I would like to subtract the mean of each variable from the mean of a variable named 'birds' and create a new data frame that will contain the results.In my real data frame I have hundreds of variables so I would like to do it automatically.Any Idea how to do so?
I tried with this line of code without the mean function and it works (on the same data frame) :
setNames(as.data.frame(cbind(g, mean(dat$birds)-mean(dat))), c(names(dat), paste0(names(dat),'_new')))
but I don't understand how to use mean as part of the code,I tried:
setNames(as.data.frame(cbind(g, mean(dat$birds)-mean(dat))), c(names(dat), paste0(names(dat),'_new')))
Here is my toy data frame.
dat <- read.table(text = " birds wolfs snakes
3 9 7
3 8 4
1 2 8
1 2 3
1 8 3
6 1 2
6 7 1
6 1 5
5 9 7
3 8 7
4 2 7
1 2 3
7 6 3
6 1 1
6 3 9
6 1 1 ",header = TRUE)
I hope I understood your question correctly.
This should create a new object, in this case - just a vector, where mean of "birds" column is substracted from the means of other columns. This should also work for any size of the data frame.
mean=mean(dat$birds)
dat2=colMeans(dat[2:dim(dat)[2]])-mean
In the future, please provide reproducible example (in your code, object 'g' is not defined) and an example of the expected output, so that it would be clear what you are trying to achieve.
Related
I am using R and working with this sample dataframe.
library(tibble)
library(stats)
set.seed(111)
conditions <- factor(c("1","2","3"))
df_sim <-
tibble::tibble(StudentID = 1:10,
Condition = sample(conditions,
size = 10,
replace = T),
XP = stats::rpois(n = 10,
lambda = 15))
This creates the following tibble.
StudentID
Condition
XP
1
2
8
2
3
11
3
3
16
4
3
12
5
1
22
6
3
16
7
1
18
8
3
8
9
2
14
10
1
17
I am trying create a new column in my dataframe called DyadID. The purpose of this column is to create a variable that is uniquely shared by two students in the dataframe — in other words, two students (e.g. Student 1 and Student 9) would share the same value (e.g. 4) in the DyadID column.
However, I only want observations linked together if they share the same Condition value. Condition contains three unique values (1, 2, 3). I want condition 1 observations linked with other condition 1 observations, 2 with 2, and 3 with 3.
Importantly, I'd like the students to be linked together randomly.
Ideally, I would like to stay within the tidyverse as that is what I am most familiar with. However, if that's not possible or ideal, any solution would be appreciated.
Here is a possible outcome I am hoping to achieve.
StudentID
Condition
XP
DyadID
1
2
8
4
2
3
11
1
3
3
16
2
4
3
12
1
5
1
22
3
6
3
16
NA
7
1
18
3
8
3
8
2
9
2
14
4
10
1
17
NA
Note that two students did not receive a pairing, because there was an odd number in condition 1 and condition 3. If there is an odd number, the DyadID can be NA.
Thank you for your help with this!
Using match to get a unique id according to Condition and sample for randomness.
library(dplyr)
df_sim <- df_sim %>% mutate(dyad_id = match(Condition,sample(unique(Condition))))
I am trying to group events based on their time of occurrence. To achieve this, I simply calculate a diff over the timestamps and want to essentially start a new group if the diff is larger than a certain value. I would have tried like the code below. However, this is not working since the dialog variable is not available during the mutate it is created by.
library(tidyverse)
df <- data.frame(time = c(1,2,3,4,5,510,511,512,513), id = c(1,2,3,4,5,6,7,8,9))
> df
time id
1 1 1
2 2 2
3 3 3
4 4 4
5 5 5
6 510 6
7 511 7
8 512 8
9 513 9
df <- df %>%
mutate(t_diff = c(NA, diff(time))) %>%
# This generates an error as dialog is not available as a variable at this point
mutate(dialog = ifelse(is.na(t_diff), id, ifelse(t_diff >= 500, id, lag(dialog, 1))))
# This is the desired result
> df
time id t_diff dialog
1 1 1 NA 1
2 2 2 1 1
3 3 3 1 1
4 4 4 1 1
5 5 5 1 1
6 510 6 505 6
7 511 7 1 6
8 512 8 1 6
9 513 9 1 6
In words, I want to add a column that points to the first element of each group. Thereby, the groups are distinguished at points at which the diff to the previous element is larger than 500.
Unfortunately, I have not found a clever workaround to achieve this in an efficient way using dplyr. Obviously, iterating over the data.frame with a loop would work, but would be very inefficient.
Is there a way to achieve this in dplyr?
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rep_sample_n <- function(tbl, size, replace = FALSE, reps = 1)
{
rep_tbl = replicate(reps, tbl[sample(1:nrow(tbl), size, replace = replace),
], simplify = FALSE) %>%
bind_rows() %>%
mutate(replicate = rep(1:reps, each = size)) %>%
select(replicate, everything()) %>%
group_by(replicate)
return(rep_tbl)
}
Hey, can anyone help me there? What is this function doing? Is the first line setting the variables of the function? And then what is this "replicate" doing? Thanks!
This formula replicates your data. lets say we have a dataset of 10 observations. In order to come up with additional like-datasets of your current one, you can replicate it by introducing random sampling of your dataset.
You can check out the wikipedia page on
statistical replication if you're more curious.
Lets take a simple dataframe:
df <- data.frame(x = 1:10, y = 1:10)
df
x y
1 1 1
2 2 2
3 3 3
4 4 4
5 5 5
6 6 6
7 7 7
8 8 8
9 9 9
10 10 10
if we want to take a random sample of this, we can use the function rep_sample_n which takes 2 arguments tbl, size, and has another 2 optional arguments replace = FALSE, reps = 1.
Here is an example of us just taking 4 randomly selected columns from our data.
rep_sample_n(df, 4)
# A tibble: 4 x 3
# Groups: replicate [1]
replicate x y
<int> <int> <int>
1 1 1 1
2 1 3 3
3 1 4 4
4 1 10 10
Now if we want to randomly sample 15 observations from a 10 observation dataset, it will throw an error. Currently the replace = FALSE argument doesn't allow that because each time a sample row is chosen, it's removed from the pool for the next sample to be taken. In the example above, it chose the 1st observation, then it went to choose the 2nd (because we asked for 4), and it only have 2 through 10 left, and it chose the 3rd, then 4th and then 10th etc. If we allow replace = TRUE, it will choose an observation from the full dataset each time.
Notice how in this example, the 5th observation was chosen twice. That wouldn't happen with replace = FALSE
rep_sample_n(df, 4, replace = TRUE)
# A tibble: 4 x 3
# Groups: replicate [1]
replicate x y
<int> <int> <int>
1 1 5 5
2 1 3 3
3 1 2 2
4 1 5 5
Lastly and most importantly, we have the reps argument which is the basis for this function, really. It allows you randomly sample your dataset multiple times, and then combine all those samples together.
Below, we have sampled our original dataset of 10 observations by selecting 4 of them in a sample, then we replicated that 5 times, so we have 5 different sample dataframes of 4 observations each that have been combined together into one 20 observation dataframe, but each of the unique 5 dataframes has been tagged with a replicate #. The replicate column will point out which 4 observations goes with which replicated dataframe.
rep_sample_n(df, 4, reps = 5)
# A tibble: 20 x 3
# Groups: replicate [5]
replicate x y
<int> <int> <int>
1 1 8 8
2 1 4 4
3 1 3 3
4 1 1 1
5 2 4 4
6 2 5 5
7 2 8 8
8 2 3 3
9 3 6 6
10 3 1 1
11 3 3 3
12 3 2 2
13 4 5 5
14 4 7 7
15 4 10 10
16 4 3 3
17 5 7 7
18 5 10 10
19 5 3 3
20 5 9 9
I hope this provided some clarity
This function takes a data frame as input (and several input preferences). It takes a random sample of size rows from the table, with or without replacement as set by the replace input. It repeats that random sampling reps times.
Then, it binds all the samples together into a single data frame, adding a new column called "replicate" indicating which repetition of the sampling produced each row.
Finally, it "groups" the resulting table, preparing it for future group-wise operations with dplyr.
For general questions about specific functions, like "What is this "replicate" doing?", you should look at the function's help page: type ?replicate or help("replicate") to get there. It includes a description of the function and examples of how to use it. If you read the description, run the examples, and are still confused, feel free to come back with a specific question and example illustrating what you are confused by.
Similarly, for "Is the first line setting the variables of the function?", the arguments to function() are the inputs to the function. If you have basic questions about R like "How do functions work", have a look at An Introduction to R, or one of the other sources in the R Tag Wiki.
Hi I am aggregating values from two columns and creating a final third column, based on priorities. If values in column 1 are missing or are NA then I go for column 2.
df=data.frame(internal=c(1,5,"",6,"NA"),external=c("",6,8,9,10))
df
internal external
1 1
2 5 6
3 8
4 6 9
5 NA 10
df$final <- df$internal
df$final <- ifelse((df$final=="" | df$final=="NA"),df$external,df$final)
df
internal external final
1 1 2
2 5 6 3
3 8 4
4 6 9 4
5 NA 10 2
How can I get final value as 4 and 2 for row 3 and row 5 when the external is 8 and 2. I don't know what's wrong but these values don't make any sense to me.
The issue arises because R converts your values to factors.
Your code will work fine with
df=data.frame(internal=c(1,5,"",6,"NA"),external=c("",6,8,9,10),stringsAsFactors = FALSE)
PS: this hideous conversion to factors should definitely belong to the R Inferno, http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
Suppose I have the following data.
x<- c(1,2, 3,4,5,1,3,8,2)
y<- c(4,2, 5,6,7,6,7,8,9)
data<-cbind(x,y)
x y
1 1 4
2 2 2
3 3 5
4 4 6
5 5 7
6 1 6
7 3 7
8 8 8
9 2 9
Now, if I subset this data to select only the observations with "x" between 1 and 3 I can do:
s1<- subset(data, x>=1 & x<=3)
and obtain my desired output:
x y
1 1 4
2 2 2
3 3 5
4 1 6
5 3 7
6 2 9
However, if I subset using the colon operator I obtained a different result:
s2<- subset(data, x==1:3)
x y
1 1 4
2 2 2
3 3 5
This time it only includes the first observation in which "x" was 1,2, or 3. Why?
I would like to use the ":" operator because I am writing a function so the user would input a range of values from which she wants to see an average calculated over the "y" variable. I would prefer if they can use ":" operator to pass this argument to the subset function inside my function but I don't know why subsetting with ":" gives me different results.
I'd appreciate any suggestions on this regard.
You can use %in% instead of ==
subset(data, x %in% 1:3)
In general, if we are comparing two vectors of unequal sizes, %in% would be used. There are cases where we can take advantage of the recycling (it can fail too) if the length of one of the vector is double that of the second. Some examples with some description is here.