I would be very grateful for any guidance on how to use the xltabr package to automatically format tables in r, please:
https://github.com/moj-analytical-services/xltabr
In SPSS for example, I would apply the relevant weight and then run a cross tab on the raw data e.g var1*var2.
How would you go about doing this in r so that the package recognises it to produce the table?
Much appreciated.
You need to create/ read in the dataframe which you want to use first.
dat <- read.spss("mydataframe.sav")
Then you need to put it in the format you want: As in your example of crosstables, you can do this:
library(reshape2)
ct <- reshape2::dcast(iris, variable1 ~ variable2, fun.aggregate = length)
#depending on what data you want, you can change the fun.aggreagte function (e.g. sum or mean).
Then you can use the xltabr package to prepare the excel file by creating a Workbook:
wb <- xltabr::auto_crosstab_to_wb(ct)
Then you can save it as .xlsx file:
library(openxlsx)
openxlsx::saveWorkbook(wb, file = "crosstable.xlsx", overwrite = T)
I hope this helps
Related
I am tring to conduct a basic bibliometrix analysis using biblioshiny. However, since I have both Scopus and WoS databases, I am finding it difficult to combine them. So far, I have been able to import both the data using codes in R, and I have also already combined them. But I can't figure out how to use this combined data as input into the biblioshiny() app.
#Importing WoS and Scopus data individually
m1 = convert2df("WOS.txt", "wos", "plaintext")
m2 = convert2df("scopus.csv", "scopus", "csv")
#Merging them
M = mergeDbSources(m1, m2, remove.duplicated = TRUE)
#Creating the results
results = biblioAnalysis(M, sep = ";")
I just need to know how to export the results in a relevant format for data input in biblioshiny. Please help!
Put all of the WOS data files (in txt format) into a zip file and upload that zip file into biblioshiny. That's all you have to do.
use this command
library(openxlsx)
write.xlsx(results, file="mergedfile.xlsx")
it will save results with a name of mergedfile
I've just started learning R so forgive me for my ignorance! I'm reading in lots of .csv files, each of which correlates to a different year (2010-2019). I then filter down the .csv files based on a variable within one of the columns (because the datasets are very large. Currently I am using the below code to do this and then repeating it for each year:
data_2010 <- data.table::fread("//Project/2010 data/2010 data.csv", select = c("date", "id", "type"))
data_b_2010 <- data_2010[which(data_2010$type=="ABC123")]
rm(data_2010)
What I would like to do is use a For-loop to create new object data_20xx for each year, and then read in the .csv files (and apply the filter of "type") for each year too.
I think I know how to create the objects in a For-loop but not entirely sure how I would also assign the .csv files and change the filepath string so it updates with each year (i.e. "//Project/2010 data/2010 data.csv" to "//Project/2011 data/2011 data.csv").
Any help would be greatly appreciated!
Next time please provide a repoducible example so we can help you.
I would use data.table which contains specialized functions to do what you want.
library(data.table)
setwd("Project")
allfiles <- list.files(recursive = T, full.names = T)
allcsv <- allfiles[grepl(".csv", allfiles)]
data_list <- list()
for(i in 1:length(allcsv)) {
print(paste(round(i/length(allcsv),2)))
data_list[i] <- fread(allcsv[i])
}
data_list_filtered <- lapply(data_list, function(x) {
y <- data.frame(x)
return(y[which(y["type"]=="ABC123",)])
})
result <- rbindlist(data_list_filtered)
First, list.files will tell you all the files contained in your working dir by default.
Second, read each csv file into the data_list list using the fast and efficient fread function.
Third, do the filtering within a loop, as requested.
Fourth, use rbindlist from data.table to rbind all of these data.table's.
Finally, if you are not familiar with the data.table syntax, you can run setDF(result) to convert your results back to a data.frame.
I strongly encourage you to learn the data.table syntax as it is quite powerful and efficient for tabular data manipulations. These vignettes will get you started.
Is there a way to import a named Excel-table into R as a data.frame?
I typically have several named Excel-tables on a single worksheet, that I want to import as data.frames, without relying on static row - and column references for the location of the Excel-tables.
I have tried to set namedRegion which is an available argument for several Excel-import functions, but that does not seem to work for named Excel-tables. I am currently using the openxlxs package, which has a function getTables() that creates a variable with Excel-table names from a single worksheet, but not the data in the tables.
To get your named table is a little bit of work.
First you need to load the workbook.
library(openxlsx)
wb <- loadWorkbook("name_excel_file.xlsx")
Next you need to extract the name of your named table.
# get the name and the range
tables <- getTables(wb = wb,
sheet = 1)
If you have multiple named tables they are all in tables. My named table is called Table1.
Next you to extract the column numbers and row numbers, which you will later use to extract the named table from the Excel file.
# get the range
table_range <- names(tables[tables == "Table1"])
table_range_refs <- strsplit(table_range, ":")[[1]]
# use a regex to extract out the row numbers
table_range_row_num <- gsub("[^0-9.]", "", table_range_refs)
# extract out the column numbers
table_range_col_num <- convertFromExcelRef(table_range_refs)
Now you re-read the Excel file with the cols and rows parameter.
# finally read it
my_df <- read.xlsx(xlsxFile = "name_excel_file.xlsx",
sheet = 1,
cols = table_range_col_num[1]:table_range_col_num[2],
rows = table_range_row_num[1]:table_range_row_num[2])
You end up with a data frame with only the content of your named table.
I used this a while ago. I found this code somewhere, but I don't know anymore from where.
This link is might be useful for you
https://stackoverflow.com/a/17709204/10235327
1. Install XLConnect package
2. Save a path to your file in a variable
3. Load workbook
4. Save your data to df
To get table names you can use function
getTables(wb,sheet=1,simplify=T)
Where:
wb - your workbook
sheet - sheet name or might be the number as well
simplify = TRUE (default) the result is simplified to a vector
https://rdrr.io/cran/XLConnect/man/getTables-methods.html
Here's the code (not mine, copied from the topic above, just a bit modified)
require(XLConnect)
sampleFile = "C:/Users/your.name/Documents/test.xlsx"
wb = loadWorkbook(sampleFile)
myTable <- getTables(wb,sheet=1)
df<-readTable(wb, sheet = 1, table = myTable)
You can check next packages:
library(xlsx)
Data <- read.xlsx('YourFile.xlsx',sheet=1)
library(readxl)
Data <- read_excel('YourFile.xlsx',sheet=1)
Both options allow you to define specific regions to load the data into R.
I use read.xlsx from package openxlsx. For example:
library(openxlsx)
fileA <- paste0(some.directory,'excel.file.xlsx')
A <- read.xlsx(fileA, startRow = 3)
hope it helps
I have a spss file which contents variables and value labels. I saw foreign package with read.spss function:
data <- read.spss("2017.sav", to.data.frame = TRUE, use.value.labels = TRUE)
If i use use.value.labels = TRUE, all string change to factor variables and i dont want it because they are not factor all.
I found one solution but i dont know if it is the best way to do it
1º First read spss file with previous sentence
2º select which variables are not factor and change it to string with:
cols <- c("x", "ab")
data[cols] <- lapply(data[cols], as.character)
if i dont use use.value.labels = TRUE i will have not value labels and i cannot export file correctly
You can also use the memisc package:
sav <- spss.system.file("file.sav")
df <- as.data.set(sav)
My company regularly deals with SAV files and we extract out the metadata separately. With the foreign package, you can get the metadata out in a few different ways (after you have loaded the file in):
data.label.table <- attr(sav, "label.table")
missings <- attr(sav, "missings")
The other bits require various lapply and sapply functions to get them out. The script I have is quite long, so I will not share it here. If you read the data in with read.spss(sav, to.data.frame = TRUE) you can get:
VariableLabels <- unname(attr(sav, "variable.labels"))
I dont know why, but I can’t install a "foreign" package.
Here is what I did instead to import a dataset from SPSS to R (through Excel):
Open your data in SPSS.
Export dataset from SPSS to Excel, but make sure to choose the "Save
value labels where defined instead of data values" option at the
very bottom.
Open R.
Import dataset from Excel.
Now, you have a dataset in R with value labels.
Use the haven package:
library(haven)
data <- read_sav("2017.sav")
The labels are shown in the RStudio viewer.
I am using sapply(tk_choose.files) to produce an interactive window where I can choose which .csv files (multiple) to import. I then do some basic data manipulation so that the mean of one particular column can be plotted using ggplot.
So far my code looks something like this:
>tfiles <- data.frame(sapply(sapply(tk_choose.files(caption="Choose T files
(hold CTRL to select multiple files)"), read.table, header=TRUE, sep=","), c))
>rfiles <- data.frame(sapply(sapply(tk_choose.files(caption="Choose R files
(hold CTRL to select multiple files)"), read.table, header=TRUE, sep=","), c))
I have then calculated the mean of a particular column for both tfiles and rfiles so that I could plot 100-tfiles-rfiles.
While this is working fine for one set of data, I would like to now import more sets of data, preferably also using sapply(tk_choose.files). Essentially I need to get t/rfiles1, t/rfiles2...and repeat the data manipulation process after that, so that I could get a plot of multiple sets of data. I have no idea how to do this without having to copy and paste my code!
Sorry if this is a stupid question, I am very new to R so I am really stuck, your help is greatly appreciated!
Assuming that the files in the working directory are as follow:
all.files<-list.files(pattern="\\.csv")
all.files
[1] "R01.csv" "R02.csv" "R03.csv" "R04.csv" "T01.csv" "T02.csv" "T03.csv" "T04.csv"
And you wish to call tfiles1 as merged data of T01 and T02; tfiles2 as merged data of T03 and T04
T <- grep("T", all.files, value=T)
T
[1] "T01.csv" "T02.csv" "T03.csv" "T04.csv"
t.list <- list(T[1:2], T[3:4])
all.T <- lapply(t.list, function(x)ldply(x, read.csv))
for (i in 1:length(all.T)) assign(paste0("tfiles", i), all.T[[i]]) #this will produce tfiles1 and tfiles2 in your R environment.