Sample size and power calculation in r as viable alternative to proc power in SAS?
By : Park Jae Hyung
Date : March 29 2020, 07:55 AM
I hope this helps you . In pwr.t.test and its derivatives, d is not the null difference (that's assumed to be zero), but the effect size/hypothesized difference between the two populations. If the difference between population means is zero, no sample size will let you detect a nonexistent difference. If population A has a proportion of 15% and population B has a proportion of 30%, then you use the function pwr::ES.h to calculate the effect size and do a test of proportions like: code :
> pwr.2p.test(h=ES.h(0.30,0.15),power=0.80,sig.level=0.05)
Difference of proportion power calculation for binomial distribution (arcsine transformation)
h = 0.3638807
n = 118.5547
sig.level = 0.05
power = 0.8
alternative = two.sided
NOTE: same sample sizes
> pwr.chisq.test(w=ES.w1(0.3,0.15),df=1,sig.level=0.05,power=0.80)
Chi squared power calculation
w = 0.2738613
N = 104.6515
df = 1
sig.level = 0.05
power = 0.8
NOTE: N is the number of observations

Add column of previous values from table of tables in Power BI / Power Query
By : crazy games
Date : March 29 2020, 07:55 AM
like below fixes the issue Similar to my answer here, instead of adding just one index, you can add two, one starting from 0 and one starting from 1, which we use to calculate the previous row by performing a self merge. code :
let
Source = Table.FromRows({{"A",#date(2019,1,1)},{"A",#date(2019,1,3)},{"B",#date(2019,1,2)},{"A",#date(2019,1,4)},{"B",#date(2019,1,5)}}, {"Name", "Date"}),
ChangeTypes = Table.TransformColumnTypes(Source,{{"Name", type text}, {"Date", type date}}),
GroupByName = Table.Group(ChangeTypes, {"Name"}, {{"tmp", each _, type table}}),
AddIndices = Table.AddColumn(GroupByName, "Custom", each Table.AddIndexColumn(Table.AddIndexColumn([tmp],"Occurrence", 1,1),"Prev",0,1)),
ExpandTables = Table.ExpandTableColumn(AddIndices, "Custom", {"Date", "Occurrence", "Prev"}, {"Date", "Occurrence", "Prev"}),
SelfMerge = Table.NestedJoin(ExpandTables,{"Name", "Prev"},ExpandTables,{"Name", "Occurrence"},"Expanded Custom",JoinKind.LeftOuter),
ExpandPriorDate = Table.ExpandTableColumn(SelfMerge, "Expanded Custom", {"Date"}, {"Prior Date"}),
RemoveExtraColumns = Table.RemoveColumns(ExpandPriorDate,{"Prev", "tmp"})
in
RemoveExtraColumns

Concatenate two more tables in power bi
By : Michaël Dohr
Date : March 29 2020, 07:55 AM
Any of those help You can use Combine > Append Queries > Append Queries as New to combine these tables into one:

Power BI: Multiple tables as output of Python in Power Query
By : user3019742
Date : March 29 2020, 07:55 AM
hop of those help? Am I correct that this second table needs to be a Pandas Dataframe? code :
# 'dataset' holds the input data for this script
import numpy as np
import pandas as pd
var1 = np.random.randint(5, size=(2, 4))
var2 = pd.DataFrame(np.random.randint(5, size=(2, 4)))
var3 = 3
var4 = pd.DataFrame([type(var3)])
var5 = pd.Series([type(var3)])
print(type(var1))
<class 'numpy.ndarray'>
print(type(var2))
<class 'pandas.core.frame.DataFrame'>
print(type(var3))
<class 'int'>
print(type(var4))
<class 'pandas.core.frame.DataFrame'>
print(type(var5))
<class 'pandas.core.series.Series'>

Power BI Aggregations  Detail Tables Must Be DirectQuery Tables?
By : user3315834
Date : March 29 2020, 07:55 AM
may help you . This is currently a limitation that Microsoft has imposed at least while aggregates are still in preview. From Microsoft's documentation:

