Study experiments and Data analysis

L5: Descriptive analysis

Read dataset into R

income = read.csv("C:\\VN trips\\VN trip 2 (Sept 2022)\\Can Tho 2022\\Income and PhDs.csv", header = T)
head(income)
##   id TimeSincePhD NPubs Sex Citations Salary
## 1  1            3    18   1        50  51876
## 2  2            6     3   1        26  54511
## 3  3            3     2   1        50  53425
## 4  4            8    17   0        34  61683
## 5  5            9    11   1        41  52926
## 6  6            6     6   0        37  47034

Characteristics by sexes

library(table1)
## 
## Attaching package: 'table1'
## The following objects are masked from 'package:base':
## 
##     units, units<-
table1(~ TimeSincePhD + NPubs + Citations + Salary | as.factor(Sex), data = income)
0
(N=35)		 1
(N=27)		 Overall
(N=62)
TimeSincePhD
Mean (SD) 7.57 (4.99) 5.78 (2.91) 6.79 (4.28)
Median [Min, Max] 6.00 [1.00, 21.0] 5.00 [2.00, 16.0] 6.00 [1.00, 21.0]
NPubs
Mean (SD) 20.1 (15.4) 15.7 (11.8) 18.2 (14.0)
Median [Min, Max] 17.0 [1.00, 69.0] 12.0 [2.00, 50.0] 13.0 [1.00, 69.0]
Citations
Mean (SD) 42.5 (18.9) 37.3 (14.5) 40.2 (17.2)
Median [Min, Max] 40.0 [1.00, 90.0] 34.0 [14.0, 83.0] 35.0 [1.00, 90.0]
Salary
Mean (SD) 56500 (10800) 50600 (11700) 53900 (11500)
Median [Min, Max] 55600 [37900, 83500] 52500 [6330, 74300] 53500 [6330, 83500] 
library("tidyverse")
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.8
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
income_n = income %>%
  mutate(gender = case_when(Sex == 0 ~ "Male",
                            Sex == 1 ~ "Female"))

table1(~ TimeSincePhD + NPubs + Citations + Salary | gender, data = income_n)
Female
(N=27)		 Male
(N=35)		 Overall
(N=62)
TimeSincePhD
Mean (SD) 5.78 (2.91) 7.57 (4.99) 6.79 (4.28)
Median [Min, Max] 5.00 [2.00, 16.0] 6.00 [1.00, 21.0] 6.00 [1.00, 21.0]
NPubs
Mean (SD) 15.7 (11.8) 20.1 (15.4) 18.2 (14.0)
Median [Min, Max] 12.0 [2.00, 50.0] 17.0 [1.00, 69.0] 13.0 [1.00, 69.0]
Citations
Mean (SD) 37.3 (14.5) 42.5 (18.9) 40.2 (17.2)
Median [Min, Max] 34.0 [14.0, 83.0] 40.0 [1.00, 90.0] 35.0 [1.00, 90.0]
Salary
Mean (SD) 50600 (11700) 56500 (10800) 53900 (11500)
Median [Min, Max] 52500 [6330, 74300] 55600 [37900, 83500] 53500 [6330, 83500]

Check whether the differences in baseline characteristics statistically significant

library(compareGroups)
createTable(compareGroups(gender ~ TimeSincePhD + NPubs + Citations + Salary, data = income_n))
## 
## --------Summary descriptives table by 'gender'---------
## 
## __________________________________________________ 
##                 Female         Male      p.overall 
##                  N=27          N=35                
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
## TimeSincePhD  5.78 (2.91)   7.57 (4.99)    0.082   
## NPubs         15.7 (11.8)   20.1 (15.4)    0.203   
## Citations     37.3 (14.5)   42.5 (18.9)    0.232   
## Salary       50613 (11666) 56510 (10777)   0.046   
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

Overall graphical assessment of the variables

library(GGally)
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
ggpairs(data = income_n, mapping = aes(color = gender))
## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero
## `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`.

vars = income_n[, c("TimeSincePhD", "NPubs", "Citations", "Salary", "gender")]
ggpairs(data = vars, mapping = aes(color = gender))
## `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`.