I have a dataframe which looks like -
Id Result
A 1
B 2
C 1
B 1
C 1
A 2
B 1
B 2
C 1
A 1
B 2
Now I need to calculate how many 1's and 2's are there for each Id and then select the number whose frequency of occurrence is the greatest.
Id Result
A 1
B 2
C 1
How can I do that? I have tried using the table function in some way but not able to use it effectively. Any help would be appreciated.
Here you can use aggregate in one step:
df <- structure(list(Id = structure(c(1L, 2L, 3L, 2L, 3L, 1L, 2L, 2L,
3L, 1L, 2L), .Label = c("A", "B", "C"), class = "factor"),
Result = c(1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L)),
.Names = c("Id", "Result"), class = "data.frame", row.names = c(NA, -11L)
)
res <- aggregate(Result ~ Id, df, FUN=function(x){which.max(c(sum(x==1), sum(x==2)))})
res
Result:
Id Result
1 A 1
2 B 2
3 C 1
With data.table you can try (df is your data.frame):
require(data.table)
dt<-as.data.table(df)
dt[,list(times=.N),by=list(Id,Result)][,list(Result=Result[which.max(times)]),by=Id]
# Id Result
#1: A 1
#2: B 2
#3: C 1
Using dplyr, you can try
library(dplyr)
df %>% group_by(Id, Result) %>% summarize(n = n()) %>% group_by(Id) %>%
filter(n == max(n)) %>% summarize(Result = Result)
Id Result
1 A 1
2 B 2
3 C 1
An option using table and ave
subset(as.data.frame(table(df1)),ave(Freq, Id, FUN=max)==Freq, select=-3)
# Id Result
# 1 A 1
# 3 C 1
# 5 B 2
Related
I have data with the status column. I want to subset my data to the condition of 'f' status, and previous condition of 'f' status.
to simplify:
df
id status time
1 n 1
1 n 2
1 f 3
1 n 4
2 f 1
2 n 2
3 n 1
3 n 2
3 f 3
3 f 4
my result should be:
id status time
1 n 2
1 f 3
2 f 1
3 n 2
3 f 3
3 f 4
How can I do this in R?
Here's a solution using dplyr -
df %>%
group_by(id) %>%
filter(status == "f" | lead(status) == "f") %>%
ungroup()
# A tibble: 6 x 3
id status time
<int> <fct> <int>
1 1 n 2
2 1 f 3
3 2 f 1
4 3 n 2
5 3 f 3
6 3 f 4
Data -
df <- structure(list(id = c(1L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 3L),
status = structure(c(2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
1L), .Label = c("f", "n"), class = "factor"), time = c(1L,
2L, 3L, 4L, 1L, 2L, 1L, 2L, 3L, 4L)), .Names = c("id", "status",
"time"), class = "data.frame", row.names = c(NA, -10L))
I need to find a running maximum of a variable by group using R. The variable is sorted by time within group using df[order(df$group, df$time),].
My variable has some NA's but I can deal with it by replacing them with zeros for this computation.
this is how the data frame df looks like:
(df <- structure(list(var = c(5L, 2L, 3L, 4L, 0L, 3L, 6L, 4L, 8L, 4L),
group = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L),
.Label = c("a", "b"), class = "factor"),
time = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L)),
.Names = c("var", "group","time"),
class = "data.frame", row.names = c(NA, -10L)))
# var group time
# 1 5 a 1
# 2 2 a 2
# 3 3 a 3
# 4 4 a 4
# 5 0 a 5
# 6 3 b 1
# 7 6 b 2
# 8 4 b 3
# 9 8 b 4
# 10 4 b 5
And I want a variable curMax as:
var | group | time | curMax
5 a 1 5
2 a 2 5
3 a 3 5
4 a 4 5
0 a 5 5
3 b 1 3
6 b 2 6
4 b 3 6
8 b 4 8
4 b 5 8
Please let me know if you have any idea how to implement it in R.
We can try data.table. Convert the 'data.frame' to 'data.table' (setDT(df1)), grouped by 'group' , we get the cummax of 'var' and assign (:=) it to a new variable ('curMax')
library(data.table)
setDT(df1)[, curMax := cummax(var), by = group]
As commented by #Michael Chirico, if the data is not ordered by 'time', we can do that in the 'i'
setDT(df1)[order(time), curMax:=cummax(var), by = group]
Or with dplyr
library(dplyr)
df1 %>%
group_by(group) %>%
mutate(curMax = cummax(var))
If df1 is tbl_sql explicit ordering might be required, using arrange
df1 %>%
group_by(group) %>%
arrange(time, .by_group=TRUE) %>%
mutate(curMax = cummax(var))
or dbplyr::window_order
library(dbplyr)
df1 %>%
group_by(group) %>%
window_order(time) %>%
mutate(curMax = cummax(var))
you can do it so:
df$curMax <- ave(df$var, df$group, FUN=cummax)
I have trouble combining slice and map.
I am interested of doing something similar to this; which is, in my case, transforming a compact person-period file to a long (sequential) person-period one. However, because my file is too big, I need to split the data first.
My data look like this
group id var ep dur
1 A 1 a 1 20
2 A 1 b 2 10
3 A 1 a 3 5
4 A 2 b 1 5
5 A 2 b 2 10
6 A 2 b 3 15
7 B 1 a 1 20
8 B 1 a 2 10
9 B 1 a 3 10
10 B 2 c 1 20
11 B 2 c 2 5
12 B 2 c 3 10
What I need is simply this (answer from this)
library(dplyr)
dt %>% slice(rep(1:n(),.$dur))
However, I am interested in introducing a split(.$group).
How I am suppose to do so ?
dt %>% split(.$group) %>% map_df(slice(rep(1:n(),.$dur)))
Is not working for example.
My desired output is the same as dt %>% slice(rep(1:n(),.$dur))
which is
group id var ep dur
1 A 1 a 1 20
2 A 1 a 1 20
3 A 1 a 1 20
4 A 1 a 1 20
5 A 1 a 1 20
6 A 1 a 1 20
7 A 1 a 1 20
8 A 1 a 1 20
9 A 1 a 1 20
10 A 1 a 1 20
.....
But I need to split this operation because the file is too big.
data
dt = structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"),
id = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L), .Label = c("1", "2"), class = "factor"), var = structure(c(1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L), .Label = c("a",
"b", "c"), class = "factor"), ep = structure(c(1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("1", "2",
"3"), class = "factor"), dur = c(20, 10, 5, 5, 10, 15, 20,
10, 10, 20, 5, 10)), .Names = c("group", "id", "var", "ep",
"dur"), row.names = c(NA, -12L), class = "data.frame")
map takes two arguments: a vector/list in .x and a function in .f. It then applies .f on all elements in .x.
The function you are passing to map is not formatted correctly. Try this:
f <- function(x) x %>% slice(rep(1:n(), .$dur))
dt %>%
split(.$group) %>%
map_df(f)
You could also use it like this:
dt %>%
split(.$group) %>%
map_df(slice, rep(1:n(), dur))
This time you directly pass the slice function to map with additional parameters.
I'm not quite sure what your desired final output is, but you could use tidyr to nest the data that you want to repeat and a simple function to expand levels of your nested data, very similar to Tutuchan's answer.
expand_df <- function(df, repeats) {
df %>% slice(rep(1:n(), repeats))
}
dt %>%
tidyr::nest(var:ep) %>%
mutate(expanded = purrr::map2(data, dur, expand_df)) %>%
select(-data) %>%
tidyr::unnest()
Tutuchan's answer gives exactly the same output as your original approach - is that what you were looking for? I don't know if it will have any advantage over your original method.
Because I am working on a very large dataset, I need to slice my dataset by groups in order to pursue my computations.
I have a person-period (melt) dataset that looks like this
group id var time
1 A 1 a 1
2 A 1 b 2
3 A 1 a 3
4 A 2 b 1
5 A 2 b 2
6 A 2 b 3
7 B 1 a 1
8 B 1 a 2
9 B 1 a 3
10 B 2 c 1
11 B 2 c 2
12 B 2 c 3
I need to do this simple transformation
library(reshape2)
library(dplyr)
dt %>% dcast(group + id ~ time, value.var = 'var')
In order to get
group id 1 2 3
1 A 1 a b a
2 A 2 b b b
3 B 1 a a a
4 B 2 c c c
So far, so good.
However, because my database is too big, I need to do this separately for each different groups, such as
a = dt %>% filter(group == 'A') %>% dcast(group + id ~ time, value.var ='var')
b = dt %>% filter(group == 'B') %>% dcast(group + id ~ time, value.var = 'var')
bind_rows(a,b)
My problem is that I would like to avoid doing it by hand. I mean, having to store separately each groups, a = ..., b = ..., c = ..., and so on
Any idea how I could have a single pipe stream that would separate each group, compute the transformation and put it back together in a dataframe ?
dt = structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"),
id = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"), var = structure(c(1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L), .Label = c("a",
"b", "c"), class = "factor"), time = structure(c(1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("1",
"2", "3"), class = "factor")), .Names = c("group", "id",
"var", "time"), row.names = c(NA, -12L), class = "data.frame")
Package purrr can be useful for working with lists. First split the dataset by group and then use map_df to dcast each list but return everything in a single data.frame.
library(purrr)
dt %>%
split(.$group) %>%
map_df(~dcast(.x, group + id ~ time, value.var = "var"))
group id 1 2 3
1 A 1 a b a
2 A 2 b b b
3 B 1 a a a
4 B 2 c c c
lapply is your friend here:
do.call(rbind, lapply(unique(dt$Group), function(grp, dt){
dt %>% filter(Group == grp) %>% dcast(group + id ~ time, value.var = "var")
}, dt = dt))
I need to find a running maximum of a variable by group using R. The variable is sorted by time within group using df[order(df$group, df$time),].
My variable has some NA's but I can deal with it by replacing them with zeros for this computation.
this is how the data frame df looks like:
(df <- structure(list(var = c(5L, 2L, 3L, 4L, 0L, 3L, 6L, 4L, 8L, 4L),
group = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L),
.Label = c("a", "b"), class = "factor"),
time = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L)),
.Names = c("var", "group","time"),
class = "data.frame", row.names = c(NA, -10L)))
# var group time
# 1 5 a 1
# 2 2 a 2
# 3 3 a 3
# 4 4 a 4
# 5 0 a 5
# 6 3 b 1
# 7 6 b 2
# 8 4 b 3
# 9 8 b 4
# 10 4 b 5
And I want a variable curMax as:
var | group | time | curMax
5 a 1 5
2 a 2 5
3 a 3 5
4 a 4 5
0 a 5 5
3 b 1 3
6 b 2 6
4 b 3 6
8 b 4 8
4 b 5 8
Please let me know if you have any idea how to implement it in R.
We can try data.table. Convert the 'data.frame' to 'data.table' (setDT(df1)), grouped by 'group' , we get the cummax of 'var' and assign (:=) it to a new variable ('curMax')
library(data.table)
setDT(df1)[, curMax := cummax(var), by = group]
As commented by #Michael Chirico, if the data is not ordered by 'time', we can do that in the 'i'
setDT(df1)[order(time), curMax:=cummax(var), by = group]
Or with dplyr
library(dplyr)
df1 %>%
group_by(group) %>%
mutate(curMax = cummax(var))
If df1 is tbl_sql explicit ordering might be required, using arrange
df1 %>%
group_by(group) %>%
arrange(time, .by_group=TRUE) %>%
mutate(curMax = cummax(var))
or dbplyr::window_order
library(dbplyr)
df1 %>%
group_by(group) %>%
window_order(time) %>%
mutate(curMax = cummax(var))
you can do it so:
df$curMax <- ave(df$var, df$group, FUN=cummax)