Peering into Anki using R
Yet another diversion to keep me from focusing on actually using Anki to learn Russian. I stumbled on the R programming language, a language that focuses on statistical analysis.
Here’s a couple snippets that begin to scratch the surface of what’s possible. Important caveat: I’m an R novice at best. There are probably much better ways of doing some of this…
Counting notes with a particular model type
Here we’ll use R to do what we did previously with Python.
First load some of the libraries we’ll need:
library(rjson)
library(RSQLite)
library(DBI)Next we’ll connect to the database and extract the model information:
# connect to the Anki database
dbpath <- "path to your collection"
con = dbConnect(RSQLite::SQLite(),dbname=dbpath)
# get information about the models
modelInfo <- as.character(dbGetQuery(con,'SELECT models FROM col'))
models <- fromJSON(modelInfo)Since the model information is stored as JSON, we’ll need to parse the JSON to build a data frame that we can use to extract the model ID that we’ll need.
names <- c()
mid <- names(models)
for(i in 1:length(mid))
{
names[i] <- models[[mid[i]]]$name
}
models <- data.frame(cbind(mid,names))Next we’ll extract the model ID (mid) from this data frame so that we can find all of the notes with that model ID:
verbmid <- as.numeric(as.character(models[models$names=="Русский - глагол","mid"]))
# query the notes database for notes with this model
query <- paste("SELECT COUNT(id) FROM notes WHERE mid =",verbmid)
res <- as.numeric(dbGetQuery(con,query))And of course, close the connection to the Anki SQLite database:
dbDisconnect(con)As of this writing, res tells me I have 702 notes with the verb model types (named “Русский - глагол” in my collection.)
Counting hours per month in Anki
Ever wonder how many hours per month you spend reviewing in Anki? Here’s an R program that will grab review time information from the database and plot it for you. I ran across the original idea in this blog post by Gene Dan, but did a little work on the x-axis scale to get it to display correctly.
library(RSQLite)
library(DBI)
library(rjson)
library(anytime)
library(sqldf)
library(zoo)
library(ggplot2)
dbpath <- "/Users/alan/Library/Application Support/Anki2/Alan - Russian/collection.anki2"
con = dbConnect(RSQLite::SQLite(),dbname=dbpath)
#get reviews
rev <- dbGetQuery(con,'select CAST(id as TEXT) as id
, CAST(cid as TEXT) as cid
, time
from revlog')
cards <- dbGetQuery(con,'select CAST(id as TEXT) as cid, CAST(did as TEXT) as did from cards')
#Get deck info - from the decks field in the col table
deckinfo <- as.character(dbGetQuery(con,'select decks from col'))
decks <- fromJSON(deckinfo)
names <- c()
did <- names(decks)
for(i in 1:length(did))
{
names[i] <- decks[[did[i]]]$name
}
decks <- data.frame(cbind(did,names))
#decks$names <- as.character(decks$names)
cards_w_decks <- merge(cards,decks,by="did")
#Date is UNIX timestamp in milliseconds, divide by 1000 to get seconds
rev$revdate <- as.yearmon(anydate(as.numeric(rev$id)/1000))
# Assign deck info to reviews
rev_w_decks <- merge(rev,cards_w_decks,by="cid")
time_summary <- sqldf("select revdate, sum(time) as Time from rev_w_decks group by revdate")
time_summary$Time <- time_summary$Time/3.6e+6
ggplot(time_summary,aes(x=revdate,y=Time))+geom_bar(stat="identity",fill="#d93d2a")+
scale_x_yearmon()+
ggtitle("Hours per Month") +
xlab("Review Date") +
ylab("Time (hrs)") +
theme(axis.text.x=element_text(hjust=2,size=rel(1))) +
theme(plot.title=element_text(size=rel(1.5),vjust=.9,hjust=.5)) +
guides(fill = guide_legend(reverse = TRUE))
dbDisconnect(con)You should get a plot like this the one at the top of the post.
I’m anxious to learn more about R and apply it to understanding my performance in Anki.
