search for: tbl_df

..._64/bujar-00check.html * installing *source* package 'bujar' ... ** package 'bujar' successfully unpacked and MD5 sums checked ** R ** data ** inst ** byte-compile and prepare package for lazy loading Warning: S3 methods '[.fun_list', '[.grouped_df', 'all.equal.tbl_df', 'anti_join.data.frame', 'anti_join.tbl_df', 'arrange.data.frame', 'arrange.default', 'arrange.grouped_df', 'arrange.tbl_df', 'arrange_.data.frame', 'arrange_.tbl_df', 'as.data.frame.grouped_df', 'as.data.frame.rowwise...

...;- wrld_simpl %>% mutate(lon = coordinates(wrld_simpl)[,1], lat = coordinates(wrld_simpl)[,2]) %>% filter(lat < -20, lon > 60) %>% dplyr::select(NAME) I got the following error messages: Error in (function (cl, name, valueClass) : c("assignment of an object of class ?tbl_df? is not valid for @?data? in an object of class ?SpatialPolygonsDataFrame?; is(value, \"data.frame\") is not TRUE", "assignment of an object of class ?tbl? is not valid for @?data? in an object of class ?SpatialPolygonsDataFrame?; is(value, \"data.frame\") is not TRUE&...

...of the form: competitionBayesOut <- anovaBF(biomass ~ clipping, data = competition) Returns the following Error message: Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ?compare? for signature ?"BFlinearModel", "missing", "tbl_df"? My guess the problem is in the ?class?, which is: [1] ?tbl_df? ?tbl? ?data.frame? The data was imported via the ?readr? package through R Studio, and then saved as a RData file. My code is below, and the data is attached as a text file. Thanks for your assistance. Best regards, James #...

..., 35833, 35768, 35949, 35861, 35828, 35652, 35555, 35597, 35850, 35229, 35290, 35411, 35161, 35024, 35065, 34769, 34490, 32760, 32833, 33354, 33755, 33656, 33092, 34240, 34997, 35409, 36074, 36474, 37139, 37532, 37637, 38045, 38351, 37510, 37495, 37720, 37686 )), class = c("tbl_ts", "tbl_df", "tbl", "data.frame"), row.names = c(NA, -100L), key = structure(list(Series_ID = "CEU0500000001", .rows = structure(list( 1:100), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", "list"))), class = c("tbl_df&quo...

...######################## # reproducible example df1 <- data.frame(wt=rnorm(90), group=paste0('reg', 1:5), var1=rep(c('male','female'), times=45), var2=rep(c('low','med','high'), each=30)) %>% tbl_df() # instead of doing this separately for each factor ... df2 <- df1 %>% group_by(group) %>% dplyr::count(var1, wt=wt) %>% mutate(prop1=n/sum(n)) df3 <- df1 %>% group_by(group) %>% dplyr::count(var2, wt=wt) %>% mutate(prop2=n/sum(n)) %>% left_join(df2, by=&...

...ains no target words.", "This sentence contains the words breast and the term metastatic. This sentence contains the word breast and the term stage IV." ) ), .Names = c("PTNO", "DATE", "TYPE", "TVAR"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,-4L) ) sampletxt2 <- group_by_at(sampletxt, vars(PTNO, DATE, TYPE)) sampletxt2 <- sampletxt2 %>% mutate( EXTRACTED = sentence_match(TVAR) )

...wt` = c(212, 241, 215, 225, 250, 241, 237, ??????????????????????????? 282, 206, 246, 194, 241, 196, 193, 224, 257, 200, 190, 208, 224 ), `plant-density` = c(137, 107, 132, 135, 115, 103, 102, 65, ?????????????????????? 149, 85, 173, 124, 157, 184, 112, 80, 165, 160, 157, 119)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -20L)) # regression model works mod2 = lm(`cob-wt` ~ `plant-density`, data = cob) # x sequence for plotting CI's # Set up x points x = seq(min(cob$`plant-density`), max(cob$`plant-density`), length = 1000) # Use predict to ge...

...uot;, > "This sentence contains the words breast and the term metastatic. This >sentence contains the word breast and the term stage IV." > ) > ), > .Names = c("PTNO", "DATE", "TYPE", "TVAR"), > class = c("tbl_df", > "tbl", "data.frame"), > row.names = c(NA,-4L) > ) > >sampletxt2 <- group_by_at(sampletxt, vars(PTNO, DATE, TYPE)) >sampletxt2 <- > sampletxt2 %>% > mutate( > EXTRACTED = sentence_match(TVAR) > ) > >____...

...uot;, > "This sentence contains the words breast and the term metastatic. This >sentence contains the word breast and the term stage IV." > ) > ), > .Names = c("PTNO", "DATE", "TYPE", "TVAR"), > class = c("tbl_df", > "tbl", "data.frame"), > row.names = c(NA,-4L) > ) > >sampletxt2 <- group_by_at(sampletxt, vars(PTNO, DATE, TYPE)) >sampletxt2 <- > sampletxt2 %>% > mutate( > EXTRACTED = sentence_match(TVAR) > ) > >____...

...ains no target words.", "This sentence contains the words breast and the term metastatic. This sentence contains the word breast and the term stage IV." ) ), .Names = c("PTNO", "DATE", "TYPE", "TVAR"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,-4L) ) sampletxt$EXTRACTED <- sapply(sampletxt$TVAR, sentence_match) sampletxt$EXTRACTED > sampletxt$EXTRACTED [1] ": "...

...0, 241, 237, > ??????????????????????????? 282, 206, 246, 194, 241, 196, 193, 224, > 257, 200, 190, 208, 224 > ), `plant-density` = c(137, 107, 132, 135, 115, 103, 102, 65, > ?????????????????????? 149, 85, 173, 124, 157, 184, 112, 80, 165, 160, > 157, 119)), > class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -20L)) > > # regression model works > mod2 = lm(`cob-wt` ~ `plant-density`, data = cob) > > # x sequence for plotting CI's > # Set up x points > x = seq(min(cob$`plant-density`), max(cob$`plant-density`),...

...ke the adjustments you devised, especially keeping the multicore approach for `make_clean_names()` and ensuring that `ID_Key` values remain intact, were the missing components you needed to fit it into your workflow. I believe the warning message regarding `dcast()` occurs because `keeptabs` is a `tbl_df` from the tidyverse rather than a base `data.frame` or `data.table`. The `data.table` implementation of `dcast()` expects a `data.table` or `data.frame`. When it detects a `tbl_df`, it tries to redirect to `reshape2::dcast()`, but since that appears to be deprecated, it will fail in future versions...

..."This sentence contains the words breast and the term metastatic. > This > sentence contains the word breast and the term stage IV." > ) > ), > .Names = c("PTNO", "DATE", "TYPE", "TVAR"), > class = c("tbl_df", > "tbl", "data.frame"), > row.names = c(NA,-4L) > ) > > sampletxt2 <- group_by_at(sampletxt, vars(PTNO, DATE, TYPE)) > sampletxt2 <- > sampletxt2 %>% > mutate( > EXTRACTED = sentence_match(TVAR) > ) >...

..., NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ... > FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, NA, NA, > FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, > FALSE, FALSE, FALSE)), class = c("tbl_df", "tbl", "data.frame" > ), row.names = c(NA, -7033L)) > > As you can see, when I do the dput, it gives me a bunch of TRUE and FALSE > values, when in reality I have records with value $0, records with amounts >> $0 and also a bunch of blank records. >...

...ains no target words.", "This sentence contains the words breast and the term metastatic. This sentence contains the word breast and the term stage IV." ) ), .Names = c("PTNO", "DATE", "TYPE", "TVAR"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,-4L) ) sampletxt$EXTRACTED <- sapply(sampletxt$TVAR, sentence_match) sampletxt$EXTRACTED > sampletxt$EXTRACTED [1] ": "...

...NA, NA, NA, NA, NA, > > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, > ... > > FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, NA, NA, > > FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, > > FALSE, FALSE, FALSE)), class = c("tbl_df", "tbl", "data.frame" > > ), row.names = c(NA, -7033L)) > > > > As you can see, when I do the dput, it gives me a bunch of TRUE and FALSE > > values, when in reality I have records with value $0, records with > amounts > >> $0 and also a...

...estion about Bayes Factor. But, just answered my own question. What is needed is to convert a 'dplyr' data frame into a standard data frame. competition2 <- data.frame(competition), where 'competition' is a 'dplyr' data frame with the following class structure. [1] "tbl_df" "tbl" "data.frame". Best regards, James

...; > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, > ... > > FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, NA, NA, > > FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, > > FALSE, FALSE, FALSE)), class = c("tbl_df", "tbl", "data.frame" > > ), row.names = c(NA, -7033L)) > > > > As you can see, when I do the dput, it gives me a bunch of TRUE > and FALSE > > values, when in reality I have records with value $0, records > with amo...

...run it on the full dataframe, but deploying data.table CONSIDERABLY reduces the time and memory overhead to the point where it only takes about 15 minutes to run one of my smaller dataframes. * I keep getting the following warning message: * The dcast generic in data.table has been passed a tbl_df and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is superseded and is no longer actively developed, and this redirection is now deprecated. Please do this redirection yourself like reshape2::dcast(keeptabs). In the next version, this warning will become an err...

...the same as the data (5625): > x, y, z, df > That's not what I get: > v <- ggplot(faithfuld, aes(waiting, eruptions, z = drmvt, df = c(13,13))) > v + geom_contour() Error in FUN(X[[i]], ...) : object 'drmvt' not found > > ? faithfuld > str(faithfuld) Classes ?tbl_df?, ?tbl? and 'data.frame': 5625 obs. of 3 variables: $ eruptions: num 1.6 1.65 1.69 1.74 1.79 ... $ waiting : num 43 43 43 43 43 43 43 43 43 43 ... $ density : num 0.00322 0.00384 0.00444 0.00498 0.00542 ... So you are apparently trying to throw together code and data that you don&...