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How do I get count of number of items in selection?

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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 8 hours ago

M-M

10.3k66 gold badges2626 silver badges4949 bronze badges

asked 9 hours ago

MetsfanMetsfan

4144 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
  9 hours ago
  
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  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
  9 hours ago
  
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Please post your desired output in the question body itself
  
  – avid_useR
  9 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  So basically you want to get the obs count for each Diet group?
  
  – avid_useR
  8 hours ago
  

  
  
  
  

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 8 hours ago

M-M

10.3k66 gold badges2626 silver badges4949 bronze badges

asked 9 hours ago

MetsfanMetsfan

4144 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
  9 hours ago
  
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  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
  9 hours ago
  
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Please post your desired output in the question body itself
  
  – avid_useR
  9 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  So basically you want to get the obs count for each Diet group?
  
  – avid_useR
  8 hours ago
  

  
  
  
  

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 8 hours ago

M-M

10.3k66 gold badges2626 silver badges4949 bronze badges

asked 9 hours ago

MetsfanMetsfan

4144 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 8 hours ago

M-M

10.3k66 gold badges2626 silver badges4949 bronze badges

asked 9 hours ago

MetsfanMetsfan

4144 bronze badges

share|improve this question

edited 8 hours ago

M-M

10.3k66 gold badges2626 silver badges4949 bronze badges

asked 9 hours ago

MetsfanMetsfan

4144 bronze badges

edited 8 hours ago

M-M

10.3k66 gold badges2626 silver badges4949 bronze badges

edited 8 hours ago

M-M

10.3k66 gold badges2626 silver badges4949 bronze badges

asked 9 hours ago

MetsfanMetsfan

4144 bronze badges

asked 9 hours ago

MetsfanMetsfan

4144 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 8 hours ago

M-MM-M

10.3k66 gold badges2626 silver badges4949 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  M-M, thanks. It works. What is the purpose of "table"? Why is it needed?
  
  – Metsfan
  8 hours ago
  
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @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
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  When I ran it without transform I got this error: "Error in subset.data.frame(ChickWeight, select = Diet, weight) : 'subset' must be logical"
  
  – Metsfan
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @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
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Okay, now it worked. I noticed that it converted the rows into columns. Interesting. Thanks again.
  
  – Metsfan
  8 hours ago
  

  
  
  
  

 | 
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 8 hours ago

ParfaitParfait

58.5k1010 gold badges5555 silver badges7575 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 6 hours ago

answered 8 hours ago

avid_useRavid_useR

14.1k44 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 6 hours ago

IceCreamToucanIceCreamToucan

13.1k11 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 8 hours ago

M-MM-M

10.3k66 gold badges2626 silver badges4949 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  M-M, thanks. It works. What is the purpose of "table"? Why is it needed?
  
  – Metsfan
  8 hours ago
  
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @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
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  When I ran it without transform I got this error: "Error in subset.data.frame(ChickWeight, select = Diet, weight) : 'subset' must be logical"
  
  – Metsfan
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @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
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Okay, now it worked. I noticed that it converted the rows into columns. Interesting. Thanks again.
  
  – Metsfan
  8 hours ago
  

  
  
  
  

 | 
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 8 hours ago

M-MM-M

10.3k66 gold badges2626 silver badges4949 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  M-M, thanks. It works. What is the purpose of "table"? Why is it needed?
  
  – Metsfan
  8 hours ago
  
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @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
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  When I ran it without transform I got this error: "Error in subset.data.frame(ChickWeight, select = Diet, weight) : 'subset' must be logical"
  
  – Metsfan
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @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
  8 hours ago
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Okay, now it worked. I noticed that it converted the rows into columns. Interesting. Thanks again.
  
  – Metsfan
  8 hours ago
  

  
  
  
  

 | 
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 8 hours ago

M-MM-M

10.3k66 gold badges2626 silver badges4949 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 8 hours ago

M-MM-M

10.3k66 gold badges2626 silver badges4949 bronze badges

answered 8 hours ago

M-MM-M

10.3k66 gold badges2626 silver badges4949 bronze badges

answered 8 hours ago

M-MM-M

10.3k66 gold badges2626 silver badges4949 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 8 hours ago

ParfaitParfait

58.5k1010 gold badges5555 silver badges7575 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 8 hours ago

ParfaitParfait

58.5k1010 gold badges5555 silver badges7575 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 8 hours ago

ParfaitParfait

58.5k1010 gold badges5555 silver badges7575 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 8 hours ago

ParfaitParfait

58.5k1010 gold badges5555 silver badges7575 bronze badges

answered 8 hours ago

ParfaitParfait

58.5k1010 gold badges5555 silver badges7575 bronze badges

answered 8 hours ago

ParfaitParfait

58.5k1010 gold badges5555 silver badges7575 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 6 hours ago

answered 8 hours ago

avid_useRavid_useR

14.1k44 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 6 hours ago

answered 8 hours ago

avid_useRavid_useR

14.1k44 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 6 hours ago

answered 8 hours ago

avid_useRavid_useR

14.1k44 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 6 hours ago

answered 8 hours ago

avid_useRavid_useR

14.1k44 gold badges2020 silver badges3333 bronze badges

answered 8 hours ago

avid_useRavid_useR

14.1k44 gold badges2020 silver badges3333 bronze badges

answered 8 hours ago

avid_useRavid_useR

14.1k44 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 6 hours ago

IceCreamToucanIceCreamToucan

13.1k11 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 6 hours ago

IceCreamToucanIceCreamToucan

13.1k11 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 6 hours ago

IceCreamToucanIceCreamToucan

13.1k11 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 6 hours ago

IceCreamToucanIceCreamToucan

13.1k11 gold badge88 silver badges1919 bronze badges

answered 6 hours ago

IceCreamToucanIceCreamToucan

13.1k11 gold badge88 silver badges1919 bronze badges

answered 6 hours ago

IceCreamToucanIceCreamToucan

13.1k11 gold badge88 silver badges1919 bronze badges