 3.30 10.00 9.95 4.45 3.00 17.00 14.00 -0.07 -0.75 0.33

share|improve this answer

answered 5 hours ago

storaged

1,4001222

add a comment | 

up vote
1
down vote

Another choice is the Desc function from the DescTools package which produce both summary stats and plot

library(DescTools)
Desc(iris3, plotit = TRUE)

#> ------------------------------------------------------------------------- 
#> iris3 (numeric)
#> 
#> length n NAs unique 0s mean meanCI
#> 600 600 0 74 0 3.46 3.31
#> 100.0% 0.0% 0.0% 3.62
#> 
#> .05 .10 .25 median .75 .90 .95
#> 0.20 1.10 1.70 3.20 5.10 6.20 6.70
#> 
#> range sd vcoef mad IQR skew kurt
#> 7.80 1.98 0.57 2.52 3.40 0.13 -1.05
#> 
#> lowest : 0.1 (5), 0.2 (29), 0.3 (7), 0.4 (7), 0.5
#> highest: 7.3, 7.4, 7.6, 7.7 (4), 7.9

The skim function from the skimr package is also a good one

library(skimr)
skim(iris)

Skim summary statistics
n obs: 150 
n variables: 5 

-- Variable type:factor --------------------------------------------------------
 variable missing complete n n_unique
Species 0 150 150 3
top_counts ordered
set: 50, ver: 50, vir: 50, NA: 0 FALSE

-- Variable type:numeric -------------------------------------------------------
 variable missing complete n mean sd p0 p25 p50
Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35
Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3 
Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8 
Sepal.Width 0 150 150 3.06 0.44 2 2.8 3 
p75 p100 hist
5.1 6.9 ▇▁▁▂▅▅▃▁
1.8 2.5 ▇▁▁▅▃▃▂▂
6.4 7.9 ▂▇▅▇▆▅▂▂
3.3 4.4 ▁▂▅▇▃▂▁▁

share|improve this answer

edited 1 hour ago

answered 1 hour ago

Tung

5,79321634

add a comment | 

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2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

active

oldest

votes

active

oldest

votes

up vote
7
down vote



accepted

How about using already existing solutions from the psych package?

my.dat <- cbind(norm = rnorm(100), pois = rpois(n = 100, 10))

library(psych)
describe(my.dat)
# vars n mean sd median trimmed mad min max range skew kurtosis se
# norm 1 100 -0.02 0.98 -0.09 -0.06 0.86 -3.25 2.81 6.06 0.13 0.74 0.10
# pois 2 100 9.91 3.30 10.00 9.95 4.45 3.00 17.00 14.00 -0.07 -0.75 0.33

share|improve this answer

answered 5 hours ago

storaged

1,4001222

add a comment | 

up vote
7
down vote



accepted

How about using already existing solutions from the psych package?

my.dat <- cbind(norm = rnorm(100), pois = rpois(n = 100, 10))

library(psych)
describe(my.dat)
# vars n mean sd median trimmed mad min max range skew kurtosis se
# norm 1 100 -0.02 0.98 -0.09 -0.06 0.86 -3.25 2.81 6.06 0.13 0.74 0.10
# pois 2 100 9.91 3.30 10.00 9.95 4.45 3.00 17.00 14.00 -0.07 -0.75 0.33

share|improve this answer

answered 5 hours ago

storaged

1,4001222

add a comment | 

up vote
7
down vote



accepted

up vote
7
down vote



accepted

How about using already existing solutions from the psych package?

my.dat <- cbind(norm = rnorm(100), pois = rpois(n = 100, 10))

library(psych)
describe(my.dat)
# vars n mean sd median trimmed mad min max range skew kurtosis se
# norm 1 100 -0.02 0.98 -0.09 -0.06 0.86 -3.25 2.81 6.06 0.13 0.74 0.10
# pois 2 100 9.91 3.30 10.00 9.95 4.45 3.00 17.00 14.00 -0.07 -0.75 0.33

share|improve this answer

answered 5 hours ago

storaged

1,4001222

How about using already existing solutions from the psych package?

my.dat <- cbind(norm = rnorm(100), pois = rpois(n = 100, 10))

library(psych)
describe(my.dat)
# vars n mean sd median trimmed mad min max range skew kurtosis se
# norm 1 100 -0.02 0.98 -0.09 -0.06 0.86 -3.25 2.81 6.06 0.13 0.74 0.10
# pois 2 100 9.91 3.30 10.00 9.95 4.45 3.00 17.00 14.00 -0.07 -0.75 0.33

share|improve this answer

answered 5 hours ago

storaged

1,4001222

share|improve this answer

share|improve this answer

answered 5 hours ago

storaged

1,4001222

answered 5 hours ago

storaged

1,4001222

answered 5 hours ago

storaged

1,4001222

1,4001222

add a comment | 

add a comment | 

up vote
1
down vote

Another choice is the Desc function from the DescTools package which produce both summary stats and plot

library(DescTools)
Desc(iris3, plotit = TRUE)

#> ------------------------------------------------------------------------- 
#> iris3 (numeric)
#> 
#> length n NAs unique 0s mean meanCI
#> 600 600 0 74 0 3.46 3.31
#> 100.0% 0.0% 0.0% 3.62
#> 
#> .05 .10 .25 median .75 .90 .95
#> 0.20 1.10 1.70 3.20 5.10 6.20 6.70
#> 
#> range sd vcoef mad IQR skew kurt
#> 7.80 1.98 0.57 2.52 3.40 0.13 -1.05
#> 
#> lowest : 0.1 (5), 0.2 (29), 0.3 (7), 0.4 (7), 0.5
#> highest: 7.3, 7.4, 7.6, 7.7 (4), 7.9

The skim function from the skimr package is also a good one

library(skimr)
skim(iris)

Skim summary statistics
n obs: 150 
n variables: 5 

-- Variable type:factor --------------------------------------------------------
 variable missing complete n n_unique
Species 0 150 150 3
top_counts ordered
set: 50, ver: 50, vir: 50, NA: 0 FALSE

-- Variable type:numeric -------------------------------------------------------
 variable missing complete n mean sd p0 p25 p50
Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35
Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3 
Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8 
Sepal.Width 0 150 150 3.06 0.44 2 2.8 3 
p75 p100 hist
5.1 6.9 ▇▁▁▂▅▅▃▁
1.8 2.5 ▇▁▁▅▃▃▂▂
6.4 7.9 ▂▇▅▇▆▅▂▂
3.3 4.4 ▁▂▅▇▃▂▁▁

share|improve this answer

edited 1 hour ago

answered 1 hour ago

Tung

5,79321634

add a comment | 

up vote
1
down vote

Another choice is the Desc function from the DescTools package which produce both summary stats and plot

library(DescTools)
Desc(iris3, plotit = TRUE)

#> ------------------------------------------------------------------------- 
#> iris3 (numeric)
#> 
#> length n NAs unique 0s mean meanCI
#> 600 600 0 74 0 3.46 3.31
#> 100.0% 0.0% 0.0% 3.62
#> 
#> .05 .10 .25 median .75 .90 .95
#> 0.20 1.10 1.70 3.20 5.10 6.20 6.70
#> 
#> range sd vcoef mad IQR skew kurt
#> 7.80 1.98 0.57 2.52 3.40 0.13 -1.05
#> 
#> lowest : 0.1 (5), 0.2 (29), 0.3 (7), 0.4 (7), 0.5
#> highest: 7.3, 7.4, 7.6, 7.7 (4), 7.9

The skim function from the skimr package is also a good one

library(skimr)
skim(iris)

Skim summary statistics
n obs: 150 
n variables: 5 

-- Variable type:factor --------------------------------------------------------
 variable missing complete n n_unique
Species 0 150 150 3
top_counts ordered
set: 50, ver: 50, vir: 50, NA: 0 FALSE

-- Variable type:numeric -------------------------------------------------------
 variable missing complete n mean sd p0 p25 p50
Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35
Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3 
Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8 
Sepal.Width 0 150 150 3.06 0.44 2 2.8 3 
p75 p100 hist
5.1 6.9 ▇▁▁▂▅▅▃▁
1.8 2.5 ▇▁▁▅▃▃▂▂
6.4 7.9 ▂▇▅▇▆▅▂▂
3.3 4.4 ▁▂▅▇▃▂▁▁

share|improve this answer

edited 1 hour ago

answered 1 hour ago

Tung

5,79321634

add a comment | 

up vote
1
down vote

up vote
1
down vote

Another choice is the Desc function from the DescTools package which produce both summary stats and plot

library(DescTools)
Desc(iris3, plotit = TRUE)

#> ------------------------------------------------------------------------- 
#> iris3 (numeric)
#> 
#> length n NAs unique 0s mean meanCI
#> 600 600 0 74 0 3.46 3.31
#> 100.0% 0.0% 0.0% 3.62
#> 
#> .05 .10 .25 median .75 .90 .95
#> 0.20 1.10 1.70 3.20 5.10 6.20 6.70
#> 
#> range sd vcoef mad IQR skew kurt
#> 7.80 1.98 0.57 2.52 3.40 0.13 -1.05
#> 
#> lowest : 0.1 (5), 0.2 (29), 0.3 (7), 0.4 (7), 0.5
#> highest: 7.3, 7.4, 7.6, 7.7 (4), 7.9

The skim function from the skimr package is also a good one

library(skimr)
skim(iris)

Skim summary statistics
n obs: 150 
n variables: 5 

-- Variable type:factor --------------------------------------------------------
 variable missing complete n n_unique
Species 0 150 150 3
top_counts ordered
set: 50, ver: 50, vir: 50, NA: 0 FALSE

-- Variable type:numeric -------------------------------------------------------
 variable missing complete n mean sd p0 p25 p50
Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35
Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3 
Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8 
Sepal.Width 0 150 150 3.06 0.44 2 2.8 3 
p75 p100 hist
5.1 6.9 ▇▁▁▂▅▅▃▁
1.8 2.5 ▇▁▁▅▃▃▂▂
6.4 7.9 ▂▇▅▇▆▅▂▂
3.3 4.4 ▁▂▅▇▃▂▁▁

share|improve this answer

edited 1 hour ago

answered 1 hour ago

Tung

5,79321634

Another choice is the Desc function from the DescTools package which produce both summary stats and plot

library(DescTools)
Desc(iris3, plotit = TRUE)

#> ------------------------------------------------------------------------- 
#> iris3 (numeric)
#> 
#> length n NAs unique 0s mean meanCI
#> 600 600 0 74 0 3.46 3.31
#> 100.0% 0.0% 0.0% 3.62
#> 
#> .05 .10 .25 median .75 .90 .95
#> 0.20 1.10 1.70 3.20 5.10 6.20 6.70
#> 
#> range sd vcoef mad IQR skew kurt
#> 7.80 1.98 0.57 2.52 3.40 0.13 -1.05
#> 
#> lowest : 0.1 (5), 0.2 (29), 0.3 (7), 0.4 (7), 0.5
#> highest: 7.3, 7.4, 7.6, 7.7 (4), 7.9

The skim function from the skimr package is also a good one

library(skimr)
skim(iris)

Skim summary statistics
n obs: 150 
n variables: 5 

-- Variable type:factor --------------------------------------------------------
 variable missing complete n n_unique
Species 0 150 150 3
top_counts ordered
set: 50, ver: 50, vir: 50, NA: 0 FALSE

-- Variable type:numeric -------------------------------------------------------
 variable missing complete n mean sd p0 p25 p50
Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35
Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3 
Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8 
Sepal.Width 0 150 150 3.06 0.44 2 2.8 3 
p75 p100 hist
5.1 6.9 ▇▁▁▂▅▅▃▁
1.8 2.5 ▇▁▁▅▃▃▂▂
6.4 7.9 ▂▇▅▇▆▅▂▂
3.3 4.4 ▁▂▅▇▃▂▁▁

share|improve this answer

edited 1 hour ago

answered 1 hour ago

Tung

5,79321634

share|improve this answer

share|improve this answer

edited 1 hour ago

edited 1 hour ago

edited 1 hour ago

answered 1 hour ago

Tung

5,79321634

answered 1 hour ago

Tung

5,79321634

answered 1 hour ago

Tung

5,79321634

5,79321634

add a comment | 

add a comment | 

 

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