Writing a single column of R object into pre-existing postgres db table - database
I have a working knowledge of R but I haven't used it in conjunction with databases too much. My question seems very similar to this question:
update table in postgresql database through r
But I can't get that code to work and the doMC package is not available for recent versions of R.
I'm able to connect to the geodatabase, read in data and manipulate it. I can also write an R object into the geodatabase as an entire table. I'm having trouble though, appending an existing geodatabase table with an R object. Unfortunately it's a secure database, so I can't give my connection information out.
-"wearing", "weekday", and "days" are blank columns in a pre-existing table in the geodatabase
-"participant_id", "date_id" and "gps_time" are populated columns in the pre-existing table that I would like to merge on.
-ucsd is the schema name and sage_choi is the existing table name
choifnl <- structure(list(wearing = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = c("nw",
"w"), class = "factor"), weekday = c("Saturday", "Saturday",
"Saturday", "Saturday", "Saturday", "Saturday"), days = c(1,
1, 1, 1, 1, 1), participant_id = c("0adf37c4-f950-40ad-9370-6bee2f93e935",
"0adf37c4-f950-40ad-9370-6bee2f93e935", "0adf37c4-f950-40ad-9370-6bee2f93e935",
"0adf37c4-f950-40ad-9370-6bee2f93e935", "0adf37c4-f950-40ad-9370-6bee2f93e935",
"0adf37c4-f950-40ad-9370-6bee2f93e935"), date = c("20130202",
"20130202", "20130202", "20130202", "20130202", "20130202"),
time = structure(1:6, .Label = c("00:00:00", "00:01:00",
"00:02:00", "00:03:00", "00:04:00", "00:05:00", "00:06:00",
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"17:07:00", "17:08:00", "17:09:00", "17:10:00", "17:11:00",
"17:12:00", "17:13:00", "17:14:00", "17:15:00", "17:16:00",
"17:17:00", "17:18:00", "17:19:00", "17:20:00", "17:21:00",
"17:22:00", "17:23:00", "17:24:00", "17:25:00", "17:26:00",
"17:27:00", "17:28:00", "17:29:00", "17:30:00", "17:31:00",
"17:32:00", "17:33:00", "17:34:00", "17:35:00", "17:36:00",
"17:37:00", "17:38:00", "17:39:00", "17:40:00", "17:41:00",
"17:42:00", "17:43:00", "17:44:00", "17:45:00", "17:46:00",
"17:47:00", "17:48:00", "17:49:00", "17:50:00", "17:51:00",
"17:52:00", "17:53:00", "17:54:00", "17:55:00", "17:56:00",
"17:57:00", "17:58:00", "17:59:00", "18:00:00", "18:01:00",
"18:02:00", "18:03:00", "18:04:00", "18:05:00", "18:06:00",
"18:07:00", "18:08:00", "18:09:00", "18:10:00", "18:11:00",
"18:12:00", "18:13:00", "18:14:00", "18:15:00", "18:16:00",
"18:17:00", "18:18:00", "18:19:00", "18:20:00", "18:21:00",
"18:22:00", "18:23:00", "18:24:00", "18:25:00", "18:26:00",
"18:27:00", "18:28:00", "18:29:00", "18:30:00", "18:31:00",
"18:32:00", "18:33:00", "18:34:00", "18:35:00", "18:36:00",
"18:37:00", "18:38:00", "18:39:00", "18:40:00", "18:41:00",
"18:42:00", "18:43:00", "18:44:00", "18:45:00", "18:46:00",
"18:47:00", "18:48:00", "18:49:00", "18:50:00", "18:51:00",
"18:52:00", "18:53:00", "18:54:00", "18:55:00", "18:56:00",
"18:57:00", "18:58:00", "18:59:00", "19:00:00", "19:01:00",
"19:02:00", "19:03:00", "19:04:00", "19:05:00", "19:06:00",
"19:07:00", "19:08:00", "19:09:00", "19:10:00", "19:11:00",
"19:12:00", "19:13:00", "19:14:00", "19:15:00", "19:16:00",
"19:17:00", "19:18:00", "19:19:00", "19:20:00", "19:21:00",
"19:22:00", "19:23:00", "19:24:00", "19:25:00", "19:26:00",
"19:27:00", "19:28:00", "19:29:00", "19:30:00", "19:31:00",
"19:32:00", "19:33:00", "19:34:00", "19:35:00", "19:36:00",
"19:37:00", "19:38:00", "19:39:00", "19:40:00", "19:41:00",
"19:42:00", "19:43:00", "19:44:00", "19:45:00", "19:46:00",
"19:47:00", "19:48:00", "19:49:00", "19:50:00", "19:51:00",
"19:52:00", "19:53:00", "19:54:00", "19:55:00", "19:56:00",
"19:57:00", "19:58:00", "19:59:00", "20:00:00", "20:01:00",
"20:02:00", "20:03:00", "20:04:00", "20:05:00", "20:06:00",
"20:07:00", "20:08:00", "20:09:00", "20:10:00", "20:11:00",
"20:12:00", "20:13:00", "20:14:00", "20:15:00", "20:16:00",
"20:17:00", "20:18:00", "20:19:00", "20:20:00", "20:21:00",
"20:22:00", "20:23:00", "20:24:00", "20:25:00", "20:26:00",
"20:27:00", "20:28:00", "20:29:00", "20:30:00", "20:31:00",
"20:32:00", "20:33:00", "20:34:00", "20:35:00", "20:36:00",
"20:37:00", "20:38:00", "20:39:00", "20:40:00", "20:41:00",
"20:42:00", "20:43:00", "20:44:00", "20:45:00", "20:46:00",
"20:47:00", "20:48:00", "20:49:00", "20:50:00", "20:51:00",
"20:52:00", "20:53:00", "20:54:00", "20:55:00", "20:56:00",
"20:57:00", "20:58:00", "20:59:00", "21:00:00", "21:01:00",
"21:02:00", "21:03:00", "21:04:00", "21:05:00", "21:06:00",
"21:07:00", "21:08:00", "21:09:00", "21:10:00", "21:11:00",
"21:12:00", "21:13:00", "21:14:00", "21:15:00", "21:16:00",
"21:17:00", "21:18:00", "21:19:00", "21:20:00", "21:21:00",
"21:22:00", "21:23:00", "21:24:00", "21:25:00", "21:26:00",
"21:27:00", "21:28:00", "21:29:00", "21:30:00", "21:31:00",
"21:32:00", "21:33:00", "21:34:00", "21:35:00", "21:36:00",
"21:37:00", "21:38:00", "21:39:00", "21:40:00", "21:41:00",
"21:42:00", "21:43:00", "21:44:00", "21:45:00", "21:46:00",
"21:47:00", "21:48:00", "21:49:00", "21:50:00", "21:51:00",
"21:52:00", "21:53:00", "21:54:00", "21:55:00", "21:56:00",
"21:57:00", "21:58:00", "21:59:00", "22:00:00", "22:01:00",
"22:02:00", "22:03:00", "22:04:00", "22:05:00", "22:06:00",
"22:07:00", "22:08:00", "22:09:00", "22:10:00", "22:11:00",
"22:12:00", "22:13:00", "22:14:00", "22:15:00", "22:16:00",
"22:17:00", "22:18:00", "22:19:00", "22:20:00", "22:21:00",
"22:22:00", "22:23:00", "22:24:00", "22:25:00", "22:26:00",
"22:27:00", "22:28:00", "22:29:00", "22:30:00", "22:31:00",
"22:32:00", "22:33:00", "22:34:00", "22:35:00", "22:36:00",
"22:37:00", "22:38:00", "22:39:00", "22:40:00", "22:41:00",
"22:42:00", "22:43:00", "22:44:00", "22:45:00", "22:46:00",
"22:47:00", "22:48:00", "22:49:00", "22:50:00", "22:51:00",
"22:52:00", "22:53:00", "22:54:00", "22:55:00", "22:56:00",
"22:57:00", "22:58:00", "22:59:00", "23:00:00", "23:01:00",
"23:02:00", "23:03:00", "23:04:00", "23:05:00", "23:06:00",
"23:07:00", "23:08:00", "23:09:00", "23:10:00", "23:11:00",
"23:12:00", "23:13:00", "23:14:00", "23:15:00", "23:16:00",
"23:17:00", "23:18:00", "23:19:00", "23:20:00", "23:21:00",
"23:22:00", "23:23:00", "23:24:00", "23:25:00", "23:26:00",
"23:27:00", "23:28:00", "23:29:00", "23:30:00", "23:31:00",
"23:32:00", "23:33:00", "23:34:00", "23:35:00", "23:36:00",
"23:37:00", "23:38:00", "23:39:00", "23:40:00", "23:41:00",
"23:42:00", "23:43:00", "23:44:00", "23:45:00", "23:46:00",
"23:47:00", "23:48:00", "23:49:00", "23:50:00", "23:51:00",
"23:52:00", "23:53:00", "23:54:00", "23:55:00", "23:56:00",
"23:57:00", "23:58:00", "23:59:00"), class = "factor")), .Names = c("wearing",
"weekday", "days", "participant_id", "date", "time"), row.names = c(NA,
6L), class = "data.frame")
update <- function(i) {
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname = "", host = "",port <- , user = "", password = "") ##connect w/username and password
txt <- paste("UPDATE ucsd.sage_choi SET wearing=",choifnl$wearing[i],",weekday=",
choifnl$weekday[i],",days=",choifnl$days[i],",where participant_id=",
choifnl$participant_id[i],",AND date_id=", choifnl$date[i],
"AND gps_time=", choifnl$time[i])
dbGetQuery(con, txt)
dbDisconnect(con)
}
library("foreach")
registerDoMC()
foreach(i = 1:length(choifnl$wearing), .inorder=FALSE,.packages="RPostgreSQL")%dopar%{
update(i)
}
####EDIT
This is the final solution (connection info omitted) that works well. I had some issues with encoding and also including the right kinds of quotes (single).
update <- function(con,i) {
txt <- paste("UPDATE ucsd.sage_final_choi SET wearing=", paste("'",choifnl$wearing[i],"'",sep=""),
", weekday=", paste("'",choifnl$weekday[i],"'",sep=""),
", days=", choifnl$days[i],
"WHERE participant_id=",paste("'",choifnl$participant_id[i],"'",sep=""),
"AND date_id=", paste("'",choifnl$date[i],"'",sep=""),
"AND gps_time=", paste("'",choifnl$time[i],"'",sep=""), ";")
dbSendQuery(con, txt) # edit 2
}
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname = "", host = "",
port <- 5432, user = "", password = "",options="-c search_path= schemaName") ##connect w/username and password
foreach(i = 1:length(choifnl$wearing), .inorder=FALSE,.packages="RPostgreSQL")%do%{
update(con,i) # passing in the open connection as an argument
}
I'm assuming here you are using the CRAN library RPostgreSQL and that your question is,
"How can I update an existing postgresql record using R?"
If I interpreted your question correctly, I have good news and I'll make minor mods to your existing code to get it working below. Now, the good news:
The error is in your SQL and
You do not need doMC (nor foreach, for that matter).
You're already connected to your database--that's usually the more onerous part!
If you really need parallelism you can see the basic format for initializing multiple workers with doSNOW. Either way, it is much easier to debug a single-threaded application so you should just straight up change your loop to a single-threaded loop by changing the %dopar% argument at the end of your foreach statement to %do% and then register your parallel backend AFTER you get the SQL working.
foreach(i = 1:length(choifnl$wearing),.inorder=FALSE,.packages="RPostgreSQL") %do% {
update(i)
}
Then you might see that your SQL has a syntax error, notably in that "where" and your first "and" erroneously follow commas. I typically break large statements into more human readable form like below so it is easier to spot inconsistencies in form. I removed the inadvertent commas in this snippet below:
### SQL error resolved
update <- function(i) {
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname = "", host = "",port <- , user = "", password = "") ##connect w/username and password
txt <- paste("UPDATE ucsd.sage_choi SET wearing=", choifnl$wearing[i],
", weekday=", choifnl$weekday[i],
", days=", choifnl$days[i],
"where participant_id=",choifnl$participant_id[i],
"AND date_id=", choifnl$date[i],
"AND gps_time=", choifnl$time[i])
dbGetQuery(con, txt)
dbDisconnect(con)
}
From a performance standpoint, you are going to do much better if you initialize your connection outside the for loop because you do not need to sink the time and cost to re-establish the connection for each record.
### SQL error resolved, connection made outside loop
update <- function(con,i) {
txt <- paste("UPDATE ucsd.sage_choi SET wearing=", choifnl$wearing[i],
", weekday=", choifnl$weekday[i],
", days=", choifnl$days[i],
"where participant_id=",choifnl$participant_id[i],
"AND date_id=", choifnl$date[i],
"AND gps_time=", choifnl$time[i])
dbSendQuery(con, txt) # edit 2
}
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname = "", host = "",port <- , user = "", password = "") ##connect w/username and password
foreach(i = 1:length(choifnl$wearing), .inorder=FALSE,.packages="RPostgreSQL")%dopar%{
update(con,i) # passing in the open connection as an argument
}
dbDisconnect(con)
If I understood the question correctly, this is fairly easy using dplyr's ability to connect to databases
library(dplyr)
library(RPostgreSQL)
my_db <- src_postgres(host="<HOST>",
user="<USER>",
password="<PASS>",
db = "ucsd")
sage_choi <- tbl(my_db,"sage_choi")
sage_choi %>%
select( participant_id, date_id, gps_time) %>%
left_join( choifnl, copy=TRUE, by=c("participant_id"="participant_id",
"date_id"="date",
"gps_time"="time")) %>%
compute(name="sage_choi2", temporary=FALSE)
Once you run the code, table sage_choi2 would contain the populated columns.
Related
using lookup tables to plot a ggplot and table
I'm creating a shiny app and i'm letting the user choose what data that should be displayed in a plot and a table. This choice is done through 3 different input variables that contain 14, 4 and two choices respectivly.
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(
selectInput(inputId = "DataSource", label = "Data source", choices =
c("Restoration plots", "all semi natural grasslands")),
selectInput(inputId = "Variabel", label = "Variable", choices =
choicesVariables)),
#choicesVariables definition is omitted here, because it's very long but it
#contains 14 string values
selectInput(inputId = "Factor", label = "Factor", choices = c("Company
type", "Region and type of application", "Approved or not approved
applications", "Age group" ))
),
dashboardBody(
plotOutput("thePlot"),
tableOutput("theTable")
))
This adds up to 73 choices (yes, i know the math doesn't add up there, but some choices are invalid). I would like to do this using a lookup table so a created one with every valid combination of choices like this:
rad1<-c(rep("Company type",20), rep("Region and type of application",20),
rep("Approved or not approved applications", 13), rep("Age group", 20))
rad2<-choicesVariable[c(1:14,1,4,5,9,10,11, 1:14,1,4,5,9,10,11, 1:7,9:14,
1:14,1,4,5,9,10,11)]
rad3<-c(rep("Restoration plots",14),rep("all semi natural grasslands",6),
rep("Restoration plots",14), rep("all semi natural grasslands",6),
rep("Restoration plots",27), rep("all semi natural grasslands",6))
rad4<-1:73
letaLista<-data.frame(rad1,rad2,rad3, rad4)
colnames(letaLista) <- c("Factor", "Variabel", "rest_alla", "id")
Now its easy to use subset to only get the choice that the user made. But how do i use this information to plot the plot and table without using a 73 line long ifelse statment?
I tried to create some sort of multidimensional array that could hold all the tables (and one for the plots) but i couldn't make it work. My experience with these kind of arrays is limited and this might be a simple issue, but any hints would be helpful!
My dataset that is the foundation for the plots and table consists of dataframe with 23 variables, factors and numerical. The plots and tabels are then created using the following code for all 73 combinations
s_A1 <- summarySE(Samlad_info, measurevar="Dist_brukcentrum",
groupvars="Companytype")
s_A1 <- s_A1[2:6,]
p_A1=ggplot(s_A1, aes(x=Companytype,
y=Dist_brukcentrum))+geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(aes(ymin=Dist_brukcentrum-se,
ymax=Dist_brukcentrum+se),width=.2,position=position_dodge(.9))+
scale_y_continuous(name = "") + scale_x_discrete(name = "")
where summarySE is the following function, burrowed from cookbook for R
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=TRUE,
conf.interval=.95, .drop=TRUE) {
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
The code in it's entirety is a bit to large but i hope this may clarify what i'm trying to do.
Well, thanks to florian's comment i think i might have found a solution my self. I'll present it here but leave the question open as there is probably far neater ways of doing it.
I rigged up the plots (that was created as lists by ggplot) into a list
plotList <- list(p_A1, p_A2, p_A3...)
tableList <- list(s_A1, s_A2, s_A3...)
I then used subset on my lookup table to get the matching id of the list to select the right plot and table.
output$thePlot <-renderPlot({
plotValue<-subset(letaLista, letaLista$Factor==input$Factor &
letaLista$Variabel== input$Variabel & letaLista$rest_alla==input$DataSource)
plotList[as.integer(plotValue[1,4])]
})
output$theTable <-renderTable({
plotValue<-subset(letaLista, letaLista$Factor==input$Factor &
letaLista$Variabel== input$Variabel & letaLista$rest_alla==input$DataSource)
skriva <- tableList[as.integer(plotValue[4])]
print(skriva)
})
How to connect to SQL Server with R?
I want to create a model in R using a connection to data stored in SQL Server datawarehouse.
I tried to use RevoScaleR library which returned
package RevoScaleR is not available (for R version 3.4.1)
so, I edited the connection string (given on the code below) for ODBC library:
install.packages("RevoScaleR")
#require("RevoScaleR")
if (!require("RODBC"))
install.packages("RODBC")
conn <- odbcDriverConnect(connection="Driver={SQL Server Native Client 11.0}; Server=CZPHADDWH01/DEV; Database=DWH_Staging; trusted_connection=true")
sqlWait <- TRUE;
sqlConsoleOutput <- FALSE;
cc <- RxInSqlServer(connectionString = conn, wait = sqlWait)
rxSetComputeContext(cc)
train_query <- "SELECT TOP(10000) * FROM dim.Contract"
formula <- as.formula("Cosi ~ ContractID + ApprovedLoanAmount + ApprovedLoadDuration")
forest_model <- rxDForest(formula = formula,
data = train_query,
nTree = 20,
maxDepth = 32,
mTry = 3,
seed = 5,
verbose = 1,
reportProgress = 1)
rxDForest_model <- as.raw(serialize(forest_model, connection = conn))
lenght(rxDForest_model)
However:
package 'RODBC' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\sjirak\AppData\Local\Temp\Rtmpqa9iKN\downloaded_packages
Error in odbcDriverConnect(connection = "Driver={SQL Server Native
Client 11.0}; Server=CZPHADDWH01/DEV; Database=DWH_Staging;
trusted_connection=true") : could not find function
"odbcDriverConnect" In library(package, lib.loc = lib.loc,
character.only = TRUE, logical.return = TRUE, : there is no
package called 'RODBC'
Any help would be appreciated.
Looking at the documentation of the ODBC, I see the following functions odbc-package dbConnect,OdbcDriver-method dbUnQuoteIdentifier odbc odbc-tables OdbcConnection odbcConnectionActions odbcConnectionIcon odbcDataType OdbcDriver odbcListColumns odbcListDataSources odbcListDrivers odbcListObjects odbcListObjectTypes odbcPreviewObject OdbcResult odbcSetTransactionIsolationLevel test_roundtrip hence I dont see your function in this list. This could be the reason why... Hence, check the documentation for the proper function.
How to import data from SQL Server into quantmod?
I'm looking for some guidance and hope that I did the right thing posting it in here. I'm looking to input data to Quantmod with GetSymbols from SQL Server. I'm new to R but have a background working with SQL Server, not a pro but finding my way.
I have imported all my data into one table in SQL Server named Quotes with the following columns;
- Ticker Varchar(10)
- Name varchar(50)
- [Date] datetime
- [Open] Decimal(19,9)
- High Decimal(19,9)
- Low Decimal(19,9)
- [Close] Decimal(19,9)
- Volume Decimal(19,9)
- Signal Decimal(19,9)
I'm able to connect to the database using the RODBC package:
- (cn <- odbcDriverConnect(connection="Driver={SQL Server Native Client 11.0};server=localhost;database=DB;trusted_connection=yes;"))
and make various select statement in R, but I'm lost in getting the data into Quantmod without having to do other workaround like exporting to csv from SQL. Importing the data from Yahoo is a problem as I cannot find a complete Yahoo-tickerlist.
Is there a way to get data directly into R and quantmod from SQL Server?
Something like this should do the trick.
getPrices.DB <- function(Symbol, from=NA) {
cn <- "add your connection info here"
qry <- sprintf("select [Date], [Open],[High],[Low],[Close],[Volume],[Signal] from MarketPrice where Ticker = '%s'", Symbol)
if (!is.na(from)) { qry <- paste(qry, sprintf(" and [Date]>= '%s'", from)) }
DB <- odbcDriverConnect(cn)
r <- sqlQuery(DB, qry, stringsAsFactors = FALSE)
odbcClose(DB)
if (!is.null(r) && NROW(r) >= 1) {
x <- xts(r[, 2:7], order.by = as.POSIXct(r[, 1], tz = "UTC"))#can eliminate tz if you want in local timezone
indexFormat(x) <- "%Y-%b-%d %H:%M:%OS3 %z" #option. I find useful for debuggging
colnames(x) <- paste(Symbol, c("Open", "High","Low", "Close", "Volume", "Signal"), sep = ".")
return(x)
} else {
return(NULL)
}
}
Now hook into the quantmod infrastructure:
getSymbols.DB <- function(Symbols, env, ...) {
importDefaults("getSymbols.DB")
this.env <- environment()
for (var in names(list(...))) {assign(var, list(...)[[var]], this.env)}
if (!hasArg(from)) from <- NA
if (!hasArg(verbose)) verbose <- FALSE
if (!hasArg(auto.assign)) auto.assign <- FALSE
for (i in 1:length(Symbols)) {
if (verbose) cat(paste("Loading ", Symbols[[i]], paste(rep(".", 10 - nchar(Symbols[[i]])), collapse = ""), sep = ""))
x <- getPrices.DB(Symbols[[i]], from = from)
if (auto.assign) assign(Symbols[[i]], x, env)
if (verbose) cat("done\n")
}
if (auto.assign)
return(Symbols)
else
return(x)
}
example usage:
APPL <- getSymbols("AAPL", src="DB", auto.assign=F)
Execute Microsoft SQL query on R Shiny
I am writing an R-Shiny app. Can some one tell me how to execute a Microsoft SQL query in R Shiny ?
This is what I have done so far:
data <- reactive({
conn <- reactive ({ databaseOpen(serverName="[serverName]", databaseName=[dbName])})
qr <- reactive ({ SELECT * from myTable })
res <- reactive ({databaseQuery(conn = conn,query = qr)})
close(conn)
View(res)
})
Any help is appreciated !
I was able to call a query by creating a function outside of the server and ui functions (in other words, in a global.r). Then the server function could call that query function using one of the inputs in the function.
Here is my code:
queryfunction <- function(zipper){
odbcChannel <- odbcConnect("myconnection")
querydoc <- paste0("
SELECT distinct *
FROM mydb
where substring(nppes_provider_zip,1,2) = '43'
and [provider_type] = 'General Practice'
")
pricetable <- sqlQuery(odbcChannel, querydoc)
close(odbcChannel)
pricetable[which(substring(pricetable$nppes_provider_zip,1,5)==zipper),]
}
server <- shinyServer(function(input, output) {
output$mytable1 <- renderDataTable(data.table(queryfunction(input$zip)))
})
I figured it out. It can be done as:
server.r
serverfun<-function(input, output){
# Storing values in myData variable
myData <- reactive({
# Opening database connection
conn <- databaseOpen(serverName = "myServer",databaseName = "myDB")
# Sample query which uses some input
qr <- paste( "SELECT name FROM Genes g WHERE Id = ",input$myId," ORDER BY name")
# Storing results
res <- databaseQuery(conn = conn,query = qr)
# closing database
databaseClose(conn)
# Returning results
res
})
output$tbTable <- renderTable({
# Checking if myData is not null
if(is.null(myData())){return ()}
# return myData
myData()
})
ui.r
library("shiny")
shinyUI(
pageWithSidebar(
headerPanel("Hide Side Bar example"),
sidebarPanel(
textInput("Id", "Enter ID below","1234")
),
mainPanel(
tabsetPanel(
tabPanel("Data", tableOutput("tbTable"))
)
)
)
)
Passing na.strings to dbWriteTable
I'm trying to run the following command in R in order to read a local tab-delimited file as a SQLite database:
library(RSQLite)
banco <- dbConnect(drv = "SQLite",
dbname = "data.sqlite")
dbWriteTable(conn = banco,
name = "Tarefas",
value = "data.tsv",
sep = "\t",
dec = ",",
na.strings = c("", NA),
row.names = FALSE,
header = TRUE)
However, the statements above yield the following error:
Error in read.table(fn, sep = sep, header = header, skip = skip, nrows
= nrows, : formal argument "na.strings" matched by multiple actual arguments
Which makes me think I'm not being able to pass na.strings explicitly as a read.delim argument. Running dbWriteTable without this argument gives me "RS-DBI driver: (RS_sqlite_import: ./data.tsv line 17696 expected 20 columns of data but found 18)". This is understandable, since I've checked line 17696 and it is almost completely blank.
Another test run using sqldf also gives me an error:
> read.csv2.sql(file = "data.tsv",
+ sql = "CREATE TABLE Tarefas AS SELECT * FROM FILE LIMIT 5",
+ dbname = "data.sqlite",
+ header = TRUE,
+ row.names = FALSE)
Error in sqliteExecStatement(con, statement, bind.data) :
RS-DBI driver: (error in statement: no such table: FILE)
Which I believe is an unrelated error, but still very confusing for someone who's pretty much an absolute SQL noob such as myself. Runnin read.csv.sql instead gives me this error:
Error in read.table(fn, sep = sep, header = header, skip = skip, nrows = nrows, :
more columns than column names
So is there a way to pass na.strings = c("", NA) at dbWriteTable? Is there a better way to read 10 GB tab-delimited files into R aside from sqldf and RSQLite? I've already tried data.table and ff.