govvilla.blogg.se - Rstudio ifelse

Rstudio ifelse how to#

We’ll place the results in a user-defined R object so that we can reuse it in the next section without re-writing the complete pipeline. For each marital status code, we’ll then count how many women and men are in each category that we’ll report in columns Mnum and Wnum (the sum of these 2 on each line should be equal to the reported Counts column.)įor simplicity with income and education we’ll simply compute the mean of the codes, which should still give us an indication of the level of income and education for each category of marital status.

We want to get some information by group, in this case we’ll group by marital status and then count the total number of observations for each case and we’ll store this in column Counts.

DMDEDUC2 - Education level - Adults 20+.

RIAGENDR: 1-male, 2-female (none missing).

The following data can be found in the DEMO_I demographic data file:

I The Story of Vector V: an R markdown example.

D.5 Download, save XPT files to hard drive.

D.4 Alternate download to R object with haven.

D.1 Download into R object with NHANES code.

14.4 Preamble, Preface and Introduction.

Rstudio ifelse how to#

13.2.2 How to create an R markdown file.

12.3.3 Changing variable status to a factor.

12.3 Data wrangling: renaming and selecting data.

11.2 ggplot2 using dplyr chapter results.

10.4.1 mutate with conditional statement.

7.4 Computing Analyte / Creatinine ratio.

7.1.1 Downloading, merging PFAS and creatinine.

4.6.3 Combining vectors to create a matrix.4.3.5 Working directory: getwd() and setwd().2.3 Working with R: objects and workspace.Tabular data analysis with R and Tidyverse: NHANES Environmental Health Datasets.