## Is there a way to sample data from multiple columns using R

r subset dataframe by column value

read specific rows and columns in r

r select columns by index

r subset dataframe by multiple column value

r subset dataframe by column name

r extract rows with certain value

r subset dataframe by list of values

Say I have the following dataset (the dataset has more than 2000 observations). I would like to get the proportion (number) of males who are left-handed, pulse greater than or equal to 80 and clap with the there right hand.

How can I do this in R?

X Sex WrHnd NWHnd WHnd Fold Pulse Clap Exer 1 1 Female 18.5 18.0 Right R on L 92 Left Some 2 2 Male 19.5 20.5 Left R on L 104 Left None 3 3 Male 18.0 13.3 Right L on R 87 Neither None 4 4 Male 18.8 18.9 Right R on L NA Neither None 5 5 Male 20.0 20.0 Right Neither 35 Right Some 6 6 Female 18.0 17.7 Right L on R 64 Right Some

Here is a way that gives you the number at the end. I deliberately calculated for `Left`

clap to produce an output for this small sample, but you can change it to `Right`

in your big data.

library(dplyr) df2 <- df %>% filter(Sex == "Male" & WHnd == "Left" & Pulse >= 80 & Clap == "Left") %>% count(.) > df2 # A tibble: 1 x 1 n <int> 1 1

**How to randomly sample rows that have multiple columns : RStudio,** Say I have the following dataset (the dataset has more than 2000 observations). I would like to get the proportion (number) of males who are left-handed, pulse� Taking a sample is easy with R because a sample is really nothing more than a subset of data. To do so, you make use of sample (), which takes a vector as input; then you tell it how many samples to draw from that list. Say you wanted to simulate rolls of a die, and you want to get ten results.

You can use `dplyr`

to first create a dataframe of all rows satisfying all your criteria

library(dplyr) select_Df <- df %>% filter(Sex == "Male" & WHnd == "Left" & Pulse >= 80 & Clap == "Right")

Then, you get the proportion of this group to the entire population by dividing the number of individuals contained in this new dataframe by the total number of individuals in the original dataframe:

nrow(select_Df) / nrow(df)

**Select Data Frame Columns in R,** How to randomly sample rows that have multiple columns want to take a random sample of 100 rows while keeping the data in each column for each row. But I need to understand better how to do the things I want to in R before I can even� How to merge data in R using R merge, dplyr, or data.table See how to join two data sets by one or more common columns using base R’s merge function, dplyr join functions, and the speedy data

You don't need `dplyr`

With your data.frame as `df`

, you can calculate this proportion as follows:

`sum(df$Sex=='Male' & df$Whnd=='Left' & df$Pulse >= 80 & df$Clap=='Right') / nrow(df)`

because in R, the `sum(TRUE)`

is `1`

, the `sum(c(TRUE, TRUE))`

is `2`

, etc.

**Subsetting Data,** In this tutorial, you will learn how to select or subset data frame columns by names and position using the R function select() and pull() [in dplyr package]. either by name or by index. select(): Extract one or multiple columns as a data table. It One can use this function to, for example, select columns if they are numeric. The most common way to select some columns of a data frame is the specification of a character vector containing the names of the columns to extract. Consider the following R code: data [ , c ("x1", "x3")] # Subset by name. data [ , c ("x1", "x3")] # Subset by name.

**How to Sort a Data Frame by Multiple Columns in R,** Learn how to use R's powerful indexing features for accessing object elements. This includes keeping or deleting variables, observations, random samples. interactively, try the selection of data frame elements exercises in the Data frames chapter weight through income (weight, income and all columns between them ). There are actually many ways to subset a data frame using R. While the subset command is the simplest and most intuitive way to handle this, you can manipulate data directly from the data frame syntax.

**Manipulating, analyzing and exporting data with tidyverse,** While perhaps not the easiest sorting method to type out in terms For example, we can use order() to simply sort a vector of� How to perform the sampling in R? The powerful sample function makes it possible to specify the weights to give to each value, i.e. the probabilities. So, if we want a sample 10 observations of this data, we can simply use this single line of code: sample(d$s,replace = TRUE,prob = d$Freq,10)

**Sort Data Frame by Multiple Columns in R (3 Examples),** Select certain columns in a data frame with the dplyr function select . Using R expressions in a non standard way, which can be confusing for new learners. the data type for each column as it reads it into R. For example, in this dataset, Once the data are grouped, you can also summarize multiple variables at the same� 4.5 Applications. The principles described above have a wide variety of useful applications. Some of the most important are described below. While many of the basic principles of subsetting have already been incorporated into functions like subset(), merge(), dplyr::arrange(), a deeper understanding of how those principles have been implemented will be valuable when you run into situations