I have a dataset like this:
1 2 3 4 5
1 1 0 0 0 0
2 1 0 0 2 0
3 0 0 0 1 5
4 2 0 0 0 0
I want to subset the rows with more than one column beeing non-zero (means rows 2,3)
I know that it has to be something like dataset[... dataset ...] but I did not find out how to access rows as such without using a for-loop
You just need rowSums really. Assuming your dataset is called "mydf", try:
> mydf[rowSums(mydf != 0) > 1, ]
X1 X2 X3 X4 X5
2 1 0 0 2 0
3 0 0 0 1 5
Here, rowSums(mydf != 0) will return a vector of how many values in each row is greater not zero. Then, adding our condition > 1 would create a logical vector which can be used to subset the rows we want.
Related
d <- data.frame(B1 = c(1,2,3,4),B2 = c(0,1,2,3))
d$total=rowSums(d)
B1 B2 total
1 0 1
2 1 3
3 2 5
4 3 7
Using the dataframe above, I want to create a new dataframe with the following logic:
Going by rows, if cells (B1:B2) matches d$total, return 1, else 0.
Ideally output to look like:
B1n B2n
1 0
0 0
0 0
0 0
What is the best way to do this in R?
Thank you.
You can compare first 2 columns with total value.
res <- +(d[1:2] == d$total)
res
# B1 B2
#[1,] 1 0
#[2,] 0 0
#[3,] 0 0
#[4,] 0 0
The result is a matrix, if you want dataframe as output you can do res <- data.frame(res).
Here is an alternate way to solve this problem. You can use dplyr::transmute which is the opposite of dplyr::mutate which will give you two separate columns. Inside transmute are just conditions.
library(dplyr)
newdf <- d %>% transmute(B1n=ifelse(B1+B2==B1,1,0),B2n=ifelse(B1+B2==B2,1,0))
> newdf
B1n B2n
1 1 0
2 0 0
3 0 0
4 0 0
From a given dataframe:
# Create dataframe with 4 variables and 10 obs
set.seed(1)
df<-data.frame(replicate(4,sample(0:1,10,rep=TRUE)))
I would like to compute a substract operation between in all columns combinations by pairs, but only keeping one substact, i.e column A- column B but not column B-column A and so on.
What I got is very manual, and this tend to be not so easy when there are lots of variables.
# Result
df_result <- as.data.frame(list(df$X1-df$X2,
df$X1-df$X3,
df$X1-df$X4,
df$X2-df$X3,
df$X2-df$X4,
df$X3-df$X4))
Also the colname of the feature name should describe the operation i.e.(x1_x2) being x1-x2.
You can use combn:
COMBI = combn(colnames(df),2)
res = data.frame(apply(COMBI,2,function(i)df[,i[1]]-df[,i[2]]))
colnames(res) = apply(COMBI,2,paste0,collapse="minus")
head(res)
X1minusX2 X1minusX3 X1minusX4 X2minusX3 X2minusX4 X3minusX4
1 0 0 -1 0 -1 -1
2 1 1 0 0 -1 -1
3 0 0 0 0 0 0
4 0 0 -1 0 -1 -1
5 1 1 1 0 0 0
6 -1 0 0 1 1 0
I have a data frame like this:
Q17a_17 Q17a_18 Q17a_19 Q17a_20 Q17a_21 Q17a_22 Q17a_23
1 NA NA NA NA NA NA NA
2 0 0 0 0 0 0 1
3 0 0 0 0 0 1 1
4 0 0 0 0 0 0 1
5 1 0 0 0 1 1 0
6 0 0 0 0 0 1 1
7 1 1 0 0 1 0 1
And I would like to merge Q17a_17, Q17a_19 and Q17a_23 in a new column with a new name. The "old" columns Q17a_17, Q17a_19 and Q17a_23 should be deleted.
In the new column should be just one value with the following conditions: "NA" if there was "NA" before, "1" if there was somewhere "1" as value before (like in row 3 or 4 or 7) and "0" if there were only zeros before.
Maybe this is really simple, but I struggle already for hours...
The approach I use here is to first compute a vector which is NA when an NA value occurs in at least one of the three columns, and zero otherwise. Also, we compute a vector containing the numerical result you want. What you want can be obtained by logically ORing together the three columns. Then, adding these two computed vectors together produces the desired result.
na.vector <- df$Q17a_17 * df$Q17a_19 * df$Q17a_23
na.vector[!is.na(na.vector)] <- 0
num.vector <- as.numeric(df$Q17a_17 | df$Q17a_19 | df$Q17a_23)
df$new_column <- na.vector + num.vector
df <- df[ , -which(names(df) %in% c("Q17a_17", "Q17a_19", "Q17a_23"))]
I have the following code: model$data
model$data
[[1]]
Category1 Category2 Category3 Category4
3555 1 0 0 0
6447 1 0 0 0
5523 1 0 1 0
7550 1 0 1 0
6330 1 0 1 0
2451 1 0 0 0
4308 1 0 1 0
8917 0 0 0 0
4780 1 0 1 0
6802 1 0 1 0
2021 1 0 0 0
5792 1 0 1 0
5475 1 0 1 0
4198 1 0 0 0
223 1 0 1 0
4811 1 0 1 0
678 1 0 1 0
I am trying to use this formula to get an index of the column names:
sample(colnames(model$data), 1)
But I receive the following error message:
Error in sample.int(length(x), size, replace, prob) :
invalid first argument
Is there a way to avoid that error?
Notice this?
model$data
[[1]]
The [[1]] means that model$data is a list, whose first component is a data frame. To do anything with it, you need to pass model$data[[1]] to your code, not model$data.
sample(colnames(model$data[[1]]), 1)
This seems to be a near-duplicate of Random rows in dataframes in R and should probably be closed as duplicate. But for completeness, adapting that answer to sampling column-indices is trivial:
you don't need to generate a vector of column-names, only their indices. Keep it simple.
sample your col-indices from 1:ncol(df) instead of 1:nrow(df)
then put those column-indices on the RHS of the comma in df[, ...]
df[, sample(ncol(df), 1)]
the 1 is because you apparently want to take a sample of size 1.
one minor complication is that your dataframe is model$data[[1]], since your model$data looks like a list with one element which is a dataframe, rather than a plain dataframe. So first, assign df <- model$data[[1]]
finally, if you really really want the sampled column-name(s) as well as their indices:
samp_col_idxs <- sample(ncol(df), 1)
samp_col_names <- colnames(df) [samp_col_idxs]
Say I got a data.table (can also be data.frame, doesn't matter to me) which has numeric columns a, b, c, d and e.
Each row of the table represents an article and a-e are numeric characteristics of the articles.
What I want to find out is which articles are similar to each other, based on columns a, b and c.
I define "similar" by allowing a, b and c to vary +/- 1 at most.
That is, article x is similar to article y if neither a, b nor c differs by more than 1. Their values for d and e don't matter and may differ significantly.
I've already tried a couple of approaches but didn't get the desired result. What I want to achieve is to get a result table which contains only those rows that are similar to at least one other row. Plus, duplicates must be excluded.
Particularly, I'm wondering if this is possible using the sqldf library. My idea is to somehow join the table with itself under the given conditions, but I don't get it together properly. Any ideas (not necessarily using sqldf)?
Suppose our input data frame is the built-in 11x8 anscombe data frame. Its first three column names are x1, x2 and x3. Then here are some solutions.
1) sqldf This returns the pairs of row numbers of similar rows:
library(sqldf)
ans <- anscombe
ans$id <- 1:nrow(ans)
sqldf("select a.id, b.id
from ans a
join ans b on abs(a.x1 - b.x1) <= 1 and
abs(a.x2 - b.x2) <= 1 and
abs(a.x3 - b.x3) <= 1")
Add another condition and a.id < b.id if each row should not be paired with itself and if we want to exclude the reverse of each pair or add and not a.id = b.id to just exclude self pairs.
2) dist This returns a matrix m whose i,j-th element is 1 if rows i and j are similar and 0 if not based on columns 1, 2 and 3.
# matrix of pairs (1 = similar, 0 = not)
m <- (as.matrix(dist(anscombe[1:3], method = "maximum")) <= 1) + 0
giving:
1 2 3 4 5 6 7 8 9 10 11
1 1 0 0 1 1 0 0 0 0 0 0
2 0 1 0 1 0 0 0 0 0 1 0
3 0 0 1 0 0 1 0 0 1 0 0
4 1 1 0 1 0 0 0 0 0 0 0
5 1 0 0 0 1 0 0 0 1 0 0
6 0 0 1 0 0 1 0 0 0 0 0
7 0 0 0 0 0 0 1 0 0 1 1
8 0 0 0 0 0 0 0 1 0 0 1
9 0 0 1 0 1 0 0 0 1 0 0
10 0 1 0 0 0 0 1 0 0 1 0
11 0 0 0 0 0 0 1 1 0 0 1
We could add m[lower.tri(m, diag = TRUE)] <- 0 to exclude self pairs and the reverse of each pair if desired or diag(m) <- 0 to just exclude self pairs.
We can create a data frame of similar row number pairs like this. To keep the output short we have excluded self pairs and the reverse of each pair.
# two-column data.frame of pairs excluding self pairs and reverses
subset(as.data.frame.table(m), c(Var1) < c(Var2) & Freq == 1)[1:2]
giving:
Var1 Var2
34 1 4
35 2 4
45 1 5
58 3 6
91 3 9
93 5 9
101 2 10
106 7 10
117 7 11
118 8 11
Here is a network graph of the above. Note that answer continues after the graph:
# network graph
library(igraph)
g <- graph.adjacency(m)
plot(g)
# raster plot
library(ggplot2)
ggplot(as.data.frame.table(m), aes(Var1, Var2, fill = factor(Freq))) +
geom_raster()
I am quite new to R so don't expect to much.
What if you create from your values (which are basically vectors) a matrix with the distance from the two values. So you can find those combinations which have a difference of less than 1 from each other. Via this way you can find the matching (a)-pairs. Repeat this with (b) and (c) and find those which are included in all pairs.
Alternatively this can probably be done as a cube as well.
Just as a thought hint.