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0

I know that there are already some threads about it, but I haven't found one yet about this specific problem.
The dependent variable in my dataset is Y and I have 144 independent variables. Y and X can take only the values 1 or 0. The data looks like

 Y A469 T593 K022K A835 Z935 U83F W5326 ...
 Person1 1 1 1 1 0 0 0 0
 Person2 1 0 1 0 1 1 0 0
 Person3 0 0 0 1 0 0 1 1
 ...
summary(dataset)

just provides descriptive statistics over all observations. What I want is (in pseudo-code):

summary(all variables if Y == 1 and Y == 0)

It would be great if I could see how often a certain X occurs in the certain value of Y. For example, mean(X4) = 0.04 and count = 6 if Y = 1.

r group-by mean

share|improve this question

edited Mar 27 at 20:22

Textime

asked Mar 27 at 14:43

TextimeTextime

4788 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Please provide a more complete data set to work with. You can and should use dput to provide sample data.
  
  – NelsonGon
  Mar 27 at 15:33
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @NelsonGon Bold assertion after it's already been nearly answered. I'm all for reproducible examples, and of course dput() is nicer, but this is plenty clear.
  
  – Gregor
  Mar 27 at 15:38
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Gregor it seemed to me that lack of data was making it hard to find the "ideal" solution. My apologies!
  
  – NelsonGon
  Mar 27 at 15:40
  

  
  
  
  

add a comment | 

0

I know that there are already some threads about it, but I haven't found one yet about this specific problem.
The dependent variable in my dataset is Y and I have 144 independent variables. Y and X can take only the values 1 or 0. The data looks like

 Y A469 T593 K022K A835 Z935 U83F W5326 ...
 Person1 1 1 1 1 0 0 0 0
 Person2 1 0 1 0 1 1 0 0
 Person3 0 0 0 1 0 0 1 1
 ...
summary(dataset)

just provides descriptive statistics over all observations. What I want is (in pseudo-code):

summary(all variables if Y == 1 and Y == 0)

It would be great if I could see how often a certain X occurs in the certain value of Y. For example, mean(X4) = 0.04 and count = 6 if Y = 1.

r group-by mean

share|improve this question

edited Mar 27 at 20:22

Textime

asked Mar 27 at 14:43

TextimeTextime

4788 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Please provide a more complete data set to work with. You can and should use dput to provide sample data.
  
  – NelsonGon
  Mar 27 at 15:33
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @NelsonGon Bold assertion after it's already been nearly answered. I'm all for reproducible examples, and of course dput() is nicer, but this is plenty clear.
  
  – Gregor
  Mar 27 at 15:38
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Gregor it seemed to me that lack of data was making it hard to find the "ideal" solution. My apologies!
  
  – NelsonGon
  Mar 27 at 15:40
  

  
  
  
  

add a comment | 

I know that there are already some threads about it, but I haven't found one yet about this specific problem.
The dependent variable in my dataset is Y and I have 144 independent variables. Y and X can take only the values 1 or 0. The data looks like

 Y A469 T593 K022K A835 Z935 U83F W5326 ...
 Person1 1 1 1 1 0 0 0 0
 Person2 1 0 1 0 1 1 0 0
 Person3 0 0 0 1 0 0 1 1
 ...
summary(dataset)

just provides descriptive statistics over all observations. What I want is (in pseudo-code):

summary(all variables if Y == 1 and Y == 0)

It would be great if I could see how often a certain X occurs in the certain value of Y. For example, mean(X4) = 0.04 and count = 6 if Y = 1.

r group-by mean

share|improve this question

edited Mar 27 at 20:22

Textime

asked Mar 27 at 14:43

TextimeTextime

4788 bronze badges

 Y A469 T593 K022K A835 Z935 U83F W5326 ...
 Person1 1 1 1 1 0 0 0 0
 Person2 1 0 1 0 1 1 0 0
 Person3 0 0 0 1 0 0 1 1
 ...
summary(dataset)

summary(all variables if Y == 1 and Y == 0)

share|improve this question

edited Mar 27 at 20:22

Textime

asked Mar 27 at 14:43

TextimeTextime

4788 bronze badges

share|improve this question

edited Mar 27 at 20:22

Textime

asked Mar 27 at 14:43

TextimeTextime

4788 bronze badges

asked Mar 27 at 14:43

TextimeTextime

4788 bronze badges

asked Mar 27 at 14:43

TextimeTextime

4788 bronze badges

1 Answer
1

active

oldest

votes

2

EDIT 2
after Akrun's and Gregor's comments here is the solution

 data_summary <- dataset %>% group_by(y) %>% 
 mutate(n = n()) %>%
 summarise_all(mean)

If you want to see more columns than fit on your screen you can try, e.g.,

• print(data_summary, width = 20)


• View(data_summary)


• select(data_summary, <<particular columns you want to see>>)


• ...

share|improve this answer

edited Mar 27 at 15:39

Gregor

73.7k1212 gold badges103103 silver badges196196 bronze badges

answered Mar 27 at 14:51

CetttCettt

4,83155 gold badges1818 silver badges4040 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I'm getting this error message: Error in UseMethod("groups") : no applicable method for 'groups' applied to an object of class "c('double', 'numeric')"
  
  – Textime
  Mar 27 at 14:52
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  can you please provide a sample of your data?
  
  – Cettt
  Mar 27 at 15:03
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  I think the count is the issue. YOu may need n()
  
  – akrun
  Mar 27 at 15:07
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  try ninstead of n()
  
  – Cettt
  Mar 27 at 15:20
  

  
  
  
  


• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  You're right, it only shows the first 10 variables. They are all there, they just aren't printed. Save the result as data_summary or something and look at it with something other than the default print method for tibbles. print.data.frame(data_summary), View(data_summary), write.csv(data_summary), print(data_summary, width = Inf), etc.
  
  – Gregor
  Mar 27 at 15:34
  
  

  
  
  
  

 | 
show 6 more comments

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2

EDIT 2
after Akrun's and Gregor's comments here is the solution

 data_summary <- dataset %>% group_by(y) %>% 
 mutate(n = n()) %>%
 summarise_all(mean)

If you want to see more columns than fit on your screen you can try, e.g.,

• print(data_summary, width = 20)


• View(data_summary)


• select(data_summary, <<particular columns you want to see>>)


• ...

share|improve this answer

edited Mar 27 at 15:39

Gregor

73.7k1212 gold badges103103 silver badges196196 bronze badges

answered Mar 27 at 14:51

CetttCettt

4,83155 gold badges1818 silver badges4040 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I'm getting this error message: Error in UseMethod("groups") : no applicable method for 'groups' applied to an object of class "c('double', 'numeric')"
  
  – Textime
  Mar 27 at 14:52
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  can you please provide a sample of your data?
  
  – Cettt
  Mar 27 at 15:03
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  I think the count is the issue. YOu may need n()
  
  – akrun
  Mar 27 at 15:07
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  try ninstead of n()
  
  – Cettt
  Mar 27 at 15:20
  

  
  
  
  


• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  You're right, it only shows the first 10 variables. They are all there, they just aren't printed. Save the result as data_summary or something and look at it with something other than the default print method for tibbles. print.data.frame(data_summary), View(data_summary), write.csv(data_summary), print(data_summary, width = Inf), etc.
  
  – Gregor
  Mar 27 at 15:34
  
  

  
  
  
  

 | 
show 6 more comments

2

EDIT 2
after Akrun's and Gregor's comments here is the solution

 data_summary <- dataset %>% group_by(y) %>% 
 mutate(n = n()) %>%
 summarise_all(mean)

If you want to see more columns than fit on your screen you can try, e.g.,

• print(data_summary, width = 20)


• View(data_summary)


• select(data_summary, <<particular columns you want to see>>)


• ...

share|improve this answer

edited Mar 27 at 15:39

Gregor

73.7k1212 gold badges103103 silver badges196196 bronze badges

answered Mar 27 at 14:51

CetttCettt

4,83155 gold badges1818 silver badges4040 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I'm getting this error message: Error in UseMethod("groups") : no applicable method for 'groups' applied to an object of class "c('double', 'numeric')"
  
  – Textime
  Mar 27 at 14:52
  
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  can you please provide a sample of your data?
  
  – Cettt
  Mar 27 at 15:03
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  I think the count is the issue. YOu may need n()
  
  – akrun
  Mar 27 at 15:07
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  try ninstead of n()
  
  – Cettt
  Mar 27 at 15:20
  

  
  
  
  


• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  You're right, it only shows the first 10 variables. They are all there, they just aren't printed. Save the result as data_summary or something and look at it with something other than the default print method for tibbles. print.data.frame(data_summary), View(data_summary), write.csv(data_summary), print(data_summary, width = Inf), etc.
  
  – Gregor
  Mar 27 at 15:34
  
  

  
  
  
  

 | 
show 6 more comments

EDIT 2
after Akrun's and Gregor's comments here is the solution

 data_summary <- dataset %>% group_by(y) %>% 
 mutate(n = n()) %>%
 summarise_all(mean)

If you want to see more columns than fit on your screen you can try, e.g.,

• print(data_summary, width = 20)


• View(data_summary)


• select(data_summary, <<particular columns you want to see>>)


• ...

share|improve this answer

edited Mar 27 at 15:39

Gregor

73.7k1212 gold badges103103 silver badges196196 bronze badges

answered Mar 27 at 14:51

CetttCettt

4,83155 gold badges1818 silver badges4040 bronze badges

 data_summary <- dataset %>% group_by(y) %>% 
 mutate(n = n()) %>%
 summarise_all(mean)

share|improve this answer

edited Mar 27 at 15:39

Gregor

73.7k1212 gold badges103103 silver badges196196 bronze badges

answered Mar 27 at 14:51

CetttCettt

4,83155 gold badges1818 silver badges4040 bronze badges

edited Mar 27 at 15:39

Gregor

73.7k1212 gold badges103103 silver badges196196 bronze badges

edited Mar 27 at 15:39

Gregor

73.7k1212 gold badges103103 silver badges196196 bronze badges

answered Mar 27 at 14:51

CetttCettt

4,83155 gold badges1818 silver badges4040 bronze badges

answered Mar 27 at 14:51

CetttCettt

4,83155 gold badges1818 silver badges4040 bronze badges