My data has many columns and subjects, but to illustrate it simpler, lets say I have 7 subjects with 3 variables/columns called x1, x2 and x3 (values range from 1 to 3 and NAs). In the analysis that I want it is important I actually call the columns I want to use (since I cannot just use the whole dataframe in my analysis because there are more variables/columns there)
>data <- data.frame(‘id’=c(1,2,3,4,5,6,7), ‘x1’=c(1,2,2,NA,3,3,1), ‘x2’=c(NA,3,1,NA,2,3,2), ‘x3’=c(NA,2,NA,NA,3,NA,1)
id x1 x2 x3
1 1 NA NA
2 2 3 2
3 2 1 NA
4 NA NA NA
5 3 2 NA
6 3 3 NA
7 1 2 1
The class of x1 x2 and x3 are numeric.
Out of that, I want to create a variable/column called ‘x4’ that:
- gives me the lowest number of row x1, x2 and x3.
-If there is an NA in a row of x1,x2,x3, the NA shall be ignored.
-If they are however ALL NAs, I would want the outcome to be NA. (NOT Inf, which is what it does with my code now)
-If there are two lowest numbers that are the same, just display any one of those two. So like this:
>data <- data.frame(‘id’=c(1,2,3,4,5,6,7), ‘x1’=c(1,2,2,NA,3,3,1), ‘x2’=c(NA,3,1,NA,2,3,2), ‘x3’=c(NA,2,NA,NA,3,NA,1), ‘x4’=c(1,2,1,NA,2,3,1)
id x1 x2 x3 x4
1 1 NA NA 1
2 2 3 2 2
3 2 1 NA 1
4 NA NA NA NA
5 3 2 NA 2
6 3 3 NA 3
7 1 2 1 1
I managed to find a very similar question, and I can mostly make it work: min for each row with dataframe in R
data$x4 <- apply(data[, c("x1","x2","x3")],1, FUN=min, na.rm = TRUE)
the problem I have now is that in case of all NAs (so id number 4), my outcome is not NA, but it is 'Inf'.
Question 1:How can I make it so it becomes an NA instead of Inf? I can of course do that afterwards like this:
is.na(data$x4) <- sapply(data$x4, is.infinite)
But I wonder if there is a nice way to do that already with/inside the previous code?
Also, rather then using sapply and the inside FUNction min, I would also like to try to make it work with code in a way like below: Question 2: is using this other code below possible?
data$x4 <- min(data[, c("x1","x2","x3")],1 , na.rm = TRUE)
for this x4 gets the outcome '1' everytime. I guess it just shows the lowest number (1) of the whole column? I dont understand why. I am already using ',1' but doesnt help.
I hope somebody can help me(r and stackoverflow newbie) out, thanks!
You are looking for pmin function which returns the (regular or parallel) minima of the input values. Below are two approaches using pmin:
df$minIget <- do.call(pmin, c(df[,-1], na.rm = TRUE)) # Approch1: using do.call
df %>% rowwise() %>% mutate(minIget = pmin(x1, x2,x3,na.rm = T))# Approch2: using tidyverse.
output:
A tibble: 7 x 5
# Rowwise:
id x1 x2 x3 minIget
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 NA NA 1
2 2 2 3 2 2
3 3 2 1 NA 1
4 4 NA NA NA NA
5 5 3 2 3 2
6 6 3 3 NA 3
7 7 1 2 1 1
You can test if all are NA before you call min like:
apply(data[, c("x1","x2","x3")], 1, function(x)
if(all(is.na(x))) NA else min(x, na.rm=TRUE))
#[1] 1 2 1 NA 2 3 1
min(data[, c("x1","x2","x3")],1 , na.rm = TRUE) gives you the minimum of 1 and data[, c("x1","x2","x3")].
Related
I am still new to R and need help. I want to change the NA value in variables x1,x2,x3 to 0 based on the value of count. Count specifies the number of observations, and the x1,x2,x3 stand for the visit to the site (or replication). The value in each 'X' variable is the number of species found. However, not all sites were visited 3 times. The variable count is telling us how many times the site was actually visited. I want to identify the actual NA and real 0 (which means no species found). I want to change the NA into 0 if the site is actually visited and keep it NA if the site is not visited. For example from the dummy data, 'zhask' site is visited 2 times, then the NA in x1 of zhask needs to be replaced with 0.
This is the dummy data:
site x1 x2 x3 count
1 miya 1 2 1 3
2 zhask NA 1 NA 2
3 balmond 3 NA 2 3
4 layla NA 1 NA 2
5 angela NA 3 NA 2
So, it the table need to be changed into:
site x1 x2 x3 count
1 miya 1 2 1 3
2 zhask 0 1 NA 2
3 balmond 3 0 2 3
4 layla 0 1 NA 2
5 angela 0 3 NA 2
I've tried many things and try to make my own function, however, it is not working:
for(i in 1:nrow(df))
{
if( is.na(df$x1[i]) && (i < df$count[i]))
{df$x1[i]=0}
else
{df$x1[i]=df$x1[i]}
}
this is the script for the dummy dataframe:
x1= c(1,NA,3, NA, NA)
x2= c(2,1, NA, 1, 3)
x3 = c(1, NA, 2, NA, NA)
count=c(3,2,3,2,2)
site=c("miya", "zhask", "balmond", "layla", "angela")
df=data.frame(site,x1,x2,x3,count)
Any help will be very much appreciated!
One way to be to apply a function over all of your count columns. Here's a way to do that.
cols <- c("x1", "x2", "x3")
df[, cols] <- mapply(function(col, idx, count) {
ifelse(idx <=count & is.na(col), 0, col)
}, df[,cols], seq_along(cols), MoreArgs=list(count=df$count))
# site x1 x2 x3 count
# 1 miya 1 2 1 3
# 2 zhask 0 1 NA 2
# 3 balmond 3 0 2 3
# 4 layla 0 1 NA 2
# 5 angela 0 3 NA 2
We use mapply to iterate over the columns and the index of the column. We also pass in the count value each time (since it's the same for all columns, it goes in the MoreArgs= parameter). This mapply will return a list and we can use that to replace the columns with the updated values.
If you wanted to use dplyr, that might look more like
library(dplyr)
cols <- c("x1"=1, "x2"=2, "x3"=3)
df %>%
mutate(across(starts_with("x"), ~if_else(cols[cur_column()]<count & is.na(.x), 0, .x)))
I used the cols vector to get the index of the column which doesn't seem to be otherwise available when using across().
But a more "tidy" way to tackle this problem would be to pivot your data first to a "tidy" format. Then you can clean the data more easily and pivot back if necessary
library(dplyr)
library(tidyr)
df %>%
pivot_longer(cols=starts_with("x")) %>%
mutate(index=readr::parse_number(name)) %>%
mutate(value=if_else(index < count & is.na(value), 0, value)) %>%
select(-index) %>%
pivot_wider(names_from=name, values_from=value)
# site count x1 x2 x3
# <chr> <dbl> <dbl> <dbl> <dbl>
# 1 miya 3 1 2 1
# 2 zhask 2 0 1 NA
# 3 balmond 3 3 0 2
# 4 layla 2 0 1 NA
# 5 angela 2 0 3 NA
Via some indexing of the columns:
vars <- c("x1","x2","x3")
df[vars][is.na(df[vars]) & (col(df[vars]) <= df$count)] <- 0
# site x1 x2 x3 count
#1 miya 1 2 1 3
#2 zhask 0 1 NA 2
#3 balmond 3 0 2 3
#4 layla 0 1 NA 2
#5 angela 0 3 NA 2
Essentially this is:
selecting the variables/columns and storing in vars
flagging the NA cells within those variables with is.na(df[vars])
col(df[vars]) returns a column number for every cell, which can be checked if it is less than the df$count in each corresponding row
the values meeting both the above criteria are overwritten <- with 0
This could be yet another solution using purrr::pmap:
purrr::pmap is used for row-wise operations when applied on a data frame. It enables us to iterate over multiple arguments at the same time. So here c(...) refers to all corresponding elements of the selected variable (all except site) in each row
I think the rest of the solution is pretty clear but please let me know if I need to explain more about this.
library(dplyr)
library(purrr)
library(tidyr)
df %>%
mutate(output = pmap(df[-1], ~ {x <- head(c(...), -1)
inds <- which(is.na(x))
req <- tail(c(...), 1) - sum(!is.na(x))
x[inds[seq_len(req)]] <- 0
x})) %>%
select(site, output, count) %>%
unnest_wider(output)
# A tibble: 5 x 5
site x1 x2 x3 count
<chr> <dbl> <dbl> <dbl> <dbl>
1 miya 1 2 1 3
2 zhask 0 1 NA 2
3 balmond 3 0 2 3
4 layla 0 1 NA 2
5 angela 0 3 NA 2
For the following sample data dat, is there a way to calculate min and max while handling NAs. My input is:
dat <- read.table(text = "ID Name PM TP2 Sigma
1 Tim 1 2 3
2 Sam 0 NA 1
3 Pam 2 1 NA
4 Ali 1 0 2
NA NA NA NA NA
6 Tim 2 0 7", header = TRUE)
My required output is:
ID Name PM TP2 Sigma Min Max
1 Tim 1 2 3 1 3
2 Sam 0 NA 1 0 1
3 Pam 2 1 NA 1 2
4 Ali 1 0 2 0 2
NA NA NA NA NA NA NA
6 Tim 2 0 7 0 7
My Effort
1- I have seen similar posts but none of them has discussed issues where all entries in a column were NAs e.g., Get the min of two columns
Based on this, I have tried pmin() and pmax(), but they do not work for me.
2- Another similar question is minimum (or maximum) value of each row across multiple columns. Again, there is no need to handle NAs.
3- Lastly, this question minimum (or maximum) value of each row across multiple columns talks about NA but not all elements in a column have missing values.
4- Also, some of the solutions require that the columns list to be included to be excluded is typed manually, my original data is quite wide, I want to have an easier solution where I can express columns by numbers rather than names.
Partial Solution
I have tried the following solution but Min column ends up having Inf and the Max column ends up having -Inf.
dat$min = apply(dat[,c(2:4)], 1, min, na.rm = TRUE)
dat$max = apply(dat[,c(2:4)], 1, max, na.rm = TRUE)
I can manually get rid of Inf by using something like:
dat$min[is.infinite(dat$min)] = NA
But I was wondering if there is a better way of achieving my desired outcome? Any advice would be greatly appreciated.
Thank you for your time.
You can use hablar's min_ and max_ function which returns NA if all values are NA.
library(dplyr)
library(hablar)
dat %>%
rowwise() %>%
mutate(min = min_(c_across(-ID)),
max = max_(c_across(-ID)))
You can also use this with apply -
cbind(dat, t(apply(dat[-1], 1, function(x) c(min = min_(x), max = max_(x)))))
# ID PM TP2 Sigma min max
#1 1 1 2 3 1 3
#2 2 0 NA 1 0 1
#3 3 2 1 NA 1 2
#4 4 1 0 2 0 2
#5 NA NA NA NA NA NA
#6 5 2 0 7 0 7
The following solution seems to work with the transform() function:
dat <- transform(dat, min = pmin(PM, TP2, Sigma))
dat <- transform(dat, max = pmin(PM, TP2, Sigma))
Without using the transform() function, the data seemed to mess up. Also, the above command requires that all column names are written explicitly. I do not understand why writing a short version like below, fails.
pmin(dat[,2:4])) or
pmax(dat[,2:4]))
I am posting the only solution that I could come up with, in case someone else stumbles upon a similar issue.
I would use data.table for this task. I use the rowSums to count the numbers of row with na and compare it to the number of columns in total. I just use in dat.new all columns where you have at least one nonNA value. Then you can use the na.rm=T as usually.
I hope this little code helps you.
library(data.table)
#your data
dat <- read.table(text = "ID PM TP2 Sigma
1 1 2 3
2 0 NA 1
3 2 1 NA
4 1 0 2
NA NA NA NA
5 2 0 7", header = TRUE)
#generate data.table and add id
dat <- data.table(dat)
number.cols <- dim(dat)[2] #4
dat[,id:=c(1:dim(dat)[1])]
# > dat
# ID PM TP2 Sigma id
# 1: 1 1 2 3 1
# 2: 2 0 NA 1 2
# 3: 3 2 1 NA 3
# 4: 4 1 0 2 4
# 5: NA NA NA NA 5
# 6: 5 2 0 7 6
#use new data.table to select all rows with at least one nonNA value
dat.new <- dat[rowSums(is.na(dat))<number.cols,]
dat.new[, MINv:=min(.SD, na.rm=T), by=id]
dat.new[, MAXv:=max(.SD, na.rm=T), by=id]
#if you need it merged to the old data
dat <- merge(dat, dat.new[,.(id,MINv,MAXv)], by="id")
On way might be to use pmin and pmax with do.call:
dat$min <- do.call(pmin, c(dat[,c(3:5)], na.rm=TRUE))
dat$max <- do.call(pmax, c(dat[,c(3:5)], na.rm=TRUE))
dat
# ID Name PM TP2 Sigma min max
#1 1 Tim 1 2 3 1 3
#2 2 Sam 0 NA 1 0 1
#3 3 Pam 2 1 NA 1 2
#4 4 Ali 1 0 2 0 2
#5 NA <NA> NA NA NA NA NA
#6 6 Tim 2 0 7 0 7
I have a dataframe with multiple columns and I want to replace NAs in one column if they are between two rows with an identical number. Here is my data:
v1 v2
1 2
NA 3
NA 2
1 1
NA 7
NA 2
3 1
I basically want to start from the beginning of the data frame and replcae NAs in column v1 with previous Non NA if the next Non NA matches the previous one. That been said, I want the result to be like this:
v1 v2
1 2
1 3
1 2
1 1
NA 7
NA 2
3 1
As you may see, rows 2 and 3 are replaced with number "1" because row 1 and 4 had an identical number but rows 5,6 stays the same because the non na values in rows 4 and 7 are not identical. I have been twicking a lot but so far no luck. Thanks
Here is an idea using zoo package. We basically fill NAs in both directions and set NA the values that are not equal between those directions.
library(zoo)
ind1 <- na.locf(df$v1, fromLast = TRUE)
df$v1 <- na.locf(df$v1)
df$v1[df$v1 != ind1] <- NA
which gives,
v1 v2
1 1 2
2 1 3
3 1 2
4 1 1
5 NA 7
6 NA 2
7 3 1
Here is a similar approach in tidyverse using fill
library(tidyverse)
df1 %>%
mutate(vNew = v1) %>%
fill(vNew, .direction = 'up') %>%
fill(v1) %>%
mutate(v1 = replace(v1, v1 != vNew, NA)) %>%
select(-vNew)
# v1 v2
#1 1 2
#2 1 3
#3 1 2
#4 1 1
#5 NA 7
#6 NA 2
#7 3 1
Here is a base R solution, the logic is almost the same as Sotos's one:
replace_na <- function(x){
f <- function(x) ave(x, cumsum(!is.na(x)), FUN = function(x) x[1])
y <- f(x)
yp <- rev(f(rev(x)))
ifelse(!is.na(y) & y == yp, y, x)
}
df$v1 <- replace_na(df$v1)
test:
> replace_na(c(1, NA, NA, 1, NA, NA, 3))
[1] 1 1 1 1 NA NA 3
I could use na.locf function to do so. Basically, I use the normal na.locf function package zoo to replace each NA with the latest previous non NA and store the data in a column. by using the same function but fixing fromlast=TRUE NAs are replaces with the first next nonNA and store them in another column. I checked these two columns and if the results in each row for these two columns are not matching I replace them with NA.
I would like to drop entire rows from a data frame if they have all NAs but for only certain subset of columns (which are named in a sequence as well as start with "X").
This is different than other SO answers that I found from what I can tell since I cannot refer to each column manually by name (too many variables) and do not only want to drop the rows if they are completely NA (rather if some variables are completely NA).
So turn sample data:
data1 <- as.data.frame(rbind(c(1,2,3), c(1, NA, 4), c(4,6,7), c(1, NA, NA), c(4, 8, NA)))
colnames(data1) <- c("Z","X1","X2")
data1
Z X1 X2
1 1 2 3
2 1 NA 4
3 4 6 7
4 1 NA NA
5 4 8 NA
into:
V1 V2 V3
1 1 2 3
2 1 NA 4
3 4 6 7
4 4 8 NA
I.e. drop the row if both X1 and X2 (all of the X sequence) are NA.
In this example there are only two variables(X1:X2)for ease but in reality I have closer to 100 of this sequence and many other important variables that may or may not be NA. I would prefer to do so in dplyr with filter but other solutions would be appreciated as well.
I feel like:
data2 %>% filter(!is.na(all(X1:X2)))
or something similar is close but R does not like the sequence reference to X1:X2 within filter.
You can use rowSums + select + starts_with + filter:
data1 %>%
filter(rowSums(!is.na(select(., starts_with("X")))) != 0)
# Z X1 X2
#1 1 2 3
#2 1 NA 4
#3 4 6 7
#4 4 8 NA
A base R solution using apply would be:
drop <- which(apply(data1[,startsWith(colnames(data1), "X")], 1, function(x) all(is.na(x))))
data1[-drop,]
# Z X1 X2
#1 1 2 3
#2 1 NA 4
#3 4 6 7
#5 4 8 NA
Another option using rowSums:
drop <- which(rowSums(is.na(data1[,c("X1","X2")]))>=2)
data1[-drop]
I would like to get rid of duplicated records in each row of my df:
df <- data.frame(a=c(1,3,5), b =c(1,2,4), c=c(2,3,7))
X1 X2 X3
1 1 1 2
2 3 2 3
3 5 4 7
I want to get this:
X1 X2 X3
1 1 NA 2
2 3 2 NA
3 5 4 7
Now, I can achieve this using apply:
data.frame(t(apply(df,1, function(row) ifelse(!duplicated(row), row, NA))))
but it seems unlikely that there isn't a more compact (and perhaps efficient) way of achieving this.
Am I missing a command or package here?