I know there are many questions asked about removing duplicates in SQL. However in my case it is slightly more complicated.
These are data with Barcode which repeats over a month. Therefore it is expected that there will be entries with the same Barcode. However it is found out that due to possibly a machine bug, same data will be recorded within 4-5 minutes timeframe 2 to 3 times. It does not happen for every entry, but it happens rather frequently.
Allow me to demonstrate with a sample table which contains the same Barcode "A00000"
Barcode No Date A B C D
A00000 1499456 10/10/2019 3:28 607 94 1743 72D
A00000 1803564 10/20/2019 22:09 589 75 1677 14D
A00000 1803666 10/20/2019 22:13 589 75 1677 14D
A00000 1803751 10/20/2019 22:17 589 75 1677 14D
A00000 2084561 10/30/2019 12:22 583 86 1677 14D
A00000 2383742 11/9/2019 23:18 594 81 1650 07D
As you can see the entries on 10/20 contains identical data which are duplicates which should be removed so only one of the entry remains (any of the entry is fine and the exact time is not the main concern). The "No" column is a pure arbitrary number which can be safely disregarded. The other entries should be remain as it is.
I know this should be done by using "Group by", but I am struggling on how to write the conditions. I have tried also using table INNER JOIN itself and then remove these selected results:
T2.A = T2.B AND
T2.[Date] > T1.[Date] AND
strftime('%s',T2.[Date]) - strftime('%s',T1.[Date]) < 600
The results still seem a bit off as some of the entries are selected twice and some are not selected. I am still not used to SQL style of thinking. Any help is appreciated.
The format of the Date column complicates things a bit, but otherwise the solution basically is to use GROUP BY in the normal way. In the following, I've assumed the name of the table is test:
WITH sane as
(SELECT *,
substr(date,1,instr(date, ' ') - 1) as time
FROM test)
SELECT Barcode, max(No), Date, A, B, C, D
FROM sane
GROUP BY barcode, time;
The use of max() is perhaps unneeded but it gives some determinacy, which might be helpful.
Related
I have a database where some people have multiple diagnoses. I posted a similar question in the past, but now have some more nuances I need to work through:
R- How to test multiple 100s of similar variables against a condition
I have this dataset (which was an import of a SAS file)
ID dx1 dx2 dx3 dx4 dx5 dx6 .... dx200
1 343 432 873 129 12 123 3445
2 34 12 44
3 12
4 34 56
Initially, I wanted to be able to create a new variable if any of the "dxs" equals a certain number without using hundreds of if statements? All the different variables have the same format (dx#). So I used the following code:
Ex:
dataset$highbloodpressure <- rowSums(screen[0:832] == "410") > 0
This worked great. However, there are many different codes for the same diagnosis. For example, a heart attack can be defined as:
410.1,
410.71,
410.62,
410.42,
...this goes on for 20 additional codes. BUT! They all start with 410.
I thought about using stringr (the variable is a string), to identify the common code components (410, for the example above), but am not sure how to use it in the context of rowsums.
If anyone has any suggestions for this, please let me know!
Thanks for all the help!
You can use the grepl() function that returns TRUE if a value is present. In order to check all columns simultaneously, just collapse all of them to one character per row:
df$dx.410 = NA
for(i in 1:dim(df)[1]){
if(grepl('410',paste(df[i,2:200],collapse=' '))){
df$dx.410[i]="Present"
}
}
This will loop through all lines, create one large character containing all diagnoses for this case and write "Present" in column dx.410 if any column contains a 410-diagnosis.
(The solution expects the data structure you have here with the dx-variables in columns 2 to 200. If there are some other columns, just adjust these numbers)
Big picture explanation is I am trying to do a sliding window analysis on environmental data in R. I have PAR (photosynthetically active radiation) data for a select number of sequential dates (pre-determined based off other biological factors) for two years (2014 and 2015) with one value of PAR per day. See below the few first lines of the data frame (data frame name is "rollingpar").
par14 par15
1356.3242 1306.7725
NaN 1232.5637
1349.3519 505.4832
NaN 1350.4282
1344.9306 1344.6508
NaN 1277.9051
989.5620 NaN
I would like to create a loop (or any other way possible) to subset the data frame (both columns!) into two week windows (14 rows) from start to finish sliding from one window to the next by a week (7 rows). So the first window would include rows 1 to 14 and the second window would include rows 8 to 21 and so forth. After subsetting, the data needs to be flipped in structure (currently using the melt function in the reshape2 package) so that the values of the PAR data are in one column and the variable of par14 or par15 is in the other column. Then I need to get rid of the NaN data and finally perform a wilcox rank sum test on each window comparing PAR by the variable year (par14 or par15). Below is the code I wrote to prove the concept of what I wanted and for the first subsetted window it gives me exactly what I want.
library(reshape2)
par.sub=rollingpar[1:14, ]
par.sub=melt(par.sub)
par.sub=na.omit(par.sub)
par.sub$variable=as.factor(par.sub$variable)
wilcox.test(value~variable, par.sub)
#when melt flips a data frame the columns become value and variable...
#for this case value holds the PAR data and variable holds the year
#information
When I tried to write a for loop to iterate the process through the whole data frame (total rows = 139) I got errors every which way I ran it. Additionally, this loop doesn't even take into account the sliding by one week aspect. I figured if I could just figure out how to get windows and run analysis via a loop first then I could try to parse through the sliding part. Basically I realize that what I explained I wanted and what I wrote this for loop to do are slightly different. The code below is sliding row by row or on a one day basis. I would greatly appreciate if the solution encompassed the sliding by a week aspect. I am fairly new to R and do not have extensive experience with for loops so I feel like there is probably an easy fix to make this work.
wilcoxvalues=data.frame(p.values=numeric(0))
Upar=rollingpar$par14
for (i in 1:length(Upar)){
par.sub=rollingpar[[i]:[i]+13, ]
par.sub=melt(par.sub)
par.sub=na.omit(par.sub)
par.sub$variable=as.factor(par.sub$variable)
save.sub=wilcox.test(value~variable, par.sub)
for (j in 1:length(save.sub)){
wilcoxvalues$p.value[j]=save.sub$p.value
}
}
If anyone has a much better way to do this through a different package or function that I am unaware of I would love to be enlightened. I did try roll apply but ran into problems with finding a way to apply it to an entire data frame and not just one column. I have searched for assistance from the many other questions regarding subsetting, for loops, and rolling analysis, but can't quite seem to find exactly what I need. Any help would be appreciated to a frustrated grad student :) and if I did not provide enough information please let me know.
Consider an lapply using a sequence of every 7 values through 365 days of year (last day not included to avoid single day in last grouping), all to return a dataframe list of Wilcox test p-values with Week indicator. Then later row bind each list item into final, single dataframe:
library(reshape2)
slidingWindow <- seq(1,364,by=7)
slidingWindow
# [1] 1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120 127
# [20] 134 141 148 155 162 169 176 183 190 197 204 211 218 225 232 239 246 253 260
# [39] 267 274 281 288 295 302 309 316 323 330 337 344 351 358
# LIST OF WILCOX P VALUES DFs FOR EACH SLIDING WINDOW (TWO-WEEK PERIODS)
wilcoxvalues <- lapply(slidingWindow, function(i) {
par.sub=rollingpar[i:(i+13), ]
par.sub=melt(par.sub)
par.sub=na.omit(par.sub)
par.sub$variable=as.factor(par.sub$variable)
data.frame(week=paste0("Week: ", i%/%7+1, "-", i%/%7+2),
p.values=wilcox.test(value~variable, par.sub)$p.value)
})
# SINGLE DF OF ALL P-VALUES
wilcoxdf <- do.call(rbind, wilcoxvalues)
I'll try to be specific: I want to create a new column on a data frame in which the values are the sum of the previous values in another column.
So I already have the first two columns (ID and Value) below and want to create the third one (Sum), but I don't know how to do this.
In the column "Sum", the values are the sum of the values in "Value), so for example, 31.098 (Sum) is the sum of 16.91 and 14.18 (Value):
ID Value Sum
157 16.91531834 16.91531834
142 14.18365203 31.09897037
205 11.93528052 43.03425089
89 11.83021643 54.86446732
53 6.3668838 61.23135112
204 3.99243539 65.22378651
202 3.21496113 68.43874764
17 1.93317924 70.37192688
220 1.74406388 72.11599076
147 1.59697415 73.71296491
33 1.42887161 75.14183652
138 1.28178189 76.42361841
154 1.19773062 77.62134903
It is the first time I'm posting here. Until now I found everything I was searching for already answered... so, sorry if this kind of question is already answered too (I must have been!), but I wasn't able to find. I'm not a native speaker (as you probably guessed already), so maybe I didn't use the proper key words...
Thanks!!
I have a table where one of the variables is country of registration.
table(df$reg_country)
returns:
AR BR ES FR IT
123 202 578 642 263
Now, if I subset the original table to exclude one of the countries
df_subset<-subset(df, reg_country!='AR')
table(df_subset$reg_country)
returns:
AR BR ES FR IT
0 202 578 642 263
This second result is very surprising to me, as R seems to somehow magically know that I have removed the the entries from AR.
Why does that happen?
Does it affect the size of the second data frame (df_subset)? If 'yes' - is there a more efficient way to to subset in order to minimize the size?
df$reg_country is a factor variable, which contains the information of all possible levels in the levels attribute. Check levels(df_subset$reg_country).
Factor levels only have a significant impact on data size if you have a huge number of them. I wouldn't expect that to be the case. However, you could use droplevels(df_subset$reg_country) to remove unused levels.
I'm trying to build quite a complex loop in R.
I have a set of data set as an object called p_int (p_int is peak intensity).
For this example the structure of p_int i.e. str(p_int) is:
num [1:1599]
The size of p_int can vary i.e. [1:688], [1:1200] etc.
What I'm trying to do with p_int is to construct a complex loop to extract the monoisotopic peaks, these are peaks with certain characteristics which will be extracted into a second object: mono_iso:
search for the first eight sets of data results in p_int. Of these eight, find the set of data with the greatest score (this score also needs to be above 50).
Once this result has been found, record it into mono_iso.
The loop will then fix on to this position of where this result is located within the large dataset. From this position it will then skip the next result along the dataset before doing the same for the next set of 8 results.
So something similar to this:
16 Results: 100 120 90 66 220 90 70 30 70 100 54 85 310 200 33 41
** So, to begin with, the loop would take the first 8 results:
100 120 90 66 220 90 70 30
**It would then decide which peak is the greatest:
220
**It would determine whether 220 was greater than 50
IF YES: It would record 220 into "mono_iso"
IF NO: It would move on to the next set of 8 results
**220 is greater than 50... so records into mono_iso
The loop would then place it's position at 220 it would then skip the "90" and begin the same thing again for the next set of 8 results beginning at the next data result in line: in this case at the 70:
70 30 70 100 54 85 310 200
It would then record the "310" value (highest value) and do the same thing again etc etc until the end of the set of data.
Hope this makes perfect sense. If anyone could possibly help me out into making such a loop work with R-script, I'd very much appreciate it.
Use this:
mono_iso <- aggregate(p_int, by=list(group=((seq_along(p_int)-1)%/%8)+1), function(x)ifelse(max(x)>50,max(x),NA))$x
This will put NA for groups such that max(...)<=50. If you want to filter those out, use this:
mono_iso <- mono_iso[!is.na(mono_iso)]