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How to split data into training/testing sets using sample function
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Closed 2 years ago.
I have a dataframe with various real estate listings similar to the following.
ADDRESS PRICE ZIP ...
123 Main St 400,000 45678
23 Green Ln 380,000 45670
29 Green Ln 385,000 45670
...
I want to perform a stratified random sample for a testing dataset. In other words, I want to take ~30% of the entries from each ZIP code and separate them into a new dataset. I am not familiar with R dataframes, so how would I perform such an operation?
I've used the sample function like so
sample(c(1:103), size=31, replace = F)
but how do I put these specific rows into a new dataframe?
8 85 5 83 66 46 39 75 101 94 10 68 63 74 22 86 42
59 52 97 62 11 44 96 88 28 9 36 2 78 49
For a stratified sampling you can use the createDataPartition function from the caret package by inserting the variable according to which you want to stratify (in your case ZIP). By using [[1]] you select the first element of the list which contains the row indices necessary for the split. Afterwards, you subset your original dataset by select only the rows given by train_index
train_index <- caret::createDataPartition(your_data$ZIP, p = 0.7)[[1]]
train_data <- your_data[train_index,]
test_data <- your_data[-train_index,]
The dplyr solution would be this one I believe:
train_set <- df %>%
group_by(ZIP) %>%
sample_frac(0.3)
It will return a dataframe with sample values for each ZIP group
Related
For my project, I have taken a data set which have 1296765 observations of 23 columns, I want to take just 10% of this data randomly. How can I do that in R.
I tried the below code but it only sampled out just 10 rows. But, I wanted to select randomly 10% of the data. I am a beginner so please help.
library(dplyr)
x <- sample_n(train, 10)
Here is a function from dplyr that select rows at random by a specific proportion:
dplyr::slice_sample(train,prop = .1)
In base R, you can subset by sampling a proportion of nrow():
set.seed(13)
train <- data.frame(id = 1:101, x = rnorm(101))
train[sample(nrow(train), nrow(train) / 10), ]
id x
69 69 1.14382456
101 101 -0.36917269
60 60 0.69967564
58 58 0.82651036
59 59 1.48369123
72 72 -0.06144699
12 12 0.46187091
89 89 1.60212039
8 8 0.23667967
49 49 0.27714729
From the following code, I got a data frame in R. I am trying to plot the data frame; however, I am only interested in the score they got on the Final. So I want the x-axis to be the number of students, which is 6, since that's how many data points their are, and I want the y-axis to be Final. Is there a way to do this from just the data frame?
data <- data.frame(Score1=c(100,36,58,77,99,92),Score2=c(56,68,68,98,15,35), Final=c(63,87,89,45,99,18))
Output listed below:
Score1 Score2 Final
1 100 56 63
2 36 68 87
3 58 68 89
4 77 98 45
5 99 15 99
6 92 35 18
Or will I have to do something like this instead? But this gives me an error that the lengths are not the same.
data <- data.frame(Score1=c(100,36,58,77,99,92),Score2=c(56,68,68,98,15,35))
Final=c(63,87,89,45,99,18)
f.data <- cbind(data,Final)
b <- 6
plot(b,Final)
Use the following
library(ggplot2);
qplot( x = 1:6, y = data$Final)
The code below can do the trick.
plot(data$Final)
I am building a streambed hydrology calculator in R using multiple tables from an Access database. I am having trouble automating and calculating the same set of indices for multiple sites. The following sample dataset describes my data structure:
> Thalweg
StationID AB0 AB1 AB2 AB3 AB4 AB5 BC1 BC2 BC3 BC4 Xdep_Vdep
1 1AAUA017.60 47 45 44 55 54 6 15 39 15 11 18.29
2 1AXKR000.77 30 27 24 19 20 18 9 12 21 13 6.46
3 2-BGU005.95 52 67 62 42 28 25 23 26 11 19 20.18
4 2-BLG011.41 66 85 77 83 63 35 10 70 95 90 67.64
5 2-CSR003.94 29 35 46 14 19 14 13 13 21 48 6.74
where each column represents certain field-measured parameters (i.e. depth of a reach section) and each row represents a different site.
I have successfully used the apply functions to simultaneously calculate simple functions on multiple rows:
> Xdepth <- apply(Thalweg[, 2:11], 1, mean) # Mean Depth
> Xdepth
1 2 3 4 5
33.1 19.3 35.5 67.4 25.2
and appending the results back to the proper station in a dataframe.
However, I am struggling when I want to calculate and save variables that are subsequently used for further calculations. I cannot seem to loop or apply the same function to multiple columns on a single row and complete the same calculations over the next row without mixing variables and data.
I want to do:
Residual_AB0 <- min(Xdep_Vdep, Thalweg$AB0)
Residual_AB1 <- min((Residual_AB0 + other_variables), Thalweg$AB1)
Residual_AB2 <- min((Residual_AB1 + other_variables), Thalweg$AB2)
Residual_AB3 <- min((Residual_AB2 + other_variables), Thalweg$AB3)
# etc.
Depth_AB0 <- (Thalweg$AB0 - Residual_AB0)
Depth_AB1 <- (Thalweg$AB1 - Residual_AB1)
Depth_AB2 <- (Thalweg$AB2 - Residual_AB2)
# etc.
I have tried and subsequently failed at for loops such as:
for (i in nrow(Thalweg)){
Residual_AB0 <- min(Xdep_Vdep, Thalweg$AB0)
Residual_AB1 <- min((Residual_AB0 + Stacks_Equation), Thalweg$AB1)
Residual_AB2 <- min((Residual_AB1 + Stacks_Equation), Thalweg$AB2)
Residual_AB3 <- min((Residual_AB2 + Stacks_Equation), Thalweg$AB3)
Residuals <- data.frame(Thalweg$StationID, Residual_AB0, Residual_AB1, Residual_AB2, Residual_AB3)
}
Is there a better way to approach looping through multiple lines of data when I need unique variables saved for each specific row that I am currently calculating? Thank you for any suggestions.
your exact problem is still a mistery to me...
but it looks like you want a double for loop
for(i in 1:nrow(thalweg)){
residual=thalweg[i,"Xdep_Vdep"]
for(j in 2:11){
residual=min(residual,thalweg[i,j])
}
}
Suppose I have a data frame with 3 columns and 10 rows as follows.
# V1 V2 V3
# 10 24 92
# 13 73 100
# 25 91 120
# 32 62 95
# 15 43 110
# 28 54 84
# 30 56 71
# 20 82 80
# 23 19 30
# 12 64 89
I want to create sub-dataframes that divide the original by the values of V1.
For example,
the first data frame will have the rows with values of V1 from 10-14,
the second will have the rows with values of V1 from 15-19,
the third from 20-24, etc.
What would be the simplest way to make this?
So if this is your data
dd<-data.frame(
V1=c(10,13,25,32,15,38,30,20,23,13),
V2=c(24,73,91,62,43,54,56,82,19,64),
V3=c(92,100,120,95,110,84,71,80,30,89)
)
then the easiest way to split is using the split() command. And since you want to split in ranges, you can use the cut() command to create those ranges. A simple split can be done with
ss<-split(dd, cut(dd$V1, breaks=seq(10,35,by=5)-1)); ss
split returns a list where each item is the subsetted data.frame. So to get at the data.frame with the values for 10-14, use ss[[1]], and for 15-19, use ss[[2]] etc.
The data set that I'm working with is similar to the one below (although the example is of a much smaller scale, the data I'm working with is 10's of thousands of rows) and I haven't been able to figure out how to get R to add up column data based on the group number. Essentially I want to be able to get the number of green(s), blue(s), and red(s) added up for all of group 81 and 66 separately and then be able to use that information to calculate percentages.
txt <- "Group Green Blue Red Total
81 15 10 21 46
81 10 10 10 30
81 4 8 0 12
81 42 2 2 46
66 11 9 1 21
66 5 14 5 24
66 7 5 2 14
66 1 16 3 20
66 22 4 2 28"
dat <- read.table(textConnection(txt), sep = " ", header = TRUE)
I've spent a good deal of time trying to figure out how to use some of the functions on my own hoping I would stumble across a proper way to do it, but since I'm such a new basic user I feel like I have hit a wall that I cannot progress past without help.
One way is via aggregate. Assuming your data is in an object x:
aggregate(. ~ Group, data=x, FUN=sum)
# Group Green Blue Red Total
# 1 66 46 48 13 107
# 2 81 71 30 33 134
Both of the answers above are perfect examples of how to address this type of problem. Two other options exist within reshape and plyr
library(reshape)
cast(melt(dat, "Group"), Group ~ ..., sum)
library(plyr)
ddply(dat, "Group", function(x) colSums(x[, -1]))
I would suggest that #Joshua's answer is neater, but two functions you should learn are apply and tapply. If a is your data set, then:
## apply calculates the sum of each row
> total = apply(a[,2:4], 1, sum)
## tapply calculates the sum based on each group
> tapply(total, a$Group, sum)
66 81
107 134