Remove participants based on a certain criteria

-1

I have an experiment with numerous participants and their choices.
For simplicity, let's assume the following:

part<-c(1,1,1,2,2,2,3,3,3)

choice<-c(6,2,9,2,3,18,3,6,8)

study<-cbind(part,choice)



       part choice

 [1,]    1      6

 [2,]    1      2

 [3,]    1      9

 [4,]    2      2

 [5,]    2      3

 [6,]    2     18

 [7,]    3      3

 [8,]    3      6

 [9,]    3      8

Now, I would like to entirely remove certain participants. For example those who made at least one choice above 10. So in the example above, because participant 2, made one choice above 10, I entirely remove him: The final data should look:

      part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

How can I do it?

Thanks!

r filtering subset data-manipulation

share|improve this question

asked Jan 2 at 11:20

YefRYefR

10119

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  With dplyr you can do study %>% group_by(part) %>% filter(all(choice < 10)).
  
  – tmfmnk
  Jan 2 at 11:24
  

  
  
  
  

add a comment | 

-1

I have an experiment with numerous participants and their choices.
For simplicity, let's assume the following:

part<-c(1,1,1,2,2,2,3,3,3)

choice<-c(6,2,9,2,3,18,3,6,8)

study<-cbind(part,choice)



       part choice

 [1,]    1      6

 [2,]    1      2

 [3,]    1      9

 [4,]    2      2

 [5,]    2      3

 [6,]    2     18

 [7,]    3      3

 [8,]    3      6

 [9,]    3      8

Now, I would like to entirely remove certain participants. For example those who made at least one choice above 10. So in the example above, because participant 2, made one choice above 10, I entirely remove him: The final data should look:

      part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

How can I do it?

Thanks!

r filtering subset data-manipulation

share|improve this question

asked Jan 2 at 11:20

YefRYefR

10119

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  With dplyr you can do study %>% group_by(part) %>% filter(all(choice < 10)).
  
  – tmfmnk
  Jan 2 at 11:24
  

  
  
  
  

add a comment | 

-1

-1

-1

I have an experiment with numerous participants and their choices.
For simplicity, let's assume the following:

part<-c(1,1,1,2,2,2,3,3,3)

choice<-c(6,2,9,2,3,18,3,6,8)

study<-cbind(part,choice)



       part choice

 [1,]    1      6

 [2,]    1      2

 [3,]    1      9

 [4,]    2      2

 [5,]    2      3

 [6,]    2     18

 [7,]    3      3

 [8,]    3      6

 [9,]    3      8

Now, I would like to entirely remove certain participants. For example those who made at least one choice above 10. So in the example above, because participant 2, made one choice above 10, I entirely remove him: The final data should look:

      part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

How can I do it?

Thanks!

r filtering subset data-manipulation

share|improve this question

asked Jan 2 at 11:20

YefRYefR

10119

I have an experiment with numerous participants and their choices.
For simplicity, let's assume the following:

part<-c(1,1,1,2,2,2,3,3,3)

choice<-c(6,2,9,2,3,18,3,6,8)

study<-cbind(part,choice)



       part choice

 [1,]    1      6

 [2,]    1      2

 [3,]    1      9

 [4,]    2      2

 [5,]    2      3

 [6,]    2     18

 [7,]    3      3

 [8,]    3      6

 [9,]    3      8

Now, I would like to entirely remove certain participants. For example those who made at least one choice above 10. So in the example above, because participant 2, made one choice above 10, I entirely remove him: The final data should look:

      part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

How can I do it?

Thanks!

r filtering subset data-manipulation

r filtering subset data-manipulation

share|improve this question

asked Jan 2 at 11:20

YefRYefR

10119

share|improve this question

asked Jan 2 at 11:20

YefRYefR

10119

share|improve this question

share|improve this question

asked Jan 2 at 11:20

YefRYefR

10119

asked Jan 2 at 11:20

YefRYefR

10119

asked Jan 2 at 11:20

YefRYefR

10119

10119

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  With dplyr you can do study %>% group_by(part) %>% filter(all(choice < 10)).
  
  – tmfmnk
  Jan 2 at 11:24
  

  
  
  
  

add a comment | 

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  With dplyr you can do study %>% group_by(part) %>% filter(all(choice < 10)).
  
  – tmfmnk
  Jan 2 at 11:24
  

  
  
  
  

2

2

With dplyr you can do study %>% group_by(part) %>% filter(all(choice < 10)).

– tmfmnk
Jan 2 at 11:24

With dplyr you can do study %>% group_by(part) %>% filter(all(choice < 10)).

– tmfmnk
Jan 2 at 11:24

add a comment | 

3 Answers
3

active

oldest

votes

4

library(dplyr)

 study %>% 

   group_by(part) %>% 

   filter(max(choice)<10)

# A tibble: 6 x 2

# Groups:   part [2]

   part choice

  <dbl>  <dbl>

1     1      6

2     1      2

3     1      9

4     3      3

5     3      6

6     3      8

share|improve this answer

answered Jan 2 at 11:23

jyjekjyjek

1,504314

add a comment | 

1

removed = which(study[ , 2]>10);

study = study[!(study[ , 1] %in% study[removed, 1]), ];



study

     part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

with this code you don't even need to install any package.

share|improve this answer

answered Jan 2 at 11:39

XinzXinz

543

add a comment | 

1

Using R base, without need of loading packages. The example uses variable names, instead of position, for better overview of the solution.

# Create object to be used in dataframe.

part   <- c(1,1,1,2,2,2,3,3,3)

choice <- c(6,2,9,2,3,18,3,6,8)

# Create dataframe.

study  <- data.frame(part, choice)



# Find rows in column [study$choice]

find_rows <- which(study$choice > 10)

# Find participant that matches [find_rows]

participant_to_be_deleted <- study[find_rows,1]



# Remove all rows that has found participant in [study$part].

result <- study[study$part!=participant_to_be_deleted,]

share|improve this answer

answered Jan 2 at 11:51

ToolboxToolbox

647312

add a comment | 

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3 Answers
3

active

oldest

votes

3 Answers
3

active

oldest

votes

active

oldest

votes

active

oldest

votes

4

library(dplyr)

 study %>% 

   group_by(part) %>% 

   filter(max(choice)<10)

# A tibble: 6 x 2

# Groups:   part [2]

   part choice

  <dbl>  <dbl>

1     1      6

2     1      2

3     1      9

4     3      3

5     3      6

6     3      8

share|improve this answer

answered Jan 2 at 11:23

jyjekjyjek

1,504314

add a comment | 

4

library(dplyr)

 study %>% 

   group_by(part) %>% 

   filter(max(choice)<10)

# A tibble: 6 x 2

# Groups:   part [2]

   part choice

  <dbl>  <dbl>

1     1      6

2     1      2

3     1      9

4     3      3

5     3      6

6     3      8

share|improve this answer

answered Jan 2 at 11:23

jyjekjyjek

1,504314

add a comment | 

4

4

4

library(dplyr)

 study %>% 

   group_by(part) %>% 

   filter(max(choice)<10)

# A tibble: 6 x 2

# Groups:   part [2]

   part choice

  <dbl>  <dbl>

1     1      6

2     1      2

3     1      9

4     3      3

5     3      6

6     3      8

share|improve this answer

answered Jan 2 at 11:23

jyjekjyjek

1,504314

library(dplyr)

 study %>% 

   group_by(part) %>% 

   filter(max(choice)<10)

# A tibble: 6 x 2

# Groups:   part [2]

   part choice

  <dbl>  <dbl>

1     1      6

2     1      2

3     1      9

4     3      3

5     3      6

6     3      8

share|improve this answer

answered Jan 2 at 11:23

jyjekjyjek

1,504314

share|improve this answer

share|improve this answer

answered Jan 2 at 11:23

jyjekjyjek

1,504314

answered Jan 2 at 11:23

jyjekjyjek

1,504314

answered Jan 2 at 11:23

jyjekjyjek

1,504314

1,504314

add a comment | 

add a comment | 

1

removed = which(study[ , 2]>10);

study = study[!(study[ , 1] %in% study[removed, 1]), ];



study

     part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

with this code you don't even need to install any package.

share|improve this answer

answered Jan 2 at 11:39

XinzXinz

543

add a comment | 

1

removed = which(study[ , 2]>10);

study = study[!(study[ , 1] %in% study[removed, 1]), ];



study

     part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

with this code you don't even need to install any package.

share|improve this answer

answered Jan 2 at 11:39

XinzXinz

543

add a comment | 

1

1

1

removed = which(study[ , 2]>10);

study = study[!(study[ , 1] %in% study[removed, 1]), ];



study

     part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

with this code you don't even need to install any package.

share|improve this answer

answered Jan 2 at 11:39

XinzXinz

543

removed = which(study[ , 2]>10);

study = study[!(study[ , 1] %in% study[removed, 1]), ];



study

     part choice

[1,]    1      6

[2,]    1      2

[3,]    1      9

[4,]    3      3

[5,]    3      6

[6,]    3      8

with this code you don't even need to install any package.

share|improve this answer

answered Jan 2 at 11:39

XinzXinz

543

share|improve this answer

share|improve this answer

answered Jan 2 at 11:39

XinzXinz

543

answered Jan 2 at 11:39

XinzXinz

543

answered Jan 2 at 11:39

XinzXinz

543

543

add a comment | 

add a comment | 

1

Using R base, without need of loading packages. The example uses variable names, instead of position, for better overview of the solution.

# Create object to be used in dataframe.

part   <- c(1,1,1,2,2,2,3,3,3)

choice <- c(6,2,9,2,3,18,3,6,8)

# Create dataframe.

study  <- data.frame(part, choice)



# Find rows in column [study$choice]

find_rows <- which(study$choice > 10)

# Find participant that matches [find_rows]

participant_to_be_deleted <- study[find_rows,1]



# Remove all rows that has found participant in [study$part].

result <- study[study$part!=participant_to_be_deleted,]

share|improve this answer

answered Jan 2 at 11:51

ToolboxToolbox

647312

add a comment | 

1

Using R base, without need of loading packages. The example uses variable names, instead of position, for better overview of the solution.

# Create object to be used in dataframe.

part   <- c(1,1,1,2,2,2,3,3,3)

choice <- c(6,2,9,2,3,18,3,6,8)

# Create dataframe.

study  <- data.frame(part, choice)



# Find rows in column [study$choice]

find_rows <- which(study$choice > 10)

# Find participant that matches [find_rows]

participant_to_be_deleted <- study[find_rows,1]



# Remove all rows that has found participant in [study$part].

result <- study[study$part!=participant_to_be_deleted,]

share|improve this answer

answered Jan 2 at 11:51

ToolboxToolbox

647312

add a comment | 

1

1

1

Using R base, without need of loading packages. The example uses variable names, instead of position, for better overview of the solution.

# Create object to be used in dataframe.

part   <- c(1,1,1,2,2,2,3,3,3)

choice <- c(6,2,9,2,3,18,3,6,8)

# Create dataframe.

study  <- data.frame(part, choice)



# Find rows in column [study$choice]

find_rows <- which(study$choice > 10)

# Find participant that matches [find_rows]

participant_to_be_deleted <- study[find_rows,1]



# Remove all rows that has found participant in [study$part].

result <- study[study$part!=participant_to_be_deleted,]

share|improve this answer

answered Jan 2 at 11:51

ToolboxToolbox

647312

Using R base, without need of loading packages. The example uses variable names, instead of position, for better overview of the solution.

# Create object to be used in dataframe.

part   <- c(1,1,1,2,2,2,3,3,3)

choice <- c(6,2,9,2,3,18,3,6,8)

# Create dataframe.

study  <- data.frame(part, choice)



# Find rows in column [study$choice]

find_rows <- which(study$choice > 10)

# Find participant that matches [find_rows]

participant_to_be_deleted <- study[find_rows,1]



# Remove all rows that has found participant in [study$part].

result <- study[study$part!=participant_to_be_deleted,]

share|improve this answer

answered Jan 2 at 11:51

ToolboxToolbox

647312

share|improve this answer

share|improve this answer

answered Jan 2 at 11:51

ToolboxToolbox

647312

answered Jan 2 at 11:51

ToolboxToolbox

647312

answered Jan 2 at 11:51

ToolboxToolbox

647312

647312

add a comment | 

add a comment | 

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• Making statements based on opinion; back them up with references or personal experience.

To learn more, see our tips on writing great answers.

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• Please be sure to answer the question. Provide details and share your research!

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