How to convert multiple columns in R - r

I have a data frame which is retrieved from a csv file. I need to get the column types of some columns and apply these types to another data.frame's corresponding columns.
For example, after certain steps, the data.frame from the csv is called Table1.
header <- names(Table1)
"Acct" "Tran"
class(Table1$Acct)
"character"
class(Table1$Tran)
"character"
Then I need to convert Table2's corresponding "Acct" and "Tran" columns to character.
I tried
class(Table1[header])
[1] "data.frame"
class(Table1$header)
[1] "NULL"
How do I apply the column types of Table1 to Table2? Do I have to use for loop to do the transfer?
Thanks
****UPDATES****
Since the data types of Table1 are not complex, I created a function to manually convert column types. as.numeric(as.character(After)) is important. if After is a factor, as.numeric(After) will change the values.
Typeconvertion<- function(Before, After){
classtype<-class(Before)
if (classtype=="factor") {After<-as.character(After)}
else if(classtype=="integer"){After<- as.numeric(as.character(After))}
else if(classtype=="numeric"){After<- as.numeric(as.character(After))}
else if(classtype=="character"){After<- as.character(After)}
else {After<- After}
}

Consider a more flexible function.
matchColClasses<- function(df1, df2){
# Purpose: protect joins from column type mismatches - a problem with multi-column empty df
# Input: df1 - master for class assignments, df2 - for col reclass and return.
# Output: df2 with shared columns classed to match df1
# Usage: df2 <- matchColClasses(df1, df2)
sharedColNames <- names(df1)[names(df1) %in% names(df2)]
sharedColTypes <- sapply(df1[,sharedColNames], class)
for (n in sharedColNames) {
class(df2[, n]) <- sharedColTypes[n]
}
return(df2)
}

Related

R function used to rename columns of a data frames

I have a data frame, say acs10. I need to relabel the columns. To do so, I created another data frame, named as labelName with two columns: The first column contains the old column names, and the second column contains names I want to use, like the table below:
column_1
column_2
oldLabel1
newLabel1
oldLabel2
newLabel2
Then, I wrote a for loop to change the column names:
for (i in seq_len(nrow(labelName))){
names(acs10)[names(acs10) == labelName[i,1]] <- labelName[i,2]}
, and it works.
However, when I tried to put the for loop into a function, because I need to rename column names for other data frames as well, the function failed. The function I wrote looks like below:
renameDF <- function(dataF,varName){
for (i in seq_len(nrow(varName))){
names(dataF)[names(dataF) == varName[i,1]] <- varName[i,2]
print(varName[i,1])
print(varName[i,2])
print(names(dataF))
}
}
renameDF(acs10, labelName)
where dataF is the data frame whose names I need to change, and varName is another data frame where old variable names and new variable names are paired. I used print(names(dataF)) to debug, and the print out suggests that the function works. However, the calling the function does not actually change the column names. I suspect it has something to do with the scope, but I want to know how to make it works.
In your function you need to return the changed dataframe.
renameDF <- function(dataF,varName){
for (i in seq_len(nrow(varName))){
names(dataF)[names(dataF) == varName[i,1]] <- varName[i,2]
}
return(dataF)
}
You can also simplify this and avoid for loop by using match :
renameDF <- function(dataF,varName){
names(dataF) <- varName[[2]][match(names(dataF), varName[[1]])]
return(dataF)
}
This should do the whole thing in one line.
colnames(acs10)[colnames(acs10) %in% labelName$column_1] <- labelName$column_2[match(colnames(acs10)[colnames(acs10) %in% labelName$column_1], labelName$column_1)]
This will work if the column name isn't in the data dictionary, but it's a bit more convoluted:
library(tibble)
df <- tribble(~column_1,~column_2,
"oldLabel1", "newLabel1",
"oldLabel2", "newLabel2")
d <- tibble(oldLabel1 = NA, oldLabel2 = NA, oldLabel3 = NA)
fun <- function(dat, dict) {
names(dat) <- sapply(names(dat), function(x) ifelse(x %in% dict$column_1, dict[dict$column_1 == x,]$column_2, x))
dat
}
fun(d, df)
You can create a function containing just on line of code.
renameDF <- function(df, varName){
setNames(df,varName[[2]][pmatch(names(df),varName[[1]])])
}

relisting resulted data.frame respect to input list

I feed inputList to my custom function, after several workflows(few simple filtration), I end up with data.frame resultDF, which needed to be relisted. I used relist to make resultDF has the same structure of inputList, but I got an error. Is there any simplest way of relisting resultDF? Can anyone point me out how to make this happen? Any idea? sorry for this simple question.
Here is input data.frame within the list:
inputList <- list(
bar=data.frame(from=c(8,18,33,53),
to=c(14,21,39,61), val=c(48,7,10,8)),
cat=data.frame(from=c(6,15,20,44),
to=c(10,17,34,51), val=c(54,21,14,12)),
foo=data.frame(from=c(11,43), to=c(36,49), val=c(49,13)))
After several workflows, I end up with this data.frame:
resultDF <- data.frame(
from=c(53,8,6,15,11,44,43,44,43),
to=c(61,14,10,17,36,51,49,51,49),
val=c(8,48,54,21,49,12,13,12,13)
)
I need to relist resultDF with the same structure of inputList. I used relit method, but I got an error.
This is my desired list:
desiredList <- list(
bar=data.frame(from=c(8,53), to=c(14,61), val=c(48,8)),
cat=data.frame(from=c(6,15,44,44), to=c(10,17,51,51), val=c(54,21,12,12)),
foo=data.frame(from=c(11,43,43), to=c(36,49,49), val=c(49,13,13))
)
How can I achieve desiredList ? Thanks in advance :)
We can loop through the 'inputList' and check whether the pasted row elements in 'resultDF' are %in% list elements and use that index to subset the 'resultDF'
lapply(inputList, function(x) resultDF[do.call(paste, resultDF) %in% do.call(paste, x),])
Another option is a join and then split. We rbind the 'inputList' to a data.table with an additional column 'grp' specifying the list names, join with the 'resultDF' on the column names of 'resultDF', and finally split the dataset using the 'grp' column
library(data.table)
dt <- rbindlist(inputList, idcol = "grp")[resultDF, on = names(resultDF)]
split(dt[,-1, with = FALSE], dt$grp)

Grab string from table and append as column in R

I have the following .csv file:
https://drive.google.com/open?id=0Bydt25g6hdY-RDJ4WG41VFpyX1k
And I would like to be able to take the date and agent name(pasting its constituent parts) and append them as columns to the right of the table, up until it finds a different name and date, doing the same for the remaining name and date items, to get the following result:
The only thing I have been able to do with the dplyr package is the following:
library(dplyr)
library(stringr)
report <- read.csv(file ="test15.csv", head=TRUE, sep=",")
date_pattern <- "(\\d+/\\d+/\\d+)"
date <- str_extract(report[,2], date_pattern)
report <- mutate(report, date = date)
Which gives me the following result:
The difficulty I am finding is probably using conditionals in order make the script get the appropriate string and append it as a column at the end of the table.
This might be crude, but I think it illustrates several things: a) setting stringsAsFactors=F; b) "pre-allocating" the columns in the data frame; and c) using the column name instead of column number to set the value.
report<-read.csv('test15.csv', header=T, stringsAsFactors=F)
# first, allocate the two additional columns (with NAs)
report$date <- rep(NA, nrow(report))
report$agent <- rep(NA, nrow(report))
# step through the rows
for (i in 1:nrow(report)) {
# grab current name and date if "Agent:"
if (report[i,1] == 'Agent:') {
currDate <- report[i+1,2]
currName=paste(report[i,2:5], collapse=' ')
# otherwise append the name/date
} else {
report[i,'date'] <- currDate
report[i,'agent'] <- currName
}
}
write.csv(report, 'test15a.csv')

Subset variables in data frame based on column type

I need to subset data frame based on column type - for example from data frame with 100 columns I need to keep only those column with type factor or integer. I've written a short function to do this, but is there any simpler solution or some built-in function or package on CRAN?
My current solution to get variable names with requested types:
varlist <- function(df=NULL, vartypes=NULL) {
type_function <- c("is.factor","is.integer","is.numeric","is.character","is.double","is.logical")
names(type_function) <- c("factor","integer","numeric","character","double","logical")
names(df)[as.logical(sapply(lapply(names(df), function(y) sapply(type_function[names(type_function) %in% vartypes], function(x) do.call(x,list(df[[y]])))),sum))]
}
The function varlist works as follows:
For every requested type and for every column in data frame call "is.TYPE" function
Sum tests for every variable (boolean is casted to integer automatically)
Cast result to logical vector
subset names in data frame
And some data to test it:
df <- read.table(file="http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data", sep=" ", header=FALSE, stringsAsFactors=TRUE)
names(df) <- c('ca_status','duration','credit_history','purpose','credit_amount','savings', 'present_employment_since','installment_rate_income','status_sex','other_debtors','present_residence_since','property','age','other_installment','housing','existing_credits', 'job','liable_maintenance_people','telephone','foreign_worker','gb')
df$gb <- ifelse(df$gb == 2, FALSE, TRUE)
df$property <- as.character(df$property)
varlist(df, c("integer","logical"))
I'm asking because my code looks really cryptic and hard to understand (even for me and I've finished the function 10 minutes ago).
Just do the following:
df[,sapply(df,is.factor) | sapply(df,is.integer)]
subset_colclasses <- function(DF, colclasses="numeric") {
DF[,sapply(DF, function(vec, test) class(vec) %in% test, test=colclasses)]
}
str(subset_colclasses(df, c("factor", "integer")))

Rename columns of a data frame by searching column name

I am writing a wrapper to ggplot to produce multiple graphs based on various datasets. As I am passing the column names to the function, I need to rename the column names so that ggplot can understand the reference.
However, I am struggling with renaming of the columns of a data frame
here's a data frame:
df <- data.frame(col1=1:3,col2=3:5,col3=6:8)
here are my column names for search:
col1_search <- "col1"
col2_search <- "col2"
col3_search <- "col3"
and here are column names to replace:
col1_replace <- "new_col1"
col2_replace <- "new_col2"
col3_replace <- "new_col3"
when I search for column names, R sorts the column indexes and disregards the search location.
for example, when I run the following code, I expected the new headers to be new_col1, new_col2, and new_col3, instead the new column names are: new_col3, new_col2, and new_col1
colnames(df)[names(df) %in% c(col3_search,col2_search,col1_search)] <- c(col3_replace,col2_replace,col1_replace)
Does anyone have a solution where I can search for column names and replace them in that order?
require(plyr)
df <- data.frame(col2=1:3,col1=3:5,col3=6:8)
df <- rename(df, c("col1"="new_col1", "col2"="new_col2", "col3"="new_col3"))
df
And you can be creative in making that second argument to rename so that it is not so manual.
> names(df)[grep("^col", names(df))] <-
paste("new", names(df)[grep("^col", names(df))], sep="_")
> names(df)
[1] "new_col1" "new_col2" "new_col3"
If you want to replace an ordered set of column names with an arbitrary character vector, then this should work:
names(df)[sapply(oldNames, grep, names(df) )] <- newNames
The sapply()-ed grep will give you the proper locations for the 'newNames' vector. I suppose you might want to make sure there are a complete set of matches if you were building this into a function.
hmm, this might be way to complicated, but the first that come into my mind:
lookup <- data.frame(search = c(col3_search,col2_search,col1_search),
replace = c(col3_replace,col2_replace,col1_replace))
colnames(df) <- lookup$replace[match(lookup$search, colnames(df))]
I second #justin's aes_string suggestion. But for future renaming you can try.
require(stringr)
df <- data.frame(col1=1:3,col2=3:5,col3=6:8)
oldNames <- c("col1", "col2", "col3")
newNames <- c("new_col1", "new_col2", "new_col3")
names(df) <- str_replace(string=names(df), pattern=oldNames, replacement=newNames)

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