I have a dataframe with two columns per sample (n > 1000 samples):
df <- data.frame(
"sample1.a" = 1:5, "sample1.b" = 2,
"sample2.a" = 2:6, "sample2.b" = c(1, 3, 3, 3, 3),
"sample3.a" = 3:7, "sample3.b" = 2)
If there is a zero in column .b, the correspsonding value in column .a should be set to NA.
I thought to write a function over colnames (without suffix) to filter each pair of columns and conditional exchaning values. Is there a simpler approach based on tidyverse?
We can split the data.frame into a list of data.frames and do the replacement in base R
df1 <- do.call(cbind, lapply(split.default(df,
sub("\\..*", "", names(df))), function(x) {
x[,1][x[2] == 0] <- NA
x}))
Or another option is Map
acols <- endsWith(names(df), "a")
bcols <- endsWith(names(df), "b")
df[acols] <- Map(function(x, y) replace(x, y == 0, NA), df[acols], df[bcols])
Or if the columns are alternate with 'a', 'b' columns, use a logical index for recycling, create the logical matrix with 'b' columns and assign the corresponding values in 'a' columns to NA
df[c(TRUE, FALSE)][df[c(FALSE, TRUE)] == 0] <- NA
or an option with tidyverse by reshaping into 'long' format (pivot_longer), changing the 'a' column to NA if there is a correspoinding 0 in 'a', and reshape back to 'wide' format with pivot_wider
library(dplyr)
library(tidyr)
df %>%
mutate(rn = row_number()) %>%
pivot_longer(cols = -rn, names_sep="\\.",
names_to = c('group', '.value')) %>%
mutate(a = na_if(b, a == 0)) %>%
pivot_wider(names_from = group, values_from = c(a, b)) %>%
select(-rn)
# A tibble: 5 x 6
# a_sample1 a_sample2 a_sample3 b_sample1 b_sample2 b_sample3
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 2 1 2 2 1 2
#2 2 3 2 2 3 2
#3 2 3 2 2 3 2
#4 2 3 2 2 3 2
#5 2 3 2 2 3 2
Related
Data:
ID
B
C
1
NA
x
2
x
NA
3
x
x
Results:
ID
Unified
1
C
2
B
3
B_C
I'm trying to combine colums B and C, using mutate and unify, but how would I scale up this function so that I can reuse this for multiple columns (think 100+), instead of having to write out the variables each time? Or is there a function that's already built in to do this?
My current solution is this:
library(tidyverse)
Data %>%
mutate(B = replace(B, B == 'x', 'B'), C = replace(C, C == 'x', 'C')) %>%
unite("Unified", B:C, na.rm = TRUE, remove= TRUE)
We may use across to loop over the column, replace the value that corresponds to 'x' with column name (cur_column())
library(dplyr)
library(tidyr)
Data %>%
mutate(across(B:C, ~ replace(., .== 'x', cur_column()))) %>%
unite(Unified, B:C, na.rm = TRUE, remove = TRUE)
-output
ID Unified
1 1 C
2 2 B
3 3 B_C
data
Data <- structure(list(ID = 1:3, B = c(NA, "x", "x"), C = c("x", NA,
"x")), class = "data.frame", row.names = c(NA, -3L))
Here are couple of options.
Using dplyr -
library(dplyr)
cols <- names(Data)[-1]
Data %>%
rowwise() %>%
mutate(Unified = paste0(cols[!is.na(c_across(B:C))], collapse = '_')) %>%
ungroup -> Data
Data
# ID B C Unified
# <int> <chr> <chr> <chr>
#1 1 NA x C
#2 2 x NA B
#3 3 x x B_C
Base R
Data$Unified <- apply(Data[cols], 1, function(x)
paste0(cols[!is.na(x)], collapse = '_'))
I have a tibble with the explicit "id" and colnames I need to convert to NA's. Is there anyway I can create the NA's without making my df a long dataset? I considered using the new rows_update function, but I'm not sure if this is correct because I only want certain columns to be NA.
library(dplyr)
to_na <- tribble(~x, ~col,
1, "z",
3, "y"
)
df <- tibble(x = c(1,2,3),
y = c(1,1,1),
z = c(2,2,2))
# desired output:
#> # A tibble: 3 x 3
#> x y z
#> <dbl> <dbl> <dbl>
#> 1 1 1 NA
#> 2 2 1 2
#> 3 3 NA 2
Created on 2020-07-03 by the reprex package (v0.3.0)
This definitely isn't the most elegant solution, but it gets the output you want.
library(dplyr)
library(purrr)
to_na <- tribble(~x, ~col,
1, "z",
3, "y"
)
df <- tibble(x = c(1,2,3),
y = c(1,1,1),
z = c(2,2,2))
map2(to_na$x, to_na$col, #Pass through these two objects in parallel
function(xval_to_missing, col) df %>% #Two objects above matched by position here.
mutate_at(col, #mutate_at the specified cols
~if_else(x == xval_to_missing, NA_real_, .) #if x == xval_to_missing, make NA, else keep as is.
) %>%
select(x, col) #keep x and the modified column.
) %>% #end of map2
reduce(left_join, by = "x") %>% #merge within the above list, by x.
relocate(x, y, z) #Keep your ordering
Output:
# A tibble: 3 x 3
x y z
<dbl> <dbl> <dbl>
1 1 1 NA
2 2 1 2
3 3 NA 2
We can use row/column indexing to assign the values to NA in base R
df <- as.data.frame(df)
df[cbind(to_na$x, match(to_na$col, names(df)))] <- NA
df
# x y z
#1 1 1 NA
#2 2 1 2
#3 3 NA 2
If we want to use rows_update
library(dplyr)
library(tidyr)
library(purrr)
lst1 <- to_na %>%
mutate(new = NA_real_) %>%
split(seq_len(nrow(.))) %>%
map(~ .x %>%
pivot_wider(names_from = col, values_from = new))
for(i in seq_along(lst1)) df <- rows_update(df, lst1[[i]])
df
# A tibble: 3 x 3
# x y z
# <dbl> <dbl> <dbl>
#1 1 1 NA
#2 2 1 2
#3 3 NA 2
I have a table with ID and other columns. I want to group the data by Ids and get the unique values of all columns.
from above table group by ID and get unique(Alt1, Alt2, Alt3)
Resul should be in vector form
A -> 1,2,3,5
B ->1,3,4,5,7
We can get data in long format and for each ID make a list of unique values.
library(dplyr)
library(tidyr)
df1 <- df %>%
pivot_longer(cols = -ID) %>%
group_by(ID) %>%
summarise(value = list(unique(value))) %>%
unnest(value)
df1
# ID value
# <fct> <dbl>
# 1 A 1
# 2 A 3
# 3 A 2
# 4 A 5
# 5 B 1
# 6 B 4
# 7 B 5
# 8 B 3
# 9 B 6
#10 B 7
We can store it as a list if needed using split.
split(df1$value, df1$ID)
#$A
#[1] 1 3 2 5
#$B
#[1] 1 4 5 3 6 7
data.table equivalent of the above would be :
library(Data.table)
setDT(df)
df2 <- melt(df, id.vars = 'ID')[, .(value = list(unique(value))), ID]
unique values are present in df2$value as a vector.
data
df <- data.frame(ID = c('A', 'A', 'B', 'B'),
Alt1 = c(1, 2, 1, 3),
Alt2 = c(3, 5, 4, 6),
Alt3 = c(1, 3, 5, 7))
I have a Data Frame with a variable with different values for another variable.
Like this:
DataFrame
So, I need a subset when the value of S contain all the possible values of B. In this example, el subset is conformed by S = a and S = b:
Subset
Any idea? Thanks!!
An option would be to group by 'S' and filter the rows having all the unique values of the column 'B' %in% 'B'
library(dplyr)
un1 <- unique(df1$B)
df1 %>%
group_by(S) %>%
filter(all(un1 %in% B))
# A tibble: 8 x 2
# Groups: S [2]
# S B
# <fct> <dbl>
#1 a 1
#2 a 2
#3 a 3
#4 a 4
#5 d 1
#6 d 2
#7 d 3
#8 d 4
Or with data.table
library(data.table)
setDT(df1)[, .SD[all(un1 %in% B)], S]
Or using base R
df1[with(df1, ave(B, S, FUN = function(x) all(un1 %in% x)) == 1),]
data
df1 <- data.frame(S = rep(letters[1:4], c(4, 3, 2, 4)),
B = c(1:4, c(1, 3, 4), 1:2, 1:4))
There is my problem that I can't solve it:
Data:
df <- data.frame(f1=c("a", "a", "b", "b", "c", "c", "c"),
v1=c(10, 11, 4, 5, 0, 1, 2))
data.frame:f1 is factor
f1 v1
a 10
a 11
b 4
b 5
c 0
c 1
c 2
# What I want is:(for example, fetch data with the number of element of some level == 2, then to data.frame)
a b
10 4
11 5
Thanks in advance!
I might be missing something simple here , but the below approach using dplyr works.
library(dplyr)
nlevels = 2
df1 <- df %>%
add_count(f1) %>%
filter(n == nlevels) %>%
select(-n) %>%
mutate(rn = row_number()) %>%
spread(f1, v1) %>%
select(-rn)
This gives
# a b
# <int> <int>
#1 10 NA
#2 11 NA
#3 NA 4
#4 NA 5
Now, if you want to remove NA's we can do
do.call("cbind.data.frame", lapply(df1, function(x) x[!is.na(x)]))
# a b
#1 10 4
#2 11 5
As we have filtered the dataframe which has only nlevels observations, we would have same number of rows for each column in the final dataframe.
split might be useful here to split df$v1 into parts corresponding to df$f1. Since you are always extracting equal length chunks, it can then simply be combined back to a data.frame:
spl <- split(df$v1, df$f1)
data.frame(spl[lengths(spl)==2])
# a b
#1 10 4
#2 11 5
Or do it all in one call by combining this with Filter:
data.frame(Filter(function(x) length(x)==2, split(df$v1, df$f1)))
# a b
#1 10 4
#2 11 5
Here is a solution using unstack :
unstack(
droplevels(df[ave(df$v1, df$f1, FUN = function(x) length(x) == 2)==1,]),
v1 ~ f1)
# a b
# 1 10 4
# 2 11 5
A variant, similar to #thelatemail's solution :
data.frame(Filter(function(x) length(x) == 2, unstack(df,v1 ~ f1)))
My tidyverse solution would be:
library(tidyverse)
df %>%
group_by(f1) %>%
filter(n() == 2) %>%
mutate(i = row_number()) %>%
spread(f1, v1) %>%
select(-i)
# # A tibble: 2 x 2
# a b
# * <dbl> <dbl>
# 1 10 4
# 2 11 5
or mixing approaches :
as_tibble(keep(unstack(df,v1 ~ f1), ~length(.x) == 2))
Using all base functions (but you should use tidyverse)
# Add count of instances
x$len <- ave(x$v1, x$f1, FUN = length)
# Filter, drop the count
x <- x[x$len==2, c('f1','v1')]
# Hacky pivot
result <- data.frame(
lapply(unique(x$f1), FUN = function(y) x$v1[x$f1==y])
)
colnames(result) <- unique(x$f1)
> result
a b
1 10 4
2 11 5
I'd like code this, may it helps for you
library(reshape2)
library(dplyr)
aa = data.frame(v1=c('a','a','b','b','c','c','c'),f1=c(10,11,4,5,0,1,2))
cc = aa %>% group_by(v1) %>% summarise(id = length((v1)))
dd= merge(aa,cc) #get the level
ee = dd[dd$aa==2,] #select number of level equal to 2
ee$id = rep(c(1,2),nrow(ee)/2) # reset index like (1,2,1,2)
dcast(ee, id~v1,value.var = 'f1')
all done!