i trying the below code where i am trying to get the row wise sum of a, b and c teams which are all numeric except for the team_league which is character, excluding this character variable i would like to derive the sum of numeric variables into a new variable league_points
to select the numeric variables i am using where(is.numeric) but it is not working, any thoughts
vital1 <- data.frame(a_team=c(1:3), b_team=c(2:4),team_league=c('dd','ee','ff'),c_team=c(5,9,1)) %>%
rowwise() %>%
mutate(league_points=sum(where(is.numeric))
)
We can use where within c_across
library(dplyr)
data.frame(a_team=c(1:3), b_team=c(2:4),
team_league=c('dd','ee','ff'),c_team=c(5,9,1)) %>%
rowwise() %>%
mutate(league_points = sum(c_across(where(is.numeric)), na.rm = TRUE)) %>%
ungroup
-output
# A tibble: 3 × 5
a_team b_team team_league c_team league_points
<int> <int> <chr> <dbl> <dbl>
1 1 2 dd 5 8
2 2 3 ee 9 14
3 3 4 ff 1 8
rowwise would be slow. Here, a vectorized function is already available i.e. rowSums
data.frame(a_team=c(1:3), b_team=c(2:4),
team_league=c('dd','ee','ff'),c_team=c(5,9,1)) %>%
mutate(league_points = rowSums(across(where(is.numeric)), na.rm = TRUE))
-output
a_team b_team team_league c_team league_points
1 1 2 dd 5 8
2 2 3 ee 9 14
3 3 4 ff 1 8
Related
Having a dataframe like:
id = c(1,1,1)
A = c(3,NA,NA)
B = c(NA,5,NA)
C= c(NA,NA,2)
df = data.frame(id,A,B,C)
id A B C
1 1 3 NA NA
2 1 NA 5 NA
3 1 NA NA 2
I want to summarize the whole dataframe in one row that it contains no NAs. It should looke like:
id A B C
1 1 3 5 2
It should work also when the dataframe is bigger and contains more ids but in the same logic.
I didnt found the right function for that and tried some variations of summarise().
You can group_by id and use max with na.rm = TRUE:
library(dplyr)
df %>%
group_by(id) %>%
summarise(across(everything(), max, na.rm = TRUE))
id A B C
1 1 3 5 2
If multiple cases, max may not be what you want, you can use sum instead.
Using fmax from collapse
library(collapse)
fmax(df[-1], df$id)
A B C
1 3 5 2
Alternatively please check the below code
data.frame(id,A,B,C) %>% group_by(id) %>% fill(c(A,B,C), .direction = 'downup') %>%
slice_head(n=1)
Created on 2023-02-03 with reprex v2.0.2
# A tibble: 1 × 4
# Groups: id [1]
id A B C
<dbl> <dbl> <dbl> <dbl>
1 1 3 5 2
This question already has answers here:
How to replace NA with mean by group / subset?
(5 answers)
Closed 7 months ago.
group = c(1,1,4,4,4,5,5,6,1,4,6)
animal = c('a','b','c','c','d','a','b','c','b','d','c')
sleep = c(14,NA,22,15,NA,96,100,NA,50,2,1)
test = data.frame(group, animal, sleep)
print(test)
group_animal = test %>% group_by(`group`, `animal`) %>% summarise(mean_sleep = mean(sleep, na.rm = T))
I would like to replace the NA values the sleep column based on the mean sleep value grouped by group and animal.
Is there any way that I can perform some sort of lookup like Excel that matches group and animal from the test dataframe to the group_animal dataframe and replaces the NA value in the sleep column from the test df with the sleep value in the group_animal df?
We could use mutate instead of summarise as summarise returns a single row per group
library(dplyr)
library(tidyr)
test <- test %>%
group_by(group, animal) %>%
mutate(sleep = replace_na(sleep, mean(sleep, na.rm = TRUE))) %>%
ungroup
-output
test
# A tibble: 11 × 3
group animal sleep
<dbl> <chr> <dbl>
1 1 a 14
2 1 b 50
3 4 c 22
4 4 c 15
5 4 d 2
6 5 a 96
7 5 b 100
8 6 c 1
9 1 b 50
10 4 d 2
11 6 c 1
df = data.frame(group=c(1,1,1,2,2,2,3,3,3),
score=c(11,NA,7,NA,NA,4,6,9,15),
MAKE=c(11,11,11,4,4,4,15,15,15))
Say you have data as above with group and score and the objective is to make new variable MAKE which is just the maximum value of score for each group repeated.
And this is my attempt yet it does not work.
df %>%
group_by(group) %>%
summarise(Value = max(is.na(score)))
For that you need
df %>% group_by(group) %>% mutate(MAKE = max(score, na.rm = TRUE))
# A tibble: 9 x 3
# Groups: group [3]
# group score MAKE
# <dbl> <dbl> <dbl>
# 1 1 11 11
# 2 1 NA 11
# 3 1 7 11
# 4 2 NA 4
# 5 2 NA 4
# 6 2 4 4
# 7 3 6 15
# 8 3 9 15
# 9 3 15 15
The issue with max(is.na(score)) is that is.na(score) is a logical vector and when max is applied, it gets coerced to a binary vector with 1 for TRUE and 0 for FALSE. A somewhat less natural solution but closer to what you tried then would be
df %>% group_by(group) %>% mutate(MAKE = max(score[!is.na(score)]))
which finds the maximal value among all those values of score that are not NA.
I have a situation where I am trying to find the number of intersections with a vector per group in another tibble.
Data example
a <- tibble(EXPERIMENT = rep(c("a","b","c"),each =4),
ECOTYPE = rep(1:12))
b <- tibble(ECOTYPE = c(1,1,5,4,8,7,6,1,4,4,2,5,6,7,1))
I want to find the number of intersections between ECOTYPE in b and ECOTYPEper EXPERIMENT in a.
I wonder if I can use dplyr to solve this, as the group_by function seems to fit this problem, but when I run:
a %>%
group_by(EXPERIMENT) %>%
summarise(INTERSECTIONS = length(intersect(b$ECOTYPE, .$ECOTYPE))
I only get the total number of intersections between a and b.
Am I missing something?
Edit:
Sorry for not posting my desired output. I would like something like this:
# A tibble: 3 x 2
EXPERIMENT INTERSECTIONS
<chr> <dbl>
1 a 8
2 b 7
3 c 0
Depending how you want to count, this will give the number of rows in b matching a:
b %>% mutate(b_flag = 1) %>%
right_join(a) %>%
group_by(EXPERIMENT) %>%
summarize(INTERSECTIONS = sum(b_flag, na.rm = T))
# # A tibble: 3 x 2
# EXPERIMENT INTERSECTIONS
# <fctr> <dbl>
# 1 a 8
# 2 b 7
# 3 c 0
I think the only problem with your code is the unnecessary .$, but it gives the counts of distinct ecotypes in b, ignoring the fact that b has three ECOTYPE = 1 rows, for example.
a %>%
group_by(EXPERIMENT) %>%
summarise(INTERSECTIONS = length(intersect(b$ECOTYPE, ECOTYPE)))
# # A tibble: 3 x 2
# EXPERIMENT INTERSECTIONS
# <fctr> <int>
# 1 a 3
# 2 b 4
# 3 c 0
This is a result of how intersect works:
intersect(c(1, 2, 3), c(1, 1, 1))
# [1] 1
Join the two and count how many are left:
inner_join(a,b, by='ECOTYPE') %>% group_by(EXPERIMENT) %>% count()
# A tibble: 2 x 2
# Groups: EXPERIMENT [2]
EXPERIMENT n
<chr> <int>
1 a 8
2 b 7
Now, if you add an indicator column to b, you can start to count absences as well:
b %>% mutate(present=TRUE) %>% right_join(a, by='ECOTYPE') %>% group_by(EXPERIMENT) %>% summarise(n(), missing=sum(is.na(present)))
# A tibble: 3 x 3
EXPERIMENT `n()` missing
<chr> <int> <int>
1 a 9 1
2 b 7 0
3 c 4 4
Got a data frame with a lot of variables (82), many of them are used for further calculations. So I've tried to convert to numerical but there's a huge work guessing distinct values for every variable and then assign numbers.
I wonder if there's a more automated way of doing it since I don't care which number is assigned to any value as it is not repeated.
My approach so far (for he sake of clarity, dummy data):
df <- data.frame(original.var1 = c("display","memory","software","display","disk","memory"),
original.var2 = c("skeptic","believer","believer","believer","skeptic","believer"),
original.var3 = c("round","square","triangle","cube","sphere","hexagon"),
original.var4 = c(10,20,30,40,50,60))
taking into account this worked fine
library(dplyr)
library(magrittr)
df$NEW1 <- as.numeric(interaction(df$original.var1, drop=TRUE))
I've tried to adapt to dplyr and pipes this way
df %<>% mutate(VAR1= as.numeric(interaction(original.var1, drop=TRUE))) %>%
mutate(VAR2= as.numeric(interaction(original.var2, drop=TRUE))) %>%
mutate(VAR3= as.numeric(interaction(original.var2, drop=TRUE)))
but results got wrong from third VAR ahead
df %>% dplyr::group_by(original.var1,VAR1) %>% tally()
# A tibble: 4 x 3
# Groups: original.var1 [?]
original.var1 VAR1 n
<fctr> <dbl> <int>
1 disk 1 1
2 display 2 2
3 memory 3 2
4 software 4 1
> df %>% dplyr::group_by(original.var2,VAR2) %>% tally()
# A tibble: 2 x 3
# Groups: original.var2 [?]
original.var2 VAR2 n
<fctr> <dbl> <int>
1 believer 1 4
2 skeptic 2 2
> df %>% dplyr::group_by(original.var3,VAR3) %>% tally()
# A tibble: 6 x 3
# Groups: original.var3 [?]
original.var3 VAR3 n
<fctr> <dbl> <int>
1 cube 1 1
2 hexagon 1 1
3 round 2 1
4 sphere 2 1
5 square 1 1
6 triangle 1 1
Any approach or package to recode not having the mapping declared previously?
You can use mutate_if,
library(dplyr)
mutate_if(df, is.factor, funs(as.numeric(interaction(., drop = TRUE))))
which gives,
original.var1 original.var2 original.var3 original.var4
1 2 2 3 10
2 3 1 5 20
3 4 1 6 30
4 2 1 1 40
5 1 2 4 50
6 3 1 2 60
Alternatively you can read your data frame with stringsAsFactors = FALSE and use is.character but it's the same thing
To address your comment, If you want to also keep your original columns, then,
mutate_if(df, is.factor, funs(new = as.numeric(interaction(., drop = TRUE))))
Using purrr Keep the factor columns only and operate on them. Merge with numerical at the end.
df %>% purrr::keep(is.factor) %>% mutate_all(funs(as.numeric(interaction(., drop = TRUE))))