Analysis of NHMRC Investigator Grants 2020
T Nguyen
21/05/2020
library(readxl)
library(ggplot2)
library(gridExtra)
library(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionReading data
ig = read_excel("~/Dropbox/NHMRC and Grants/NHMRC 2019/Investigator Grant v2/Outcome/List of investigator grants.xlsx")
ig$Budget = ig$Total/1000
ig$Rank[ig$Rank=="Ms" | ig$Rank=="Mr"] <- "Mr/Ms"
ig$Rank[ig$Rank=="Dr"] <- "Dr"
ig$Rank[ig$Rank=="A/Prof"] <- "A/Prof"
ig$Rank[ig$Rank=="Prof"] <- "Prof"
ig$Rank = factor(ig$Rank, levels=c("Mr/Ms", "Dr", "A/Prof", "Prof"))
head(ig)## # A tibble: 6 x 17
## ID Rank Name Type SubType Institution State Sector Total Area Field
## <dbl> <fct> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 1.19e6 A/Pr… A/Pr… Inte… Collab… University… VIC Unive… 1.22e6 Clin… Clin…
## 2 1.19e6 Dr Dr S… Inte… Collab… Commonweal… ACT Gover… 4.89e5 Clin… Neur…
## 3 1.19e6 Dr Dr V… Inte… Collab… Griffith U… QLD Unive… 4.22e5 Publ… Ment…
## 4 1.19e6 Prof Prof… Inte… Collab… University… VIC Unive… 4.98e5 Clin… Medi…
## 5 1.20e6 Prof Prof… Inte… Collab… RMIT Unive… VIC Unive… 4.97e5 Clin… Paed…
## 6 1.19e6 Prof Prof… Inte… Collab… University… NSW Unive… 4.99e5 Clin… Neur…
## # … with 6 more variables: KW1 <chr>, KW2 <chr>, KW3 <chr>, KW4 <chr>,
## # KW5 <chr>, Budget <dbl>ig1 = filter(ig, Type %in% "Investigator Grants")Analysis
table1(~SubType + Budget | Rank, data=ig1)| Mr/Ms (N=3) |
Dr (N=91) |
A/Prof (N=34) |
Prof (N=109) |
Overall (N=237) |
|
|---|---|---|---|---|---|
| SubType | |||||
| EL1 | 3 (100%) | 76 (83.5%) | 4 (11.8%) | 0 (0%) | 83 (35.0%) |
| EL2 | 0 (0%) | 14 (15.4%) | 20 (58.8%) | 5 (4.6%) | 39 (16.5%) |
| L1 | 0 (0%) | 1 (1.1%) | 10 (29.4%) | 31 (28.4%) | 42 (17.7%) |
| L2 | 0 (0%) | 0 (0%) | 0 (0%) | 28 (25.7%) | 28 (11.8%) |
| L3 | 0 (0%) | 0 (0%) | 0 (0%) | 45 (41.3%) | 45 (19.0%) |
| Budget | |||||
| Mean (SD) | 645 (0) | 766 (384) | 1520 (602) | 2240 (590) | 1550 (856) |
| Median [Min, Max] | 645 [645, 645] | 645 [280, 2590] | 1470 [463, 3140] | 2180 [900, 3760] | 1540 [280, 3760] |
table1(~ SubType + Budget | Area , data=ig1)| Basic Science (N=71) |
Clinical Medicine and Science (N=100) |
Health Services Research (N=20) |
Public Health (N=46) |
Overall (N=237) |
|
|---|---|---|---|---|---|
| SubType | |||||
| EL1 | 21 (29.6%) | 36 (36.0%) | 6 (30.0%) | 20 (43.5%) | 83 (35.0%) |
| EL2 | 13 (18.3%) | 11 (11.0%) | 7 (35.0%) | 8 (17.4%) | 39 (16.5%) |
| L1 | 14 (19.7%) | 15 (15.0%) | 2 (10.0%) | 11 (23.9%) | 42 (17.7%) |
| L2 | 5 (7.0%) | 15 (15.0%) | 4 (20.0%) | 4 (8.7%) | 28 (11.8%) |
| L3 | 18 (25.4%) | 23 (23.0%) | 1 (5.0%) | 3 (6.5%) | 45 (19.0%) |
| Budget | |||||
| Mean (SD) | 1750 (913) | 1540 (869) | 1350 (664) | 1340 (758) | 1550 (856) |
| Median [Min, Max] | 1790 [570, 3760] | 1540 [280, 3740] | 1340 [448, 2740] | 1390 [359, 3290] | 1540 [280, 3760] |
table1(~ KW1, data=ig1)| Overall (N=237) |
|
|---|---|
| KW1 | |
| aboriginal child | 2 (0.8%) |
| aboriginal health | 3 (1.3%) |
| addiction | 1 (0.4%) |
| adjuvant | 1 (0.4%) |
| adolescent health | 1 (0.4%) |
| age-related | 1 (0.4%) |
| aged care | 1 (0.4%) |
| ageing | 1 (0.4%) |
| alcohol use disorders | 1 (0.4%) |
| alternative splicing | 1 (0.4%) |
| androgens | 1 (0.4%) |
| antibiotic resistance | 1 (0.4%) |
| antibiotics | 2 (0.8%) |
| antimicrobial resistance | 2 (0.8%) |
| ataxia | 1 (0.4%) |
| autoimmunity | 1 (0.4%) |
| axonal reconnection | 1 (0.4%) |
| bacteria | 1 (0.4%) |
| bioinformatics | 2 (0.8%) |
| biomarkers | 2 (0.8%) |
| biomedical engineering | 1 (0.4%) |
| biostatistics | 1 (0.4%) |
| blood transfusion | 1 (0.4%) |
| blood-borne communicable diseases | 1 (0.4%) |
| brain imaging | 1 (0.4%) |
| brain mapping | 1 (0.4%) |
| breast cancer | 5 (2.1%) |
| breast cancer aetiology | 1 (0.4%) |
| bronchiolitis | 1 (0.4%) |
| cancer biology | 1 (0.4%) |
| cancer control | 1 (0.4%) |
| cancer immunotherapy | 1 (0.4%) |
| cancer screening | 1 (0.4%) |
| cardiology | 1 (0.4%) |
| cardiovascular disease prevention | 2 (0.8%) |
| cerebral palsy treatments | 1 (0.4%) |
| child development | 2 (0.8%) |
| child health | 1 (0.4%) |
| chronic infection | 1 (0.4%) |
| chronic obstructive pulmonary disease (copd) | 1 (0.4%) |
| chronic pain | 1 (0.4%) |
| circadian rhythms | 1 (0.4%) |
| cognition | 1 (0.4%) |
| colorectal cancer | 2 (0.8%) |
| colorectal cancer prevention | 2 (0.8%) |
| combination therapy | 1 (0.4%) |
| crohn's disease | 2 (0.8%) |
| cytokine receptor | 1 (0.4%) |
| dementia | 2 (0.8%) |
| dementia care | 1 (0.4%) |
| dendritic cell lineages | 1 (0.4%) |
| depression | 1 (0.4%) |
| developmental disorders | 1 (0.4%) |
| diabetes | 1 (0.4%) |
| diagnostic imaging | 1 (0.4%) |
| diagnostic methods | 1 (0.4%) |
| diarrhoeal disease | 1 (0.4%) |
| diet | 1 (0.4%) |
| dna replication | 1 (0.4%) |
| drug delivery | 1 (0.4%) |
| drug delivery systems | 2 (0.8%) |
| dyspnoea | 1 (0.4%) |
| eating disorders | 1 (0.4%) |
| electroconvulsive therapy (ect) | 1 (0.4%) |
| endometrial cancer | 1 (0.4%) |
| enteric bacteria | 1 (0.4%) |
| enteric nervous system | 1 (0.4%) |
| epidemiology | 2 (0.8%) |
| epigenetics | 2 (0.8%) |
| epilepsy | 2 (0.8%) |
| evidence-based health care | 1 (0.4%) |
| exercise intolerance | 1 (0.4%) |
| falls prevention | 1 (0.4%) |
| fatty liver disease | 1 (0.4%) |
| food | 1 (0.4%) |
| functional magnetic resonance imaging (fmri) | 1 (0.4%) |
| g protein-coupled receptors | 2 (0.8%) |
| gene regulation | 1 (0.4%) |
| genetics | 1 (0.4%) |
| genomics | 2 (0.8%) |
| haematology | 1 (0.4%) |
| haemodialysis | 1 (0.4%) |
| health literacy | 1 (0.4%) |
| health services research | 1 (0.4%) |
| human immunodeficiency virus (hiv) | 1 (0.4%) |
| illicit drug use | 1 (0.4%) |
| infection | 3 (1.3%) |
| infection control | 1 (0.4%) |
| infectious diseases | 1 (0.4%) |
| inflammation | 2 (0.8%) |
| inflammatory bowel disease (ibd) | 1 (0.4%) |
| influenza | 3 (1.3%) |
| influenza virus | 1 (0.4%) |
| insomnia | 1 (0.4%) |
| intensive care | 1 (0.4%) |
| iron | 1 (0.4%) |
| iron transport | 1 (0.4%) |
| knee osteoarthritis | 1 (0.4%) |
| lipid metabolism | 1 (0.4%) |
| low back pain | 1 (0.4%) |
| lung development | 1 (0.4%) |
| lupus | 1 (0.4%) |
| macrophage biology | 1 (0.4%) |
| magnetic resonance imaging (mri) | 1 (0.4%) |
| malaria | 3 (1.3%) |
| mammography | 1 (0.4%) |
| maternal health | 2 (0.8%) |
| medical physics | 1 (0.4%) |
| medications | 1 (0.4%) |
| melanoma | 2 (0.8%) |
| memory t-cells | 1 (0.4%) |
| menopause | 1 (0.4%) |
| mental health | 2 (0.8%) |
| microbial ecology | 1 (0.4%) |
| midwifery | 1 (0.4%) |
| molecular chaperones | 1 (0.4%) |
| molecular epidemiology | 1 (0.4%) |
| motor disability | 1 (0.4%) |
| mouse models | 1 (0.4%) |
| multiple sclerosis (ms) | 2 (0.8%) |
| muscle strength | 1 (0.4%) |
| muscular dystrophy | 1 (0.4%) |
| musculoskeletal disorders | 2 (0.8%) |
| myeloid leukaemia | 1 (0.4%) |
| nanotechnology | 1 (0.4%) |
| natural killer cells | 1 (0.4%) |
| nephrology | 1 (0.4%) |
| neurodegenerative disorders | 1 (0.4%) |
| neurodevelopmental disorders | 1 (0.4%) |
| neuropsychiatric disorders | 1 (0.4%) |
| neuroradiology | 1 (0.4%) |
| neuroscience | 1 (0.4%) |
| nursing | 1 (0.4%) |
| nutrition | 1 (0.4%) |
| obesity | 1 (0.4%) |
| ophthalmology | 1 (0.4%) |
| organ growth and development | 1 (0.4%) |
| osteoarthritis | 2 (0.8%) |
| osteoporosis | 3 (1.3%) |
| paediatric | 1 (0.4%) |
| pain | 1 (0.4%) |
| pain management | 1 (0.4%) |
| parkinson disease | 1 (0.4%) |
| patient participation | 1 (0.4%) |
| patient safety | 1 (0.4%) |
| periodontitis | 1 (0.4%) |
| pharmacoepidemiology | 1 (0.4%) |
| physical activity | 2 (0.8%) |
| poisoning | 1 (0.4%) |
| pregnancy complications | 2 (0.8%) |
| premature birth | 1 (0.4%) |
| prevention | 1 (0.4%) |
| preventive health | 1 (0.4%) |
| primary care | 1 (0.4%) |
| prostate cancer | 1 (0.4%) |
| protein structure | 1 (0.4%) |
| psychopathology | 1 (0.4%) |
| public health policy | 1 (0.4%) |
| randomised trial | 1 (0.4%) |
| rehabilitation | 1 (0.4%) |
| retinal degeneration | 1 (0.4%) |
| rhinosinusitis | 1 (0.4%) |
| schistosoma | 1 (0.4%) |
| schizophrenia | 1 (0.4%) |
| schizophrenia and related disorders | 1 (0.4%) |
| scleroderma | 1 (0.4%) |
| secondary prevention | 1 (0.4%) |
| sepsis | 2 (0.8%) |
| sleep | 1 (0.4%) |
| sleep apnoea | 1 (0.4%) |
| sleep breathing disorders | 1 (0.4%) |
| streptococcus pyogenes | 1 (0.4%) |
| stroke | 1 (0.4%) |
| structural biology | 4 (1.7%) |
| survivorship | 1 (0.4%) |
| systemic lupus erythematosus (sle) | 1 (0.4%) |
| t cell immunity | 2 (0.8%) |
| thromboembolic disease | 1 (0.4%) |
| tobacco control | 1 (0.4%) |
| transcranial magnetic stimulation (tms) | 1 (0.4%) |
| translational research | 2 (0.8%) |
| transplantation | 1 (0.4%) |
| tropoelastin | 1 (0.4%) |
| tuberculosis | 3 (1.3%) |
| vaccination | 1 (0.4%) |
| ventricular fibrillation | 1 (0.4%) |
| virus eradication | 1 (0.4%) |
| visual development | 1 (0.4%) |
| zebrafish | 1 (0.4%) |
ggplot(data=ig1, aes(x=Total/1000, col="blue")) + geom_histogram(color="white", fill="blue") + facet_grid(. ~SubType) + labs(x="Budget ($1000)", y="Number of grants", title="Budget by fellowship type")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data=ig1, aes(x=Rank, fill=Rank)) + geom_bar(stat="count") + theme(legend.position="none") + labs(x="Academic rank", y="Number of grants", title="Number of successful grants by academic rank")
ggplot(data=ig1, aes(x=SubType, fill=Rank)) + geom_bar(stat="count") + labs(x="Subtype", y="Number of grants", title="Number of successful grants by Type and Rank")
ggplot(data=ig1, aes(x=SubType, fill=Area)) + geom_bar(stat="count") + labs(x="Subtype", y="Number of grants", title="Number of successful grants by Type and Area")