I have two data frames.
The first describes a set of households:
#df1
street house etc
1 1 ...
1 2 ...
2 1 ...
2 2 ...
2 3 ...
3 1 ...
The second describes the individuals who live in those houses
#df2
street house person etc
1 1 1 ...
1 1 2 ...
1 2 1 ...
1 2 2 ...
1 2 3 ...
3 1 1 ...
I would like to add a new column to df1 called "member_count" and populate this column with the number of rows in df2 matching both "street" and "house". What is the most readable way of accomplishing this with base R?
tmpdf <- data.frame(table(df2$street, df2$house))
names(tmpdf) <- c("street", "house", "member_count")
df1 <- merge(df1, tmpdf, by = c("street", "house"), all.x = TRUE)
In base R, perhaps the easiest way is
df1$membercount <- mapply(function(s,h) nrow(df2[df2$street==s & df2$house==h,]),
df1$street,df1$house)
df1
street house membercount
1 1 1 2
2 1 2 3
3 2 1 0
4 2 2 0
5 2 3 0
6 3 1 1
Related
I need to subset the columns of a dataframe taking into account the rownames of another dataframe.(in R)
Im trying to select the representative species of Brazilian Amazon subseting a great Brazilian database taking into account the percentage of representative location, information which is in another dataframe
> a <- data.frame("John" = c(2,1,1,2), "Dora" = c(1,1,3,2), "camilo" = c(1:4),"alex"=c(1,2,1,2))
> a
John Dora camilo alex
1 2 1 1 1
2 1 1 2 2
3 1 3 3 1
4 2 2 4 2
> b <- data.frame("SN" = 1:3, "Age" = c(15,31,2), "Name" = c("John","Dora","alex"))
> b
SN Age Name
1 1 15 John
2 2 31 Dora
3 3 2 alex
> result <- a[,rownames(b)[1:3]]
Error in `[.data.frame`(a, , rownames(b)[1:3]) :
undefined columns selected
I want to get this dataframe
John Dora alex
1 2 1 1
2 1 1 2
3 1 3 1
4 2 2 2
The simple a[,b$Name] does not work because b$Name is considered a factor. Be careful because it won't throw an error but you will get the wrong answer!
But this is easy to fit by using a[,as.character(b$Name)]instead!
There is a injury score called ISS score
I have a table of injury data in rows according to pt ID.
I would like to obtain the top three values for the 6 injury columns.
Column values range from 0-5.
pt_id head face abdo pelvis Extremity External
1 4 0 0 1 0 3
2 3 3 5 0 3 2
3 0 0 2 1 1 1
4 2 0 0 0 0 1
5 5 0 0 2 0 1
My output for the above example would be
pt-id n1 n2 n3
1 4 3 1
2 5 3 3
3 2 1 1
4 2 1 0
5 5 2 1
values can be in a list or in new columns as calculating the score is simple from that point on.
I had thought that I would be able to create a list for the 6 injury columns and then apply a sort to each list taking the top three values. My code for that was:
ais$ais_list <- setNames(split(ais[,2:7], seq(nrow(ais))), rownames(ais))
But I struggled to apply the sort to the lists within the data frame as unfortunately some of the data in my data set includes NA values
We could use apply row-wise and sort the dataframe and take only first three values in each row.
cbind(df[1], t(apply(df[-1], 1, sort, decreasing = TRUE)[1:3, ]))
# pt_id 1 2 3
#1 1 4 3 1
#2 2 5 3 3
#3 3 2 1 1
#4 4 2 1 0
#5 5 5 2 1
As some values may contain NA it is better we apply sort using anonymous function and then take take top 3 values using head.
cbind(df[1], t(apply(df[-1], 1, function(x) head(sort(x, decreasing = TRUE), 3))))
A tidyverse option is to first gather the data, arrange it in descending order and for every row select only first three values. We then replace the injury column with the column names which we want and finally spread the data back to wide format.
library(tidyverse)
df %>%
gather(injury, value, -pt_id) %>%
arrange(desc(value)) %>%
group_by(pt_id) %>%
slice(1:3) %>%
mutate(injury = 1:3) %>%
spread(injury, value)
# pt_id `1` `2` `3`
# <int> <int> <int> <int>
#1 1 4 3 1
#2 2 5 3 3
#3 3 2 1 1
#4 4 2 1 0
#5 5 5 2 1
I'd like to add rows to a dataframe based on a vector within the dataframe. Here are the dataframes (df2 is the one I'd like to add rows to; df1 is the one I'd like to take the rows from):
ID=c(1:5)
x=c(rep("a",3),rep("b",2))
y=c(rep(0,5))
df1=data.frame(ID,x,y)
df2=df1[2:4,1:2]
df2$y=c(5,2,3)
df1
ID x y
1 1 a 0
2 2 a 0
3 3 a 0
4 4 b 0
5 5 b 0
df2
ID x y
2 2 a 5
3 3 a 2
4 4 b 3
I'd like to add to df2 any rows that aren't in df1, based on the ID vector. so my output dataframe would look like this:
ID x y
1 b 0
5 b 0
2 a 5
3 a 2
4 b 3
Can anyone see a way of doing this neatly, please? I need to do it for a lot of dataframes, all with different numbers of rows. I've tried using merge or rbind but I haven't been able to work out how to do it based on the vector.
Thank you!
A solution with dplyr:
bind_rows(df2,anti_join(df1,df2,by="ID"))
# ID x y
#1 2 a 5
#2 3 a 2
#3 4 b 3
#4 1 a 0
#5 5 b 0
You can do the following:
missingIDs <- which(!df1$ID %in% df2$ID) #check which df1 ID's are not in df2, see function is.element()
df.toadd <- df1[missingIDs,] #define the data frame to add to df2
result <- rbind(df.toadd, df2) #use rbind to add it
result
ID x y
1 1 a 0
5 5 b 0
2 2 a 5
3 3 a 2
4 4 b 3
What about this one-liner?
rbind(df2, df1[!df1$ID %in% df2$ID,])
ID x y
2 2 a 5
3 3 a 2
4 4 b 3
1 1 a 0
5 5 b 0
I was writing a loop with if function in R. The table is like below:
ID category
1 a
1 b
1 c
2 a
2 b
3 a
3 b
4 a
5 a
I want to use the for loop with if function to add another column to count each grouped ID, like below count column:
ID category Count
1 a 1
1 b 2
1 c 3
2 a 1
2 b 2
3 a 1
3 b 2
4 a 1
5 a 1
My code is (output is the table name):
for (i in 2:nrow(output1)){
if(output1[i,1] == output[i-1,1]){
output1[i,"rn"]<- output1[i-1,"rn"]+1
}
else{
output1[i,"rn"]<-1
}
}
But the result returns as all count column values are all "1".
ID category Count
1 a 1
1 b 1
1 c 1
2 a 1
2 b 1
3 a 1
3 b 1
4 a 1
5 a 1
Please help me out... Thanks
There are packages and vectorized ways to do this task, but if you are practicing with loops try:
output1$rn <- 1
for (i in 2:nrow(output1)){
if(output1[i,1] == output1[i-1,1]){
output1[i,"rn"]<- output1[i-1,"rn"]+1
}
else{
output1[i,"rn"]<-1
}
}
With your original code, when you made this call output1[i-1,"rn"]+1 in the third line of your loop, you were referencing a row that didn't exist on the first pass. By first creating the row and filling it with the value 1, you give the loop something explicit to refer to.
output1
# ID category rn
# 1 1 a 1
# 2 1 b 2
# 3 1 c 3
# 4 2 a 1
# 5 2 b 2
# 6 3 a 1
# 7 3 b 2
# 8 4 a 1
# 9 5 a 1
With the package dplyr you can accomplish it quickly with:
library(dplyr)
output1 %>% group_by(ID) %>% mutate(rn = 1:n())
Or with data.table:
library(data.table)
setDT(output1)[,rn := 1:.N, by=ID]
With base R you can also use:
output1$rn <- with(output1, ave(as.character(category), ID, FUN=seq))
There are vignettes and tutorials on the two packages mentioned, and by searching ?ave in the R console for the last approach.
looping solution will be painfully slow for bigger data. Here is one line solution using data.table:
require(data.table)
a<-data.table(ID=c(1,1,1,2,2,3,3,4,5),category=c('a','b','c','a','b','a','b','a','a'))
a[,':='(category_count = 1:.N),by=.(ID)]
what you want is actually a column of factor level. do this
df$count=as.numeric(df$category)
this will give out put as
ID category count
1 1 a 1
2 1 b 2
3 1 c 3
4 2 a 1
5 2 b 2
6 3 a 1
7 3 b 2
8 4 a 1
9 5 a 1
provided your category is already a factor. if not first convert to factor
df$category=as.factor(df$category)
df$count=as.numeric(df$category)
Im am trying to figure out how to subset my dataset according to the repeated value of the variable s, taking also into account the id associated to the row.
Suppose my dataset is:
dat <- read.table(text = "
id s
1 2
1 2
1 1
1 3
1 3
1 3
2 3
2 3
3 2
3 2",
header=TRUE)
What I would like to do is, for each id, to keep only the first row for which s = 3. The result with dat would be:
id s
1 2
1 2
1 1
1 3
2 3
3 2
3 2
I have tried to use both duplicated() and which() for using subset() in a second moment, but I am not going anywhere. The main problem is that it is not sufficient to isolate the first row of the s = 3 "blocks", because in some cases (as here between id = 1 and id = 2) the 3's overlap between one id and another.. Which strategy would you adopt?
Like this:
subset(dat, s != 3 | s == 3 & !duplicated(dat))
# id s
# 1 1 2
# 2 1 2
# 3 1 1
# 4 1 3
# 7 2 3
# 9 3 2
# 10 3 2
Note that subset can be dangerous to work with (see Why is `[` better than `subset`?), so the longer but safer version would be:
dat[dat$s != 3 | dat$s == 3 & !duplicated(dat), ]