Welcome To STEM MA Quantitative methods in Educational Research Blog

We will use this blog to display student work from the MA STEM Quantitative methods in Educational Research module at King’s College London. You can see the course book here MASTEMR

# Create a dataframe of country latitude and longitude data
world_data <- map_data(map="world")

# Create a dataframe of mean PISA science scores, and rename CNT to region
# for the leftjoin mergining of dataframes
WorldSci <- PISA_2022 %>%
  select(CNT, PV1SCIE) %>%
  group_by(CNT) %>%
  summarise(meanSci = mean(PV1SCIE)) %>%
  rename(region = CNT)

levels(WorldSci$region)[levels(WorldSci$region)=="United Kingdom"] <- "UK"
levels(WorldSci$region)[levels(WorldSci$region)=="United States"] <- "USA"

Viet<-PISA_2022%>%
  select(PV1SCIE, CNT)%>%
  filter(CNT=="Vietnam")
  
# Add the country latitude and longitude data to the PISA scores
WorldSci<-left_join(world_data, WorldSci, by="region")
# Use geom_map to plot the basic world map (fill is white, line colour is black)
# Use geom_polygon to plot the PISA data
# Add a colour scale

Labels<-WorldSci%>%
  group_by(region)%>%
  summarise(meanSci=mean(meanSci), lat=mean(lat), long=mean(long))%>%
  na.omit()



ggplot(data = WorldSci, aes(x=long, y=lat, group=group)) +
  geom_map(data=world_data, map=world_data, aes(map_id=region), fill="white", colour="black")+
  geom_polygon(aes(fill=meanSci)) +
  scale_fill_viridis_c(option = "viridis")+
  geom_text_repel(data=Labels, inherit.aes = F, aes(x=long, y=lat,label=region),
                  size=3)+
  ggtitle("Mean PISA 2022 scoures by country")+
  theme(plot.title = element_text(hjust = 0.5))