Using grep to subset columns of a dataframe by names(dataframe) - r

I am trying to use grep to subset columns of a data frame with one row. When grep returns multiple columns the new data frame has the corresponding column names from the grep. When only one column is returned the column name is NULL... I am using this method because I am looping over many sites that may contain different combinations of HVAC sensor data.
I am trying to create subsets for each unit 'HVAC1', 'HVAC2', 'HVAC3' and a subset for columns that are common to all units. In this case, there is only one column that is common to all units : 'IAT' or indoor ambient temperature. Also, there is no third HVAC unit so the grep on HVAC 3 rightly returns names(sensordata.h3) as character(0).
Here is my code.
sensordata <- data.frame(sitetime = c("2015-10-22 14:15:17"), HVAC1RT = c(70.7), HVAC1ST = c(74.75), HVAC2RT = c(66.875), HVAC2ST = c(46.4), IAT = c(72.5))
sensordata
names(sensordata)
sensordata.h1 <- sensordata[,c(grep("HVAC1",names(sensordata)))]
sensordata.h1
names(sensordata.h1)
sensordata.h2 <- sensordata[,c(grep("HVAC2",names(sensordata)))]
sensordata.h2
names(sensordata.h2)
sensordata.h3 <- sensordata[,c(grep("HVAC3",names(sensordata)))]
sensordata.h3
names(sensordata.h3)
sensordata.common <- sensordata[,c(grep("IAT|OAT|IAH",names(sensordata)))]
sensordata.common
names(sensordata.common)

Try this:
sensordata.common <- sensordata[,c(grep("IAT|OAT|IAH",names(sensordata))), drop=F]
sensordata.common
IAT
1 72.5
names(sensordata.common)
[1] "IAT"
The option drop=F prevents [ to reduce the output to a vector. See ?[ (you need to use backticks around [, can't get it formatted here correctly...
Alternatively, you could use dplyr::select, as in select(sensordata.common, contains("your_names_here")). dplyr's default is to never change the output class.

Related

Dynamically change part of variable name in R

I am trying to automatise some post-hoc analysis, but I will try to explain myself with a metaphor that I believe will illustrate what I am trying to do.
Suppose I have a list of strings in two lists, in the first one I have a list of names and in the other a list of adjectives:
list1 <- c("apt", "farm", "basement", "lodge")
list2 <- c("tiny", "noisy")
Let's suppose also I have a data frame with a bunch of data that I have named something like this as they are the results of some previous linear analysis.
> head(df)
qt[apt_tiny,Intercept] qt[apt_noisy,Intercept] qt[farm_tiny,Intercept]
1 4.196321 -0.4477012 -1.0822793
2 3.231220 -0.4237787 -1.1433449
3 2.304687 -0.3149331 -0.9245896
4 2.768691 -0.1537728 -0.9925387
5 3.771648 -0.1109647 -0.9298861
6 3.370368 -0.2579591 -1.0849262
and so on...
Now, what I am trying to do is make some automatic operations where the strings in the previous lists dynamically change as they go in a for loop. I have made a list with all the distinct combinations and called it distinct. Now I am trying to do something like this:
for (i in 1:nrow(distinct)){
var1[[i]] <- list1[[i]]
var2[[i]] <- list2[[i]]
#this being the insertable name part for the rest of the variables and parts of variable,
#i'll put it inside %var[[i]]% for the sake of the explanation.
%var1[[i]]%_%var2[[i]]%_INT <- df$`qt[%var1[[i]]%_%var2[[i]]%,Intercept]`+ df$`qt[%var1[[i]]%,Intercept]`
}
The difficult thing for me here is %var1[[i]]% is at the same time inside a variable and as the name of a column inside a data frame.
Any help would be much appreciated.
You cannot use $ to extract column values with a character variable. So df$`qt[%var1[[i]]%_%var2[[i]]%,Intercept] will not work.
Create the name of the column using sprintf and use [[ to extract it. For example to construct "qt[apt_tiny,Intercept]" as column name you can do :
i <- 1
sprintf('qt[%s_%s,Intercept]', list1[i], list2[i])
#[1] "qt[apt_tiny,Intercept]"
Now use [[ to subset that column from df
df[[sprintf('qt[%s_%s,Intercept]', list1[i], list2[i])]]
You can do the same for other columns.

Iterate through and conditionally append string values in a Pandas dataframe

I've got a dataframe of research participants whose IDs are stored in the following format "0000.000".
Where the first four digits are their family ID number, and the final three digits are their individual index within the family. The majority of individuals have a suffix of ".000", but some have ".001", ".002", etc.
As a result of some inefficiencies, these numbers are stored as floats. I'm trying to import them as strings so that I can use them in a join to another data frame that is formatted correctly.
Those IDs that end in .000 are imported as "0000", rather than "0000.000". All others are imported correctly.
I'm trying to iterate through the IDs and append ".000" to those that are missing the suffix.
If I were using R, I could do it like this.
df %>% mutate(StudyID = ifelse(length(StudyID)<5,
paste(StudyID,".000",sep=""),
StudyID)
I've found a Python solution (below), but it's pretty janky.
row = 0
for i in df["StudyID"]:
if len(i)<5:
df.iloc[row,3] = i + ".000"
else: df.iloc[row,3] = i
index += 1
I think it'd be ideal to do it as a list comprehension, but I haven't been able to find a solution that lets me iterate through the column, changing a single value at a time.
For example, this solution iterates and checks the logic properly, but it replaces every single value that evaluates True during each iteration. I only want the value currently being evaluated to change.
[i + ".000" if len(i)<5 else i for i in df["StudyID"]]
Is this possible?
As you said, your code is doing the trick. One other way of doing what you want that i could think of is the following :
# Start by creating a mask that gives you the index you want to change
mask = [len(i)<5 for i in df.StudyID]
# Change the value of the dataframe on the mask
df.StudyID.iloc[mask] += ".000"
I think by length(StudyID), you meant nchar(StudyID), as #akrun pointed out.
You can do it in the dplyr way in python using datar:
>>> from datar.all import f, tibble, mutate, nchar, if_else, paste
>>>
>>> df = tibble(
... StudyID = ["0000", "0001", "0000.000", "0001.001"]
... )
>>> df
StudyID
<object>
0 0000
1 0001
2 0000.000
3 0001.001
>>>
>>> df >> mutate(StudyID=if_else(
... nchar(f.StudyID) < 5,
... paste(f.StudyID, ".000", sep=""),
... f.StudyID
... ))
StudyID
<object>
0 0000.000
1 0001.000
2 0000.000
3 0001.001
Disclaimer: I am the author of the datar package.
Ultimately, I needed to do this for a few different dataframes so I ended up defining a function to solve the problem so that I could apply it to each one.
I think the list comprehension idea was going to become too complex and potentially too difficult to understand when reviewing so I stuck with a plain old for-loop.
def create_multi_index(data, col_to_split, sep = "."):
"""
This function loops through the original ID column and splits it into
multiple parts (multi-IDs) on the defined separator.
By default, the function assumes the unique ID is formatted like a decimal number
The new multi-IDs are appended into a new list.
If the original ID was formatted like an integer, rather than a decimal
the function assumes the latter half of the ID to be ".000"
"""
# Take a copy of the dataframe to modify
new_df = data
# generate two new lists to store the new multi-index
Family_ID = []
Family_Index = []
# iterate through the IDs, split and allocate the pieces to the appropriate list
for i in new_df[col_to_split]:
i = i.split(sep)
Family_ID.append(i[0])
if len(i)==1:
Family_Index.append("000")
else:
Family_Index.append(i[1])
# Modify and return the dataframe including the new multi-index
return new_df.assign(Family_ID = Family_ID,
Family_Index = Family_Index)
This returns a duplicate dataframe with a new column for each part of the multi-id.
When joining dataframes with this form of ID, as long as both dataframes have the multi index in the same format, these columns can be used with pd.merge as follows:
pd.merge(df1, df2, how= "inner", on = ["Family_ID","Family_Index"])

How do I refer to defined objects using the reshape function in R?

I'm unable to get the reshape function ( stats::reshape ) to accept a reference to a defined character vector in one of its arguments. I don't know if this reflects wrong syntax on my part, a limitation of the function, or a more general issue related to how R itself operates.
I am using reshape to change data from wide to long format. I have a dataset with many repeated measures that are sorted appropriately for reshape (x.1, x.2, x.3, y.1, y.2, y.3, etc). I've defined a variable firstlastmeasure that contains the index to the first and last column of repeated measures data that needs to be processed by reshape (this is to prevent having to change the index every time columns are added or removed from the original data).
This is how it's defined (in a convoluted way):
temp0 <- subset(p, select=nameoffirstcolumn:nameoflastcolumn)
lastmeasname = names(temp0[ncol(temp0)])
firstmeasname = names(temp0[1])
firstmeasindex = grep(firstmesname,colnames(p))
lastmeasindex = grep(lastmesname,colnames(p))
firstlastmeasure <- paste(firstmesindex,lastmesindex,sep=":")
I'm using this variable as an argument to reshape's varying parameter, like so:
reshape(dataset, direction = "long", varying = firstlastmeasure)
Reshape always returns:
"Error in guess(varying) : failed to guess time-varying variables from their names".
Using the numerical index explicitly (i.e. varying = 6:34) works fine.
paste creates a string, if you look at firstlastmeasure it will be something like "6:34". If you look at 6:34 it will be a vector 6 7 8 9 ... 34. You need to define the vector, not paste together a string. (Note that subset does a bit of special processing to make : work with column names.)
If I'm interepreting your code correctly, temp0 has all the column you want, so you could just do
firstlastmeasure = names(temp0)
and be done with it. A little more complicated, you could keep you grep code and just not use paste:
firstlastmeasure = firstmeasindex:lastmeasindex
Since you are inputting names, the subset is unnecessary. Simplest of all would be to skip temp0 and do
firstlastmeasure = grep(nameoffirstcolumn, names(p)):grep(nameoflastcolumn, names(p))

Optimization of R Data.table combination with for loop function

I have a 'Agency_Reference' table containing column 'agency_lookup', with 200 entries of strings as below :
alpha
beta
gamma etc..
I have a dataframe 'TEST' with a million rows containing a 'Campaign' column with entries such as :
Alpha_xt2010
alpha_xt2014
Beta_xt2016 etc..
i want to loop through for each entry in reference table and find which string is present within each campaign column entries and create a new agency_identifier column variable in table.
my current code is as below and is slow to execute. Requesting guidance on how to optimize the same. I would like to learn how to do it in the data.table way
Agency_Reference <- data.frame(agency_lookup = c('alpha','beta','gamma','delta','zeta'))
TEST <- data.frame(Campaign = c('alpha_xt123','ALPHA345','Beta_xyz_34','BETa_testing','code_delta_'))
TEST$agency_identifier <- 0
for (agency_lookup in as.vector(Agency_Reference$agency_lookup)) {
TEST$Agency_identifier <- ifelse(grepl(tolower(agency_lookup), tolower(TEST$Campaign)),agency_lookup,TEST$Agency_identifier)}
Expected Output :
Campaign----Agency_identifier
alpha_xt123---alpha
ALPHA34----alpha
Beta_xyz_34----beta
BETa_testing----beta
code_delta_-----delta
Try
TEST <- data.frame(Campaign = c('alpha_xt123','ALPHA345','Beta_xyz_34','BETa_testing','code_delta_'))
pattern = tolower(c('alpha','Beta','gamma','delta','zeta'))
TEST$agency_identifier <- sub(pattern = paste0('.*(', paste(pattern, collapse = '|'), ').*'),
replacement = '\\1',
x = tolower(TEST$Campaign))
This will not answer your question per se, but from what I understand you want to dissect the Campaign column and do something with the values it provides.
Take a look at Tidy data, more specifically the part "Multiple variables stored in one column". I think you'll make some great progress using tidyr::separate. That way you don't have to use a for-loop.

How to find common variables in a list of datasets & reshape them in R?

setwd("C:\\Users\\DATA")
temp = list.files(pattern="*.dta")
for (i in 1:length(temp)) assign(temp[i], read.dta13(temp[i], nonint.factors = TRUE))
grep(pattern="_m", temp, value=TRUE)
Here I create a list of my datasets and read them into R, I then attempt to use grep in order to find all variable names with pattern _m, obviously this doesn't work because this simply returns all filenames with pattern _m. So essentially what I want, is my code to loop through the list of databases, find variables ending with _m, and return a list of databases that contain these variables.
Now I'm quite unsure how to do this, I'm quite new to coding and R.
Apart from needing to know in which databases these variables are, I also need to be able to make changes (reshape them) to these variables.
First, assign will not work as you think, because it expects a string (or character, as they are called in R). It will use the first element as the variable (see here for more info).
What you can do depends on the structure of your data. read.dta13 will load each file as a data.frame.
If you look for column names, you can do something like that:
myList <- character()
for (i in 1:length(temp)) {
# save the content of your file in a data frame
df <- read.dta13(temp[i], nonint.factors = TRUE))
# identify the names of the columns matching your pattern
varMatch <- grep(pattern="_m", colnames(df), value=TRUE)
# check if at least one of the columns match the pattern
if (length(varMatch)) {
myList <- c(myList, temp[i]) # save the name if match
}
}
If you look for the content of a column, you can have a look at the dplyr package, which is very useful when it comes to data frames manipulation.
A good introduction to dplyr is available in the package vignette here.
Note that in R, appending to a vector can become very slow (see this SO question for more details).
Here is one way to figure out which files have variables with names ending in "_m":
# setup
setwd("C:\\Users\\DATA")
temp = list.files(pattern="*.dta")
# logical vector to be filled in
inFileVec <- logical(length(temp))
# loop through each file
for (i in 1:length(temp)) {
# read file
fileTemp <- read.dta13(temp[i], nonint.factors = TRUE)
# fill in vector with TRUE if any variable ends in "_m"
inFileVec[i] <- any(grepl("_m$", names(fileTemp)))
}
In the final line, names returns the variable names, grepl returns a logical vector for whether each variable name matches the pattern, and any returns a logical vector of length 1 indicating whether or not at least one TRUE was returned from grepl.
# print out these file names
temp[inFileVec]

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