This question already has answers here:
Subtraction within Groups using R
(3 answers)
Closed 2 years ago.
I want to create a new column similar to newvar. I need to subtract the values of group 1 from group 1 at the respective times and then the values of group 2 from group 1 at the respective times. The base values are of group 1 at the respective time.
id group time var newvar
1 1 1 0 0 0
2 1 1 1 1 0
3 1 1 2 5 0
4 1 2 0 1 1
5 1 2 1 2 1
6 1 2 2 3 -2
7 2 1 0 0 0
8 2 1 1 2 0
9 2 1 2 4 0
10 2 2 0 1 1
11 2 2 1 2 0
12 2 2 2 5 1
A dplyr solution:
library(dplyr)
df %>%
group_by(id, time) %>%
mutate(result = var - var[1])
# # A tibble: 12 x 6
# # Groups: id, time [6]
# id group time var newvar result
# <int> <int> <int> <int> <int> <int>
# 1 1 1 0 0 0 0
# 2 1 1 1 1 0 0
# 3 1 1 2 5 0 0
# 4 1 2 0 1 1 1
# 5 1 2 1 2 1 1
# 6 1 2 2 3 -2 -2
# 7 2 1 0 0 0 0
# 8 2 1 1 2 0 0
# 9 2 1 2 4 0 0
# 10 2 2 0 1 1 1
# 11 2 2 1 2 0 0
# 12 2 2 2 5 1 1
The corresponding solution with ave() in stats:
within(df, result <- ave(var, id, time, FUN = function(x) x - x[1]))
Data
df <- structure(list(id = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L),
group = c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L),
time = c(0L, 1L, 2L, 0L, 1L, 2L, 0L, 1L, 2L, 0L, 1L, 2L),
var = c(0L, 1L, 5L, 1L, 2L, 3L, 0L, 2L, 4L, 1L, 2L, 5L),
newvar = c(0L, 0L, 0L, 1L, 1L, -2L, 0L, 0L, 0L, 1L, 0L, 1L)),
class = "data.frame", row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"))
Base R one-liner using higher-order functions:
do.call("c", Map(function(x){x - x[1]}, with(df, split(var, paste0(id, time)))))
Related
See table below: I want to assign 1 or 0 to a new_col but the sum of 1s per unique hhid column should not exceed the value of any element in the column "nets" as seen in the table below, assuming new_col doesn't exist
hhid nets new_col
1 1 3 1
1 1 3 1
1 1 3 1
1 1 3 0
1 2 2 1
1 2 2 1
1 2 2 0
1 3 2 1
1 3 2 1
1 3 2 0
1 3 2 0
I tried code below
df %>% group_by(hhid) %>% mutate(new_col = ifelse(summarise(across(new_col), sum)<= df$nets),1,0)
Try this:
Data:
df <- structure(list(hhid = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
3L), nets = c(3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L)), class = "data.frame", row.names = c(NA,
-11L))
hhid nets
1 1 3
2 1 3
3 1 3
4 1 3
5 2 2
6 2 2
7 2 2
8 3 2
9 3 2
10 3 2
11 3 2
Code:
df %>%
group_by(hhid) %>%
mutate(new_col = ifelse(row_number() <= nets,1,0))
Output:
# A tibble: 11 x 3
# Groups: hhid [3]
hhid nets new_col
<int> <int> <dbl>
1 1 3 1
2 1 3 1
3 1 3 1
4 1 3 0
5 2 2 1
6 2 2 1
7 2 2 0
8 3 2 1
9 3 2 1
10 3 2 0
11 3 2 0
Same solution but using data.table instead of dplyr
dt <- structure(list(hhid = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
3L), nets = c(3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L)), row.names = c(NA,
-11L), class = c("data.frame"))
library(data.table)
setDT(dt)
dt[, new_col := +(seq_len(.N) <= nets), by = hhid]
dt
hhid nets new_col
1: 1 3 1
2: 1 3 1
3: 1 3 1
4: 1 3 0
5: 2 2 1
6: 2 2 1
7: 2 2 0
8: 3 2 1
9: 3 2 1
10: 3 2 0
11: 3 2 0
I have this dataframe:
treatment hh_id hh_size sex yob g2000 g2002 g2004 p2000
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Civic Duty 1 2 1 1941 1 1 1 0
2 Civic Duty 1 2 1 1947 1 1 1 0
3 Hawthorne 2 3 1 1951 1 1 1 0
4 Hawthorne 2 3 1 1950 1 1 1 0
5 Hawthorne 2 3 1 1982 1 1 1 0
6 Control 3 3 1 1981 0 0 1 0
7 Control 3 3 1 1959 1 1 1 0
8 Control 3 3 1 1956 1 1 1 0
9 Control 4 2 1 1968 0 0 1 0
10 Control 4 2 1 1967 1 1 1 0
I want to group it by hh_id & treatment and summarize the rest of the columns by their mean.
Except, I also want two other columns to count the number of males and females in each household, where in the "sex" column female == 1 and male == 0.
Here's what I have so far:
households <- df %>%
mutate_if(is.character, factor) %>%
group_by(hh_id, treatment) %>%
summarise_if(is.numeric, mean)
View(households)
which gives me this dataframe:
hh_id treatment hh_size sex yob g2000 g2002 g2004 p2000
<dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 Civic Duty 2 1 1944 1 1 1 0
2 2 Hawthorne 3 1 1961 1 1 1 0
3 3 Control 3 1 1965. 0.667 0.667 1 0
4 4 Control 2 1 1968. 0.5 0.5 1 0
5 5 Control 1 1 1941 1 1 1 0
6 6 Hawthorne 2 1 1947 1 1 1 0
7 7 Control 1 1 1969 1 0 1 0
8 8 Control 2 1 1964 1 1 1 0.5
9 9 Self 2 1 1956 0.5 0.5 1 0
10 10 Control 1 1 1943 1 1 1 0
Instead of summarise_if, use summarise with across (which is much more flexible). Also, the _if/_at/_all are deprecated
library(dplyr)
df1 %>%
group_by(hh_id, treatment) %>%
summarise(across(where(is.numeric), mean),
n_female = sum(sex == 1), n_male = sum(sex == 0))
The flexibility is that, we can pass multiple set of columns with difference functions in across as well as computation on a single column without across
data
df1 <- structure(list(treatment = c("Civic Duty", "Civic Duty", "Hawthorne",
"Hawthorne", "Hawthorne", "Control", "Control", "Control", "Control",
"Control"), hh_id = c(1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L),
hh_size = c(2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L), sex = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), yob = c(1941L, 1947L,
1951L, 1950L, 1982L, 1981L, 1959L, 1956L, 1968L, 1967L),
g2000 = c(1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L), g2002 = c(1L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L), g2004 = c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), p2000 = c(0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"))
I need to filter the data frame below according to the number of samples each otu occurs in.
samples otu1 otu2 otu3 otu4 otu5
1 a 2 1 0 0 3
2 b 2 4 1 4 3
3 c 0 0 0 1 0
4 d 0 0 1 4 4
5 e 1 2 0 2 3
6 f 1 1 2 4 2
7 g 1 0 0 4 3
8 h 0 0 2 0 4
9 i 1 2 2 1 6
10 j 0 0 2 3 4
For example, to keep only the otus that occur in >=80% of the samples, the output would be like:
samples otu4 otu5
1 a 0 3
2 b 4 3
3 c 1 0
4 d 4 4
5 e 2 3
6 f 4 2
7 g 4 3
8 h 0 4
9 i 1 6
10 j 3 4
We can use select
library(dplyr)
df1 %>%
select(samples, where(~ is.numeric(.) && mean(. != 0) >= 0.8))
-output
# samples otu4 otu5
#1 a 0 3
#2 b 4 3
#3 c 1 0
#4 d 4 4
#5 e 2 3
#6 f 4 2
#7 g 4 3
#8 h 0 4
#9 i 1 6
#10 j 3 4
Or if we are using an older dplyr version, use select_if
df1 %>%
select_if(~ is.character(.)|is.numeric(.) && mean(. != 0) >= 0.8)
data
df1 <- structure(list(samples = c("a", "b", "c", "d", "e", "f", "g",
"h", "i", "j"), otu1 = c(2L, 2L, 0L, 0L, 1L, 1L, 1L, 0L, 1L,
0L), otu2 = c(1L, 4L, 0L, 0L, 2L, 1L, 0L, 0L, 2L, 0L), otu3 = c(0L,
1L, 0L, 1L, 0L, 2L, 0L, 2L, 2L, 2L), otu4 = c(0L, 4L, 1L, 4L,
2L, 4L, 4L, 0L, 1L, 3L), otu5 = c(3L, 3L, 0L, 4L, 3L, 2L, 3L,
4L, 6L, 4L)), class = "data.frame", row.names = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10"))
This question already has answers here:
Numbering rows within groups in a data frame
(10 answers)
Closed 3 years ago.
I have a data frame.
household person trip loop
1 1 1 1
1 1 2 1
1 1 3 1
1 1 4 2
1 1 5 2
1 2 1 1
1 2 2 1
1 2 3 2
2 1 1 1
2 1 2 1
2 1 3 2
2 1 4 2
for each person in each household I want to change some of index in column trip as below:
when loop is changed I want the trip index Strats from 1 agin.
output
household person trip loop
1 1 1 1
1 1 2 1
1 1 3 1
1 1 1 2
1 1 2 2
1 2 1 1
1 2 2 1
1 2 1 2
2 1 1 1
2 1 2 1
2 1 1 2
2 1 2 2
We can use
library(dplyr)
df1 %>%
group_by(household, person, loop) %>%
mutate(trip = row_number())
# A tibble: 12 x 4
# Groups: household, person, loop [6]
# household person trip loop
# <int> <int> <int> <int>
# 1 1 1 1 1
# 2 1 1 2 1
# 3 1 1 3 1
# 4 1 1 1 2
# 5 1 1 2 2
# 6 1 2 1 1
# 7 1 2 2 1
# 8 1 2 1 2
# 9 2 1 1 1
#10 2 1 2 1
#11 2 1 1 2
#12 2 1 2 2
data
df1 <- structure(list(household = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L), person = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L), trip = c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 1L, 2L,
3L, 4L), loop = c(1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
2L)), class = "data.frame", row.names = c(NA, -12L))
Using data.table :
library(data.table)
df <- setDT(df) # Making sure your data is a data table
df[, trip := seq_len(.N), by = .(household, person, loop)]
I'm trying to print all rows belonging to the same group on the console.
However, skip records that belong to a unique group.
id1 id2 name dob sex group
1 1 2 0 1 0 1
2 1 3 0 0 1 2
3 1 4 1 1 1 2
4 2 3 0 0 0 3
5 2 4 0 1 0 4
6 3 4 0 0 1 4
4 2 3 0 0 0 4
5 2 4 0 1 0 5
6 3 4 0 0 1 6
Result :
id1 id2 name dob sex group
2 1 3 0 0 1 2
3 1 4 1 1 1 2
id1 id2 name dob sex group
5 2 4 0 1 0 4
6 3 4 0 0 1 4
4 2 3 0 0 0 4
The below works great to get the non-unique ones, but I need a for loop for this to print to the screen.
library(dplyr)
dfC %>%
group_by(group) %>%
filter(n()>1)
We can use group_walk with print
library(dplyr)
dfC %>%
group_by(grp = group) %>%
filter(n() > 1) %>%
group_walk(print)
# A tibble: 2 x 6
# id1 id2 name dob sex group
# <int> <int> <int> <int> <int> <int>
#1 1 3 0 0 1 2
#2 1 4 1 1 1 2
# A tibble: 3 x 6
# id1 id2 name dob sex group
# <int> <int> <int> <int> <int> <int>
#1 2 4 0 1 0 4
#2 3 4 0 0 1 4
#3 2 3 0 0 0 4
data
dfC <- structure(list(id1 = c(1L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 3L), id2 = c(2L,
3L, 4L, 3L, 4L, 4L, 3L, 4L, 4L), name = c(0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L), dob = c(1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L),
sex = c(0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L), group = c(1L,
2L, 2L, 3L, 4L, 4L, 4L, 5L, 6L)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9"))