Ontario Research Fund - Research Excellence Program - Open Ontario

Organizing Dataset

The following code is used to download the dataset, pick important variables to form a new dataset and convert some variables from character format into their specific one.

The head of the well-organized dataset is shown at the end of the code and from that, we can easily found those most important variables. After forming the new dataset, the rest of the paper is going to use this dataset to create some graphs.

# library
library(tidyverse)
library(lubridate)
library(viridis)
library(hrbrthemes)
library(ggridges) # for geom_density_ridges()

# Download the CSV file and organize the dataset
data <- read_csv("https://files.ontario.ca/opendata/orfre_apr_20_18_0.csv", skip=1)




# Get rid of empty rows and change the formats of date and currency variables
data <- data |> rename_all(make.names)|> 
  subset(Program!="") |>
  mutate(Ontario.Commitment = as.numeric(gsub("[$,]", "", Ontario.Commitment))) |>
  mutate(Total.Project.Costs = as.numeric(gsub("[$,]", "", Total.Project.Costs))) |>
  mutate(Approval.Date = as.Date(Approval.Date, "%d-%b-%y"))
head(data)

## # A tibble: 6 × 21
##   Program Project.Number Project.Title       Project.Description    Area.Primary
##   <chr>   <chr>          <chr>               <chr>                  <chr>       
## 1 ORF RE  RE01-076       Structural and Fun… "Dr. Robert Hegele, a… 4.1         
## 2 ORF RE  RE01-025       Intelligent Multip… "Global wireless tele… 7.6.2       
## 3 ORF RE  RE01-063       The Ontario BioCar… "Manufacturing and po… 7.5.2       
## 4 ORF RE  RE01-065       SHARCNET: New Oppo… "The Shared Hierarchi… 7.6.3       
## 5 ORF RE  RE01-067       Centre for Brain a… "Dr. Melvyn A. Goodal… 4.1         
## 6 ORF RE  RE01-027       Thermo-mechanical … "Hydrogen has potenti… 5.7         
## # … with 16 more variables: Area.Secondary <chr>, Discipline.Primary <dbl>,
## #   Discipline.Secondary <dbl>, Round <chr>, Approval.Date <date>,
## #   Lead.Research.Institution <chr>, City <chr>, Ontario.Commitment <dbl>,
## #   Total.Project.Costs <dbl>, Keyword <chr>, EXPENDITURE_TYPE <chr>,
## #   Salutation <chr>, First_Name <chr>, Middle_Name <chr>, Last_Name <chr>,
## #   OIA_AREA <chr>

# Plot the Paired graph
#extract the year from the approval date
data |> 
  separate(Approval.Date, into = c("year","month", "date"), sep = "-")|>
ggplot( )+
  #geom_point(aes(x=year, y=Ontario.Commitment))+
  geom_boxplot(aes(y=Ontario.Commitment, x=factor(year)))+
   ggtitle("Funding per Year (From Ontario)")+
  labs(y="Dollars of funding", x = "Year of approval")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

• The graph shows that most of the projects were for $1,000,000 - $4,000,000 Canadian dollars.

Frequency of Study Area

The following code is used to draw the different research areas funding. Note that first we need to sum within each category (OIA_AREA) and then fill in the table so that we have zeros in all the missing years.

data |> 
  separate(Approval.Date, into = c("year","month", "date"), sep = "-")|>
  group_by(year,OIA_AREA) |>
  summarize(TotalFunding = sum(Ontario.Commitment))|>
  ungroup()|>
  complete(OIA_AREA,year,fill = list(TotalFunding = 0))|>
ggplot( aes(x=year, y=TotalFunding, group=OIA_AREA )) + 
  geom_line(aes(color = OIA_AREA)) +
  ggtitle("Total Funding Per Year Within Each Category")+
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

Summary

• Research programs are separated into 5 areas: Advanced health technologies, Bio-economy and clean technologies, Digital media information and communications technologies, Pharmaceutical research and manufacturing, and other unspecified groups.

# percentage of study are in research fund
data |> 
  separate(Approval.Date, into = c("year","month", "date"), sep = "-")|>
  group_by(year,OIA_AREA) |>
  summarize(TotalFunding = sum(Ontario.Commitment))|>
  ungroup()|>
  complete(OIA_AREA,year,fill = list(TotalFunding = 0))|>
ggplot( aes(x = year,fill=OIA_AREA, y=TotalFunding)) + 
    geom_bar(position="fill", stat="identity") +
    scale_fill_viridis(discrete = T) +
    ggtitle("Funding proportion in each research area ") +
    theme_ipsum() +
    xlab("year")+
    ylab("proportion of funding")+
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.