TRM Practical Data Analysis - Basic level
Thach Tran
2024-02-15
TRM Practical Data Analysis - Basic level
Lecture 2. Descriptive analysis
Task 1. Read the “Professorial Salaries” and name this dataset “salary”
salary = read.csv("C:\\Thach\\UTS\\Teaching\\TRM\\Practical Data Analysis\\2024_Autumn semester\\Data\\Professorial Salaries.csv")Task 2. Examine the overall relationship between variables
library(GGally)## Loading required package: ggplot2## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2vars = salary[, c("Rank", "Discipline", "Yrs.since.phd", "Yrs.service", "NPubs", "Ncits", "Sex", "Salary")]
ggpairs(data = vars, mapping = aes(color = Sex))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Task 3. Describe the study population
library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-table1(~ Rank + Discipline + Yrs.since.phd + Yrs.service + NPubs + Ncits | Sex, data = salary)| Female (N=39) |
Male (N=358) |
Overall (N=397) |
|
|---|---|---|---|
| Rank | |||
| AssocProf | 10 (25.6%) | 54 (15.1%) | 64 (16.1%) |
| AsstProf | 11 (28.2%) | 56 (15.6%) | 67 (16.9%) |
| Prof | 18 (46.2%) | 248 (69.3%) | 266 (67.0%) |
| Discipline | |||
| A | 18 (46.2%) | 163 (45.5%) | 181 (45.6%) |
| B | 21 (53.8%) | 195 (54.5%) | 216 (54.4%) |
| Yrs.since.phd | |||
| Mean (SD) | 16.5 (9.78) | 22.9 (13.0) | 22.3 (12.9) |
| Median [Min, Max] | 17.0 [2.00, 39.0] | 22.0 [1.00, 56.0] | 21.0 [1.00, 56.0] |
| Yrs.service | |||
| Mean (SD) | 11.6 (8.81) | 18.3 (13.2) | 17.6 (13.0) |
| Median [Min, Max] | 10.0 [0, 36.0] | 18.0 [0, 60.0] | 16.0 [0, 60.0] |
| NPubs | |||
| Mean (SD) | 20.2 (14.4) | 17.9 (13.9) | 18.2 (14.0) |
| Median [Min, Max] | 18.0 [1.00, 50.0] | 13.0 [1.00, 69.0] | 13.0 [1.00, 69.0] |
| Ncits | |||
| Mean (SD) | 40.7 (16.2) | 40.2 (17.0) | 40.2 (16.9) |
| Median [Min, Max] | 36.0 [14.0, 70.0] | 35.0 [1.00, 90.0] | 35.0 [1.00, 90.0] |
Task 4. Describe the study population with mean (SD) presented for continuous variables
library(table1)
table1(~ Rank + Discipline + Yrs.since.phd + Yrs.service + NPubs + Ncits | Sex, data = salary, render.continuous = c(. = "Median [Q1, Q3]"))| Female (N=39) |
Male (N=358) |
Overall (N=397) |
|
|---|---|---|---|
| Rank | |||
| AssocProf | 10 (25.6%) | 54 (15.1%) | 64 (16.1%) |
| AsstProf | 11 (28.2%) | 56 (15.6%) | 67 (16.9%) |
| Prof | 18 (46.2%) | 248 (69.3%) | 266 (67.0%) |
| Discipline | |||
| A | 18 (46.2%) | 163 (45.5%) | 181 (45.6%) |
| B | 21 (53.8%) | 195 (54.5%) | 216 (54.4%) |
| Yrs.since.phd | |||
| Median [Q1, Q3] | 17.0 [10.0, 23.5] | 22.0 [12.0, 33.0] | 21.0 [12.0, 32.0] |
| Yrs.service | |||
| Median [Q1, Q3] | 10.0 [4.00, 17.5] | 18.0 [7.00, 27.0] | 16.0 [7.00, 27.0] |
| NPubs | |||
| Median [Q1, Q3] | 18.0 [11.0, 27.0] | 13.0 [8.00, 25.8] | 13.0 [8.00, 26.0] |
| Ncits | |||
| Median [Q1, Q3] | 36.0 [28.5, 54.5] | 35.0 [28.0, 50.0] | 35.0 [28.0, 50.0] |
Task 5. Compare characteristics of female professors and male professors
library(compareGroups)
createTable(compareGroups(Sex ~ Rank + Discipline + Yrs.since.phd + Yrs.service + NPubs + Ncits, data = salary))##
## --------Summary descriptives table by 'Sex'---------
##
## _______________________________________________
## Female Male p.overall
## N=39 N=358
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Rank: 0.014
## AssocProf 10 (25.6%) 54 (15.1%)
## AsstProf 11 (28.2%) 56 (15.6%)
## Prof 18 (46.2%) 248 (69.3%)
## Discipline: 1.000
## A 18 (46.2%) 163 (45.5%)
## B 21 (53.8%) 195 (54.5%)
## Yrs.since.phd 16.5 (9.78) 22.9 (13.0) <0.001
## Yrs.service 11.6 (8.81) 18.3 (13.2) <0.001
## NPubs 20.2 (14.4) 17.9 (13.9) 0.352
## Ncits 40.7 (16.2) 40.2 (17.0) 0.851
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯