I would like to extract a selection of rows from a data frame based on multiple identifying variables contained in another data frame. Consider the following illustrative data set:
df <- data.frame(id=c(1,2,2,3,4,4,4,4,5), ref=c("A","B","C","D","E","F","F","G","H"), amount=c(10,15,20,25,30,35,-35,40,45))
required <- data.frame(id=c(2,3,4,4), ref=c("B","D","E","F"))
I would like the output in a data frame with id, ref and amount as follows:
id ref amount
2 B 15
3 D 25
4 E 30
4 F 35
4 F -35
Note in particular that id 4 and ref F have two matches from the df with amounts 35 and -35.
You want to merge:
merge(df, required)
## id ref amount
## 1 2 B 15
## 2 3 D 25
## 3 4 E 30
## 4 4 F 35
## 5 4 F -35
Related
there is data.frame df, i want to arrange (sort) it by index column according the factor levels.
the result as "the wished data.frame". anyone can help ？ thanks!
#create data frame
df<-data.frame(index=c("b","a","e"),amount=c(10,76,60))
df$index<-factor(df$index,levels=c("a","b","e"))
# current df
index amount
1 b 10
2 a 76
3 e 60
# the wished data.frame
index amount
1 a 76
2 b 10
3 e 60
Like this?
arrange(df, match(df$index, levels(df$index)))
index amount
1 a 76
2 b 10
3 e 60
Data
df<-data.frame(index=c("b","a","e"),amount=c(10,76,60))
df$index<-factor(df$index,levels=c("b","e","a"))
You can use order :
df[order(df$index), ]
# index amount
#2 a 76
#1 b 10
#3 e 60
How does one go about switching a data frame based on column names between to tables with a lookup table in between.
Orig
A B C
1 2 3
2 2 2
4 5 6
Ret
D E
7 8
8 9
2 4
lookup <- data.frame(Orig=c('A','B','C'),Ret=c('D','D','E'))
Orig Ret
1 A D
2 B D
3 C E
So that the final data frame would be
A B C
7 7 8
8 8 9
2 2 4
We can match the 'Orig' column in 'lookup' with the column names of 'Orig' to find the numeric index (although, it is in the same order, it could be different in other cases), get the corresponding 'Ret' elements based on that. We use that to subset the 'Ret' dataset and assign the output back to the original dataset. Here I made a copy of "Orig".
OrigN <- Orig
OrigN[] <- Ret[as.character(lookup$Ret[match(as.character(lookup$Orig),
colnames(Orig))])]
OrigN
# A B C
#1 7 7 8
#2 8 8 9
#3 2 2 4
NOTE: as.character was used as the columns in 'lookup' were factor class.
I believe that the following will work as well.
OrigN <- Orig
OrigN[, as.character(lookup$Orig)] <- Ret[, as.character(lookup$Ret)]
This method applies a column shuffle to Orig (actually a copy OrigN following #Akrun) and then fills these columns with the appropriately ordered columns of Ret using the lookup.
This question already has an answer here:
R programming - data frame manoevur
(1 answer)
Closed 7 years ago.
Suppose I have the following dataframe:
Categories Variable
1 a 11
2 b 21
3 c 34
4 d 45
5 e 52
6 f 65
7 g 76
8 a 13
9 b 24
I'd like to turn it into a new dataframe like the following:
Categories Variable
1 a 11
2 b 21
3 c 34
4 d+e 97
5 f 65
6 g 76
7 a 13
8 b 24
How can I do it? (Surely, the dataframe is much larger, but I want the sum of all categories of d and e and group it into a new category, say 'H').
Many thanks!
This is a good question but unfortunately OT here. So I'll answer until it get migrated.
I'm assuming Variable is of class factor, so you'll need to properly re-level it (assuming your data is called df)
levels(df$Categories)[levels(df$Categories) %in% c("d", "e")] <- "h"
Next, I'll use the data.table package as you have a large data set and it's devel version (v >= 1.9.5) has a convinient function called rleid (download from GitHub)
library(data.table) ## v >= 1.9.5
setDT(df)[, .(Variable = sum(Variable)), by = .(indx = rleid(Categories), Categories)]
# indx Categories Variable
# 1: 1 a 11
# 2: 2 b 21
# 3: 3 c 34
# 4: 4 h 97
# 5: 5 f 65
# 6: 6 g 76
# 7: 7 a 13
# 8: 8 b 24
You can try this:
# plyr package provides rbind.fill() function for row binding
library(plyr)
# Assuming you have a rows.cvs containing the data, read it into a data frame
data<-read.csv("rows.csv",stringsAsFactors=FALSE)
# Find the lowest index of d or e (whichever comes first)
index<-min(match("d",data$Var1.nominal.), match("e",data$Var1.nominal.))
# Returns all rows containing d and e in Var1(nominal) column
tempData<-data[data$Var1.nominal. %in% c("d","e"),]
# Remove all the rows containing d and e from original data frame
data<-data[!data$Var1.nominal. %in% c("d","e"),]
# Reorder row index numbers in data
rownames(data)<-NULL
# Combine rows containing d and e in Var1(nominal)column, and sum up the column Var2(numeric)
tempData<-data.frame(Var1.nominal.="d+e",Var2.numeric.=sum(tempData[,2]))
# Combine original data and tempData frame with use of index
data<-rbind.fill(data[1:(index-1),],tempData,data[index:length(data[,1]),])
# Renaming "d+e" to"h"
data[index,1]="h"
# Getting rid of the tempData data frame
rm(tempData)
Output:
> data
Var1.nominal. Var2.numeric.
1 a 11
2 b 21
3 c 34
4 h 97
5 f 65
6 g 76
7 a 13
8 b 24
I have a dataframe and it has four columns. Now I want to take a subset of this dataframe according to an integer vector. I tried to use subset and looked at other posts in vain.
b=c('p','q','r','s','t','u')
a=c('at','bt','ct','dt','et','ft')
d=c(22,23,24,25,26,27)
e=c(1,2,3,4,5,6)
dat=data.frame(b,a,d,e)
dat
b a d e
1 p at 22 1
2 q bt 23 2
3 r ct 24 3
4 s dt 25 4
5 t et 26 5
6 u ft 27 6
test=c(2,5)
Now I want to select all the rows (keeping all the columns too) that are in test that is 2nd and 5th rows and keep all other columns.
Given your definitions of dat and test,
dat[test,]
# b a d e
# 2 q bt 23 2
# 5 t et 26 5
or
dat[dat$e %in% test,]
# b a d e
# 2 q bt 23 2
# 5 t et 26 5
The first approach just treats the elements of test as row numbers of dat. The second extracts all rows of dat for which dat$e is in test.
I am stumped again.
I have two data frames
dataframe1
a b c
[1] 21 12 22
[2] 11 9 6
[3] 4 6 7
and
dataframe2
f g h
[1] 21 12 22
[2] 11 9 6
[3] 4 6 7
I want to take the first column of dataframe1 and make three new dataframes with the second column being each of the three f,g and h
Obviously I could just do a subset over and over
subset1 <- cbind(dataframe1[,1]dataframe2[,1])
subset2 <- cbind(dataframe1[,1]dataframe2[,2])
but my dataframes will have variable numbers of columns and are very long row numberwise. So I am looking for a little more something general. My data frames will always be the same length.
The closest I have come to getting anything was with apply and cbind but I got either a set of three rows that were a and f, a and g, a and h each combined as single numeric vector or I get a single data frame with four columns, a,f,g,h.
Help is deeply appreciated.
You can use lapply it iterate over the columns of dataframe2 like so:
lapply(dataframe2, function(x) as.data.frame(cbind(dataframe1[,1], x)))
This will result in a list object where each entry corresponds to a column of dataframe2. For example:
$f
V1 x
1 21 21
2 11 11
3 4 4
$g
V1 x
1 21 12
2 11 9
3 4 6
$h
V1 x
1 21 22
2 11 6
3 4 7