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
Related
I have a tibble dt given as follows:
library(tidyverse)
dt <- tibble(x=as.integer(c(0,0,1,0,0,0,1,1,0,1))) %>%
mutate(grp = as.factor(c(rep("A",3), rep("B",4), rep("C",1), rep("D",2))))
dt
As one can observe the rule for grouping is:
starts 0 and ends with 1 (e.g., groups A, B, D) or
it solely contains 1 (e.g., group C)
Problem: Given a tibble with column integer vector x of zeros and 1 that starts with 0 and ends in 1, what is the most efficient way to obtain a grouping using R? (You can use any grouping symbols/factors.)
We can get the cumulative sum of 'x' (assuming it is binary), take the lag add 1 and use that index to replace it with LETTERS (Note that LETTERS was used only as part of matching with the expected output - it can take go up to certain limit)
library(dplyr)
dt %>%
mutate(grp2 = LETTERS[lag(cumsum(x), default = 0)+ 1])
-output
# A tibble: 10 x 3
x grp grp2
<int> <fct> <chr>
1 0 A A
2 0 A A
3 1 A A
4 0 B B
5 0 B B
6 0 B B
7 1 B B
8 1 C C
9 0 D D
10 1 D D
Though the strategy proposed by Akrun is fantastic, yet to show that it can be managed through accumulate also
library(tidyverse)
dt <- tibble(x=as.integer(c(0,0,1,0,0,0,1,1,0,1))) %>%
mutate(grp = as.factor(c(rep("A",3), rep("B",4), rep("C",1), rep("D",2))))
dt %>%
mutate(GRP = accumulate(lag(x, default = 0),.init =1, ~ if(.y != 1) .x else .x+1)[-1])
#> # A tibble: 10 x 3
#> x grp GRP
#> <int> <fct> <dbl>
#> 1 0 A 1
#> 2 0 A 1
#> 3 1 A 1
#> 4 0 B 2
#> 5 0 B 2
#> 6 0 B 2
#> 7 1 B 2
#> 8 1 C 3
#> 9 0 D 4
#> 10 1 D 4
Created on 2021-06-13 by the reprex package (v2.0.0)
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
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
For the dataframe below I want to add the original values for Var_x after a group_by on ID and event and a max() on quest, but I cannot get my code right. Any suggestions? By the way, in my original dataframe more than 1 column needs to be added.
df <- data.frame(ID = c(1,1,1,1,1,1,2,2,2,3,3,3),
quest = c(1,1,2,2,3,3,1,2,3,1,2,3),
event = c("A","B","A","B","A",NA,"C","D","C","D","D",NA),
VAR_X = c(2,4,3,6,3,NA,6,4,5,7,5,NA))
Code:
df %>%
group_by(ID,event) %>%
summarise(quest = max(quest))
Desired output:
ID quest event VAR_X
1 1 2 B 6
2 1 3 A 3
3 2 2 D 4
4 2 3 C 5
5 3 2 D 5
Start by omiting the na values and in the end do an inner_join with the original data set.
df %>%
na.omit() %>%
group_by(ID, event) %>%
summarise(quest = max(quest)) %>%
inner_join(df, by = c("ID", "event", "quest"))
## A tibble: 5 x 4
## Groups: ID [3]
# ID event quest VAR_X
# <dbl> <fct> <dbl> <dbl>
#1 1 A 3 3
#2 1 B 2 6
#3 2 C 3 5
#4 2 D 2 4
#5 3 D 2 5
df %>%
drop_na() %>% # remove if necessary ..
group_by(ID, event) %>%
filter(quest == max(quest)) %>%
ungroup()
# A tibble: 5 x 4
# ID quest event VAR_X
#<dbl> <dbl> <chr> <dbl>
# 1 1 2 B 6
# 2 1 3 A 3
# 3 2 2 D 4
# 4 2 3 C 5
# 5 3 2 D 5
I have a dataframe df with three columns a,b,c.
df <- data.frame(a = c('a','b','c','d','e','f','g','e','f','g'),
b = c('X','Y','Z','X','Y','Z','X','X','Y','Z'),
c = c('cat','dog','cat','dog','cat','cat','dog','cat','cat','dog'))
df
# output
a b c
1 a X cat
2 b Y dog
3 c Z cat
4 d X dog
5 e Y cat
6 f Z cat
7 g X dog
8 e X cat
9 f Y cat
10 g Z dog
I have to group_by using the column b followed by summarise using the column c with counts of available values in it.
df %>% group_by(b) %>%
summarise(nCat = sum(c == 'cat'),
nDog = sum(c == 'dog'))
#output
# A tibble: 3 × 3
b nCat nDog
<fctr> <int> <int>
1 X 2 2
2 Y 2 1
3 Z 2 1
However, before doing the above task, I should remove the rows belonging to a value in a which has more than one value in b.
df %>% group_by(a) %>% summarise(count = n())
#output
# A tibble: 7 × 2
a count
<fctr> <int>
1 a 1
2 b 1
3 c 1
4 d 1
5 e 2
6 f 2
7 g 2
For example, in this dataframe, all the rows having value e(values: Y,X), f(values: Z,Y), g(values: X,Z) in column a.
# Expected output
# A tibble: 3 × 3
b nCat nDog
<fctr> <int> <int>
1 X 1 1
2 Y 0 1
3 Z 1 0
We can use filter with n_distinct to filter the values in 'b' that have only one unique element for each 'a' group, then grouped by 'b', we do the summarise
df %>%
group_by(a) %>%
filter(n_distinct(b)==1) %>%
group_by(b) %>%
summarise(nCat =sum(c=='cat'), nDog = sum(c=='dog'), Total = n())
# A tibble: 3 × 4
# b nCat nDog Total
# <fctr> <int> <int> <int>
#1 X 1 1 2
#2 Y 0 1 1
#3 Z 1 0 1