Random sampling using the dplyr package
The dplyr package is a well known R package for data manipulation and transformation. It gives a bunch of functions that make it simpler to work with data casings and data tables in R. One common undertaking in data analysis is random sampling, which can be accomplished using the sample_n() and sample_frac() functions in dplyr.
1: Randomly Sampling Rows from a Data Frame
In this code, we’ll randomly sample a specified number of rows from a data frame.
R
# Load the dplyr packagelibrary(dplyr)# Create a sample data frame# Set seed for reproducibilityset.seed(123) data <- data.frame( ID = 1:100, Value = rnorm(100))# Randomly sample 10 rows from the data framesampled_data <- data %>% sample_n(10)# View the sampled dataprint(sampled_data) |
Output
ID Value
1 7 0.56047565
2 9 -0.23017749
3 15 1.55870831
4 16 0.07050839
5 20 1.71506598
6 23 -0.68685285
7 42 1.78691314
8 46 1.06782371
9 50 0.49850701
10 68 -0.29472045
In this code, we first load the dplyr package and create a sample data frame called data. We then use the sample_n() function to randomly sample 10 rows from the data frame and store the result in the sampled_data variable.
2: Random Sampling a Fraction of Rows from a Data Frame
In this code, we’ll randomly sample a specified fraction of rows from a data frame.
R
# Load the dplyr packagelibrary(dplyr)# Create a sample data frame# Set seed for reproducibilityset.seed(456) data <- data.frame( ID = 1:200, Value = rnorm(200))# Randomly sample 20% of the rows from the data framesampled_data <- data %>% sample_frac(0.20)# View the sampled dataprint(sampled_data) |
Output
ID Value
1 151 1.200410172
2 140 -0.181812198
3 88 0.920529800
4 68 -1.431378346
5 191 -0.697237001
6 27 -0.462854969
7 75 -0.020014663
8 90 -0.236867797
9 46 0.120851803
10 71 -0.169987994
11 163 -1.035274763
12 62 -0.982060062
13 175 -1.549384356
14 85 0.708817307
15 174 0.309910662
16 119 -1.433778349
17 49 -1.175402402
18 126 -1.126327533
19 69 -0.544594202
20 130 0.355610384
21 193 1.232308978
22 36 1.815652319
23 60 0.577150467
24 132 1.149194486
25 118 1.207347447
26 42 0.393037377
27 131 0.004052138
28 167 1.772544877
29 181 -1.388188492
30 45 2.078874614
31 17 1.736936177
32 77 -0.112933852
33 26 1.134284565
34 124 0.313843454
35 133 -0.496614335
36 83 2.020634788
37 35 0.170625252
38 197 -0.196112610
39 116 0.982940735
40 149 1.210757937
In this code, we again load the dplyr package and create a sample data frame called data. We use the sample_frac() function to randomly sample 20% of the rows from the data frame and store the result in the sampled_data variable.
Sample from a Population Using R
Sampling from a population is a critical technique in statistics and data analysis. It allows you to draw conclusions about a large group (the population) by examining a smaller, representative subset (the sample). In R, you can easily perform random sampling to obtain a sample from a population, which is useful for various applications such as hypothesis testing, data visualization, and model building.