Human Resources Management
Kornél
2023-02-28
My database
I use the database I found in Mendeley Data web page. So far, we do not now many things about the database. Hopefully, the situation changes in a very short time.
adat <-read.csv2("Kornel.csv") # conversion from csv to data.frame
knitr::kable(head(adat[, 1:8]), "pipe") #printing the first 5 records on the screen to check, whether everything is correct| Gender | Age | Education | Tenure | Position | JS1 | JS2 | JS3 |
|---|---|---|---|---|---|---|---|
| 1 | 5 | 1 | 3 | 3 | 4 | 4 | 4 |
| 1 | 5 | 1 | 3 | 3 | 5 | 5 | 5 |
| 1 | 5 | 1 | 3 | 3 | 4 | 1 | 4 |
| 1 | 5 | 1 | 3 | 3 | 1 | 1 | 4 |
| 1 | 5 | 1 | 3 | 3 | 2 | 4 | 1 |
| 1 | 5 | 1 | 3 | 3 | 5 | 3 | 4 |
Missing data - eliminating the rows
We want to get overview about missing data in our database. The Amelia package solves it.
library(Amelia)## Loading required package: Rcpp## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.8.1, built: 2022-11-18)
## ## Copyright (C) 2005-2023 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##missmap(adat)
A nice image.
# we eliminate the rows with missing data
na.omit(adat)Working with graphics
library(ggplot2)
ggplot(adat, aes(x=Education, y=Position)) +
geom_point()## Warning: Removed 1 rows containing missing values (`geom_point()`).A nice image.