I have a data set like this:
df <- data.frame(situation1=rnorm(30),
situation2=rnorm(30),
situation3=rnorm(30),
models=c(rep("A",10), rep("B",10), rep("C", 10)))
where I compare three models (A,B,C) in three situations. I have 10 measurements for each model.
I now want to summarise this into ranks, i.e. how often each models wins in each situtation. Win is defined by the highest value.
A final output could be something like this:
model situation1 situtation2 situtation3
A 4 3 3
B 7 1 2
C 1 4 5
In base R:
table(df$models,colnames(df[-4])[max.col(df[-4])])
# situation1 situation2 situation3
# A 2 4 4
# B 4 5 1
# C 2 4 4
Results may change from your OP, since you didn't set a seed.
Here is an option using data.table
library(data.table)
setDT(df)[, lapply(Map(`==`, .SD, list(do.call(pmax, .SD))), sum), models]
Here's a dplyr option:
df %>%
group_by(models) %>%
mutate_all(funs(. == pmax(situation1, situation2, situation3))) %>%
summarise_all(sum)
Or possibly a little more efficient:
df %>%
mutate_at(vars(-models), funs(. == pmax(situation1, situation2, situation3))) %>%
group_by(models) %>%
summarise_all(sum)
## A tibble: 3 × 4
# models situation1 situation2 situation3
# <chr> <int> <int> <int>
#1 A 3 3 3
#2 B 3 5 1
#3 C 6 1 2
If you're looking for the minimum, use pmin instead of pmax. And in case there may be NAs, use the na.rm-argument in pmax/pmin.
Final note: the result doesn't match OP's because the sample data was generated without setting a seed.
Related
I have a data frame with three columns. Each row contains three unique numbers between 1 and 5 (inclusive).
df <- data.frame(a=c(1,4,2),
b=c(5,3,1),
c=c(3,1,5))
I want to use mutate to create two additional columns that, for each row, contain the two numbers between 1 and 5 that do not appear in the initial three columns in ascending order. The desired data frame in the example would be:
df2 <- data.frame(a=c(1,4,2),
b=c(5,3,1),
c=c(3,1,5),
d=c(2,2,3),
e=c(4,5,4))
I tried to use the below mutate function utilizing setdiff to accomplish this, but returned NAs rather than the values I was looking for:
df <- df %>% mutate(d=setdiff(c(a,b,c),c(1:5))[1],
e=setdiff(c(a,b,c),c(1:5))[2])
I can get around this by looping through each row (or using an apply function) but would prefer a mutate approach if possible.
Thank you for your help!
Base R:
cbind(df, t(apply(df, 1, setdiff, x = 1:5)))
# a b c 1 2
# 1 1 5 3 2 4
# 2 4 3 1 2 5
# 3 2 1 5 3 4
Warning: if there are any non-numerical columns, apply will happily up-convert things (converting to a matrix internally).
We can use pmap to loop over the rows, create a list column and then unnest it to create two new columns
library(dplyr)
librayr(purrr)
library(tidyr)
df %>%
mutate(out = pmap(., ~ setdiff(1:5, c(...)) %>%
as.list%>%
set_names(c('d', 'e')))) %%>%
unnest_wider(c(out))
# A tibble: 3 x 5
# a b c d e
# <dbl> <dbl> <dbl> <int> <int>
#1 1 5 3 2 4
#2 4 3 1 2 5
#3 2 1 5 3 4
Or using base R
df[c('d', 'e')] <- do.call(rbind, lapply(asplit(df, 1), function(x) setdiff(1:5, x)))
I have data with a grouping variable ("from") and values ("number"):
from number
1 1
1 1
2 1
2 2
3 2
3 2
I want to subset the data and select groups which have two or more unique values. In my data, only group 2 has more than one distinct 'number', so this is the desired result:
from number
2 1
2 2
Several possibilities, here's my favorite
library(data.table)
setDT(df)[, if(+var(number)) .SD, by = from]
# from number
# 1: 2 1
# 2: 2 2
Basically, per each group we are checking if there is any variance, if TRUE, then return the group values
With base R, I would go with
df[as.logical(with(df, ave(number, from, FUN = var))), ]
# from number
# 3 2 1
# 4 2 2
Edit: for a non numerical data you could try the new uniqueN function for the devel version of data.table (or use length(unique(number)) > 1 instead
setDT(df)[, if(uniqueN(number) > 1) .SD, by = from]
You could try
library(dplyr)
df1 %>%
group_by(from) %>%
filter(n_distinct(number)>1)
# from number
#1 2 1
#2 2 2
Or using base R
indx <- rowSums(!!table(df1))>1
subset(df1, from %in% names(indx)[indx])
# from number
#3 2 1
#4 2 2
Or
df1[with(df1, !ave(number, from, FUN=anyDuplicated)),]
# from number
#3 2 1
#4 2 2
Using concept of variance shared by David but doing it dplyr way:
library(dplyr)
df %>%
group_by(from) %>%
mutate(variance=var(number)) %>%
filter(variance!=0) %>%
select(from,number)
#Source: local data frame [2 x 2]
#Groups: from
#from number
#1 2 1
#2 2 2
I have data with a grouping variable ("from") and values ("number"):
from number
1 1
1 1
2 1
2 2
3 2
3 2
I want to subset the data and select groups which have two or more unique values. In my data, only group 2 has more than one distinct 'number', so this is the desired result:
from number
2 1
2 2
Several possibilities, here's my favorite
library(data.table)
setDT(df)[, if(+var(number)) .SD, by = from]
# from number
# 1: 2 1
# 2: 2 2
Basically, per each group we are checking if there is any variance, if TRUE, then return the group values
With base R, I would go with
df[as.logical(with(df, ave(number, from, FUN = var))), ]
# from number
# 3 2 1
# 4 2 2
Edit: for a non numerical data you could try the new uniqueN function for the devel version of data.table (or use length(unique(number)) > 1 instead
setDT(df)[, if(uniqueN(number) > 1) .SD, by = from]
You could try
library(dplyr)
df1 %>%
group_by(from) %>%
filter(n_distinct(number)>1)
# from number
#1 2 1
#2 2 2
Or using base R
indx <- rowSums(!!table(df1))>1
subset(df1, from %in% names(indx)[indx])
# from number
#3 2 1
#4 2 2
Or
df1[with(df1, !ave(number, from, FUN=anyDuplicated)),]
# from number
#3 2 1
#4 2 2
Using concept of variance shared by David but doing it dplyr way:
library(dplyr)
df %>%
group_by(from) %>%
mutate(variance=var(number)) %>%
filter(variance!=0) %>%
select(from,number)
#Source: local data frame [2 x 2]
#Groups: from
#from number
#1 2 1
#2 2 2
This is an approximation of the original dataframe. In the original, there are many more columns than are shown here.
id init_cont family description value
1 K S impacteach 1
1 K S impactover 3
1 K S read 2
2 I S impacteach 2
2 I S impactover 4
2 I S read 1
3 K D impacteach 3
3 K D impactover 5
3 K D read 3
I want to combine the values for impacteach and impactover to generate an average value that is just called impact. I would like the final table to look like the following:
id init_cont family description value
1 K S impact 2
1 K S read 2
2 I S impact 3
2 I S read 1
3 K D impact 4
3 K D read 3
I have not been able to figure out how to generate this table. However, I have been able to create a dataframe that looks like this:
id description value
1 impact 2
1 read 2
2 impact 3
2 read 1
3 impact 4
3 read 3
What is the best way for me to take these new values and add them to the original dataframe? I also need to remove the original values (like impacteach and impactover) in the original dataframe. I would prefer to modify the original dataframe as opposed to creating an entirely new dataframe because the original dataframe has many columns.
In case it is useful, this is a summary of the code I used to create the shorter dataframe with impact as a combination of impacteach and impactover:
df %<%
mutate(newdescription = case_when(description %in% c("impacteach", "impactoverall") ~ "impact", TRUE ~ description)) %<%
group_by(id, newdescription) %<%
summarise(value = mean(as.numeric(value)))
What if you changed the description column first so that it could be included in the grouping:
df %>%
mutate(description = substr(description, 1, 6)) %>%
group_by(id, init_cont, family, description) %>%
summarise(value = mean(value))
# A tibble: 6 x 5
# Groups: id, init_cont, family [?]
# id init_cont family description value
# <int> <chr> <chr> <chr> <dbl>
# 1 1 K S impact 2.
# 2 1 K S read 2.
# 3 2 I S impact 3.
# 4 2 I S read 1.
# 5 3 K D impact 4.
# 6 3 K D read 3.
You just need to modify your group_by statement. Try group_by(id, init_cont, family)
Because your id seems to be mapped to init_cont and family already, adding in these values won't change your summarization result. Then you have all the columns you want with no extra work.
If you have a lot of columns you could trying something like the code below. Essentially, do a left_join onto your original data with your summarised data, but doing it using the . to not store off a new dataframe. Then, once joined (by id and description which we modified in place) you'll have two value columns which should be prepeneded with a .x and .y, drop the original and then use distinct to get rid of the duplicate 'impact' columns.
df %>%
mutate(description = case_when(description %in% c("impacteach", "impactoverall") ~ "impact", TRUE ~ description)) %>%
left_join(. %>%
group_by(id, description)
summarise(value = mean(as.numeric(value))
,by=c('id','description')) %>%
select(-value.x) %>%
distinct()
gsub can be used to replace description containing imact as impact and then group_by from dplyr package will help in summarising the value.
df %>% group_by(id, init_cont, family,
description = gsub("^(impact).*","\\1", description)) %>%
summarise(value = mean(value))
# # A tibble: 6 x 5
# # Groups: id, init_cont, family [?]
# id init_cont family description value
# <int> <chr> <chr> <chr> <dbl>
# 1 1 K S impact 2.00
# 2 1 K S read 2.00
# 3 2 I S impact 3.00
# 4 2 I S read 1.00
# 5 3 K D impact 4.00
# 6 3 K D read 3.00
I have data with a grouping variable ("from") and values ("number"):
from number
1 1
1 1
2 1
2 2
3 2
3 2
I want to subset the data and select groups which have two or more unique values. In my data, only group 2 has more than one distinct 'number', so this is the desired result:
from number
2 1
2 2
Several possibilities, here's my favorite
library(data.table)
setDT(df)[, if(+var(number)) .SD, by = from]
# from number
# 1: 2 1
# 2: 2 2
Basically, per each group we are checking if there is any variance, if TRUE, then return the group values
With base R, I would go with
df[as.logical(with(df, ave(number, from, FUN = var))), ]
# from number
# 3 2 1
# 4 2 2
Edit: for a non numerical data you could try the new uniqueN function for the devel version of data.table (or use length(unique(number)) > 1 instead
setDT(df)[, if(uniqueN(number) > 1) .SD, by = from]
You could try
library(dplyr)
df1 %>%
group_by(from) %>%
filter(n_distinct(number)>1)
# from number
#1 2 1
#2 2 2
Or using base R
indx <- rowSums(!!table(df1))>1
subset(df1, from %in% names(indx)[indx])
# from number
#3 2 1
#4 2 2
Or
df1[with(df1, !ave(number, from, FUN=anyDuplicated)),]
# from number
#3 2 1
#4 2 2
Using concept of variance shared by David but doing it dplyr way:
library(dplyr)
df %>%
group_by(from) %>%
mutate(variance=var(number)) %>%
filter(variance!=0) %>%
select(from,number)
#Source: local data frame [2 x 2]
#Groups: from
#from number
#1 2 1
#2 2 2