I am trying to filter a data set to only include subjects who have data in all conditions (levels of a factor).
I have tried to filter by calculating the number of levels for each subject, but that does not work.
library(tidyverse)
Data <- data.frame(
Subject = factor(c(rep(1, 3),
rep(2, 3),
rep(3, 1))),
Condition = factor(c("A", "B", "C",
"A", "B", "C",
"A")),
Val = c(1, 0, 1,
0, 0, 1,
1)
)
Data %>%
semi_join(
.,
Data %>%
group_by(Subject) %>%
summarize(Num_Cond = length(levels(Condition))) %>%
filter(Num_Cond == 3),
by = "Subject"
)
This attempt yields:
Subject Condition Val
1 1 A 1
2 1 B 0
3 1 C 1
4 2 A 0
5 2 B 0
6 2 C 1
7 3 A 1
Desired output:
Subject Condition Val
1 1 A 1
2 1 B 0
3 1 C 1
4 2 A 0
5 2 B 0
6 2 C 1
I want to filter subject 3 out because they only have data for one condition.
Is there a dplyr/tidyverse approach for this problem?
We can create a condition with all and levels
library(dplyr)
Data %>%
group_by(Subject) %>%
filter(all(levels(Condition) %in% Condition))
# A tibble: 6 x 3
# Groups: Subject [2]
# Subject Condition Val
# <fct> <fct> <dbl>
#1 1 A 1
#2 1 B 0
#3 1 C 1
#4 2 A 0
#5 2 B 0
#6 2 C 1
Or with n_distinct and nlevels
Data %>%
group_by(Subject) %>%
filter(nlevels(Condition) == n_distinct(Condition))
# A tibble: 6 x 3
# Groups: Subject [2]
# Subject Condition Val
# <fct> <fct> <dbl>
#1 1 A 1
#2 1 B 0
#3 1 C 1
#4 2 A 0
#5 2 B 0
#6 2 C 1
Here is a solution testing wether the number of rows of each groupis equal to the number of levels of Condition.
Data %>%
group_by(Subject) %>%
filter(n() == nlevels(Condition))
## A tibble: 6 x 3
## Groups: Subject [2]
# Subject Condition Val
# <fct> <fct> <dbl>
#1 1 A 1
#2 1 B 0
#3 1 C 1
#4 2 A 0
#5 2 B 0
#6 2 C 1
Edit
Following the comment by user #akrun I tested with a data set having duplicate values for each row and the code above does fail.
bind_rows(Data, Data) %>%
group_by(Subject) %>%
#distinct() %>%
filter(n() == nlevels(Condition))
## A tibble: 0 x 3
## Groups: Subject [0]
## ... with 3 variables: Subject <fct>, Condition <fct>, Val <dbl>
To run the commented out code line would solve the problem.
I found a relatively simple solution by sub-setting on Subject:
Data %>%
semi_join(
.,
Data %>%
group_by(Subject) %>%
droplevels() %>%
summarize(Num_Cond = length(levels(Condition)[Subject])) %>%
filter(Num_Cond == 3),
by = "Subject"
)
This gives the desired output:
Subject Condition Val
1 1 A 1
2 1 B 0
3 1 C 1
4 2 A 0
5 2 B 0
6 2 C 1
Related
I have the following dataframe:
df <-read.table(header=TRUE, text="id code
1 A
1 B
1 C
2 A
2 A
2 A
3 A
3 B
3 A")
Per id, I would love to find those individuals that have at least 2 conditions, namely:
conditionA = "A"
conditionB = "B"
conditionC = "C"
and create a new colum with "index", 1 if there are two or more conditions met and 0 otherwise:
df_output <-read.table(header=TRUE, text="id code index
1 A 1
1 B 1
1 C 1
2 A 0
2 A 0
2 A 0
3 A 1
3 B 1
3 A 1")
So far I have tried the following:
df_output = df %>%
group_by(id) %>%
mutate(index = ifelse(grepl(conditionA|conditionB|conditionC, code), 1, 0))
and as you can see I am struggling to get the threshold count into the code.
You can create a vector of conditions, and then use %in% and sum to count the number of occurrences in each group. Use + (or ifelse) to convert logical into 1 and 0:
conditions = c("A", "B", "C")
df %>%
group_by(id) %>%
mutate(index = +(sum(unique(code) %in% conditions) >= 2))
id code index
1 1 A 1
2 1 B 1
3 1 C 1
4 2 A 0
5 2 A 0
6 2 A 0
7 3 A 1
8 3 B 1
9 3 A 1
You could use n_distinct(), which is a faster and more concise equivalent of length(unique(x)).
df %>%
group_by(id) %>%
mutate(index = +(n_distinct(code) >= 2)) %>%
ungroup()
# # A tibble: 9 × 3
# id code index
# <int> <chr> <int>
# 1 1 A 1
# 2 1 B 1
# 3 1 C 1
# 4 2 A 0
# 5 2 A 0
# 6 2 A 0
# 7 3 A 1
# 8 3 B 1
# 9 3 A 1
You can check conditions using intersect() function and check whether resulting list is of minimal (eg- 2) length.
conditions = c('A', 'B', 'C')
df_output2 =
df %>%
group_by(id) %>%
mutate(index = as.integer(length(intersect(code, conditions)) >= 2))
I have a data frame with a group, a condition that differs by group, and an index within each group:
df <- data.frame(group = c(rep(c("A", "B", "C"), each = 3)),
condition = rep(c(0,1,1), each = 3),
index = c(1:3,1:3,2:4))
> df
group condition index
1 A 0 1
2 A 0 2
3 A 0 3
4 B 1 1
5 B 1 2
6 B 1 3
7 C 1 2
8 C 1 3
9 C 1 4
I would like to slice the data within each group, filtering out all but the row with the lowest index. However, this filter should only be applied when the condition applies, i.e., condition == 1. My solution was to compute a ranking on the index within each group and filter on the combination of condition and rank:
df %>%
group_by(group) %>%
mutate(rank = order(index)) %>%
filter(case_when(condition == 0 ~ TRUE,
condition == 1 & rank == 1 ~ TRUE))
# A tibble: 5 x 4
# Groups: group [3]
group condition index rank
<chr> <dbl> <int> <int>
1 A 0 1 1
2 A 0 2 2
3 A 0 3 3
4 B 1 1 1
5 C 1 2 1
This left me wondering whether there is a faster solution that does not require a separate ranking variable, and potentially uses slice_min() instead.
You can use filter() to keep all cases where the condition is zero or the index equals the minimum index.
library(dplyr)
df %>%
group_by(group) %>%
filter(condition == 0 | index == min(index))
# A tibble: 5 x 3
# Groups: group [3]
group condition index
<chr> <dbl> <int>
1 A 0 1
2 A 0 2
3 A 0 3
4 B 1 1
5 C 1 2
An option with slice
library(dplyr)
df %>%
group_by(group) %>%
slice(unique(c(which(condition == 0), which.min(index))))
I have a dataframe...
df <- tibble(
id = 1:7,
family = c("a","a","b","b","c", "d", "e")
)
Families will only contain 2 members at most (so they're either individuals or pairs).
I need a new column 'random' that assigns the number 1 to families where there is only one member (e.g. c, d and e) and randomly assigns 0 or 1 to families containing 2 members (a and b in the example).
By the end the data should look like the following (depending on the random assignment of 0/1)...
df <- tibble(
id = 1:7,
family = c("a","a","b","b","c", "d", "e"),
random = c(1, 0, 0, 1, 1, 1, 1)
)
I would like to be able to do this with a combination of group_by and mutate since I am mostly using Tidyverse.
I tried the following (but this didn't randomly assign 0/1 within families)...
df %>%
group_by(family) %>%
mutate(
random = if_else(
condition = n() == 1,
true = 1,
false = as.double(sample(0:1,1,replace = T))
)
You could sample along the sequence length of the family group and take the answer modulo 2:
df %>%
group_by(family) %>%
mutate(random = sample(seq(n())) %% 2)
#> # A tibble: 7 x 3
#> # Groups: family [5]
#> id family random
#> <int> <chr> <dbl>
#> 1 1 a 0
#> 2 2 a 1
#> 3 3 b 0
#> 4 4 b 1
#> 5 5 c 1
#> 6 6 d 1
#> 7 7 e 1
We can use if/else
library(dplyr)
df %>%
group_by(family) %>%
mutate(random = if(n() == 1) 1 else sample(rep(0:1, length.out = n())))
# A tibble: 7 x 3
# Groups: family [5]
# id family random
# <int> <chr> <dbl>
#1 1 a 0
#2 2 a 1
#3 3 b 1
#4 4 b 0
#5 5 c 1
#6 6 d 1
#7 7 e 1
Another option
df %>%
group_by(family) %>%
mutate(random = 2 - sample(1:n()))
# A tibble: 7 x 3
# Groups: family [5]
id family random
# <int> <chr> <dbl>
# 1 1 a 1
# 2 2 a 0
# 3 3 b 1
# 4 4 b 0
# 5 5 c 1
# 6 6 d 1
# 7 7 e 1
I was wondering if there's a more elegant way of taking a dataframe, grouping by x to see how many x's occur in the dataset, then mutating to find the first occurrence of every x (y)
test <- data.frame(x = c("a", "b", "c", "d",
"c", "b", "e", "f", "g"),
y = c(1,1,1,1,2,2,2,2,2))
x y
1 a 1
2 b 1
3 c 1
4 d 1
5 c 2
6 b 2
7 e 2
8 f 2
9 g 2
Current Output
output <- test %>%
group_by(x) %>%
summarise(count = n())
x count
<fct> <int>
1 a 1
2 b 2
3 c 2
4 d 1
5 e 1
6 f 1
7 g 1
Desired Output
x count first_seen
<fct> <int> <dbl>
1 a 1 1
2 b 2 1
3 c 2 1
4 d 1 1
5 e 1 2
6 f 1 2
7 g 1 2
I can filter the test dataframe for the first occurrences then use a left_join but was hoping there's a more elegant solution using mutate?
# filter for first occurrences of y
right <- test %>%
group_by(x) %>%
filter(y == min(y)) %>%
slice(1) %>%
ungroup()
# bind to the output dataframe
left_join(output, right, by = "x")
We can use first after grouping by 'x' to create a new column, use that also in group_by and get the count with n()
library(dplyr)
test %>%
group_by(x) %>%
group_by(first_seen = first(y), add = TRUE) %>%
summarise(count = n())
# A tibble: 7 x 3
# Groups: x [7]
# x first_seen count
# <fct> <dbl> <int>
#1 a 1 1
#2 b 1 2
#3 c 1 2
#4 d 1 1
#5 e 2 1
#6 f 2 1
#7 g 2 1
I have a question. Why not keep it simple? for example
test %>%
group_by(x) %>%
summarise(
count = n(),
first_seen = first(y)
)
#> # A tibble: 7 x 3
#> x count first_seen
#> <chr> <int> <dbl>
#> 1 a 1 1
#> 2 b 2 1
#> 3 c 2 1
#> 4 d 1 1
#> 5 e 1 2
#> 6 f 1 2
#> 7 g 1 2
Given a dataset such as:
set.seed(134)
df<- data.frame(ID= rep(LETTERS[1:5], each=2),
condition=rep(0:1, 5),
value=rpois(10, 3)
)
df
ID condition value
1 A 0 2
2 A 1 3
3 B 0 5
4 B 1 2
5 C 0 3
6 C 1 1
7 D 0 2
8 D 1 4
9 E 0 1
10 E 1 5
For each ID, when the value for condition==0 is less than the value for condition==1, I want to keep both observations. When the value for condition==0 is greater than condition==1, I want to keep only the row for condition==0.
The subset returned should be this:
ID condition value
1 A 0 2
2 A 1 3
3 B 0 5
5 C 0 3
7 D 0 2
8 D 1 4
9 E 0 1
10 E 1 5
Using dplyr the first step is:
df %>% group_by(ID) %>%
But not sure where to go from there.
Translating fairly literally,
library(dplyr)
set.seed(134)
df <- data.frame(ID = rep(LETTERS[1:5], each = 2),
condition = rep(0:1, 5),
value = rpois(10, 3))
df %>% group_by(ID) %>%
filter(condition == 0 |
(condition == 1 & value > value[condition == 0]))
#> # A tibble: 8 x 3
#> # Groups: ID [5]
#> ID condition value
#> <fct> <int> <int>
#> 1 A 0 2
#> 2 A 1 3
#> 3 B 0 5
#> 4 C 0 3
#> 5 D 0 2
#> 6 D 1 4
#> 7 E 0 1
#> 8 E 1 5
This depends on each group having a single observation with condition == 0, but should otherwise be fairly robust.
This is may not be the easiest way, but should work as you want.
library(reshape2)
df %>%
dcast(ID ~ condition, value.var = 'value') %>% # cast to wide format
mutate(`1` = ifelse(`1` > `0`, `1`, NA)) %>% # turn 0>1 values as NA
melt('ID') %>% # melt as long format
arrange(ID) %>% # sort by ID
filter(complete.cases(.)) # remove NA rows
Output:
ID variable value
1 A 0 2
2 A 1 3
3 B 0 5
4 C 0 3
5 D 0 2
6 D 1 4
7 E 0 1
8 E 1 5
You always want the value from the first row in each group. You only want the value from the second row in each group if it's larger than the first.
This works:
df %>%
group_by(ID) %>%
filter(row_number() == 1 | value > lag(value))
Edit: as #alistaire points out, this method depends on a particular order in, which is might be a good idea to guarantee as follows:
df %>%
arrange(ID, condition) %>%
group_by(ID) %>%
filter(row_number() == 1 | value > lag(value))