> my_query <- paste("select * from", query_table, "where Arrived_Date_Time >=", arrived_earliest_date, "and Arrived_Date_Time < ", arrived_latest_date)
> dfDataIn <- sqlQuery(NSSP, my_query, stringsAsFactors=FALSE)
> odbcCloseAll()
> table(dfDataIn$Discharge_Disposition)
1 2 3 4 5 6 7 8 9 20 21
64059 336 1522 32 306 1166 2343 1 35423 312 36
30 41 43 50 51 61 62 63 64 65 66
26 18 295 133 200 5 270 76 3 1121 811
70 100
249 24
Actually dfDataIn$Discharge_Disposition is a character variable, and most importantly, most 1 here are supposed to be "01" in the database, whereas only minority are truly "1" in the database. (similarly for 2-9)
Is there any way to read the data in the right format?
You could try as.is = TRUE.
dfDataIn <- sqlQuery(NSSP, my_query, as.is = TRUE)
This will bring the data as is from the data source.
Related
It works that it iterates over word, but the variable "word" contains a word, instead of the number (position) of that word in the row. For example, in the first row, 'yzi' has number 1, and 'runner' has number 3. Can anyone help?
Are you looking for this?
lapply(strsplit(output$text, ' '), function(x) seq_along(x)^2)
#[[1]]
# [1] 1 4 9 16 25 36 49 64 81 100 121 144 169 196
#[[2]]
# [1] 1 4 9 16 25 36 49 64 81 100 121 144 169 196
#[[3]]
# [1] 1 4 9 16 25 36 49 64 81 100 121 144 169
#[[4]]
# [1] 1 4 9 16 25 36 49 64 81 100
#...
#...
Or in a loop -
for(row in 1:nrow(output)){
list=strsplit(output$text[row], " ")[[1]]
for(i in seq_along(list)){
print(i^2)
}
}
We can use map
library(purrr)
map(strsplit(output$text, ' '), ~ seq_along(.x)^2)
I am using a code based on Deseq2. One of my goals is to plot a heatmap of data.
heatmap.data <- counts(dds)[topGenes,]
The error I am getting is
Error in counts(dds)[topGenes, ]: subscript out of bounds
the first few line sof my counts(dds) function looks like this.
99h1 99h2 99h3 99h4 wth1 wth2
ENSDARG00000000002 243 196 187 117 91 96
ENSDARG00000000018 42 55 53 32 48 48
ENSDARG00000000019 91 91 108 64 95 94
ENSDARG00000000068 3 10 10 10 30 21
ENSDARG00000000069 55 47 43 53 51 30
ENSDARG00000000086 46 26 36 18 37 29
ENSDARG00000000103 301 289 289 199 347 386
ENSDARG00000000151 18 19 17 14 22 19
ENSDARG00000000161 16 17 9 19 10 20
ENSDARG00000000175 10 9 10 6 16 12
ENSDARG00000000183 12 8 15 11 8 9
ENSDARG00000000189 16 17 13 10 13 21
ENSDARG00000000212 227 208 259 234 78 69
ENSDARG00000000229 68 72 95 44 71 64
ENSDARG00000000241 71 92 67 76 88 74
ENSDARG00000000324 11 9 6 2 8 9
ENSDARG00000000370 12 5 7 8 0 5
ENSDARG00000000394 390 356 339 283 313 286
ENSDARG00000000423 0 0 2 2 7 1
ENSDARG00000000442 1 1 0 0 1 1
ENSDARG00000000472 16 8 3 5 7 8
ENSDARG00000000476 2 1 2 4 6 3
ENSDARG00000000489 221 203 169 144 84 114
ENSDARG00000000503 133 118 139 89 91 112
ENSDARG00000000529 31 25 17 26 15 24
ENSDARG00000000540 25 17 17 10 28 19
ENSDARG00000000542 15 9 9 6 15 12
How do I ensure all the elements of the top genes are present in it?
When I try to see 20 top genes in the dataset. it looks like a list of genes
6339" "12416" "1241" "3025" "12791" "846" "15090"
[8] "6529" "14564" "4863" "12777" "1122" "7454" "13716"
[15] "5790" "3328" "1231" "13734" "2797" "9072" with the column head V1.
I have used both
topGenes <- read.table("E://mir99h50 Cheng data//topGenesresordered.txt",header = TRUE)
and
topGenes <- read.table("E://mir99h50 Cheng data//topGenesresordered.txt",header = FALSE)
to see if the out of bounds error is removed. However it was of no use. I guess the V1 head is causing the issue.
The top genes function has been generated using the above code snippet.
resordered <- res[order(res$padj),]
#Reorder gene list by increasing pAdj
resordered <- as.data.frame(res[order(res$padj),])
#Filter for genes that are differentially expressed with an FDR < 0.01
ii <- which(res$padj < 0.01)
length(ii)
# Use the rownames() function to get the top 20 differentially expressed genes from our results table
topGenes <- rownames(resordered[1:20,])
topGenes
# Get the counts from the DESeqDataSet using the counts() function
heatmap.data <- counts(dds)[topGenes,]
Perhaps this will do what you want?
counts_dds <- counts(dds)
topgenes <- c("ENSDARG00000000002", "ENSDARG00000000489", "ENSDARG00000000503",
"ENSDARG00000000540", "ENSDARG00000000529", "ENSDARG00000000542")
heatmap.data <- counts_dds[rownames(counts_dds) %in% topgenes,]
If you provide more information it will be easier to advise you on how to fix your problem.
I am having difficulties trying to order a list element-wise by decreasing order...
I have a ByPos_Mindex object or a list of 1000 IRange objects (CG_seqP) from
C <- vmatchPattern(CG, CPGi_Seq, max.mismatch = 0, with.indels = FALSE)
IRanges object with 27 ranges and 0 metadata columns:
start end width
<integer> <integer> <integer>
[1] 1 2 2
[2] 3 4 2
[3] 9 10 2
[4] 27 28 2
[5] 34 35 2
... ... ... ...
[23] 189 190 2
[24] 207 208 2
[25] 212 213 2
[26] 215 216 2
[27] 218 219 2
length(1000 of these IRanges)
I then change this to a list of only the start integers (which I want)
CG_SeqP <- sapply(C, function(x) sapply(as.vector(x), "[", 1))
[[1]]
[1] 1 3 9 27 34 47 52 56 62 66 68 70 89 110 112
[16] 136 140 146 154 160 163 178 189 207 212 215 218
(1000 of these)
The Problem happens when I try and order the list of elements using
CG_SeqP <- sapply(as.vector(CG_SeqP),order, decreasing = TRUE)
I get a list of what I think is row numbers so if the first IRAnge object is 27 I get this...
CG_SeqP[1]
[[1]]
[1] 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8
[21] 7 6 5 4 3 2 1
So the decreasing has worked but not for my actual list of elements>?
Any suggestions, thanks in advance.
Order returns order of the sequence not the actual elements of your vector, to extract it let us look at a toy example (I am following your idea here) :
set.seed(1)
alist1 <- list(a = sample(1:100, 30))
So, If you print alist1 with the current seed value , you will have below results:
> alist1
$a
[1] 99 51 67 59 23 25 69 43 17 68 10 77 55 49 29 39 93 16 44
[20] 7 96 92 80 94 34 97 66 31 5 24
Now to sort them either you use sort function or you can use order, sort just sorts the data, whereas order just returns the order number of the elements in a sorted sequence. It doesn't return the actual sequence, it returns the position. Hence we need to put those positions in the actual vector using square notation brackets to get the right sorted outcome.
lapply(as.vector(alist1),function(x)x[order(x, decreasing = TRUE)])
I have used lapply instead of sapply just to enforce the outcome as a list. You are free to choose any command basis your need
Will return:
#> lapply(as.vector(alist1),function(x)x[order(x, decreasing = TRUE)])
#$a
# [1] 99 97 96 94 93 92 80 77 69 68 67 66 59 55 51 49 44 43 39
#[20] 34 31 29 25 24 23 17 16 10 7 5
I hope this clarifies your doubt. Thanks
I've a csv data file with the following formats
Stock prices over the period of Jan 1, 2015 to Sep 26, 2017
Now I use the following code to import the data as zoo object:
sensexzoo1<- read.zoo(file = "/home/bidyut/Downloads/SENSEX.csv",
format="%d-%B-%Y", header=T, sep=",")
It produces the following error:
Error in read.zoo(file = "/home/bidyut/Downloads/SENSEX.csv", format =
"%d-%B-%Y", : index has 679 bad entries at data rows: 1 2 3 4 5 6
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76
77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
100 ...
What is the wrong with this? Please suggest
The problem is the mismatch between the header and the data. The header line has 5 fields and the remaining lines of the file have 6 fields:
head(count.fields("SENSEX.csv", sep = ","))
## [1] 5 6 6 6 6 6
When that happens it assumes that the first field of the data is the row names so by default the next field (which in fact contains the Open data) is assumed to be the time index.
We can address this in several alternative ways:
1) The easiest way to fix this is to add a field called Volume, say, to the header so that the header looks like this:
Date,Open,High,Low,Close,Volume
2) If you have many files of this format so that it is not feasible to modify them we can read the data in without the headers and then add them on in a second pass. The [, -5] drops the column of NAs and the [-1] on the second line drops the Date header.
z <- read.zoo("SENSEX.csv", format="%d-%B-%Y", sep = ",", skip = 1)[, -5]
names(z) <- unlist(read.table("SENSEX.csv", sep = ",", nrow = 1))[-1]
giving:
> head(z)
Open High Low Close
2015-01-01 27485.77 27545.61 27395.34 27507.54
2015-01-02 27521.28 27937.47 27519.26 27887.90
2015-01-05 27978.43 28064.49 27786.85 27842.32
2015-01-06 27694.23 27698.93 26937.06 26987.46
2015-01-07 26983.43 27051.60 26776.12 26908.82
2015-01-08 27178.77 27316.41 27101.94 27274.71
3) A third approach is to read the file in as text, use R to append ",Volume" to the first line and then read the text with read.zoo:
Lines <- readLines("SENSEX.csv")
Lines[1] <- paste0(Lines[1], ",Volume")
z <- read.zoo(text = Lines, header = TRUE, sep = ",", format="%d-%B-%Y")
Note: The first few lines of SENSEX.csv are shown below to make this self-contained (not dependent on the link in the question which could disappear in the future):
Date,Open,High,Low,Close
1-January-2015,27485.77,27545.61,27395.34,27507.54,
2-January-2015,27521.28,27937.47,27519.26,27887.90,
5-January-2015,27978.43,28064.49,27786.85,27842.32,
6-January-2015,27694.23,27698.93,26937.06,26987.46,
7-January-2015,26983.43,27051.60,26776.12,26908.82,
8-January-2015,27178.77,27316.41,27101.94,27274.71,
9-January-2015,27404.19,27507.67,27119.63,27458.38,
12-January-2015,27523.86,27620.66,27323.74,27585.27,
13-January-2015,27611.56,27670.19,27324.58,27425.73,
14-January-2015,27432.14,27512.80,27203.25,27346.82,
I am trying to solve the DSC(Differential scanning calorimetry) data with R but it seems that I ran into some troubles. All this used to be done in Origin or Qtiplot tediously in my lab.But I wonder if there is another way to do it in batch.But the result did not goes well. For example, maybe I have used the wrong colnames of my data.frame,the code
dat$0.5min
Error: unexpected numeric constant in "dat$0.5"
can not reach my data.
So below is the full description of my purpose, thank you in advance!
the DSC data is like this（I store the CSV file in my GoogleDrive Link ） :
T1 0.5min T2 1min
40.59 -0.2904 40.59 -0.2545
40.81 -0.281 40.81 -0.2455
41.04 -0.2747 41.04 -0.2389
41.29 -0.2728 41.29 -0.2361
41.54 -0.2553 41.54 -0.2239
41.8 -0.07 41.8 -0.0732
42.06 0.1687 42.06 0.1414
42.32 0.3194 42.32 0.2817
42.58 0.3814 42.58 0.3421
42.84 0.3863 42.84 0.3493
43.1 0.3665 43.11 0.3322
43.37 0.3438 43.37 0.3109
43.64 0.3265 43.64 0.2937
43.9 0.3151 43.9 0.2819
44.17 0.3072 44.17 0.2735
44.43 0.2995 44.43 0.2656
44.7 0.2899 44.7 0.2563
44.96 0.2779 44.96 0.245
in fact I have merge the data into a data.frame and hope I can adjust it and do something further.
the command is:
dat<-read.csv("Book1.csv",header=F)
colnames(dat)<-c('T1','0.5min','T2','1min','T3','2min','T4','4min','T5','8min','T6','10min',
'T7','20min','T8','ascast1','T9','ascast2','T10','ascast3','T11','ascast4',
'T12','ascast5'
)
so actually dat is a data.frame with 1163 obs. of 24 variables.
T1,T2,T3.....T12 means temperature that the samples were tested of DSC although in the same interval they do differ a little due to the unstability of the machine.
And the colname along T1~T12 is Heat Flow of different heat treatment durations that records by the machine and ascast1~ascast5 means nothing done to the sample to check the accuracy of the machine.
Now I need to do something like the following:
for T1~T2 is in Celsius Degrees，I need to change them into Kelvin Degrees whichi means every data plus 273.16.
Two temperature is chosen to compare the result that is Ts=180.25,Te=240.45(all is discussed in Celsius Degrees and I have seen it Qtiplot to make sure). To be clear I list the two temperature and the first 6 columns data.
T1 0.5min T2 1min T3 2min T4 4min
180.25 -0.01710000 180.25 -0.01780000 180.25 -0.02120000 180.25 -0.02020000
. . . .
. . . .
240.45 0.05700000 240.45 0.04500000 240.45 0.05780000 240.45 0.05580000
That all Heat Flow in Ts should be the same that can be made 0 for convenience. So based on the different values Heat Flow of different times like 0.5min,1min,2min,4min,8min,10min,20min and ascas1~ascast5 all Heat Flow value should be minus the Heat Flow value in Ts.
And for Heat Flow in Te, the value should be adjust to make sure that all the Heat Flow data are the same in Te. The purpose is like the following, (1) calculate mean of the 12 heat flow data in Te. Let's use Hmean for the mean heat flow.So Hmean is the value that all Heat Flow should be. (2) for data in column 0.5min,I use col("0.5min") to denote, and the lineal transform formula is like the following:
col("0.5min")-[([0.05700000-(-0.01710000)]-Hmean)/(Te-Ts)]*(col(T1)-Ts)
Actually, [0.05700000-(-0.01710000)] is done in step 2,but I write it for your reference. And this formula is used for different pair of T1~T12 and columns,like (T1,0.5min),(T2, 1min),(T3,1min).....all is 12 pairs.
Now we can plot the 12 pairs of data on the same plot with intervals from 180~240(also in Celsius Degrees) to magnify the details of differences between the different scans of DSC.
I have been stuck on this problems for 2 days , so I return to stackoverflow for help.
Thanks!
I am assuming that your question was right in the beginning where you got the following error,
dat$0.5min
Error: unexpected numeric constant in "dat$0.5"
As I could not find a question in the rest of the steps. They just seemed like a step by step procedure of an experiment.
To fix that error, the problem is the column name has a number in it so to use the column name in the way you want (to reference a column), you should use "`", accent mark, symbol.
>dataF <- data.frame("0.5min"=1:10,"T2"=11:20,check.names = F)
> dataF$`0.5min`
[1] 1 2 3 4 5 6 7 8 9 10
Based on comments adding more information,
You can add a constant to add to alternate columns in the following manner,
dataF <- data.frame(matrix(1:100,10,10))
const <- 237
> print(dataF)
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
1 1 11 21 31 41 51 61 71 81 91
2 2 12 22 32 42 52 62 72 82 92
3 3 13 23 33 43 53 63 73 83 93
4 4 14 24 34 44 54 64 74 84 94
5 5 15 25 35 45 55 65 75 85 95
6 6 16 26 36 46 56 66 76 86 96
7 7 17 27 37 47 57 67 77 87 97
8 8 18 28 38 48 58 68 78 88 98
9 9 19 29 39 49 59 69 79 89 99
10 10 20 30 40 50 60 70 80 90 100
dataF[,seq(1,ncol(dataF),by = 2)] <- dataF[,seq(1,ncol(dataF),by = 2)] + const
> print(dataF)
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
1 238 11 258 31 278 51 298 71 318 91
2 239 12 259 32 279 52 299 72 319 92
3 240 13 260 33 280 53 300 73 320 93
4 241 14 261 34 281 54 301 74 321 94
5 242 15 262 35 282 55 302 75 322 95
6 243 16 263 36 283 56 303 76 323 96
7 244 17 264 37 284 57 304 77 324 97
8 245 18 265 38 285 58 305 78 325 98
9 246 19 266 39 286 59 306 79 326 99
10 247 20 267 40 287 60 307 80 327 100
To generalize, we know that the columns of a dataframe can be referenced with a vector of numbers/column names. Most operations in R are vectorized. You can use column names or numbers based on the pattern you are looking for.
For example, I change the name of my first two columns and want to access just those I do this,
colnames(dataF)[c(1,2)] <- c("Y1","Y2")
#Reference all column names with "Y" in it. You can do any operation you want on this.
dataF[,grep("Y",colnames(dataF))]
Y1 Y2
1 238 11
2 239 12
3 240 13
4 241 14
5 242 15
6 243 16
7 244 17
8 245 18
9 246 19
10 247 20