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Samenvoegen met base R

Hiervoor gebruiken we de functie-aggregatie, die als volgt kan worden gebruikt:

aggregate(formula,function,data)

De volgende code toont verschillende manieren om de aggregatiefunctie te gebruiken.

CODE:

df = data.frame(group=c("Group 1","Group 1","Group 2","Group 2","Group 2"), subgroup = c("A","A","A","A","B"),value = c(2,2.5,1,2,1.5))

# sum, grouping by one column
aggregate(value~group, FUN=sum, data=df)

# mean, grouping by one column
aggregate(value~group, FUN=mean, data=df)

# sum, grouping by multiple columns
aggregate(value~group+subgroup,FUN=sum,data=df)

# custom function, grouping by one column
# in this example we want the sum of all values larger than 2 per group.
aggregate(value~group, FUN=function(x) sum(x[x>2]), data=df)

OUTPUT:

> df = data.frame(group=c("Group 1","Group 1","Group 2","Group 2","Group 2"), subgroup = c("A","A","A","A","B"),value = c(2,2.5,1,2,1.5))
> print(df)
    group subgroup value
1 Group 1        A   2.0
2 Group 1        A   2.5
3 Group 2        A   1.0
4 Group 2        A   2.0
5 Group 2        B   1.5
> 
> # sum, grouping by one column
> aggregate(value~group, FUN=sum, data=df)
    group value
1 Group 1   4.5
2 Group 2   4.5
> 
> # mean, grouping by one column
> aggregate(value~group, FUN=mean, data=df)
    group value
1 Group 1  2.25
2 Group 2  1.50
> 
> # sum, grouping by multiple columns
> aggregate(value~group+subgroup,FUN=sum,data=df)
    group subgroup value
1 Group 1        A   4.5
2 Group 2        A   3.0
3 Group 2        B   1.5
> 
> # custom function, grouping by one column
> # in this example we want the sum of all values larger than 2 per group.
> aggregate(value~group, FUN=function(x) sum(x[x>2]), data=df)
    group value
1 Group 1   2.5
2 Group 2   0.0

Samenvoegen met dplyr

Aggregeren met dplyr is eenvoudig! U kunt hiervoor de functies group_by () en summize () gebruiken. Enkele voorbeelden worden hieronder gegeven.

CODE:

# Aggregating with dplyr
library(dplyr)

df = data.frame(group=c("Group 1","Group 1","Group 2","Group 2","Group 2"), subgroup = c("A","A","A","A","B"),value = c(2,2.5,1,2,1.5))
print(df)

# sum, grouping by one column
df %>% group_by(group) %>% summarize(value = sum(value)) %>% as.data.frame()

# mean, grouping by one column
df %>% group_by(group) %>% summarize(value = mean(value)) %>% as.data.frame()

# sum, grouping by multiple columns
df %>% group_by(group,subgroup) %>% summarize(value = sum(value)) %>% as.data.frame()

# custom function, grouping by one column
# in this example we want the sum of all values larger than 2 per group.
df %>% group_by(group) %>% summarize(value = sum(value[value>2])) %>% as.data.frame()

OUTPUT:

> library(dplyr)
> 
> df = data.frame(group=c("Group 1","Group 1","Group 2","Group 2","Group 2"), subgroup = c("A","A","A","A","B"),value = c(2,2.5,1,2,1.5))
> print(df)
    group subgroup value
1 Group 1        A   2.0
2 Group 1        A   2.5
3 Group 2        A   1.0
4 Group 2        A   2.0
5 Group 2        B   1.5
> 
> # sum, grouping by one column
> df %>% group_by(group) %>% summarize(value = sum(value)) %>% as.data.frame()
    group value
1 Group 1   4.5
2 Group 2   4.5
> 
> # mean, grouping by one column
> df %>% group_by(group) %>% summarize(value = mean(value)) %>% as.data.frame()
    group value
1 Group 1  2.25
2 Group 2  1.50
> 
> # sum, grouping by multiple columns
> df %>% group_by(group,subgroup) %>% summarize(value = sum(value)) %>% as.data.frame()
    group subgroup value
1 Group 1        A   4.5
2 Group 2        A   3.0
3 Group 2        B   1.5
> 
> # custom function, grouping by one column
> # in this example we want the sum of all values larger than 2 per group.
> df %>% group_by(group) %>% summarize(value = sum(value[value>2])) %>% as.data.frame()
    group value
1 Group 1   2.5
2 Group 2   0.0

Aggregeren met data.table

Groeperen met het data.table pakket wordt gedaan met behulp van de syntaxis dt[i, j, by] Deze kan luid worden voorgelezen als: " Neem dt, deel rijen in met i, bereken dan j, gegroepeerd op door. " Binnen de dt-instructie , meerdere berekeningen of groepen moeten in een lijst worden geplaatst. Aangezien een alias voor list() .() list() Is, kunnen beide door elkaar worden gebruikt. In de onderstaande voorbeelden gebruiken we .() .

CODE:

# Aggregating with data.table
library(data.table)

dt = data.table(group=c("Group 1","Group 1","Group 2","Group 2","Group 2"), subgroup = c("A","A","A","A","B"),value = c(2,2.5,1,2,1.5))
print(dt)

# sum, grouping by one column
dt[,.(value=sum(value)),group]

# mean, grouping by one column
dt[,.(value=mean(value)),group]

# sum, grouping by multiple columns
dt[,.(value=sum(value)),.(group,subgroup)]

# custom function, grouping by one column
# in this example we want the sum of all values larger than 2 per group.
dt[,.(value=sum(value[value>2])),group]

OUTPUT:

> # Aggregating with data.table
> library(data.table)
> 
> dt = data.table(group=c("Group 1","Group 1","Group 2","Group 2","Group 2"), subgroup = c("A","A","A","A","B"),value = c(2,2.5,1,2,1.5))
> print(dt)
     group subgroup value
1: Group 1        A   2.0
2: Group 1        A   2.5
3: Group 2        A   1.0
4: Group 2        A   2.0
5: Group 2        B   1.5
> 
> # sum, grouping by one column
> dt[,.(value=sum(value)),group]
     group value
1: Group 1   4.5
2: Group 2   4.5
> 
> # mean, grouping by one column
> dt[,.(value=mean(value)),group]
     group value
1: Group 1  2.25
2: Group 2  1.50
> 
> # sum, grouping by multiple columns
> dt[,.(value=sum(value)),.(group,subgroup)]
     group subgroup value
1: Group 1        A   4.5
2: Group 2        A   3.0
3: Group 2        B   1.5
> 
> # custom function, grouping by one column
> # in this example we want the sum of all values larger than 2 per group.
> dt[,.(value=sum(value[value>2])),group]
     group value
1: Group 1   2.5
2: Group 2   0.0