sqldf

I have a data frame with 3 variables: place, time, and value (P, T, X). I want to create a fourth variable which will be the cumulative sum of X. Normally I like to do grouping calculations with sqldf , but can't seem to find an equivalent for cumsum . That is: sqldf("select P,T,X, cumsum(X) as X_CUM from df group by P,T") doesn't work. Is this even possible with sqldf ? I tried doBy , but that doesn't all cumsum either. Set up some test data: DF <- data.frame(t = 1:4, p = rep(1:3, each = 4), value = 1:12) and now we have three solutions. First we use sqldf , as requested, using the default

I have some query like this: sqldf("select TenScore from data where State_P = 'AndhraPradesh'") But I have "AndhraPradesh" in a variable stateValue . How can I use this variable in a select query in R to get the same result as above. Please show me the syntax. You can use sprintf : sqldf(sprintf("select TenScore from data where State_P = '%s'", stateValue)) G. Grothendieck See Example 5 on the sqldf GitHub page . Example 5. Insert Variables Here is an example of inserting evaluated variables into a query using gsubfn quasi-perl-style string interpolation. gsubfn is used by sqldf so its already

I am using read.csv.sql from the package sqldf to try and read in a subset of rows, where the subset selects from multiple values - these values are stored in another vector. I have hacked a way to a form that works but I would like to see the correct way to pass the sql statement. Code below gives minimum example. library(sqldf) # some data write.csv(mtcars, "mtcars.csv", quote = FALSE, row.names = FALSE) # values to select from variable 'carb' cc <- c(1, 2) # This only selects last value from 'cc' vector read.csv.sql("mtcars.csv", sql = paste("select * from file where carb = ", cc )) # So

This question already has an answer here: How do I use regex in a SQLite query? 13 answers I am using sqldf package and sql analyze one table generated by a classification model. But when I use the code: table<-sqldf(" SELECT a, b, c, d, e, f, CASE WHEN (REGEXP_LIKE(t, '\b(2nd time|3rd time|4th time)\b')) = TRUE THEN 1 ELSE 0 END AS UPSET_NOT_LIKE, regexp_extract(t, '\b(2nd time|3rd time|4th time)\b')) as Word FROM cls ") It looks like that the sqldf package don't have regexp_like and regexp_extract function. Is there any sql-advanced packages that I can use to do the query? G. Grothendieck

I have two data-frames df1 and df2 that each have around 10 million rows and 4 columns. I read them into R using RODBC/sqlQuery with no problems, but when I try to rbind them, I get that most dreaded of R error messages: cannot allocate memory . There have got to be more efficient ways to do an rbind more efficiently -- anyone have their favorite tricks on this they want to share? For instance I found this example in the doc for sqldf : # rbind a7r <- rbind(a5r, a6r) a7s <- sqldf("select * from a5s union all select * from a6s") Is that the best/recommended way to do it? UPDATE I got it to work

I'll illustrate my question with an example. Sample data: df <- data.frame(ID = c(1, 1, 2, 2, 3, 5), A = c("foo", "bar", "foo", "foo", "bar", "bar"), B = c(1, 5, 7, 23, 54, 202)) df ID A B 1 1 foo 1 2 1 bar 5 3 2 foo 7 4 2 foo 23 5 3 bar 54 6 5 bar 202 What I want to do is to summarize, by ID, the sum of B and the sum of B when A is "foo". I can do this in a couple steps like: require(magrittr) require(dplyr) df1 <- df %>% group_by(ID) %>% summarize(sumB = sum(B)) df2 <- df %>% filter(A == "foo") %>% group_by(ID) %>% summarize(sumBfoo = sum(B)) left_join(df1, df2) ID sumB sumBfoo 1 1 6 1 2 2