How to subset dataframe based on a “not equal to” criteria applied to a large number of columns?How to...
"You are your self first supporter", a more proper way to say it
Is it legal for company to use my work email to pretend I still work there?
Why was the small council so happy for Tyrion to become the Master of Coin?
Method of fabrication patents, Is it okay to import from abroad?
Smoothness of finite-dimensional functional calculus
Have astronauts in space suits ever taken selfies? If so, how?
Is it tax fraud for an individual to declare non-taxable revenue as taxable income? (US tax laws)
Font hinting is lost in Chrome-like browsers (for some languages )
A newer friend of my brother's gave him a load of baseball cards that are supposedly extremely valuable. Is this a scam?
Why Is Death Allowed In the Matrix?
Why don't electron-positron collisions release infinite energy?
Can an x86 CPU running in real mode be considered to be basically an 8086 CPU?
Equivalence principle before Einstein
What defenses are there against being summoned by the Gate spell?
To string or not to string
In Japanese, what’s the difference between “Tonari ni” (となりに) and “Tsugi” (つぎ)? When would you use one over the other?
I'm planning on buying a laser printer but concerned about the life cycle of toner in the machine
Is it possible to do 50 km distance without any previous training?
When a company launches a new product do they "come out" with a new product or do they "come up" with a new product?
Assigning pointers to atomic type to pointers to non atomic type
Writing rule stating superpower from different root cause is bad writing
Can a Warlock become Neutral Good?
Can I ask the recruiters in my resume to put the reason why I am rejected?
Hiring someone is unethical to Kantians because you're treating them as a means?
How to subset dataframe based on a “not equal to” criteria applied to a large number of columns?
How to sort a dataframe by multiple column(s)Extract a subset of a dataframe based on a condition involving a fieldHow to change the order of DataFrame columns?How to apply a function to two columns of Pandas dataframeHow to drop rows of Pandas DataFrame whose value in certain columns is NaNSelect rows from a DataFrame based on values in a column in pandasHow to convert index of a pandas dataframe into a column?How to count the NaN values in a column in pandas DataFramesubset a dataframe based on sum of a columnSubset dataframe based on number of observations in each column
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
}
I'm new to R and currently trying to subset my data according to my predefined exclusion criteria for analysis. I'm presently trying to remove all cases that have dementia, as coded by the ICD-10. Problem is that there are multiple variables containing information on each individual's disease status (~70 variables), although as they are coded in the same way, the same condition can be applied to all of them.
Some simulated data:
#Create dataframe containing simulated data
df = data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'))
#data is structured as below:
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1003 G560 G20 NA
4 1004 D235 NA I802
5 1005 B178 NA NA
6 1006 F011 A049 A481
7 1007 F023 NA NA
8 1008 C761 NA NA
9 1009 H653 G300 NA
10 1010 A049 G308 NA
11 1011 J679 A045 D352
Here, I'm trying to remove any case that has a 'dementia code' across any of the "disease_code" variables.
#Remove cases with dementia from dataframe (e.g. F023, G20)
Newdata_df <- subset(df, (2:4 != "F023"|"G20"|"F009"|"F002"|"F001"|"F000"|"F00"|
"G309"| "G308"|"G301"|"G300"|"G30"| "F01"|"F018"|"F013"|
"F012"| "F011"| "F010"|"F01"))
The error that I recieve is:
Error in 2:4 != "F023" | "G20" :
operations are possible only for numeric, logical or complex types
Ideally, the subsetted dataframe would look like this:
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
I know that there is an error in my code although I'm not sure how exactly to fix it. I've tried a few other ways (using dplyr) although haven't had any luck so far.
Any help is greatly appreciated!
r dataframe filter subset
edited Mar 29 at 12:55
Sotos
31.2k51741
asked Mar 29 at 12:40
M_OxfordM_Oxford
593
add a comment |
I'm new to R and currently trying to subset my data according to my predefined exclusion criteria for analysis. I'm presently trying to remove all cases that have dementia, as coded by the ICD-10. Problem is that there are multiple variables containing information on each individual's disease status (~70 variables), although as they are coded in the same way, the same condition can be applied to all of them.
Some simulated data:
#Create dataframe containing simulated data
df = data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'))
#data is structured as below:
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1003 G560 G20 NA
4 1004 D235 NA I802
5 1005 B178 NA NA
6 1006 F011 A049 A481
7 1007 F023 NA NA
8 1008 C761 NA NA
9 1009 H653 G300 NA
10 1010 A049 G308 NA
11 1011 J679 A045 D352
Here, I'm trying to remove any case that has a 'dementia code' across any of the "disease_code" variables.
#Remove cases with dementia from dataframe (e.g. F023, G20)
Newdata_df <- subset(df, (2:4 != "F023"|"G20"|"F009"|"F002"|"F001"|"F000"|"F00"|
"G309"| "G308"|"G301"|"G300"|"G30"| "F01"|"F018"|"F013"|
"F012"| "F011"| "F010"|"F01"))
The error that I recieve is:
Error in 2:4 != "F023" | "G20" :
operations are possible only for numeric, logical or complex types
Ideally, the subsetted dataframe would look like this:
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
I know that there is an error in my code although I'm not sure how exactly to fix it. I've tried a few other ways (using dplyr) although haven't had any luck so far.
Any help is greatly appreciated!
r dataframe filter subset
edited Mar 29 at 12:55
Sotos
31.2k51741
asked Mar 29 at 12:40
M_OxfordM_Oxford
593
1
You should reshape your data to long format. That will make your life (and analysis) much easier.
– docendo discimus
Mar 29 at 12:51
add a comment |
I'm new to R and currently trying to subset my data according to my predefined exclusion criteria for analysis. I'm presently trying to remove all cases that have dementia, as coded by the ICD-10. Problem is that there are multiple variables containing information on each individual's disease status (~70 variables), although as they are coded in the same way, the same condition can be applied to all of them.
Some simulated data:
#Create dataframe containing simulated data
df = data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'))
#data is structured as below:
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1003 G560 G20 NA
4 1004 D235 NA I802
5 1005 B178 NA NA
6 1006 F011 A049 A481
7 1007 F023 NA NA
8 1008 C761 NA NA
9 1009 H653 G300 NA
10 1010 A049 G308 NA
11 1011 J679 A045 D352
Here, I'm trying to remove any case that has a 'dementia code' across any of the "disease_code" variables.
#Remove cases with dementia from dataframe (e.g. F023, G20)
Newdata_df <- subset(df, (2:4 != "F023"|"G20"|"F009"|"F002"|"F001"|"F000"|"F00"|
"G309"| "G308"|"G301"|"G300"|"G30"| "F01"|"F018"|"F013"|
"F012"| "F011"| "F010"|"F01"))
The error that I recieve is:
Error in 2:4 != "F023" | "G20" :
operations are possible only for numeric, logical or complex types
Ideally, the subsetted dataframe would look like this:
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
I know that there is an error in my code although I'm not sure how exactly to fix it. I've tried a few other ways (using dplyr) although haven't had any luck so far.
Any help is greatly appreciated!
r dataframe filter subset
edited Mar 29 at 12:55
Sotos
31.2k51741
asked Mar 29 at 12:40
M_OxfordM_Oxford
593
I'm new to R and currently trying to subset my data according to my predefined exclusion criteria for analysis. I'm presently trying to remove all cases that have dementia, as coded by the ICD-10. Problem is that there are multiple variables containing information on each individual's disease status (~70 variables), although as they are coded in the same way, the same condition can be applied to all of them.
Some simulated data:
#Create dataframe containing simulated data
df = data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'))
#data is structured as below:
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1003 G560 G20 NA
4 1004 D235 NA I802
5 1005 B178 NA NA
6 1006 F011 A049 A481
7 1007 F023 NA NA
8 1008 C761 NA NA
9 1009 H653 G300 NA
10 1010 A049 G308 NA
11 1011 J679 A045 D352
Here, I'm trying to remove any case that has a 'dementia code' across any of the "disease_code" variables.
#Remove cases with dementia from dataframe (e.g. F023, G20)
Newdata_df <- subset(df, (2:4 != "F023"|"G20"|"F009"|"F002"|"F001"|"F000"|"F00"|
"G309"| "G308"|"G301"|"G300"|"G30"| "F01"|"F018"|"F013"|
"F012"| "F011"| "F010"|"F01"))
The error that I recieve is:
Error in 2:4 != "F023" | "G20" :
operations are possible only for numeric, logical or complex types
Ideally, the subsetted dataframe would look like this:
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
I know that there is an error in my code although I'm not sure how exactly to fix it. I've tried a few other ways (using dplyr) although haven't had any luck so far.
Any help is greatly appreciated!
r dataframe filter subset
r dataframe filter subset
edited Mar 29 at 12:55
Sotos
31.2k51741
asked Mar 29 at 12:40
M_OxfordM_Oxford
593
edited Mar 29 at 12:55
Sotos
31.2k51741
asked Mar 29 at 12:40
M_OxfordM_Oxford
593
edited Mar 29 at 12:55
Sotos
31.2k51741
edited Mar 29 at 12:55
Sotos
31.2k51741
edited Mar 29 at 12:55
Sotos
31.2k51741
31.2k51741
asked Mar 29 at 12:40
M_OxfordM_Oxford
593
asked Mar 29 at 12:40
M_OxfordM_Oxford
593
asked Mar 29 at 12:40
M_OxfordM_Oxford
593
593
1
You should reshape your data to long format. That will make your life (and analysis) much easier.
– docendo discimus
Mar 29 at 12:51
add a comment |
1
You should reshape your data to long format. That will make your life (and analysis) much easier.
– docendo discimus
Mar 29 at 12:51
1
1
You should reshape your data to long format. That will make your life (and analysis) much easier.
– docendo discimus
Mar 29 at 12:51
You should reshape your data to long format. That will make your life (and analysis) much easier.
– docendo discimus
Mar 29 at 12:51
add a comment |
6 Answers
6
active
oldest
votes
One dplyr possibility could be:
df %>%
filter_at(vars(2:4), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1004 D235 NA I802
4 1005 B178 NA NA
5 1008 C761 NA NA
6 1011 J679 A045 D352
In this case, it checks whether any of the columns 2:4 contains any of the given codes.
Or:
df %>%
filter_at(vars(contains("disease_code")), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
In this case, it checks whether any of the columns with names disease_code contains any of the given codes.
answered Mar 29 at 12:52
tmfmnktmfmnk
3,6661516
1
Thanks everyone for your suggestions! I appreciate that you also explained what your suggested code does @tmfmnk - really useful!
– M_Oxford
Mar 29 at 14:22
add a comment |
We can create a vector with the codes to be removed and use rowSums to remove, i.e.
codes_to_remove <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
"G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
df[rowSums(sapply(df[-1], `%in%`, codes_to_remove)) == 0,]
which gives,
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
answered Mar 29 at 12:52
SotosSotos
31.2k51741
add a comment |
As mentioned in comments by @docendo discimus we can convert the dataframe to long format using gather, group_by ID and select only those IDs which do not have dementia_code in them and then spread them back to wide format.
library(tidyverse)
df %>%
gather(key, value, -ID) %>%
group_by(ID) %>%
filter(!any(value %in% dementia_code)) %>%
spread(key, value)
# ID disease_code_1 disease_code_2 disease_code_3
# <dbl> <chr> <chr> <chr>
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#3 1004 D235 NA I802
#4 1005 B178 NA NA
#5 1008 C761 NA NA
#6 1011 J679 A045 D352
data
dementia_code <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309",
"G308","G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
answered Mar 29 at 13:09
Ronak ShahRonak Shah
45.3k104266
Why load all oftidyverse? Isn't this justtidyranddplyr?
– Dunois
Mar 29 at 13:25
1
@Dunois yes, it is. I have a habit of loading it all up by default :P
– Ronak Shah
Mar 29 at 13:30
3
We could also do it using ananti_joinsuch asNewdata_df <- df %>% anti_join(df %>% gather(DiseaseCodeNumber, CodeValue, -ID) %>% filter(CodeValue %in% c("F023","G20","F009","F002","F001","F000","F00", "G309", "G308","G301","G300","G30","F01","F018","F013", "F012", "F011","F010","F01")), by = "ID")
– Kerry Jackson
Mar 29 at 13:39
add a comment |
How about this:
> dementia <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
+ "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
>
> dementia <- apply(sapply(df[, -1], function(x) {x %in% dementia}), 1, any)
>
> df[!dementia,]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
>
Edit:
An even more elegant solution, thanks to @ Ronan Shah:
> df[apply(df[-1], 1, function(x) {!any(x %in% dementia)}),]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
Hope it helps.
answered Mar 29 at 12:52
Santiago CapobiancoSantiago Capobianco
491310
@ Ronan Shah Nice! Its a more elegant solution. You should post it.
– Santiago Capobianco
Mar 29 at 13:19
1
Yes! Sorry, I will change it right away.
– Santiago Capobianco
Mar 29 at 13:36
add a comment |
We can use melt/dcast from data.table
library(data.table)
dcast(melt(setDT(df), id.var = 'ID')[,
if(!any(value %in% dementia_codes)) .SD, .(ID)], ID ~ variable)
# ID disease_code_1 disease_code_2 disease_code_3
#1: 1001 I802 A071 H250
#2: 1002 H356 NA NA
#3: 1004 D235 NA I802
#4: 1005 B178 NA NA
#5: 1008 C761 NA NA
#6: 1011 J679 A045 D352
Or this can be done more compactly in base R with no reshaping
df[!Reduce(`|`, lapply(df[-1], `%in%` , dementia_codes)),]
# ID disease_code_1 disease_code_2 disease_code_3
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#4 1004 D235 NA I802
#5 1005 B178 NA NA
#8 1008 C761 NA NA
#11 1011 J679 A045 D352
data
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000",
"F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013",
"F012", "F011", "F010", "F01")
answered Mar 29 at 14:24
akrunakrun
419k13207284
add a comment |
A for loop version with base R, in case you prefer that.
df <- data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'), stringsAsFactors = FALSE)
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
new_df <- df[0,]
for(i in 1:nrow(df)){
currRow <- df[i,]
if(any(dementia_codes %in% as.character(currRow)) == FALSE){
new_df <- rbind(new_df, currRow)
}
}
new_df
# ID disease_code_1 disease_code_2 disease_code_3
# 1 1001 I802 A071 H250
# 2 1002 H356 NA NA
# 4 1004 D235 NA I802
# 5 1005 B178 NA NA
# 8 1008 C761 NA NA
# 11 1011 J679 A045 D352
answered Mar 29 at 13:10
DunoisDunois
858
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55417645%2fhow-to-subset-dataframe-based-on-a-not-equal-to-criteria-applied-to-a-large-nu%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
6 Answers
6
active
oldest
votes
6 Answers
6
active
oldest
votes
active
oldest
votes
active
oldest
votes
One dplyr possibility could be:
df %>%
filter_at(vars(2:4), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1004 D235 NA I802
4 1005 B178 NA NA
5 1008 C761 NA NA
6 1011 J679 A045 D352
In this case, it checks whether any of the columns 2:4 contains any of the given codes.
Or:
df %>%
filter_at(vars(contains("disease_code")), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
In this case, it checks whether any of the columns with names disease_code contains any of the given codes.
answered Mar 29 at 12:52
tmfmnktmfmnk
3,6661516
1
Thanks everyone for your suggestions! I appreciate that you also explained what your suggested code does @tmfmnk - really useful!
– M_Oxford
Mar 29 at 14:22
add a comment |
One dplyr possibility could be:
df %>%
filter_at(vars(2:4), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1004 D235 NA I802
4 1005 B178 NA NA
5 1008 C761 NA NA
6 1011 J679 A045 D352
In this case, it checks whether any of the columns 2:4 contains any of the given codes.
Or:
df %>%
filter_at(vars(contains("disease_code")), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
In this case, it checks whether any of the columns with names disease_code contains any of the given codes.
answered Mar 29 at 12:52
tmfmnktmfmnk
3,6661516
1
Thanks everyone for your suggestions! I appreciate that you also explained what your suggested code does @tmfmnk - really useful!
– M_Oxford
Mar 29 at 14:22
add a comment |
One dplyr possibility could be:
df %>%
filter_at(vars(2:4), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1004 D235 NA I802
4 1005 B178 NA NA
5 1008 C761 NA NA
6 1011 J679 A045 D352
In this case, it checks whether any of the columns 2:4 contains any of the given codes.
Or:
df %>%
filter_at(vars(contains("disease_code")), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
In this case, it checks whether any of the columns with names disease_code contains any of the given codes.
answered Mar 29 at 12:52
tmfmnktmfmnk
3,6661516
One dplyr possibility could be:
df %>%
filter_at(vars(2:4), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
3 1004 D235 NA I802
4 1005 B178 NA NA
5 1008 C761 NA NA
6 1011 J679 A045 D352
In this case, it checks whether any of the columns 2:4 contains any of the given codes.
Or:
df %>%
filter_at(vars(contains("disease_code")), all_vars(! . %in% c("F023","G20","F009","F002","F001","F000","F00",
"G309", "G308","G301","G300","G30", "F01","F018","F013",
"F012", "F011", "F010","F01")))
In this case, it checks whether any of the columns with names disease_code contains any of the given codes.
answered Mar 29 at 12:52
tmfmnktmfmnk
3,6661516
edited Mar 29 at 13:29
answered Mar 29 at 12:52
tmfmnktmfmnk
3,6661516
answered Mar 29 at 12:52
tmfmnktmfmnk
3,6661516
answered Mar 29 at 12:52
tmfmnktmfmnk
3,6661516
3,6661516
1
Thanks everyone for your suggestions! I appreciate that you also explained what your suggested code does @tmfmnk - really useful!
– M_Oxford
Mar 29 at 14:22
add a comment |
1
Thanks everyone for your suggestions! I appreciate that you also explained what your suggested code does @tmfmnk - really useful!
– M_Oxford
Mar 29 at 14:22
1
1
Thanks everyone for your suggestions! I appreciate that you also explained what your suggested code does @tmfmnk - really useful!
– M_Oxford
Mar 29 at 14:22
Thanks everyone for your suggestions! I appreciate that you also explained what your suggested code does @tmfmnk - really useful!
– M_Oxford
Mar 29 at 14:22
add a comment |
We can create a vector with the codes to be removed and use rowSums to remove, i.e.
codes_to_remove <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
"G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
df[rowSums(sapply(df[-1], `%in%`, codes_to_remove)) == 0,]
which gives,
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
answered Mar 29 at 12:52
SotosSotos
31.2k51741
add a comment |
We can create a vector with the codes to be removed and use rowSums to remove, i.e.
codes_to_remove <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
"G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
df[rowSums(sapply(df[-1], `%in%`, codes_to_remove)) == 0,]
which gives,
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
answered Mar 29 at 12:52
SotosSotos
31.2k51741
add a comment |
We can create a vector with the codes to be removed and use rowSums to remove, i.e.
codes_to_remove <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
"G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
df[rowSums(sapply(df[-1], `%in%`, codes_to_remove)) == 0,]
which gives,
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
answered Mar 29 at 12:52
SotosSotos
31.2k51741
We can create a vector with the codes to be removed and use rowSums to remove, i.e.
codes_to_remove <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
"G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
df[rowSums(sapply(df[-1], `%in%`, codes_to_remove)) == 0,]
which gives,
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
answered Mar 29 at 12:52
SotosSotos
31.2k51741
answered Mar 29 at 12:52
SotosSotos
31.2k51741
answered Mar 29 at 12:52
SotosSotos
31.2k51741
answered Mar 29 at 12:52
SotosSotos
31.2k51741
31.2k51741
add a comment |
add a comment |
As mentioned in comments by @docendo discimus we can convert the dataframe to long format using gather, group_by ID and select only those IDs which do not have dementia_code in them and then spread them back to wide format.
library(tidyverse)
df %>%
gather(key, value, -ID) %>%
group_by(ID) %>%
filter(!any(value %in% dementia_code)) %>%
spread(key, value)
# ID disease_code_1 disease_code_2 disease_code_3
# <dbl> <chr> <chr> <chr>
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#3 1004 D235 NA I802
#4 1005 B178 NA NA
#5 1008 C761 NA NA
#6 1011 J679 A045 D352
data
dementia_code <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309",
"G308","G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
answered Mar 29 at 13:09
Ronak ShahRonak Shah
45.3k104266
Why load all oftidyverse? Isn't this justtidyranddplyr?
– Dunois
Mar 29 at 13:25
1
@Dunois yes, it is. I have a habit of loading it all up by default :P
– Ronak Shah
Mar 29 at 13:30
3
We could also do it using ananti_joinsuch asNewdata_df <- df %>% anti_join(df %>% gather(DiseaseCodeNumber, CodeValue, -ID) %>% filter(CodeValue %in% c("F023","G20","F009","F002","F001","F000","F00", "G309", "G308","G301","G300","G30","F01","F018","F013", "F012", "F011","F010","F01")), by = "ID")
– Kerry Jackson
Mar 29 at 13:39
add a comment |
As mentioned in comments by @docendo discimus we can convert the dataframe to long format using gather, group_by ID and select only those IDs which do not have dementia_code in them and then spread them back to wide format.
library(tidyverse)
df %>%
gather(key, value, -ID) %>%
group_by(ID) %>%
filter(!any(value %in% dementia_code)) %>%
spread(key, value)
# ID disease_code_1 disease_code_2 disease_code_3
# <dbl> <chr> <chr> <chr>
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#3 1004 D235 NA I802
#4 1005 B178 NA NA
#5 1008 C761 NA NA
#6 1011 J679 A045 D352
data
dementia_code <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309",
"G308","G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
answered Mar 29 at 13:09
Ronak ShahRonak Shah
45.3k104266
Why load all oftidyverse? Isn't this justtidyranddplyr?
– Dunois
Mar 29 at 13:25
1
@Dunois yes, it is. I have a habit of loading it all up by default :P
– Ronak Shah
Mar 29 at 13:30
3
We could also do it using ananti_joinsuch asNewdata_df <- df %>% anti_join(df %>% gather(DiseaseCodeNumber, CodeValue, -ID) %>% filter(CodeValue %in% c("F023","G20","F009","F002","F001","F000","F00", "G309", "G308","G301","G300","G30","F01","F018","F013", "F012", "F011","F010","F01")), by = "ID")
– Kerry Jackson
Mar 29 at 13:39
add a comment |
As mentioned in comments by @docendo discimus we can convert the dataframe to long format using gather, group_by ID and select only those IDs which do not have dementia_code in them and then spread them back to wide format.
library(tidyverse)
df %>%
gather(key, value, -ID) %>%
group_by(ID) %>%
filter(!any(value %in% dementia_code)) %>%
spread(key, value)
# ID disease_code_1 disease_code_2 disease_code_3
# <dbl> <chr> <chr> <chr>
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#3 1004 D235 NA I802
#4 1005 B178 NA NA
#5 1008 C761 NA NA
#6 1011 J679 A045 D352
data
dementia_code <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309",
"G308","G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
answered Mar 29 at 13:09
Ronak ShahRonak Shah
45.3k104266
As mentioned in comments by @docendo discimus we can convert the dataframe to long format using gather, group_by ID and select only those IDs which do not have dementia_code in them and then spread them back to wide format.
library(tidyverse)
df %>%
gather(key, value, -ID) %>%
group_by(ID) %>%
filter(!any(value %in% dementia_code)) %>%
spread(key, value)
# ID disease_code_1 disease_code_2 disease_code_3
# <dbl> <chr> <chr> <chr>
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#3 1004 D235 NA I802
#4 1005 B178 NA NA
#5 1008 C761 NA NA
#6 1011 J679 A045 D352
data
dementia_code <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309",
"G308","G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
answered Mar 29 at 13:09
Ronak ShahRonak Shah
45.3k104266
edited Mar 29 at 13:18
answered Mar 29 at 13:09
Ronak ShahRonak Shah
45.3k104266
answered Mar 29 at 13:09
Ronak ShahRonak Shah
45.3k104266
answered Mar 29 at 13:09
Ronak ShahRonak Shah
45.3k104266
45.3k104266
Why load all oftidyverse? Isn't this justtidyranddplyr?
– Dunois
Mar 29 at 13:25
1
@Dunois yes, it is. I have a habit of loading it all up by default :P
– Ronak Shah
Mar 29 at 13:30
3
We could also do it using ananti_joinsuch asNewdata_df <- df %>% anti_join(df %>% gather(DiseaseCodeNumber, CodeValue, -ID) %>% filter(CodeValue %in% c("F023","G20","F009","F002","F001","F000","F00", "G309", "G308","G301","G300","G30","F01","F018","F013", "F012", "F011","F010","F01")), by = "ID")
– Kerry Jackson
Mar 29 at 13:39
add a comment |
Why load all oftidyverse? Isn't this justtidyranddplyr?
– Dunois
Mar 29 at 13:25
1
@Dunois yes, it is. I have a habit of loading it all up by default :P
– Ronak Shah
Mar 29 at 13:30
3
We could also do it using ananti_joinsuch asNewdata_df <- df %>% anti_join(df %>% gather(DiseaseCodeNumber, CodeValue, -ID) %>% filter(CodeValue %in% c("F023","G20","F009","F002","F001","F000","F00", "G309", "G308","G301","G300","G30","F01","F018","F013", "F012", "F011","F010","F01")), by = "ID")
– Kerry Jackson
Mar 29 at 13:39
Why load all of
tidyverse? Isn't this just tidyr and dplyr?– Dunois
Mar 29 at 13:25
Why load all of
tidyverse? Isn't this just tidyr and dplyr?– Dunois
Mar 29 at 13:25
1
1
@Dunois yes, it is. I have a habit of loading it all up by default :P
– Ronak Shah
Mar 29 at 13:30
@Dunois yes, it is. I have a habit of loading it all up by default :P
– Ronak Shah
Mar 29 at 13:30
3
3
We could also do it using an
anti_join such as Newdata_df <- df %>% anti_join(df %>% gather(DiseaseCodeNumber, CodeValue, -ID) %>% filter(CodeValue %in% c("F023","G20","F009","F002","F001","F000","F00", "G309", "G308","G301","G300","G30","F01","F018","F013", "F012", "F011","F010","F01")), by = "ID")– Kerry Jackson
Mar 29 at 13:39
We could also do it using an
anti_join such as Newdata_df <- df %>% anti_join(df %>% gather(DiseaseCodeNumber, CodeValue, -ID) %>% filter(CodeValue %in% c("F023","G20","F009","F002","F001","F000","F00", "G309", "G308","G301","G300","G30","F01","F018","F013", "F012", "F011","F010","F01")), by = "ID")– Kerry Jackson
Mar 29 at 13:39
add a comment |
How about this:
> dementia <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
+ "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
>
> dementia <- apply(sapply(df[, -1], function(x) {x %in% dementia}), 1, any)
>
> df[!dementia,]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
>
Edit:
An even more elegant solution, thanks to @ Ronan Shah:
> df[apply(df[-1], 1, function(x) {!any(x %in% dementia)}),]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
Hope it helps.
answered Mar 29 at 12:52
Santiago CapobiancoSantiago Capobianco
491310
@ Ronan Shah Nice! Its a more elegant solution. You should post it.
– Santiago Capobianco
Mar 29 at 13:19
1
Yes! Sorry, I will change it right away.
– Santiago Capobianco
Mar 29 at 13:36
add a comment |
How about this:
> dementia <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
+ "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
>
> dementia <- apply(sapply(df[, -1], function(x) {x %in% dementia}), 1, any)
>
> df[!dementia,]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
>
Edit:
An even more elegant solution, thanks to @ Ronan Shah:
> df[apply(df[-1], 1, function(x) {!any(x %in% dementia)}),]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
Hope it helps.
answered Mar 29 at 12:52
Santiago CapobiancoSantiago Capobianco
491310
@ Ronan Shah Nice! Its a more elegant solution. You should post it.
– Santiago Capobianco
Mar 29 at 13:19
1
Yes! Sorry, I will change it right away.
– Santiago Capobianco
Mar 29 at 13:36
add a comment |
How about this:
> dementia <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
+ "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
>
> dementia <- apply(sapply(df[, -1], function(x) {x %in% dementia}), 1, any)
>
> df[!dementia,]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
>
Edit:
An even more elegant solution, thanks to @ Ronan Shah:
> df[apply(df[-1], 1, function(x) {!any(x %in% dementia)}),]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
Hope it helps.
answered Mar 29 at 12:52
Santiago CapobiancoSantiago Capobianco
491310
How about this:
> dementia <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308",
+ "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
>
> dementia <- apply(sapply(df[, -1], function(x) {x %in% dementia}), 1, any)
>
> df[!dementia,]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
>
Edit:
An even more elegant solution, thanks to @ Ronan Shah:
> df[apply(df[-1], 1, function(x) {!any(x %in% dementia)}),]
ID disease_code_1 disease_code_2 disease_code_3
1 1001 I802 A071 H250
2 1002 H356 NA NA
4 1004 D235 NA I802
5 1005 B178 NA NA
8 1008 C761 NA NA
11 1011 J679 A045 D352
Hope it helps.
answered Mar 29 at 12:52
Santiago CapobiancoSantiago Capobianco
491310
edited Mar 29 at 13:37
answered Mar 29 at 12:52
Santiago CapobiancoSantiago Capobianco
491310
answered Mar 29 at 12:52
Santiago CapobiancoSantiago Capobianco
491310
answered Mar 29 at 12:52
Santiago CapobiancoSantiago Capobianco
491310
491310
@ Ronan Shah Nice! Its a more elegant solution. You should post it.
– Santiago Capobianco
Mar 29 at 13:19
1
Yes! Sorry, I will change it right away.
– Santiago Capobianco
Mar 29 at 13:36
add a comment |
@ Ronan Shah Nice! Its a more elegant solution. You should post it.
– Santiago Capobianco
Mar 29 at 13:19
1
Yes! Sorry, I will change it right away.
– Santiago Capobianco
Mar 29 at 13:36
@ Ronan Shah Nice! Its a more elegant solution. You should post it.
– Santiago Capobianco
Mar 29 at 13:19
@ Ronan Shah Nice! Its a more elegant solution. You should post it.
– Santiago Capobianco
Mar 29 at 13:19
1
1
Yes! Sorry, I will change it right away.
– Santiago Capobianco
Mar 29 at 13:36
Yes! Sorry, I will change it right away.
– Santiago Capobianco
Mar 29 at 13:36
add a comment |
We can use melt/dcast from data.table
library(data.table)
dcast(melt(setDT(df), id.var = 'ID')[,
if(!any(value %in% dementia_codes)) .SD, .(ID)], ID ~ variable)
# ID disease_code_1 disease_code_2 disease_code_3
#1: 1001 I802 A071 H250
#2: 1002 H356 NA NA
#3: 1004 D235 NA I802
#4: 1005 B178 NA NA
#5: 1008 C761 NA NA
#6: 1011 J679 A045 D352
Or this can be done more compactly in base R with no reshaping
df[!Reduce(`|`, lapply(df[-1], `%in%` , dementia_codes)),]
# ID disease_code_1 disease_code_2 disease_code_3
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#4 1004 D235 NA I802
#5 1005 B178 NA NA
#8 1008 C761 NA NA
#11 1011 J679 A045 D352
data
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000",
"F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013",
"F012", "F011", "F010", "F01")
answered Mar 29 at 14:24
akrunakrun
419k13207284
add a comment |
We can use melt/dcast from data.table
library(data.table)
dcast(melt(setDT(df), id.var = 'ID')[,
if(!any(value %in% dementia_codes)) .SD, .(ID)], ID ~ variable)
# ID disease_code_1 disease_code_2 disease_code_3
#1: 1001 I802 A071 H250
#2: 1002 H356 NA NA
#3: 1004 D235 NA I802
#4: 1005 B178 NA NA
#5: 1008 C761 NA NA
#6: 1011 J679 A045 D352
Or this can be done more compactly in base R with no reshaping
df[!Reduce(`|`, lapply(df[-1], `%in%` , dementia_codes)),]
# ID disease_code_1 disease_code_2 disease_code_3
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#4 1004 D235 NA I802
#5 1005 B178 NA NA
#8 1008 C761 NA NA
#11 1011 J679 A045 D352
data
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000",
"F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013",
"F012", "F011", "F010", "F01")
answered Mar 29 at 14:24
akrunakrun
419k13207284
add a comment |
We can use melt/dcast from data.table
library(data.table)
dcast(melt(setDT(df), id.var = 'ID')[,
if(!any(value %in% dementia_codes)) .SD, .(ID)], ID ~ variable)
# ID disease_code_1 disease_code_2 disease_code_3
#1: 1001 I802 A071 H250
#2: 1002 H356 NA NA
#3: 1004 D235 NA I802
#4: 1005 B178 NA NA
#5: 1008 C761 NA NA
#6: 1011 J679 A045 D352
Or this can be done more compactly in base R with no reshaping
df[!Reduce(`|`, lapply(df[-1], `%in%` , dementia_codes)),]
# ID disease_code_1 disease_code_2 disease_code_3
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#4 1004 D235 NA I802
#5 1005 B178 NA NA
#8 1008 C761 NA NA
#11 1011 J679 A045 D352
data
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000",
"F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013",
"F012", "F011", "F010", "F01")
answered Mar 29 at 14:24
akrunakrun
419k13207284
We can use melt/dcast from data.table
library(data.table)
dcast(melt(setDT(df), id.var = 'ID')[,
if(!any(value %in% dementia_codes)) .SD, .(ID)], ID ~ variable)
# ID disease_code_1 disease_code_2 disease_code_3
#1: 1001 I802 A071 H250
#2: 1002 H356 NA NA
#3: 1004 D235 NA I802
#4: 1005 B178 NA NA
#5: 1008 C761 NA NA
#6: 1011 J679 A045 D352
Or this can be done more compactly in base R with no reshaping
df[!Reduce(`|`, lapply(df[-1], `%in%` , dementia_codes)),]
# ID disease_code_1 disease_code_2 disease_code_3
#1 1001 I802 A071 H250
#2 1002 H356 NA NA
#4 1004 D235 NA I802
#5 1005 B178 NA NA
#8 1008 C761 NA NA
#11 1011 J679 A045 D352
data
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000",
"F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013",
"F012", "F011", "F010", "F01")
answered Mar 29 at 14:24
akrunakrun
419k13207284
edited Mar 29 at 14:41
answered Mar 29 at 14:24
akrunakrun
419k13207284
answered Mar 29 at 14:24
akrunakrun
419k13207284
answered Mar 29 at 14:24
akrunakrun
419k13207284
419k13207284
add a comment |
add a comment |
A for loop version with base R, in case you prefer that.
df <- data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'), stringsAsFactors = FALSE)
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
new_df <- df[0,]
for(i in 1:nrow(df)){
currRow <- df[i,]
if(any(dementia_codes %in% as.character(currRow)) == FALSE){
new_df <- rbind(new_df, currRow)
}
}
new_df
# ID disease_code_1 disease_code_2 disease_code_3
# 1 1001 I802 A071 H250
# 2 1002 H356 NA NA
# 4 1004 D235 NA I802
# 5 1005 B178 NA NA
# 8 1008 C761 NA NA
# 11 1011 J679 A045 D352
answered Mar 29 at 13:10
DunoisDunois
858
add a comment |
A for loop version with base R, in case you prefer that.
df <- data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'), stringsAsFactors = FALSE)
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
new_df <- df[0,]
for(i in 1:nrow(df)){
currRow <- df[i,]
if(any(dementia_codes %in% as.character(currRow)) == FALSE){
new_df <- rbind(new_df, currRow)
}
}
new_df
# ID disease_code_1 disease_code_2 disease_code_3
# 1 1001 I802 A071 H250
# 2 1002 H356 NA NA
# 4 1004 D235 NA I802
# 5 1005 B178 NA NA
# 8 1008 C761 NA NA
# 11 1011 J679 A045 D352
answered Mar 29 at 13:10
DunoisDunois
858
add a comment |
A for loop version with base R, in case you prefer that.
df <- data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'), stringsAsFactors = FALSE)
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
new_df <- df[0,]
for(i in 1:nrow(df)){
currRow <- df[i,]
if(any(dementia_codes %in% as.character(currRow)) == FALSE){
new_df <- rbind(new_df, currRow)
}
}
new_df
# ID disease_code_1 disease_code_2 disease_code_3
# 1 1001 I802 A071 H250
# 2 1002 H356 NA NA
# 4 1004 D235 NA I802
# 5 1005 B178 NA NA
# 8 1008 C761 NA NA
# 11 1011 J679 A045 D352
answered Mar 29 at 13:10
DunoisDunois
858
A for loop version with base R, in case you prefer that.
df <- data.frame(ID = c(1001, 1002, 1003, 1004, 1005,1006,1007,1008,1009,1010,1011),
disease_code_1 = c('I802','H356','G560','D235','B178','F011','F023','C761','H653','A049','J679'),
disease_code_2 = c('A071','NA','G20','NA','NA','A049','NA','NA','G300','G308','A045'),
disease_code_3 = c('H250','NA','NA','I802','NA','A481','NA','NA','NA','NA','D352'), stringsAsFactors = FALSE)
dementia_codes <- c("F023", "G20", "F009", "F002", "F001", "F000", "F00", "G309", "G308", "G301", "G300", "G30", "F01", "F018", "F013", "F012", "F011", "F010", "F01")
new_df <- df[0,]
for(i in 1:nrow(df)){
currRow <- df[i,]
if(any(dementia_codes %in% as.character(currRow)) == FALSE){
new_df <- rbind(new_df, currRow)
}
}
new_df
# ID disease_code_1 disease_code_2 disease_code_3
# 1 1001 I802 A071 H250
# 2 1002 H356 NA NA
# 4 1004 D235 NA I802
# 5 1005 B178 NA NA
# 8 1008 C761 NA NA
# 11 1011 J679 A045 D352
answered Mar 29 at 13:10
DunoisDunois
858
edited Mar 29 at 13:23
answered Mar 29 at 13:10
DunoisDunois
858
answered Mar 29 at 13:10
DunoisDunois
858
answered Mar 29 at 13:10
DunoisDunois
858
858
add a comment |
add a comment |
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55417645%2fhow-to-subset-dataframe-based-on-a-not-equal-to-criteria-applied-to-a-large-nu%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
1
You should reshape your data to long format. That will make your life (and analysis) much easier.
– docendo discimus
Mar 29 at 12:51