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Sum column based on conditions in another column in a data frame

Drop factor levels in a subsetted data frameHow to join (merge) data frames (inner, outer, left, right)Convert a list of data frames into one data frameR - list to data frameDrop data frame columns by nameHow to drop columns by name in a data frameChanging column names of a data frameExtracting specific columns from a data frameSum of hybrid data frames depending on multiple conditions in RSelect rows from a DataFrame based on values in a column in pandas

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1

0

I have a data frame, which contains two columns, Time and Response

df = cbind.data.frame(
 Time = c(1, 1.2, 1.9, 2.2, 2.9, 3.1, 3.2, 3.2, 3.2, 3.6, 3.9, 4, 5.1, 5.99), 
 Response = c(1, 1, 1, 2, 3, 3, 3, 4, 3.5, 3.6, 3.3, 6, 11, 13)
 )

I want to transform it by summing the Response within the same minute (Time) . [1-2), [2-3), [3-4), [4-5), and [5 and above].

The expected data frame will be

dfe = cbind.data.frame(
 time.range = c(1, 2, 3, 4, 5), 
 Response = c(3, 5, 19.4, 6, 24)
 )

r dataframe

share|improve this question

asked Mar 28 at 10:29

WangWang

21611 silver badge99 bronze badges

add a comment
 | 

1

0

I have a data frame, which contains two columns, Time and Response

df = cbind.data.frame(
 Time = c(1, 1.2, 1.9, 2.2, 2.9, 3.1, 3.2, 3.2, 3.2, 3.6, 3.9, 4, 5.1, 5.99), 
 Response = c(1, 1, 1, 2, 3, 3, 3, 4, 3.5, 3.6, 3.3, 6, 11, 13)
 )

I want to transform it by summing the Response within the same minute (Time) . [1-2), [2-3), [3-4), [4-5), and [5 and above].

The expected data frame will be

dfe = cbind.data.frame(
 time.range = c(1, 2, 3, 4, 5), 
 Response = c(3, 5, 19.4, 6, 24)
 )

r dataframe

share|improve this question

asked Mar 28 at 10:29

WangWang

21611 silver badge99 bronze badges

add a comment
 | 

I have a data frame, which contains two columns, Time and Response

df = cbind.data.frame(
 Time = c(1, 1.2, 1.9, 2.2, 2.9, 3.1, 3.2, 3.2, 3.2, 3.6, 3.9, 4, 5.1, 5.99), 
 Response = c(1, 1, 1, 2, 3, 3, 3, 4, 3.5, 3.6, 3.3, 6, 11, 13)
 )

I want to transform it by summing the Response within the same minute (Time) . [1-2), [2-3), [3-4), [4-5), and [5 and above].

The expected data frame will be

dfe = cbind.data.frame(
 time.range = c(1, 2, 3, 4, 5), 
 Response = c(3, 5, 19.4, 6, 24)
 )

r dataframe

share|improve this question

asked Mar 28 at 10:29

WangWang

21611 silver badge99 bronze badges

df = cbind.data.frame(
 Time = c(1, 1.2, 1.9, 2.2, 2.9, 3.1, 3.2, 3.2, 3.2, 3.6, 3.9, 4, 5.1, 5.99), 
 Response = c(1, 1, 1, 2, 3, 3, 3, 4, 3.5, 3.6, 3.3, 6, 11, 13)
 )

dfe = cbind.data.frame(
 time.range = c(1, 2, 3, 4, 5), 
 Response = c(3, 5, 19.4, 6, 24)
 )

share|improve this question

asked Mar 28 at 10:29

WangWang

21611 silver badge99 bronze badges

share|improve this question

asked Mar 28 at 10:29

WangWang

21611 silver badge99 bronze badges

asked Mar 28 at 10:29

WangWang

21611 silver badge99 bronze badges

asked Mar 28 at 10:29

WangWang

21611 silver badge99 bronze badges

2 Answers
2

active

oldest

votes

6

We can use floor to group it for every minute

library(dplyr)

df %>%
 group_by(minute = floor(Time)) %>%
 summarise(Response = sum(Response))

# minute Response
# <dbl> <dbl>
#1 1 3 
#2 2 5 
#3 3 20.4
#4 4 6 
#5 5 24 

---

Using aggregate in base R

aggregate(Response~floor(Time), df, sum)

Also with tapply

tapply(df$Response, floor(df$Time), sum)

---

And for completion data.table option

library(data.table)
setDT(df)[,sum(Response), by = floor(Time)]

share|improve this answer

edited Mar 28 at 10:42

answered Mar 28 at 10:32

Ronak ShahRonak Shah

82k1313 gold badges5050 silver badges8787 bronze badges

add a comment
 | 

1

We can use rowsum from base R

rowsum(df$Response, as.integer(df$Time))
# [,1]
#1 3.0
#2 5.0
#3 20.4
#4 6.0
#5 24.0

share|improve this answer

answered Mar 28 at 17:02

akrunakrun

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6

We can use floor to group it for every minute

library(dplyr)

df %>%
 group_by(minute = floor(Time)) %>%
 summarise(Response = sum(Response))

# minute Response
# <dbl> <dbl>
#1 1 3 
#2 2 5 
#3 3 20.4
#4 4 6 
#5 5 24 

---

Using aggregate in base R

aggregate(Response~floor(Time), df, sum)

Also with tapply

tapply(df$Response, floor(df$Time), sum)

---

And for completion data.table option

library(data.table)
setDT(df)[,sum(Response), by = floor(Time)]

share|improve this answer

edited Mar 28 at 10:42

answered Mar 28 at 10:32

Ronak ShahRonak Shah

82k1313 gold badges5050 silver badges8787 bronze badges

add a comment
 | 

6

We can use floor to group it for every minute

library(dplyr)

df %>%
 group_by(minute = floor(Time)) %>%
 summarise(Response = sum(Response))

# minute Response
# <dbl> <dbl>
#1 1 3 
#2 2 5 
#3 3 20.4
#4 4 6 
#5 5 24 

---

Using aggregate in base R

aggregate(Response~floor(Time), df, sum)

Also with tapply

tapply(df$Response, floor(df$Time), sum)

---

And for completion data.table option

library(data.table)
setDT(df)[,sum(Response), by = floor(Time)]

share|improve this answer

edited Mar 28 at 10:42

answered Mar 28 at 10:32

Ronak ShahRonak Shah

82k1313 gold badges5050 silver badges8787 bronze badges

add a comment
 | 

We can use floor to group it for every minute

library(dplyr)

df %>%
 group_by(minute = floor(Time)) %>%
 summarise(Response = sum(Response))

# minute Response
# <dbl> <dbl>
#1 1 3 
#2 2 5 
#3 3 20.4
#4 4 6 
#5 5 24 

---

Using aggregate in base R

aggregate(Response~floor(Time), df, sum)

Also with tapply

tapply(df$Response, floor(df$Time), sum)

---

And for completion data.table option

library(data.table)
setDT(df)[,sum(Response), by = floor(Time)]

share|improve this answer

edited Mar 28 at 10:42

answered Mar 28 at 10:32

Ronak ShahRonak Shah

82k1313 gold badges5050 silver badges8787 bronze badges

library(dplyr)

df %>%
 group_by(minute = floor(Time)) %>%
 summarise(Response = sum(Response))

# minute Response
# <dbl> <dbl>
#1 1 3 
#2 2 5 
#3 3 20.4
#4 4 6 
#5 5 24 

aggregate(Response~floor(Time), df, sum)

tapply(df$Response, floor(df$Time), sum)

library(data.table)
setDT(df)[,sum(Response), by = floor(Time)]

share|improve this answer

edited Mar 28 at 10:42

answered Mar 28 at 10:32

Ronak ShahRonak Shah

82k1313 gold badges5050 silver badges8787 bronze badges

answered Mar 28 at 10:32

Ronak ShahRonak Shah

82k1313 gold badges5050 silver badges8787 bronze badges

answered Mar 28 at 10:32

Ronak ShahRonak Shah

82k1313 gold badges5050 silver badges8787 bronze badges

1

We can use rowsum from base R

rowsum(df$Response, as.integer(df$Time))
# [,1]
#1 3.0
#2 5.0
#3 20.4
#4 6.0
#5 24.0

share|improve this answer

answered Mar 28 at 17:02

akrunakrun

466k1515 gold badges261261 silver badges343343 bronze badges

add a comment
 | 

1

We can use rowsum from base R

rowsum(df$Response, as.integer(df$Time))
# [,1]
#1 3.0
#2 5.0
#3 20.4
#4 6.0
#5 24.0

share|improve this answer

answered Mar 28 at 17:02

akrunakrun

466k1515 gold badges261261 silver badges343343 bronze badges

add a comment
 | 

We can use rowsum from base R

rowsum(df$Response, as.integer(df$Time))
# [,1]
#1 3.0
#2 5.0
#3 20.4
#4 6.0
#5 24.0

share|improve this answer

answered Mar 28 at 17:02

akrunakrun

466k1515 gold badges261261 silver badges343343 bronze badges

rowsum(df$Response, as.integer(df$Time))
# [,1]
#1 3.0
#2 5.0
#3 20.4
#4 6.0
#5 24.0

share|improve this answer

answered Mar 28 at 17:02

akrunakrun

466k1515 gold badges261261 silver badges343343 bronze badges

answered Mar 28 at 17:02

akrunakrun

466k1515 gold badges261261 silver badges343343 bronze badges

answered Mar 28 at 17:02

akrunakrun

466k1515 gold badges261261 silver badges343343 bronze badges