I have a huge table where there is information of 2 professionals in each line that goes like this:
df1 <- data.frame("Date" = c(1,2,3,4), "prof1" = c(25,59,10,5), "prof2" = c(5,7,8,25))
# Date prof1 prf2
#1 1 25 5
#2 2 59 7
#3 3 10 8
#4 4 5 25
... ... ...
I want to delete the line 4 because its the same with line 1, just with alternate values.
So I created a copy os that table with the values of the columns B and C switched like this:
df2 <- data.frame("Date" = c(1,2,3,4), "prof2" = c(5,7,8,25), "prof1" = c(25,59,10,5))
# Date prof2 prof1
#1 1 5 25
#2 2 7 59
#3 3 8 10
#4 4 25 5
... ... ...
And executed the code:
df1<- df1[!do.call(paste, df1[2:3]) %in% do.call(paste, df2[2:3]), ]
But it end up deleting the line 1 as well. Giving me this table:
# Date prof2 prof1
#2 2 7 59
#3 3 8 10
... ... ...
when what I wanted was this:
# Date prof2 prof1
#1 1 5 25
#2 2 7 59
#3 3 8 10
... ... ...
How can I delete only one of the lines that are similar to another?
If you don't care about which one of the duplicates you keep, you can just make sure that
prof2 > prof1 and then remove duplicates.
SWAP = which(df2$prof2 < df2$prof1)
temp = df2$prof2
df2$prof2[SWAP] = df2$prof1[SWAP]
df2$prof1[SWAP] = temp[SWAP]
df2 = df2[!duplicated(df2[,2:3]), ]
df2
Date prof2 prof1
1 1 25 5
2 2 59 7
3 3 10 8
We can do this with apply to loop over the rows of the dataset, sort, them, get the transpose, apply duplicated on it to get a logical vector and subset
df1[!duplicated(t(apply(df1[-1], 1, sort))),]
# Date prof1 prof2
#1 1 25 5
#2 2 59 7
#3 3 10 8
Or another option is pmin/pmax
subset(df1, !duplicated(cbind(pmin(prof1, prof2), pmax(prof1, prof2))))
# Date prof1 prof2
#1 1 25 5
#2 2 59 7
#3 3 10 8
Or using filter from dplyr
library(dplyr)
df1 %>%
filter( !duplicated(cbind(pmin(prof1, prof2), pmax(prof1, prof2))))
Related
I have a data frame as follows:
index val sample_id
1 1 14 5
2 2 22 6
3 3 1 6
4 4 25 7
5 5 3 7
6 6 34 7
For each row with the sample_id, I would like to add a unique identifier as follows:
index val sample_id
1 1 14 5
2 2 22 6-A
3 3 1 6-B
4 4 25 7-A
5 5 3 7-B
6 6 34 7-C
Any suggestion? Thank you for your help.
Base R
dat$id2 <- ave(dat$sample_id, dat$sample_id,
FUN = function(z) if (length(z) > 1) paste(z, LETTERS[seq_along(z)], sep = "-") else as.character(z))
dat
# index val sample_id id2
# 1 1 14 5 5
# 2 2 22 6 6-A
# 3 3 1 6 6-B
# 4 4 25 7 7-A
# 5 5 3 7 7-B
# 6 6 34 7 7-C
tidyverse
library(dplyr)
dat %>%
group_by(sample_id) %>%
mutate(id2 = if (n() > 1) paste(sample_id, LETTERS[row_number()], sep = "-") else as.character(sample_id)) %>%
ungroup()
Minor note: it might be tempting to drop the as.character(z) from either or both of the code blocks. In the first, nothing will change (here): base R allows you to be a little sloppy; if we rely on that and need the new field to always be character, then in that one rare circumstance where all rows have unique sample_id, then the column will remain integer. dplyr is much more careful in guarding against this; if you run the tidyverse code without as.character, you'll see the error.
Using dplyr:
library(dplyr)
dplyr::group_by(df, sample_id) %>%
dplyr::mutate(sample_id = paste(sample_id, LETTERS[seq_along(sample_id)], sep = "-"))
index val sample_id
<int> <dbl> <chr>
1 1 14 5-A
2 2 22 6-A
3 3 1 6-B
4 4 25 7-A
5 5 3 7-B
6 6 34 7-C
If you just want to create unique tags for the same sample_id, maybe you can try make.unique like below
transform(
df,
sample_id = ave(as.character(sample_id),sample_id,FUN = function(x) make.unique(x,sep = "_"))
)
which gives
index val sample_id
1 1 14 5
2 2 22 6
3 3 1 6_1
4 4 25 7
5 5 3 7_1
6 6 34 7_2
I am trying to add additional data from a reference table onto my primary dataframe. I see similar questions have been asked about this however cant find anything for my specific case.
An example of my data frame is set up like this
df <- data.frame("participant" = rep(1:3,9), "time" = rep(1:9, each = 3))
lookup <- data.frame("start.time" = c(1,5,8), "end.time" = c(3,6,10), "var1" = c("A","B","A"),
"var2" = c(8,12,3), "var3"= c("fast","fast","slow"))
print(df)
participant time
1 1 1
2 2 1
3 3 1
4 1 2
5 2 2
6 3 2
7 1 3
8 2 3
9 3 3
10 1 4
11 2 4
12 3 4
13 1 5
14 2 5
15 3 5
16 1 6
17 2 6
18 3 6
19 1 7
20 2 7
21 3 7
22 1 8
23 2 8
24 3 8
25 1 9
26 2 9
27 3 9
> print(lookup)
start.time end.time var1 var2 var3
1 1 3 A 8 fast
2 5 6 B 12 fast
3 8 10 A 3 slow
What I want to do is merge or join these two dataframes in a way which also includes the times in between both the start and end time of the look up data frame. So the columns var1, var2 and var3 are added onto the df at each instance where the time lies between the start time and end time.
for example, in the above case - the look up value in the first row has a start time of 1, an end time of 3, so for times 1, 2 and 3 for each participant, the first row data should be added.
the output should look something like this.
print(output)
participant time var1 var2 var3
1 1 1 A 8 fast
2 2 1 A 8 fast
3 3 1 A 8 fast
4 1 2 A 8 fast
5 2 2 A 8 fast
6 3 2 A 8 fast
7 1 3 A 8 fast
8 2 3 A 8 fast
9 3 3 A 8 fast
10 1 4 <NA> NA <NA>
11 2 4 <NA> NA <NA>
12 3 4 <NA> NA <NA>
13 1 5 B 12 fast
14 2 5 B 12 fast
15 3 5 B 12 fast
16 1 6 B 12 fast
17 2 6 B 12 fast
18 3 6 B 12 fast
19 1 7 <NA> NA <NA>
20 2 7 <NA> NA <NA>
21 3 7 <NA> NA <NA>
22 1 8 A 3 slow
23 2 8 A 3 slow
24 3 8 A 3 slow
25 1 9 A 3 slow
26 2 9 A 3 slow
27 3 9 A 3 slow
I realise that column names don't match and they should for merging data sets.
One option would be to use the sqldf package, and phrase your problem as a SQL left join:
sql <- "SELECT t1.participant, t1.time, t2.var1, t2.var2, t2.var3
FROM df t1
LEFT JOIN lookup t2
ON t1.time BETWEEN t2.\"start.time\" AND t2.\"end.time\""
output <- sqldf(sql)
A dplyr solution:
output <- df %>%
# Create an id for the join
mutate(merge_id=1) %>%
# Use full join to create all the combinations between the two datasets
full_join(lookup %>% mutate(merge_id=1), by="merge_id") %>%
# Keep only the rows that we want
filter(time >= start.time, time <= end.time) %>%
# Select the relevant variables
select(participant,time,var1:var3) %>%
# Right join with initial dataset to get the missing rows
right_join(df, by = c("participant","time")) %>%
# Sort to match the formatting asked by OP
arrange(time, participant)
This produces the output asked by OP, but it will only work for data of reasonable size, as the full join produces a data frame with number of rows equal to the product of the number of rows of both initial datasets.
Using tidyverse and creating an auxiliary table:
df <- data.frame("participant" = rep(1:3,9), "time" = rep(1:9, each = 3))
lookup <- data.frame("start.time" = c(1,5,8), "end.time" = c(3,6,10), "var1" = c("A","B","A"),
"var2" = c(8,12,3), "var3"= c("fast","fast","slow"))
lookup_extended <- lookup %>%
mutate(time = map2(start.time, end.time, ~ c(.x:.y))) %>%
unnest(time) %>%
select(-start.time, -end.time)
df2 <- df %>%
left_join(lookup_extended, by = "time")
Problem
I want to create a loop from data in df1 it's important the data is taken one ID value at a time.
I'm unsure how this can be done with R.
#original dataset
id=c(1,1,1,2,2,2,3,3,3)
dob=c("11-08","12-04","04-03","10-04","03-07","06-02","12-09","01-01","03-08")
count=c(1,6,3,2,5,6,8,6,4)
outcome=rep(1:0,length.out=9)
df1=data.frame(id,dob,count,outcome)
#changes for each value this needs to be completed separately for each value
df2<-df1[df1$id==1,]
df2<-df2[,-4]
addition<-df2$count+45
df2<-cbind(df2,addition)
df3<-df1[df1$id==2,]
df3<-df3[,-4]
addition<-df3$count+45
df3<-cbind(df3,addition)
df4<-df1[df1$id==3,]
df4<-df4[,-4]
addition<-df4$count+45
df4<-cbind(df4,addition)
df5<-rbind(df2,df3,df4)
Expected Output
df5<-rbind(df2,df3,df4)
1 1 11-08 1 46
2 1 12-04 6 51
3 1 04-03 3 48
4 2 10-04 2 47
5 2 03-07 5 50
6 2 06-02 6 51
7 3 12-09 8 53
8 3 01-01 6 51
9 3 03-08 4 49
In the present context (could be a simplified example) it doesn't even need that to loop, as we can directly add the 'count' with a number
df1$addition <- df1$count + 45
However, if it is a complicated operation and needs to look into the 'id' separately, then do a group_by operation
library(dplyr)
df1 %>%
group_by(id) %>%
mutate(addition = count + 45)
# A tibble: 9 x 5
# Groups: id [3]
# id dob count outcome addition
# <dbl> <fct> <dbl> <int> <dbl>
#1 1 11-08 1 1 46
#2 1 12-04 6 0 51
#3 1 04-03 3 1 48
#4 2 10-04 2 0 47
#5 2 03-07 5 1 50
#6 2 06-02 6 0 51
#7 3 12-09 8 1 53
#8 3 01-01 6 0 51
#9 3 03-08 4 1 49
Also, data.table syntax would be
library(data.table)
setDT(df1)[, addition := count + 45, by = id]
or simply
setDT(df1)[, addition := count + 45]
I need to find the first two times my df meets a certain condition grouped by two variables. I am trying to use the ddply function, but I am doing something wrong with the ".variables" command.
So in this example, I'm trying to find the first two times x > 30 and y > 30 in each group / trial.
The way I'm using ddply is giving me the first two times in the dataset, then repeating that for every group.
set.seed(1)
df <- data.frame((matrix(nrow=200,ncol=5)))
colnames(df) <- c("group","trial","x","y","hour")
df$group <- rep(c("A","B","C","D"),each=50)
df$trial <- rep(c(rep(1,times=25),rep(2,times=25)),times=4)
df[,3:4] <- runif(400,0,50)
df$hour <- rep(1:25,time=8)
library(plyr)
ddply(.data=df, .variables=c("group","trial"), .fun=function(x) {
i <- which(df$x > 30 & df$y >30 )[1:2]
if (!is.na(i)) x[i, ]
})
Expected results:
group trial x y hour
13 A 1 34.3511423 38.161134 13
15 A 1 38.4920710 40.931734 15
36 A 2 33.4233369 34.481392 11
37 A 2 39.7119930 34.470671 12
52 B 1 43.0604738 46.645491 2
65 B 1 32.5435234 35.123126 15
But instead, my code is finding c(1,4) from the first grouptrial and repeating that over for every grouptrial:
group trial x y hour
1 A 1 34.351142 38.161134 13
2 A 1 38.492071 40.931734 15
3 A 2 5.397181 27.745031 13
4 A 2 20.563721 22.636003 15
5 B 1 22.953286 13.898301 13
6 B 1 32.543523 35.123126 15
I would also like for there to be rows of NA if a second occurrence isn't present in a group*trial.
Thanks,
I think this is what you want:
library(tidyverse)
df %>% group_by(group, trial) %>% filter(x > 30 & y > 30) %>% slice(1:2)
Result:
# A tibble: 16 x 5
# Groups: group, trial [8]
group trial x y hour
<chr> <dbl> <dbl> <dbl> <int>
1 A 1 33.5 46.3 4
2 A 1 32.6 42.7 11
3 A 2 35.9 43.6 4
4 A 2 30.5 42.7 14
5 B 1 33.0 38.1 2
6 B 1 40.5 30.4 7
7 B 2 48.6 33.2 2
8 B 2 34.1 30.9 4
9 C 1 33.0 45.1 1
10 C 1 30.3 36.7 17
11 C 2 44.8 33.9 1
12 C 2 41.5 35.6 6
13 D 1 44.2 34.3 12
14 D 1 39.1 40.0 23
15 D 2 39.4 47.5 4
16 D 2 42.1 40.1 10
(slightly different from your results, probably a different R version)
I reccomend using dplyr or data.table rather than plyr. From the plyr github page:
plyr is retired: this means only changes necessary to keep it on CRAN
will be made. We recommend using dplyr (for data frames) or purrr (for
lists) instead.
Since someone has already provided a solution with dplyr, here is one option with data.table.
In the selection df[i, j, k] I am selecting rows which match your criteria in i, grouping by the given variables in k, and selecting the first two rows (head) of each group-specific subset of the data .SD. All of this inside the brackets is data.table specific, and only works because I converted df to a data.table first with setDT.
library(data.table)
setDT(df)
df[x > 30 & y > 30, head(.SD, 2), by = .(group, trial)]
# group trial x y hour
# 1: A 1 34.35114 38.16113 13
# 2: A 1 38.49207 40.93173 15
# 3: A 2 33.42334 34.48139 11
# 4: A 2 39.71199 34.47067 12
# 5: B 1 43.06047 46.64549 2
# 6: B 1 32.54352 35.12313 15
# 7: B 2 48.03090 38.53685 5
# 8: B 2 32.11441 49.07817 18
# 9: C 1 32.73620 33.68561 1
# 10: C 1 32.00505 31.23571 20
# 11: C 2 32.13977 40.60658 9
# 12: C 2 34.13940 49.47499 16
# 13: D 1 36.18630 34.94123 19
# 14: D 1 42.80658 46.42416 23
# 15: D 2 37.05393 43.24038 3
# 16: D 2 44.32255 32.80812 8
To try a solution that is closer to what you've tried so far we can do the following
ddply(.data=df, .variables=c("group","trial"), .fun=function(df_temp) {
i <- which(df_temp$x > 30 & df_temp$y >30 )[1:2]
df_temp[i, ]
})
Some explanation
One problem with the code that you provided is that you used df inside of ddply. So you defined fun= function(x) but you didn't look for cases of x> 30 & y> 30 in x but in df. Further, your code uses i for x, but i was defined with df. Finally, to my understanding there is no need for if (!is.na(i)) x[i, ]. If there is only one row that meets your condition, you will get a row with NAs anayway, because you use which(df_temp$x > 30 & df_temp$y >30 )[1:2].
Using dplyr, you can also do:
df %>%
group_by(group, trial) %>%
slice(which(x > 30 & y > 30)[1:2])
group trial x y hour
<chr> <dbl> <dbl> <dbl> <int>
1 A 1 34.4 38.2 13
2 A 1 38.5 40.9 15
3 A 2 33.4 34.5 11
4 A 2 39.7 34.5 12
5 B 1 43.1 46.6 2
6 B 1 32.5 35.1 15
7 B 2 48.0 38.5 5
8 B 2 32.1 49.1 18
Since everything else is covered here is a base R version using split
output <- do.call(rbind, lapply(split(df, list(df$group, df$trial)),
function(new_df) new_df[with(new_df, head(which(x > 30 & y > 30), 2)), ]
))
rownames(output) <- NULL
output
# group trial x y hour
#1 A 1 34.351 38.161 13
#2 A 1 38.492 40.932 15
#3 B 1 43.060 46.645 2
#4 B 1 32.544 35.123 15
#5 C 1 32.736 33.686 1
#6 C 1 32.005 31.236 20
#7 D 1 36.186 34.941 19
#8 D 1 42.807 46.424 23
#9 A 2 33.423 34.481 11
#10 A 2 39.712 34.471 12
#11 B 2 48.031 38.537 5
#12 B 2 32.114 49.078 18
#13 C 2 32.140 40.607 9
#14 C 2 34.139 49.475 16
#15 D 2 37.054 43.240 3
#16 D 2 44.323 32.808 8
I have the following data:
a <- data.frame(ID=c("A","B","Z","H"), a=c(0,1,2,45), b=c(3,4,5,22), c=c(6,7,8,3))
> a
ID a b c
1 A 0 3 6
2 B 1 4 7
3 Z 2 5 8
4 H 45 22 3
b <- data.frame(ID=c("A","B","E","W","Z","H"), a=c(9,10,11,39,5,0), b=c(4,2,7,54,12,34), c=c(12,0,34,23,13,14))
> b
ID a b c
1: A 9 4 12
2: B 10 2 0
3: E 11 7 34
4: W 39 54 23
5: Z 5 12 13
6: H 0 34 14
I want to merge both dataframes, keeping only rows of data.frame a and summarize the same columns, so at the end I get:
> z
ID a b c
1 A 9 7 18
2 B 11 6 7
3 Z 7 17 21
4 H 45 56 17
So far I have tried the following:
merge(a,b,by="ID",all.x=T,all.y=F)
> merge(a,b,by="ID",all.x=T,all.y=F)
ID a.x b.x c.x a.y b.y c.y
1 A 0 3 6 9 4 12
2 B 1 4 7 10 2 0
3 H 45 22 3 0 34 14
4 Z 2 5 8 5 12 13
> join(a,b,type="left",by="ID")
ID a b c a b c
1 A 0 3 6 9 4 12
2 B 1 4 7 10 2 0
3 Z 2 5 8 5 12 13
4 H 45 22 3 0 34 14
I cannot manage to summarize the columns.
My dataframe is pretty big so if the solution can speed up things that would even be better.
If your data.frame is very big, then you may consider this option:
library(data.table)
## convert data.frame to data.table
setDT(a)
## convert data.frame to data.table
setDT(b)
## merge the two data.tables
c <- merge(a,b,by='ID')
## extract names of all columns except the first one i.e. ID
col_names <- colnames(a)[-1]
## query building
col_1 <- paste0(col_names,'.x')
col_2 <- paste0(col_names,'.y')
cols <- paste(col_1,col_2,sep=',')
cols_2 <- paste0(col_names," = sum(",cols,")")
cols_3 <- paste(cols_2,collapse=',')
query <- paste0("z <- c[,.(",cols_3,"),by=ID]")
## query execution
eval(parse(text = query))
This works at least for your example:
a <- data.frame(ID=c("A","B","Z","H"), a=c(0,1,2,45), b=c(3,4,5,22), c=c(6,7,8,3))
b <- data.frame(ID=c("A","B","E","W","Z","H"), a=c(9,10,11,39,5,0), b=c(4,2,7,54,12,34), c=c(12,0,34,23,13,14))
match_a <- na.omit(match(b$ID, a$ID))
match_b <- na.omit(match(a$ID, b$ID))
df <- cbind(ID = a$ID[match_a], a[match_a, -1] + b[match_b, -1])
First, get matching rows from a in b and vice versa, so we can be sure that we only have those rows that appear in both data frames (and we now know their row-indices in both data frames). Then, simply use vectorized additions for those matching rows, but omit ID, as factor cannot be summed up; add ID back manually.
You cannot directly add both data frame is because both the data frames are of unequal size. To make them of equal size you can check for IDs in a which are present in b and then add them element wise.
new <- b[b$ID %in% a$ID, ]
cbind(ID = a$ID, a[-1] + new[-1])
# ID a b c
#1 A 9 7 18
#2 B 11 6 7
#3 Z 7 17 21
#4 H 45 56 17