I have a data frame as follows
V2 V4 V6 V8
1 5 5.2 5.1 4.8
2 4.4 4.1 4.5 4.3
3 4.2 3.8 4.2 4.1
4 5 3.2 3.3 4.0
In actual data V value goes from V2 to V200 and row goes from 1 to 99. I want to select columns if its values ever goes less than 4.
Result should be,
V4 V6
1 5.2 5.1
2 4.1 4.5
3 3.8 4.2
4 3.2 3.3
Also want to select columns whose value never goes less than 4. Result should be
V2 V8
1 5 4.8
2 4.4 4.3
3 4.2 4.1
4 5 4.0
I am trying with subset command, but not able to get it done yet.
You have not specified whether you want to do this for each row or for the whole data.frame. For a full data.frame:
mins <- sapply(df, min)
moreThan4 <- df[which(mins > 4)]
lessThan4 <- df[which(mins < 4)]
Related
I have this line of code but I don't know what it means especially the note_ind part.
apply(mydat[,-c(1,2,3,note_ind:ncol(dataset))],c(1,2),as.numeric)
The notation x:y is used to create numeric vector sequences where each element is the previous element incremented by 1. It is shorthand for `seq(x, y, by = 1). It is most commonly used for integer sequences, but it works on doubles also.
1:10
[1] 1 2 3 4 5 6 7 8 9 10
1.1:10.1
[1] 1.1 2.1 3.1 4.1 5.1 6.1 7.1 8.1 9.1 10.1
1.5:10.2 # sequence stops after 9.5 because 10.2 < 9.5 + 1 - seq() behaves the same way
[1] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5
Presumably note_ind is an integer value from somewhere else in your code. ncol(data.set) is the number of columns, so note_ind:ncol(dataset) generates a seqence between those two values, incrementing by 1 for each element.
I need some help with R coding here.
The data set Glass consists of 214 rows of data in which each row corresponds to a glass sample. Each row consists of 10 columns. When viewed as a classification problem, column 10
(Type) specifies the class of each observation/instance. The remaining columns are attributes that might beused to infer column 10. Here is an example of the first row
RI Na Mg Al Si K Ca Ba Fe Type
1 1.52101 13.64 4.49 1.10 71.78 0.06 8.75 0.0 0.0 1
First, I casted column 10 so that it is interpreted by R as a factor instead of an integer value.
Now I need to create a vector with indices for all observations (must have values 1-214). This needs to be done to creating training data for Naive Bayes. I know how to create a vector with 214 values, but not one that has specific indices for observations from a data frame.
If it helps this is being done to set up training data for Naive Bayes, thanks
I'm not totally sure that I get what you're trying to do... So please forgive me if my solution isn't helpful. If your df's name is 'df', just use the dplyr package for reordering your columns and write
library(dplyr)
df['index'] <- 1:214
df <- df %>% select(index,everything())
Here's an example. So that I can post full dataframes, my dataframes will only have 10 rows...
Let's say my dataframe is:
df <- data.frame(col1 = c(2.3,6.3,9.2,1.7,5.0,8.5,7.9,3.5,2.2,11.5),
col2 = c(1.5,2.8,1.7,3.5,6.0,9.0,12.0,18.0,20.0,25.0))
So it looks like
col1 col2
1 2.3 1.5
2 6.3 2.8
3 9.2 1.7
4 1.7 3.5
5 5.0 6.0
6 8.5 9.0
7 7.9 12.0
8 3.5 18.0
9 2.2 20.0
10 11.5 25.0
If I want to add another column that just is 1,2,3,4,5,6,7,8,9,10... and I'll call it 'index' ...I could do this:
library(dplyr)
df['index'] <- 1:10
df <- df %>% select(index, everything())
That will give me
index col1 col2
1 1 2.3 1.5
2 2 6.3 2.8
3 3 9.2 1.7
4 4 1.7 3.5
5 5 5.0 6.0
6 6 8.5 9.0
7 7 7.9 12.0
8 8 3.5 18.0
9 9 2.2 20.0
10 10 11.5 25.0
Hope this will help
df$ind <- seq.int(nrow(df))
I have a tibble structured as follows:
day theta
1 1 2.1
2 1 2.1
3 2 3.2
4 2 3.2
5 5 9.5
6 5 9.5
7 5 9.5
Note that the tibble contains multiple rows for each day, and for each day the same value for theta is repeated an arbitrary number of times. (The tibble contains other arbitrary columns necessitating this repeating structure.)
I'd like to use dplyr to cumulatively sum values for theta across days such that, in the example above, 2.1 is added only a single time to 3.2, etc. The tibble would be mutated so as to append the new cumulative sum (c.theta) as follows:
day theta c.theta
1 1 2.1 2.1
2 1 2.1 2.1
3 2 3.2 5.3
4 2 3.2 5.3
5 5 9.5 14.8
6 5 9.5 14.8
7 5 9.5 14.8
...
My initial efforts to group_by day and then cumsum over theta resulted only in cumulative summing over the full set of data (e.g., 2.1 + 2.1 + 3.2 ...) which is undesirable. In my Stack Overflow searches, I can find many examples of cumulative summing within groups, but never between groups, as I describe above. Nudges in the right direction would be much appreciated.
Doing this in dplyr I came up with a very similar solution to PoGibas - use distinct to just get one row per day, find the sum and merge back in:
df = read.table(text="day theta
1 1 2.1
2 1 2.1
3 2 3.2
4 2 3.2
5 5 9.5
6 5 9.5
7 5 9.5", header = TRUE)
cumsums = df %>%
distinct(day, theta) %>%
mutate(ctheta = cumsum(theta))
df %>%
left_join(cumsums %>% select(day, ctheta), by = 'day')
Not a dplyr, but just an alternative data.table solution:
library(data.table)
# Original table is called d
setDT(d)
merge(d, unique(d)[, .(c.theta = cumsum(theta), day)])
day theta c.theta
1: 1 2.1 2.1
2: 1 2.1 2.1
3: 2 3.2 5.3
4: 2 3.2 5.3
5: 5 9.5 14.8
6: 5 9.5 14.8
7: 5 9.5 14.8
PS: If you want to preserve other columns you have to use unique(d[, .(day, theta)])
In base R you could use split<- and tapply to return the desired result.
# construct 0 vector to fill in
dat$temp <- 0
# fill in with cumulative sum for each day
split(dat$temp, dat$day) <- cumsum(tapply(dat$theta, dat$day, head, 1))
Here, tapply returns the first element of theta for each day which is is fed to cumsum. The elements of cumulative sum are fed to each day using split<-.
This returns
dat
day theta temp
1 1 2.1 2.1
2 1 2.1 2.1
3 2 3.2 5.3
4 2 3.2 5.3
5 5 9.5 14.8
6 5 9.5 14.8
7 5 9.5 14.8
I have information about revenue in cumulative form for the whole year, I would like to get monthly revenue. Lets say first month revenue is 3.2M, and second month revenue is 2.2M, but my second entry is sum of first two months.
Revenue
3.2
5.4
7.6
9.2
I would like to extract revenue as below
ExRevenue
3.2
2.2
2.2
1.6
How can I extract the revenue using R functions. Please help.
You could do
df <- read.table(header=T,text="Revenue
3.2
5.4
7.6
9.2")
df$ExRevenue <- c(df$Revenue[1], diff(df$Revenue))
df
# Revenue ExRevenue
# 1 3.2 3.2
# 2 5.4 2.2
# 3 7.6 2.2
# 4 9.2 1.6
This question already has answers here:
Remove rows with all or some NAs (missing values) in data.frame
(18 answers)
Closed 7 years ago.
I apologize if this is elementary or has been answered before, but I haven't found an answer to my question despite extensive searching. I'm also very new to programming so please bear with me here.
I have a bunch of 25 by 2 matrices of data, however some of the cells have NA values. I'm looking to extract a subset of the matrix consisting of only the complete paired values (so no NA values).
So say I have:
3.6 4.2
9.2 8.4
4.8 NA
1.1 8.2
NA 11.6
NA NA
2.7 3.5
I want:
3.6 4.2
9.2 8.4
1.1 8.2
2.7 3.5
Is there some function that would do this easily?
Thanks!
Try this
df <- read.table(text = "3.6 4.2
9.2 8.4
4.8 NA
1.1 8.2
NA 11.6
NA NA
2.7 3.5")
df[complete.cases(df), ]
# V1 V2
# 1 3.6 4.2
# 2 9.2 8.4
# 4 1.1 8.2
# 7 2.7 3.5
df[ apply(!is.na(df), 1, all) , ]
df <- data.frame(V1 = c(3.6,9.2,4.8,1.1,NA,NA,2.7),
V2 = c(4.2,8.4,NA,8.2,11.6,NA,3.5))
EDIT: I forgot na.omit or complete.cases Doh.