I have a table that looks like this:
Source | Values
------------------------
Tester | Sample01
Tester | AnotherSample
Tester | Samples
Tester2 | Isitasample
Tester2 | sampledagain
Tester2 | testdata
Tester3 | approximation
Tester3 | <null>
and as a result I only want to have a single entry from each source, like:
Source | Values
-----------------------
Tester | Sample01
Tester2 | sampledagain
Tester3 | <null>
I have thought about distinct and join but I can only come up with solutions that filter out values alltogether. Same with unions. I am stumped.
let t = datatable (Source:string, Values:string)
[
'Tester' ,'Sample01'
,'Tester' ,'AnotherSample'
,'Tester' ,'Samples'
,'Tester2' ,'Isitasample'
,'Tester2' ,'sampledagain'
,'Tester2' ,'testdata'
,'Tester3' ,'approximation'
,'Tester3' ,'<null>'
];
// Option 1
t
| summarize arg_max("", *) by Source
| project-away max_
Source
Values
Tester
Sample01
Tester2
Isitasample
Tester3
approximation
Fiddle
Related
I have two dataframes. One is a set of ≈4000 entries that looks similar to this:
| grade_col1 | grade_col2 |
| --- | --- |
| A-| A-|
| B | 86|
| C+| C+|
| B-| D |
| A | A |
| C-| 72|
| F | 96|
| B+| B+|
| B | B |
| A-| A-|
The other is a set of ≈700 entries that look similar to this:
| grade | scale |
| --- | --- |
| A+|100|
| A+| 99|
| A+| 98|
| A+| 97|
| A | 96|
| A | 95|
| A | 94|
| A | 93|
| A-| 92|
| A-| 91|
| A-| 90|
| B+| 89|
| B+| 88|
...and so on.
What I'm trying to do is create a new column that shows whether grade_col2 matches grade_col1 with a binary, 0-1 output (0 = no match, 1 = match). Most of grade_col2 is shown by letter grade. But every once in awhile an entry in grade_col2 was accidentally entered as a numeric grade instead. I want this match column to give me a "1" even when grade_col2 is a numeric grade instead of a letter grade. In other words, if grade_col1 is B and grade_col2 is 86, I want this to still be read as a match. Only when grade_col1 is F and grade_col2 is 96 would this not be a match (similar to when grade_col1 is B- and grade_col2 is D = not a match).
The second data frame gives me the information I need to translate between one and the other (entries between 97-100 are A+, between 93-96 are A, and so on). I just don't know how to run a script that uses this information to find matches through all ≈4000 entries. Theoretically, I could do this manually, but the real dataset is so lengthy that this isn't realistic.
I had been thinking of using nested if_else statements with dplyr. But once I got past the first "if" statement, I got stuck. I'd appreciate any help with this people can offer.
You can do this using a join.
Let your first dataframe be grades_df and your second dataframe be lookup_df, then you want something like the following:
output = grades_df %>%
# join on look up, keeping everything grades table
left_join(lookup_df, by = c(grade_col2 = "scale")) %>%
# combine grade_col2 from grades_df and grade from lookup_df
mutate(grade_col2b = ifelse(is.na(grade), grade_col2, grade)) %>%
# indicator column
mutate(indicator = ifelse(grade_col1 == grade_col2b, 1, 0))
I use WGCNA package for analyzing the co-expressed genes. Here I try to Form a data frame analogous to expression data that will hold the clinical traits. and i use the following codes:
table for traitData
| x | sample | NoduleperPlant |
|- |- |- |
| 1 | 1021_verbena_rep_1 | 2 |
| 2 | 1021_verbena_rep_2 | 3 |
| 3 | 1021_verbena_rep_3 | 1 |
| 4 | 1021_camporegio_rep_1 | 2 |
| 5 | 1021_camporegio_rep_2 | 3 |
| 6 | 1021_camporegio_rep_3 | 4 |
| 7 | BL225C_camporegio_rep_1 | 5 |
| 8 | BL225C_camporegio_rep_2 | 4 |
| 9 | BL225C_camporegio_rep_3 | 1 |
Table dfxpr (some of the genes are presented in table)
|FIELD1 |aacC-1|aacC4-1|aapJ-1|aapM-1|aapP-1|aapQ-1|aarF-1|
|-----------------------|------|-------|------|------|------|------|------|
|X1021_verbena_rep_1 |42 |46 |12412 |935 |3354 |2876 |550 |
|X1021_verbena_rep_2 |52 |37 |11775 |946 |2970 |2824 |514 |
|X1021_verbena_rep_3 |12 |22 |5077 |397 |1462 |1228 |230 |
|X1021_camporegio_rep_1 |52 |71 |12983 |1454 |3408 |3248 |707 |
|X1021_camporegio_rep_2 |20 |65 |9240 |803 |2807 |3146 |445 |
|X1021_camporegio_rep_3 |28 |53 |11030 |1065 |3480 |3410 |582 |
|BL225C_camporegio_rep_1|29 |19 |6346 |375 |938 |768 |118 |
|BL225C_camporegio_rep_2|51 |62 |12938 |781 |1765 |1629 |291 |
|BL225C_camporegio_rep_3|52 |43 |6462 |504 |1120 |1091 |238 |
traitData = read.csv("NodulPerPlantTraitForLowGroup.csv"); #this csv file contains 3 columns as the first column is non-relevant information, second column contains the names of samples and the third column holds the values measured for the traits.
# remove columns that hold information I do not need.
allTraits = traitData[, -1];
allTraits = allTraits[, 1:2];
# Form a data frame analogous to expression data that will hold the clinical traits.
lowNoduleSamples = rownames(dfxpr) #dfxpr is a data frame containing 9 observations (i.e. samples) and 6398 variables (i.e. genes)
traitRows = match(lowNoduleSamples, allTraits$sample); #here is the line i get wrong values as NAs while i know they all should match
datTraits = allTraits[traitRows, -1]; #then this lines result NAs too
rownames(datTraits) = allTraits[traitRows, 1];
collectGarbage();
how can I fix the problem?
I have Added a "drop = FALSE" to this line: datTraits = allTraits[traitRows, -1]
datTraits = allTraits[traitRows, -1, drop = FALSE]
I realized that my allTraits contains only 2 columns; when I remove the first one, I'm left with just one column and R converts that into a single vector unless I add the drop = FALSE argument.
I'm quite new to SQLite and I'm trying to use an EXCEPT statement in order to compare two tables with very similar data. The data comes from a CSV file I download daily, and within the file new rows are added and deleted, and old rows can have one or more columns change each day. I'm trying to find a way to select rows that have had a column's data change, when I am unable to predict which column's data will change.
Say for example I have:
TABLE contracts:
|ID|Description|Name|Contract Type|
|1 |Plumbing |Bob |Paper |
|2 |Cooking |Ryan|Paper |
|3 |Driving |Eric|Paper |
|4 |Dancing |Emma|Paper |
and:
TABLE updated_contracts:
|ID|Description|Name|Contract Type|
|1 |Hiking |Bob |Paper |
|2 |Cooking |Ryan|Paper |
|3 |Driving |Eric|Paper |
|4 |Dancing |Emma|Digital |
I'd like it to return:
|1 |Hiking |Bob |Paper |
|4 |Dancing |Emma|Digital |
because contract 1 has changed the description and contract 4 has changed the contract type.
Is it possible to do this in SQLite?
One way to do it is with a LEFT join of updated_contracts to contracts where the matching rows are filtered out:
select uc.*
from updated_contracts uc left join contracts c
using(id, Description, Name, `Contract Type`)
where c.id is null
EXCEPT can also be used like this:
select * from updated_contracts
except
select * from contracts
This will work only if the tables have the same number of columns and its advantage is that it compares null values in columns and returns true if they are both null.
See the demo.
Results:
| ID | Description | Name | Contract Type |
| --- | ----------- | ---- | ------------- |
| 1 | Hiking | Bob | Paper |
| 4 | Dancing | Emma | Digital |
I want to create a calculated field to use with the rpivotTable package, similar to the functionality seen in excel.
For instance, consider the following table:
+--------------+--------+---------+-------------+-----------------+
| Manufacturer | Vendor | Shipper | Total Units | Defective Units |
+--------------+--------+---------+-------------+-----------------+
| A | P | X | 173247 | 34649 |
| A | P | Y | 451598 | 225799 |
| A | P | Z | 759695 | 463414 |
| A | Q | X | 358040 | 225565 |
| A | Q | Y | 102068 | 36744 |
| A | Q | Z | 994961 | 228841 |
| A | R | X | 454672 | 231883 |
| A | R | Y | 275994 | 124197 |
| A | R | Z | 691100 | 165864 |
| B | P | X | 755594 | 302238 |
| . | . | . | . | . |
| . | . | . | . | . |
+--------------+--------+---------+-------------+-----------------+
(my actual table has many more columns, both dimensions and measures, time, etc. and I need to define multiple such "calculated columns")
If I want to calculate defect rate (which would be Defective Units/Total Units) and I want to aggregate by either of the first three columns, I'm not able to.
I tried assignment by reference (:=), but that still didn't seem to work and summed up defect rates (i.e., sum(Defective_Units/Total_Units)), instead of sum(Defective_Units)/sum(Total_Units):
myData[, Defect.Rate := Defective_Units / Total_Units]
This ended up giving my defect rates greater than 1. Is there anywhere I can declare a calculated field, which is just a formula evaluated post aggregation?
You're lucky - the creator of pivottable.js foresaw cases like yours (and mine, earlier today) by implementing an aggregator called "Sum over Sum" and a few more, likewise, cf. https://github.com/nicolaskruchten/pivottable/blob/master/src/pivot.coffee#L111 and https://github.com/nicolaskruchten/pivottable/blob/master/src/pivot.coffee#L169.
So we'll use "Sum over Sum" as parameter "aggregatorName", and the columns whose quotient we want in the "vals" parameter.
Here's a meaningless usage example from the mtcars data for reproducibility:
require(rpivotTable)
data(mtcars)
rpivotTable(mtcars,rows="gear", cols=c("cyl","carb"),
aggregatorName = "Sum over Sum",
vals =c("mpg","disp"),
width="100%", height="400px")
I am wondering if there is simple way to achieve this in Julia besides iterating over the rows in a for-loop.
I have a table with two columns that looks like this:
| Name | Interest |
|------|----------|
| AJ | Football |
| CJ | Running |
| AJ | Running |
| CC | Baseball |
| CC | Football |
| KD | Cricket |
...
I'd like to create a table where each Name in first column is matched with a combined Interest column as follows:
| Name | Interest |
|------|----------------------|
| AJ | Football, Running |
| CJ | Running |
| CC | Baseball, Football |
| KD | Cricket |
...
How do I achieve this?
UPDATE: OK, so after trying a few things including print_joint and grpby, I realized that the easiest way to do this would be by() function. I'm 99% there.
by(myTable, :Name, df->DataFrame(Interest = string(df[:Interest])))
This gives me my :Interest column as "UTF8String[\"Running\"]", and I can't figure out which method I should use instead of string() (or where to typecast) to get the desired ASCIIString output.