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6

I want to list in array format how many in each Diet group (there are four) have Time > 21.

I have tried to solve this in RStudio.

data(ChickWeight)
newdata <- subset(ChickWeight, Time >= 21, select=Diet)

In order to find how many observations are in newdata, I used
nrow(newdata),
but I would like to find out how many observations meet the criteria just by making it a part of this expression:

newdata <- subset(ChickWeight, Time >= 21, select=Diet) 

so that when I display newdata the table will also contain the number of observations that meet the criteria in a new column.

Desire output:

Diet Number Observations
1 200 (I just created the numbers for this column as examples)
2 75
3 150
4 100 

Is there a way to do that?

r subset frequency

share|improve this question

edited Jul 12 at 17:36

M-M

10.6k66 gold badges2727 silver badges5050 bronze badges

asked Jul 12 at 17:04

MetsfanMetsfan

5355 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  and the obs count would be a repeating number in a different column of newdata? What about newdata$obs_count <- nrow(newdata)?
  
  – avid_useR
  Jul 12 at 17:13
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I would like it displayed this way: Diet Number Observations 1 200 (what # is) 2 300 (what # is) 3 75 (what # is) 4 25 (what # is) avid_useR: When I ran yours, I got NULL.
  
  – Metsfan
  Jul 12 at 17:19
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Please post your desired output in the question body itself
  
  – avid_useR
  Jul 12 at 17:20
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  So basically you want to get the obs count for each Diet group?
  
  – avid_useR
  Jul 12 at 17:25
  

  
  
  
  

add a comment | 

6

I want to list in array format how many in each Diet group (there are four) have Time > 21.

I have tried to solve this in RStudio.

data(ChickWeight)
newdata <- subset(ChickWeight, Time >= 21, select=Diet)

In order to find how many observations are in newdata, I used
nrow(newdata),
but I would like to find out how many observations meet the criteria just by making it a part of this expression:

newdata <- subset(ChickWeight, Time >= 21, select=Diet) 

so that when I display newdata the table will also contain the number of observations that meet the criteria in a new column.

Desire output:

Diet Number Observations
1 200 (I just created the numbers for this column as examples)
2 75
3 150
4 100 

Is there a way to do that?

r subset frequency

share|improve this question

edited Jul 12 at 17:36

M-M

10.6k66 gold badges2727 silver badges5050 bronze badges

asked Jul 12 at 17:04

MetsfanMetsfan

5355 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  and the obs count would be a repeating number in a different column of newdata? What about newdata$obs_count <- nrow(newdata)?
  
  – avid_useR
  Jul 12 at 17:13
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I would like it displayed this way: Diet Number Observations 1 200 (what # is) 2 300 (what # is) 3 75 (what # is) 4 25 (what # is) avid_useR: When I ran yours, I got NULL.
  
  – Metsfan
  Jul 12 at 17:19
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Please post your desired output in the question body itself
  
  – avid_useR
  Jul 12 at 17:20
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  So basically you want to get the obs count for each Diet group?
  
  – avid_useR
  Jul 12 at 17:25
  

  
  
  
  

add a comment | 

I want to list in array format how many in each Diet group (there are four) have Time > 21.

I have tried to solve this in RStudio.

data(ChickWeight)
newdata <- subset(ChickWeight, Time >= 21, select=Diet)

In order to find how many observations are in newdata, I used
nrow(newdata),
but I would like to find out how many observations meet the criteria just by making it a part of this expression:

newdata <- subset(ChickWeight, Time >= 21, select=Diet) 

so that when I display newdata the table will also contain the number of observations that meet the criteria in a new column.

Desire output:

Diet Number Observations
1 200 (I just created the numbers for this column as examples)
2 75
3 150
4 100 

Is there a way to do that?

r subset frequency

share|improve this question

edited Jul 12 at 17:36

M-M

10.6k66 gold badges2727 silver badges5050 bronze badges

asked Jul 12 at 17:04

MetsfanMetsfan

5355 bronze badges

data(ChickWeight)
newdata <- subset(ChickWeight, Time >= 21, select=Diet)

newdata <- subset(ChickWeight, Time >= 21, select=Diet) 

Diet Number Observations
1 200 (I just created the numbers for this column as examples)
2 75
3 150
4 100 

share|improve this question

edited Jul 12 at 17:36

M-M

10.6k66 gold badges2727 silver badges5050 bronze badges

asked Jul 12 at 17:04

MetsfanMetsfan

5355 bronze badges

share|improve this question

edited Jul 12 at 17:36

M-M

10.6k66 gold badges2727 silver badges5050 bronze badges

asked Jul 12 at 17:04

MetsfanMetsfan

5355 bronze badges

edited Jul 12 at 17:36

M-M

10.6k66 gold badges2727 silver badges5050 bronze badges

edited Jul 12 at 17:36

M-M

10.6k66 gold badges2727 silver badges5050 bronze badges

asked Jul 12 at 17:04

MetsfanMetsfan

5355 bronze badges

asked Jul 12 at 17:04

MetsfanMetsfan

5355 bronze badges

4 Answers
4

active

oldest

votes

5

It can be done in base:

transform(table(Diet=subset(ChickWeight, Time >= 21, select=Diet)))

#> Diet Freq
#> 1 1 16
#> 2 2 10
#> 3 3 10
#> 4 4 9

share|improve this answer

answered Jul 12 at 17:29

M-MM-M

10.6k66 gold badges2727 silver badges5050 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  M-M, thanks. It works. What is the purpose of "table"? Why is it needed?
  
  – Metsfan
  Jul 12 at 17:56
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Metsfan You can read about it by running ?table(). In short, table gives a cross tab with frequencies. I am just transforming it later to change the direction of output (run the code without transform to see). You should run table on couple more dataframes that you have to know what it does better.
  
  – M-M
  Jul 12 at 18:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  When I ran it without transform I got this error: "Error in subset.data.frame(ChickWeight, select = Diet, weight) : 'subset' must be logical"
  
  – Metsfan
  Jul 12 at 18:09
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Metsfan Are you assigning that to a column or something? For testing, just run that line without anything else before or after: table(Diet=subset(ChickWeight, Time >= 21, select=Diet))
  
  – M-M
  Jul 12 at 18:12
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Okay, now it worked. I noticed that it converted the rows into columns. Interesting. Thanks again.
  
  – Metsfan
  Jul 12 at 18:17
  

  
  
  
  

 | 
show 1 more comment

1

Consider a straightforward aggregate after the subset call:

newdata <- subset(ChickWeight, Time >= 21, select=Diet)

aggregate(cbind(Obs=Diet) ~ Diet, newdata, FUN=length)

# Diet Obs
# 1 1 16
# 2 2 10
# 3 3 10
# 4 4 9

share|improve this answer

answered Jul 12 at 17:29

ParfaitParfait

59k1010 gold badges5555 silver badges7676 bronze badges

add a comment | 

1

We can do this with summarize from dplyr:

library(dplyr)

newdata %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

We can even combine the subset to a single dplyr workflow:

ChickWeight %>%
 filter(Time >= 21) %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

Output:

# A tibble: 4 x 2
 Diet Num_Obs
 <fct> <int>
1 1 16
2 2 10
3 3 10
4 4 9

share|improve this answer

edited Jul 12 at 19:46

answered Jul 12 at 17:27

avid_useRavid_useR

14.2k44 gold badges2020 silver badges3333 bronze badges

add a comment | 

0

Here is a data table approach

library(data.table)
df <- as.data.table(ChickWeight)

df[Time >= 21, .(Number = .N), by = Diet]
# Diet Number
# 1: 1 16
# 2: 2 10
# 3: 3 10
# 4: 4 9

share|improve this answer

answered Jul 12 at 19:37

IceCreamToucanIceCreamToucan

13.3k11 gold badge88 silver badges1919 bronze badges

add a comment | 

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5

It can be done in base:

transform(table(Diet=subset(ChickWeight, Time >= 21, select=Diet)))

#> Diet Freq
#> 1 1 16
#> 2 2 10
#> 3 3 10
#> 4 4 9

share|improve this answer

answered Jul 12 at 17:29

M-MM-M

10.6k66 gold badges2727 silver badges5050 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  M-M, thanks. It works. What is the purpose of "table"? Why is it needed?
  
  – Metsfan
  Jul 12 at 17:56
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Metsfan You can read about it by running ?table(). In short, table gives a cross tab with frequencies. I am just transforming it later to change the direction of output (run the code without transform to see). You should run table on couple more dataframes that you have to know what it does better.
  
  – M-M
  Jul 12 at 18:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  When I ran it without transform I got this error: "Error in subset.data.frame(ChickWeight, select = Diet, weight) : 'subset' must be logical"
  
  – Metsfan
  Jul 12 at 18:09
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Metsfan Are you assigning that to a column or something? For testing, just run that line without anything else before or after: table(Diet=subset(ChickWeight, Time >= 21, select=Diet))
  
  – M-M
  Jul 12 at 18:12
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Okay, now it worked. I noticed that it converted the rows into columns. Interesting. Thanks again.
  
  – Metsfan
  Jul 12 at 18:17
  

  
  
  
  

 | 
show 1 more comment

5

It can be done in base:

transform(table(Diet=subset(ChickWeight, Time >= 21, select=Diet)))

#> Diet Freq
#> 1 1 16
#> 2 2 10
#> 3 3 10
#> 4 4 9

share|improve this answer

answered Jul 12 at 17:29

M-MM-M

10.6k66 gold badges2727 silver badges5050 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  M-M, thanks. It works. What is the purpose of "table"? Why is it needed?
  
  – Metsfan
  Jul 12 at 17:56
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Metsfan You can read about it by running ?table(). In short, table gives a cross tab with frequencies. I am just transforming it later to change the direction of output (run the code without transform to see). You should run table on couple more dataframes that you have to know what it does better.
  
  – M-M
  Jul 12 at 18:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  When I ran it without transform I got this error: "Error in subset.data.frame(ChickWeight, select = Diet, weight) : 'subset' must be logical"
  
  – Metsfan
  Jul 12 at 18:09
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Metsfan Are you assigning that to a column or something? For testing, just run that line without anything else before or after: table(Diet=subset(ChickWeight, Time >= 21, select=Diet))
  
  – M-M
  Jul 12 at 18:12
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Okay, now it worked. I noticed that it converted the rows into columns. Interesting. Thanks again.
  
  – Metsfan
  Jul 12 at 18:17
  

  
  
  
  

 | 
show 1 more comment

It can be done in base:

transform(table(Diet=subset(ChickWeight, Time >= 21, select=Diet)))

#> Diet Freq
#> 1 1 16
#> 2 2 10
#> 3 3 10
#> 4 4 9

share|improve this answer

answered Jul 12 at 17:29

M-MM-M

10.6k66 gold badges2727 silver badges5050 bronze badges

transform(table(Diet=subset(ChickWeight, Time >= 21, select=Diet)))

#> Diet Freq
#> 1 1 16
#> 2 2 10
#> 3 3 10
#> 4 4 9

share|improve this answer

answered Jul 12 at 17:29

M-MM-M

10.6k66 gold badges2727 silver badges5050 bronze badges

answered Jul 12 at 17:29

M-MM-M

10.6k66 gold badges2727 silver badges5050 bronze badges

answered Jul 12 at 17:29

M-MM-M

10.6k66 gold badges2727 silver badges5050 bronze badges

1

Consider a straightforward aggregate after the subset call:

newdata <- subset(ChickWeight, Time >= 21, select=Diet)

aggregate(cbind(Obs=Diet) ~ Diet, newdata, FUN=length)

# Diet Obs
# 1 1 16
# 2 2 10
# 3 3 10
# 4 4 9

share|improve this answer

answered Jul 12 at 17:29

ParfaitParfait

59k1010 gold badges5555 silver badges7676 bronze badges

add a comment | 

1

Consider a straightforward aggregate after the subset call:

newdata <- subset(ChickWeight, Time >= 21, select=Diet)

aggregate(cbind(Obs=Diet) ~ Diet, newdata, FUN=length)

# Diet Obs
# 1 1 16
# 2 2 10
# 3 3 10
# 4 4 9

share|improve this answer

answered Jul 12 at 17:29

ParfaitParfait

59k1010 gold badges5555 silver badges7676 bronze badges

add a comment | 

Consider a straightforward aggregate after the subset call:

newdata <- subset(ChickWeight, Time >= 21, select=Diet)

aggregate(cbind(Obs=Diet) ~ Diet, newdata, FUN=length)

# Diet Obs
# 1 1 16
# 2 2 10
# 3 3 10
# 4 4 9

share|improve this answer

answered Jul 12 at 17:29

ParfaitParfait

59k1010 gold badges5555 silver badges7676 bronze badges

newdata <- subset(ChickWeight, Time >= 21, select=Diet)

aggregate(cbind(Obs=Diet) ~ Diet, newdata, FUN=length)

# Diet Obs
# 1 1 16
# 2 2 10
# 3 3 10
# 4 4 9

share|improve this answer

answered Jul 12 at 17:29

ParfaitParfait

59k1010 gold badges5555 silver badges7676 bronze badges

answered Jul 12 at 17:29

ParfaitParfait

59k1010 gold badges5555 silver badges7676 bronze badges

answered Jul 12 at 17:29

ParfaitParfait

59k1010 gold badges5555 silver badges7676 bronze badges

1

We can do this with summarize from dplyr:

library(dplyr)

newdata %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

We can even combine the subset to a single dplyr workflow:

ChickWeight %>%
 filter(Time >= 21) %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

Output:

# A tibble: 4 x 2
 Diet Num_Obs
 <fct> <int>
1 1 16
2 2 10
3 3 10
4 4 9

share|improve this answer

edited Jul 12 at 19:46

answered Jul 12 at 17:27

avid_useRavid_useR

14.2k44 gold badges2020 silver badges3333 bronze badges

add a comment | 

1

We can do this with summarize from dplyr:

library(dplyr)

newdata %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

We can even combine the subset to a single dplyr workflow:

ChickWeight %>%
 filter(Time >= 21) %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

Output:

# A tibble: 4 x 2
 Diet Num_Obs
 <fct> <int>
1 1 16
2 2 10
3 3 10
4 4 9

share|improve this answer

edited Jul 12 at 19:46

answered Jul 12 at 17:27

avid_useRavid_useR

14.2k44 gold badges2020 silver badges3333 bronze badges

add a comment | 

We can do this with summarize from dplyr:

library(dplyr)

newdata %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

We can even combine the subset to a single dplyr workflow:

ChickWeight %>%
 filter(Time >= 21) %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

Output:

# A tibble: 4 x 2
 Diet Num_Obs
 <fct> <int>
1 1 16
2 2 10
3 3 10
4 4 9

share|improve this answer

edited Jul 12 at 19:46

answered Jul 12 at 17:27

avid_useRavid_useR

14.2k44 gold badges2020 silver badges3333 bronze badges

library(dplyr)

newdata %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

ChickWeight %>%
 filter(Time >= 21) %>%
 group_by(Diet) %>%
 summarize(Num_Obs = n())

# A tibble: 4 x 2
 Diet Num_Obs
 <fct> <int>
1 1 16
2 2 10
3 3 10
4 4 9

share|improve this answer

edited Jul 12 at 19:46

answered Jul 12 at 17:27

avid_useRavid_useR

14.2k44 gold badges2020 silver badges3333 bronze badges

answered Jul 12 at 17:27

avid_useRavid_useR

14.2k44 gold badges2020 silver badges3333 bronze badges

answered Jul 12 at 17:27

avid_useRavid_useR

14.2k44 gold badges2020 silver badges3333 bronze badges

0

Here is a data table approach

library(data.table)
df <- as.data.table(ChickWeight)

df[Time >= 21, .(Number = .N), by = Diet]
# Diet Number
# 1: 1 16
# 2: 2 10
# 3: 3 10
# 4: 4 9

share|improve this answer

answered Jul 12 at 19:37

IceCreamToucanIceCreamToucan

13.3k11 gold badge88 silver badges1919 bronze badges

add a comment | 

0

Here is a data table approach

library(data.table)
df <- as.data.table(ChickWeight)

df[Time >= 21, .(Number = .N), by = Diet]
# Diet Number
# 1: 1 16
# 2: 2 10
# 3: 3 10
# 4: 4 9

share|improve this answer

answered Jul 12 at 19:37

IceCreamToucanIceCreamToucan

13.3k11 gold badge88 silver badges1919 bronze badges

add a comment | 

Here is a data table approach

library(data.table)
df <- as.data.table(ChickWeight)

df[Time >= 21, .(Number = .N), by = Diet]
# Diet Number
# 1: 1 16
# 2: 2 10
# 3: 3 10
# 4: 4 9

share|improve this answer

answered Jul 12 at 19:37

IceCreamToucanIceCreamToucan

13.3k11 gold badge88 silver badges1919 bronze badges

library(data.table)
df <- as.data.table(ChickWeight)

df[Time >= 21, .(Number = .N), by = Diet]
# Diet Number
# 1: 1 16
# 2: 2 10
# 3: 3 10
# 4: 4 9

share|improve this answer

answered Jul 12 at 19:37

IceCreamToucanIceCreamToucan

13.3k11 gold badge88 silver badges1919 bronze badges

answered Jul 12 at 19:37

IceCreamToucanIceCreamToucan

13.3k11 gold badge88 silver badges1919 bronze badges

answered Jul 12 at 19:37

IceCreamToucanIceCreamToucan

13.3k11 gold badge88 silver badges1919 bronze badges