I am having trouble combining multiple rows into 1 row, below is my current data:
I want one row of symptoms for each VAERS_ID. However, because the number of rows each VAERS_ID is inconsistent, I am having trouble.
I have tried this:
test= data %>%
select(VAERS_ID, SYMPTOM1, SYMPTOM2, SYMPTOM3, SYPMTOM4, SYMPTOM5) %>%
group_by(VAERS_ID) %>%
mutate(Grp = paste0(SYMPTOM1,SYMPTOM1, SYMPTOM2, SYMPTOM3, SYPMTOM4, SYMPTOM5, collapse
= ",")) %>%
distinct(VAERS_ID, Grp, .keep_all = TRUE)
This gives me the original data, plus another column labeled Grp containing all of the symptoms for each VAERS_ID pasted together, with a comma between each set.
Any help would be appreciated.
Your approach seems right but since data cannot be copied and tested, I am not able to reproduce your error. Some changes suggested, which you can try.
since you want "ALL Symptoms" in 1 place for each VAERS_ID, which is a common real world use case and I face this often. If you don't need original data in output, simply use this
data%>%
group_by(VAERS_ID) %>%
summarise("Symptoms" = paste0(SYMPTOM1,SYMPTOM1, SYMPTOM2, SYMPTOM3, SYPMTOM4, SYMPTOM5, collapse = ",")
With mutate you get original data since it adds a new column.
To address the warning to ungroup, just added %>%ungroup at end or within summarise add .groups="drop"
Related
I am sort of a newbie with R and data manipulation and I am trying to transpose the UCI words dataset. The default dataset is currently structured as so.
Where the first column is the document number, the second column is the word number referencing another text file and the last column is the number of times the word occurs in the document. (For now, we can forget about the third column and I know how to drop it from the dataset.)
What I am trying to do is to transpose the dataset so that I can have each document's words in one row. So a simple example would be like this.
I tried using the t() function but it would transpose the entire dataset all together which is not what I want. I looked in using the dplyr package to help with the data manipulation but I am not getting any solid leads. If you guys have any sources or a particular direction you can nudge me towards accomplishing this that would helpfull.
Thank you!
Here's a solution using the tidyverse package (which includes dplyr). The trick is to first add another column to differentiate entries with the same value in the first column (document number) and then just change the data to wide format using pivot_wider.
library(tidyverse)
# Your data
df <- read.csv(text = "num word
1 61
2 76
1 89
3 211
3 296", sep = " ")
df %>%
# Group by num
group_by(num) %>%
# Add a rownumber to differentiate entries for the same first column value
mutate(rownum = row_number()) %>%
# Change data to wide format
pivot_wider(id = num,
names_from = rownum,
values_from = word)
So I was able to figure out how to accomplish this task. Hopefully, it helps other DS's in the future.
data <- read.table("docword.kos.txt", sep = " ")
data <- data %>% select(V1, V2)
trans <- data %>%
group_by(V1) %>%
summarise(words = paste(V2, collapse = ","))
trans <- trans %>% select(words)
What I ended up doing is using the tidyr package to perform some data wrangling and group my dataset by the first column. Then I exported and re uploaded the dataset after making some slight adjustments in notepad (Replaced the " from the generated csv file)
write.csv(trans, "~\trend.csv", row.names = FALSE)
I have a dataframe with the columns doc_id and feats (both character vectors). I'm trying to create a new column n_rel_prn, which has the number of total occurrences of the value 'PronType=Rel' in the feats column, for each doc_id.
I can't use filter(), because it filters out all of the other data I need (i.e. where the value for feats is not 'PronType=Rel'), but otherwise it does the trick. (Here's that code snippet:)
tcorpus %>% group_by(doc_id) %>%
filter(feats=='PronType=Rel') %>%
mutate(n_rel_prn = n())
Basically, I need something that works like the following code (except that actually works--this obviously doesn't):
tcorpus %>% group_by(doc_id) %>%
mutate(n_rel_prn = NROW(feats == 'PronType=Rel'))
Is there a way I can count the number of 'PronType=Rel' observations (grouped by doc_id) and add these totals to a new column? (I'm assuming at the very least group_by %>% mutate() is the way to go.)
You are almost there. Try this:
tcorpus %>% group_by(doc_id) %>% mutate(n_rel_prn = sum(feats == 'PronType=Rel'))
I have a calculation that I have to perform for 23 people (they have varying number of rows allocated to each person so difficult to do in excel. What I'd like to do is take the total time each person took to complete a test and divide it into 5 time categories (20%) so that I can look at their reaction time in more detail.
I will just do this by hand but it will take quite a while because they have 8 sets of data each. I'm hoping someone can show me the best way to use a loop or automate this process even just a little. I have tried to understand the examples but I'm afraid I don't have the skill. So by hand I would do it like I have below where I just filter by each subject.
I started by selecting the relevant columns, then filtered by subject so that I could calculate the time they started and the time they finished and used that to create a variable (testDuration) that could be used to create the 20% proportions of RTs that I'm after. I have no idea how to get the individual subjects' test start, end, duration and timeBin sizes to appear in one column. Any help very gratefully received.
Subj1 <- rtTrialsYA_s1 %>%
select(Subject, RetRating.OnsetTime, RetRating.RT, RetRating.RTTime) %>%
filter(Subject==1) %>%
summarise(
testStart =
min(RetRating.OnsetTime),
testEnd = max(RetRating.RTTime)
) %>%
mutate(
testDuration = testEnd - testStart,
timeBin =
testDuration/5
)
Subj2 <- rtTrialsYA_s1 %>%
select(Subject, RetRating.OnsetTime, RetRating.RT, RetRating.RTTime) %>%
filter(Subject==2) %>%
summarise(
testStart =
min(RetRating.OnsetTime),
testEnd = max(RetRating.RTTime)
) %>%
mutate(
testDuration = testEnd - testStart,
timeBin =
testDuration/5
)
I'm not positive that I understand your code, but this function can be called for any Subject value and then return the output:
myfunction <- function(subjectNumber){
Subj <- rtTrialsYA_s1 %>%
select(Subject, RetRating.OnsetTime, RetRating.RT, RetRating.RTTime) %>%
filter(Subject==subjectNumber) %>%
summarise(testStart = min(RetRating.OnsetTime), testEnd = max(RetRating.RTTime)) %>%
mutate(testDuration = testEnd -testStart) %>%
mutate(timeBin = testDuration/5)
return(Subj)
}
Subj1 <- myfunction(1)
Subj2 <- myfunction(2)
To loop through this, I'll need to know what your data and the desired output looks like.
I think you're missing one piece and that is simply dplyr::group_by.
You can use it as follows to break your dataset into groups, each containing the observations belonging to only one subject, and then summarise on those groups with whatever it is you want to analyze.
library(dplyr)
df <- rtTrialsYA_s1 %>%
group_by(Subject) %>%
summarise(
testStart = min(RetRating.OnsetTime),
testEnd = max(RetRating.RTTime),
testDuration = testEnd - testStart,
timeBin = testDuration/5,
.groups = "drop"
)
There is no need to do separate mutate calls in your code, btw. Also, you can continue to do column calculations right within summarise, as long as the result vectors have the same length as your aggregated columns.
And since summarise retains only the grouping columns and whatever you are defining, there is no real need to do a select statement before, either.
// update
You say you need all your calculated columns to appear within one single column. For that you can use tidyr::pivot_longer. Using the df we calculated above:
library(tidyr)
df_long <- df %>%
pivot_longer(-Subject)
Above will take all columns, except Subject and pivot them into two columns, one containing the former col name and one containing the former value.
I am currently struggling to transition one of my columns in my data to a row as an observation. Below is a representative example of what my data looks like:
library(tidyverse)
test_df <- tibble(unit_name=rep("Chungcheongbuk-do"),unit_n=rep(2),
can=c("Cho Bong-am","Lee Seung-man","Lee Si-yeong","Shin Heung-woo"),
pev1=rep(510014),vot1=rep(457815),vv1=rep(445955),
ivv1=rep(11860),cv1=c(25875,386665,23006,10409),
abstention=rep(52199))
As seen above, the abstention column exists at the end of my data frame, and I would like my data to look like the following:
library(tidyverse)
desired_df <- tibble(unit_name=rep("Chungcheongbuk-do"),unit_n=rep(2),
can=c("Cho Bong-am","Lee Seung-man","Lee Si-yeong","Shin Heung-woo","abstention"),
pev1=rep(510014),vot1=rep(457815),vv1=rep(445955),
ivv1=rep(11860),cv1=c(25875,386665,23006,10409,52199))
Here, abstentions are treated like a candidate, in the can column. Thus, the rest of the data is maintained, and the abstention values are their own observation in the cv1 column.
I have tried using pivot_wider, but I am unsure how to use the arguments to get what I want. I have also considered t() to transpose the column into a row, but also having a hard time slotting it back into my data. Any help is appreciated! Thanks!
Here's a strategy what will work if you have multiple unit_names
test_df %>%
group_split(unit_name) %>%
map( function(group_data) {
slice(group_data, 1) %>%
mutate(can="abstention", cv1=abstention) %>%
add_row(group_data, .) %>%
select(-abstention)
}) %>%
bind_rows()
Basically we split the data up by unit_name, then we grab the first row for each group and move the values around. Append that as a new row to each group, and then re-combine all the groups.
I have an HMMSCAN result file of protein domains with 10 columns. please see the link for the CSV file.
https://docs.google.com/spreadsheets/d/10d_YQwD41uj0q5pKinIo7wElhDj3BqilwWxThfIg75s/edit?usp=sharing
But I want it to look like this:-
1BVN:P|PDBID|CHAIN|SEQUENCE Alpha-amylase Alpha-amylase_C A_amylase_inhib
3EF3:A|PDBID|CHAIN|SEQUENCE Cutinase
3IP8:A|PDBID|CHAIN|SEQUENCE Amdase
4Q1U:A|PDBID|CHAIN|SEQUENCE Arylesterase
4ROT:A|PDBID|CHAIN|SEQUENCE Esterase
5XJH:A|PDBID|CHAIN|SEQUENCE DLH
6QG9:A|PDBID|CHAIN|SEQUENCE Tannase
The repeated entries of column 3 should get grouped and its corresponding values of column 1, which are in different rows, should be arranged in separate columns.
This is what i wrote till now:
df <- read.csv ("hydrolase_sorted.txt" , header = FALSE, sep ="\t")
new <- df %>% select (V1,V3) %>% group_by(V3) %>% spread(V1, V3)
I hope I am clear with the problem statement. Thanks in advance!!
Your input data set has two unregular rows. However, the approach in your solution is right but one more step is required:
library(dplyr)
df %>% select(V3,V1) %>% group_by(V3) %>% mutate(x = paste(V1,collapse=" ")) %>% select(V3,x)
What we did here is simply concentrating strings by V3. Before running the abovementioned code in this solution you should preprocess and fix some improper rows manually. The rows (TIM, Dannase, and DLH). To do that you can use the Convert texts into column function of in Excel.
Required steps defined are below. Problematic columns highlighted yellow:
Sorry for the non-English interface of my Excel but the way is self-explanatory.