similar to: Lahman Baseball Data Using R DBI Package

The double quotes are required by SQL if a name is not of the form letter-followed-by-any-number-of-letters-or-numbers or if the name is a SQL keyword like 'where' or 'select'. If you are doing this from a function, you may as well quote all the names. -Bill On Fri, Oct 2, 2020 at 6:18 PM Philip <herd_dog at cox.net> wrote: > The \?2B\? worked. Have no idea why. Can

Hi Philip, You've probably realized by now that R doesn't like column names that start with a number. If you try to access an R-dataframe column named 2B or 3B with the familiar "$" notation, you'll get an error: > library(DBI) > library(RSQLite) > con2 <- dbConnect(SQLite(), "~/R_Dir/lahmansbaseballdb.sqlite") > Hack12Batting <-

This is really a feature of SQL, not R. SQL requires that you double quote column names that start with numbers, include spaces, etc., or that are SQL key words. E.g., > d <- data.frame(Order=c("sit","stay","heel"), Where=c("here","there","there"), From=c("me","me","you")) >

Greetings! I'm have a hard time working with some data I imported from a baseball database. Several of the database columns have numbers in them (2B, 3B), and when I try to use these vectors from the data frame, I get syntax errors, probably because it's interpreting the name as a number: > show(batting2005) playerID yearID stint teamID lgID G AB R H 2B 3B HR RBI SB CS BB

I'm updating the Lahman package of baseball statistics to the 2013 release. In addition to the main data sets, the package also contains several convenience functions that make use of these data sets. These now trigger the notes below from R CMD check run with Win builder, R-devel. How can I avoid these? * using R Under development (unstable) (2014-08-25 r66471) * using platform:

I have a query which returns a data set like so: > salaries yearID POS pct 1 2009 RF 203 2 2009 DH 200 3 2009 1B 198 4 2009 3B 180 5 2009 LF 169 6 2009 SS 156 7 2009 CF 148 8 2009 2B 97 9 2009 C 86 10 2008 DH 234 11 2008 1B 199 12 2008 RF 197 13 2008 3B 191 14 2008 SS 180 15 2008 CF 164 16 2008 LF 156 17 2008 2B 104 18 2008

Hello all. I have the following: plot(salaries$yearID, salaries$salary, type='n', xaxt='n', xlab='', yaxt='n', ylab='') axis(1, at=unique(salaries$yearID), labels=unique(salaries$yearID), lwd=.25, tck=-0.05) axis(2, axTicks(2), format(axTicks(2), scientific = F)) Which nicely creates the Y axis with the raw numbers, which are in the range of .5 - 7

I'm looking for guidance or advice about the R license to use in preparing a package containing the Baseball Database from http://baseball1.com/statistics/ My main purpose is to make it available to students in a course, and to develop it with others I'd like to put it on R-Forge, and then perhaps make it public on CRAN. However, the page above bears a very restrictive copyright notice

Can't figure this out. I have the following list of salary averages per year, per position. The dput output is: > dput(salaries) structure(list(yearID = c(2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008), AVG = c(8956855.61, 7886684.166126, 7534048.43102, 7406439.339471, 7219148.437934, 6697734.908336, 6400379.88398,

Im having trouble with a sports site im creating for a client of mine. The part is where the Team Captians for each team in the league go into the admin section to submit a match report for each match. The form looks like this: TEAM 1 BATTING: (these are all drop down menus that pull the player names from the database) There are 10 of these rows that look like this, 1 row for each player.

I am creating a blog to learn ruby on rails. from the layout page i pass all the posts as a collection to _archive.rhtml <%= render :partial => "archive", :collection => @archive %> On _archive.rhtml i have access to the collection. I am then gone to render partial another page _archivecount.rhtml to display the number of posts for each month. Can anyone give a clue

Hi, I recently ran into an inconsistency in the way model.matrix.default handles factor encoding for higher level interactions with categorical variables when the full hierarchy of effects is not present. Depending on which lower level interactions are specified, the factor encoding changes for a higher level interaction. Consider the following minimal reproducible example: -------------- >

Hello Tyler, You write that you understand what I am saying. However, I am now at loss about what exactly is the problem with the behavior of R. Here is a script which reproduces your experiments with three variables (excluding the full model): m=expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C")) model.matrix(~(X1+X2+X3)^3-X1:X3,data=m)

Hello Tyler, I want to bring to your attention the following document: "What happens if you omit the main effect in a regression model with an interaction?" (https://stats.idre.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-in-a-regression-model-with-an-interaction). This gives a useful review of the problem. Your example is Case 2: a continuous and a categorical regressor.

I think it is not a bug. It is a general property of interactions. This property is best observed if all variables are factors (qualitative). For example, you have three variables (factors). You ask for as many interactions as possible, except an interaction term between two particular variables. When this interaction is not a constant, it is different for different values of the remaining

Hello Tyler, I rephrase my previous mail, as follows: In your example, T_i = X1:X2:X3. Let F_j = X3. (The numerical variables X1 and X2 are not encoded at all.) Then T_{i(j)} = X1:X2, which in the example is dropped from the model. Hence the X3 in T_i must be encoded by dummy variables, as indeed it is. Arie On Thu, Nov 2, 2017 at 4:11 PM, Tyler <tylermw at gmail.com> wrote: > Hi

Hi Arie, Given the heuristic, in all of my examples with a missing two-factor interaction the three-factor interaction should be coded with dummy variables. In reality, it is encoded by dummy variables only when the numeric:numeric interaction is missing, and by contrasts for the other two. The heuristic does not specify separate behavior for numeric vs categorical factors (When the author of

Hello Tyler, Thank you for searching for, and finding, the basic description of the behavior of R in this matter. I think your example is in agreement with the book. But let me first note the following. You write: "F_j refers to a factor (variable) in a model and not a categorical factor". However: "a factor is a vector object used to specify a discrete classification"

Hi Arie, Thank you for your further research into the issue. Regarding Stata: On the other hand, JMP gives model matrices that use the main effects contrasts in computing the higher order interactions, without the dummy variable encoding. I verified this both by analyzing the linear model given in my first example and noting that JMP has one more degree of freedom than R for the same model, as

Hi Arie, The book out of which this behavior is based does not use factor (in this section) to refer to categorical factor. I will again point to this sentence, from page 40, in the same section and referring to the behavior under question, that shows F_j is not limited to categorical factors: "Numeric variables appear in the computations as themselves, uncoded. Therefore, the rule does not