I have two data frames which are loaded from csv files. Basically from different environment but similar format/columns, They can have differences in rows/values. I would like to find the differences and create them in a new data frame. Also both data frame will have same order. I have 100's of files to be compared. Thanks in advance.
Dataframe1: df1test
product | country | partner | value
------------------------------------
prdct1 | china | part1 | ["563,45"]
prdct2 | UK | part4 | ["52,455"]
prdct3 | USA | part2 | ["563,45"]
prdct4 | ITALY | part6 | ["674,45"]
prdct5 | UK | part7 | ["563,578"]
Dataframe2: df1prod
product | country | partner | value
------------------------------------
prdct1 | china | part1 | ["563,45"]
prdct2 | UK | part4 | ["247,455"]
prdct3 | USA | part41 | ["563,45"]
prdct4 | UK | part6 | ["0,45"]
I would like to show the differences in third data frame
Dataframe3: dfDifference
Env:test Env:prod
product| country|partner| value product| country | partner | value
------------------------------------ -----------------------------------
prdct2 | UK |part4 | ["52,455"] prdct2 |UK |part4 | ["247,455"]
prdct3 | USA |part2 | ["563,45"] prdct3 |USA|part41 | ["563,45"]
prdct4 | ITALY |part6 | ["674,45"] prdct4 |UK |part6 | ["0,45"]
prdct5 | UK |part7 | ["563,578"] Not Available
I tried the following functions and methods but did'nt workout
Compare function
comptest<-compare(df1test,df1prod,allowAll = TRUE)
Variable combine
df1test$Varcomp <- apply(df1test,1,paste,collapse=';')
df1prod$Varcomp <- apply(df1prod,1,paste,collapse=';')
aabb<-sapply(df1prod$Varcomp,FUN = function(x){x==df1test$Varcomp})
A good way to do this ist the setdiff() function, whicht takes the two dataframes as arguments.
newdata <- setdiff(df1, df2)
Related
Hi everyone. I'm so sorry for my english. I need to separate the
domain data of some emails in a table. Then, if these mail data have
the domain of a country, this information must be moved to another
column that is incomplete in which the participants of a congress are
included. This for a relatively large database. I put an example
below.
| email | country |
| -------- | -------------- |
| naco#gmail.com | CO |
| monic45814#gmail.com | AR |
| jsalazar#chapingo.mx | |
| andresramirez#urosario.edu.co | |
| jeimy861491#hotmail.com | CL |
|jytvc#hotmail.com | |
Outcome should be
| email | country |
| -------- | -------------- |
| naco#gmail.com | CO |
| monic45814#gmail.com | AR |
| jsalazar#chapingo.mx | MX |
| andresramirez#urosario.edu.co | CO |
|jeimy861491#hotmail.com | CL |
|jytvc#hotmail.com | *NA* |
Thank you so much.
You can use str_extract to get the string after the last occurrence of "." and if_else to ignore rows that already have a country and rows which e-mail doesn't end with a country code:
df %>%
mutate(country = if_else(is.na(country) & str_extract(email, "[^.]+$") != "com", toupper(str_extract(email, "[^.]+$")), country))
small but not so small PS: I would always recommend to provide fake data when you are mentioning personal data like e-mail addresses
Here is a solution in base R.
Suppose:
df<-data.frame(email,country)
Then:
df$country<-ifelse(is.na(df$country)&sub(".*(.*?)[\\.|:]", "",df$email)!="com",sub(".*(.*?)[\\.|:]", "",df$email),paste(df$country))
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'm able to do forecasts with an ARIMA model, but when I try to do a forecast for a linear model, I do not get any actual forecasts - it stops at the end of the data set (which isn't useful for forecasting since I already know what's in the data set). I've found countless examples online where using this same code works just fine, but I haven't found anyone else having this same error.
library("stats")
library("forecast")
y <- data$Mfg.Shipments.Total..USA.
model_a1 <- auto.arima(y)
forecast_a1 <- forecast.Arima(model_a1, h = 12)
The above code works perfectly. However, when I try to do a linear model....
model1 <- lm(y ~ Mfg.NO.Total..USA. + Mfg.Inv.Total..USA., data = data )
f1 <- forecast.lm(model1, h = 12)
I get an error message saying that I MUST provide a new data set (which seems odd to me, since the documentation for the forecast package says that it is an optional argument).
f1 <- forecast.lm(model1, newdata = x, h = 12)
If I do this, I am able to get the function to work, but the forecast only predicts values for the existing data - it doesn't predict the next 12 periods. I have also tried using the append function to add additional rows to see if that would fix the issue, but when trying to forecast a linear model, it immediately stops at the most recent point in the time series.
Here's the data that I'm using:
+------------+---------------------------+--------------------+---------------------+
| | Mfg.Shipments.Total..USA. | Mfg.NO.Total..USA. | Mfg.Inv.Total..USA. |
+------------+---------------------------+--------------------+---------------------+
| 2110-01-01 | 3.59746e+11 | 3.58464e+11 | 5.01361e+11 |
| 2110-01-01 | 3.59746e+11 | 3.58464e+11 | 5.01361e+11 |
| 2110-02-01 | 3.62268e+11 | 3.63441e+11 | 5.10439e+11 |
| 2110-03-01 | 4.23748e+11 | 4.24527e+11 | 5.10792e+11 |
| 2110-04-01 | 4.08755e+11 | 4.02769e+11 | 5.16853e+11 |
| 2110-05-01 | 4.08187e+11 | 4.02869e+11 | 5.18180e+11 |
| 2110-06-01 | 4.27567e+11 | 4.21713e+11 | 5.15675e+11 |
| 2110-07-01 | 3.97590e+11 | 3.89916e+11 | 5.24785e+11 |
| 2110-08-01 | 4.24732e+11 | 4.16304e+11 | 5.27734e+11 |
| 2110-09-01 | 4.30974e+11 | 4.35043e+11 | 5.28797e+11 |
| 2110-10-01 | 4.24008e+11 | 4.17076e+11 | 5.38917e+11 |
| 2110-11-01 | 4.11930e+11 | 4.09440e+11 | 5.42618e+11 |
| 2110-12-01 | 4.25940e+11 | 4.34201e+11 | 5.35384e+11 |
| 2111-01-01 | 4.01629e+11 | 4.07748e+11 | 5.55057e+11 |
| 2111-02-01 | 4.06385e+11 | 4.06151e+11 | 5.66058e+11 |
| 2111-03-01 | 4.83827e+11 | 4.89904e+11 | 5.70990e+11 |
| 2111-04-01 | 4.54640e+11 | 4.46702e+11 | 5.84808e+11 |
| 2111-05-01 | 4.65124e+11 | 4.63155e+11 | 5.92456e+11 |
| 2111-06-01 | 4.83809e+11 | 4.75150e+11 | 5.86645e+11 |
| 2111-07-01 | 4.44437e+11 | 4.40452e+11 | 5.97201e+11 |
| 2111-08-01 | 4.83537e+11 | 4.79958e+11 | 5.99461e+11 |
| 2111-09-01 | 4.77130e+11 | 4.75580e+11 | 5.93065e+11 |
| 2111-10-01 | 4.69276e+11 | 4.59579e+11 | 6.03481e+11 |
| 2111-11-01 | 4.53706e+11 | 4.55029e+11 | 6.02577e+11 |
| 2111-12-01 | 4.57872e+11 | 4.81454e+11 | 5.86886e+11 |
| 2112-01-01 | 4.35834e+11 | 4.45037e+11 | 6.04042e+11 |
| 2112-02-01 | 4.55996e+11 | 4.70820e+11 | 6.12071e+11 |
| 2112-03-01 | 5.04869e+11 | 5.08818e+11 | 6.11717e+11 |
| 2112-04-01 | 4.76213e+11 | 4.70666e+11 | 6.16375e+11 |
| 2112-05-01 | 4.95789e+11 | 4.87730e+11 | 6.17639e+11 |
| 2112-06-01 | 4.91218e+11 | 4.87857e+11 | 6.09361e+11 |
| 2112-07-01 | 4.58087e+11 | 4.61037e+11 | 6.19166e+11 |
| 2112-08-01 | 4.97438e+11 | 4.74539e+11 | 6.22773e+11 |
| 2112-09-01 | 4.86994e+11 | 4.85560e+11 | 6.23067e+11 |
| 2112-10-01 | 4.96744e+11 | 4.92562e+11 | 6.26796e+11 |
| 2112-11-01 | 4.70810e+11 | 4.64944e+11 | 6.23999e+11 |
| 2112-12-01 | 4.66721e+11 | 4.88615e+11 | 6.08900e+11 |
| 2113-01-01 | 4.51585e+11 | 4.50763e+11 | 6.25881e+11 |
| 2113-02-01 | 4.56329e+11 | 4.69574e+11 | 6.33157e+11 |
| 2113-03-01 | 5.04023e+11 | 4.92978e+11 | 6.31055e+11 |
| 2113-04-01 | 4.84798e+11 | 4.76750e+11 | 6.35643e+11 |
| 2113-05-01 | 5.04478e+11 | 5.04488e+11 | 6.34376e+11 |
| 2113-06-01 | 4.99043e+11 | 5.13760e+11 | 6.25715e+11 |
| 2113-07-01 | 4.75700e+11 | 4.69012e+11 | 6.34892e+11 |
| 2113-08-01 | 5.05244e+11 | 4.90404e+11 | 6.37735e+11 |
| 2113-09-01 | 5.00087e+11 | 5.04849e+11 | 6.34665e+11 |
| 2113-10-01 | 5.05965e+11 | 4.99682e+11 | 6.38945e+11 |
| 2113-11-01 | 4.78876e+11 | 4.80784e+11 | 6.34442e+11 |
| 2113-12-01 | 4.80640e+11 | 4.98807e+11 | 6.19458e+11 |
| 2114-01-01 | 4.56779e+11 | 4.57684e+11 | 6.36568e+11 |
| 2114-02-01 | 4.62195e+11 | 4.70312e+11 | 6.48982e+11 |
| 2114-03-01 | 5.19472e+11 | 5.25900e+11 | 6.47038e+11 |
| 2114-04-01 | 5.04217e+11 | 5.06090e+11 | 6.52612e+11 |
| 2114-05-01 | 5.14186e+11 | 5.11149e+11 | 6.58990e+11 |
| 2114-06-01 | 5.25249e+11 | 5.33247e+11 | 6.49512e+11 |
| 2114-07-01 | 4.99198e+11 | 5.52506e+11 | 6.57645e+11 |
| 2114-08-01 | 5.17184e+11 | 5.07622e+11 | 6.59281e+11 |
| 2114-09-01 | 5.23682e+11 | 5.24051e+11 | 6.55582e+11 |
| 2114-10-01 | 5.17305e+11 | 5.09549e+11 | 6.59237e+11 |
| 2114-11-01 | 4.71921e+11 | 4.70093e+11 | 6.57044e+11 |
| 2114-12-01 | 4.84948e+11 | 4.86804e+11 | 6.34120e+11 |
+------------+---------------------------+--------------------+---------------------+
Edit - Here's the code I used for adding new datapoints for forecasting.
library(xts)
library(mondate)
d <- as.mondate("2115-01-01")
d11 <- d + 11
seq(d, d11)
newdates <- seq(d, d11)
new_xts <- xts(order.by = as.Date(newdates))
new_xts$Mfg.Shipments.Total..USA. <- NA
new_xts$Mfg.NO.Total..USA. <- NA
new_xts$Mfg.Inv.Total..USA. <- NA
x <- append(data, new_xts)
Not sure if you ever figured this out, but just in case I thought I'd point out what's going wrong.
The documentation for forecast.lm says:
An optional data frame in which to look for variables with which to predict. If omitted, it is assumed that the only variables are trend and season, and h forecasts are produced.
so it's optional if trend and season are your only predictors.
The ARIMA model works because it's using lagged values of the time series in the forecast. For the linear model, it uses the given predictors (Mfg.NO.Total..USA. and Mfg.Inv.Total..USA. in your case) and thus needs their corresponding future values; without these, there are no independent variables to predict from.
In the edit, you added those variables to your future dataset, but they still have values of NA for all future points, thus the forecasts are also NA.
Gabe is correct. You need future values of your causals.
You should consider the Transfer Function modeling process instead of regression(ie developed for use with cross-sectional data). By using prewhitening your X variables (ie build a model for each one), you can calculate the Cross correlation function to see any lead or lag relationship.
It is very apparent that Inv.Total is a lead variable(b**-1) from the standardized graph of Y and the two x's. When Invto moves down so does shipments. In addition, there is also AR seasonal component beyond the causals that is driving the data. There are a few outliers as well so this is a robust solution. I am developer of this software used here, but this can be run in any tool.
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.
Sum of var values by group with certain values excluded conditioned on the other variable.
How to do it elegantly without transposing?
So in the table below for each (fTicker, DATE_f), I seek to sum the values of wght with the value of wght conditioned on sTicker excluded from the sum.
In the table below, (excl_val,sTicker=A) |(fTicker=XLK, DATE_f = 6/20/2003) = wght_AAPL_6/20/2003_XLK + wght_AA_6/20/2003_XLK but not the wght for sTicker=A
+---------+---------+-----------+-------------+-------------+
| sTicker | fTicker | DATE_f | wght | excl_val |
+---------+---------+-----------+-------------+-------------+
| A | XLK | 6/20/2003 | 0.087600002 | 1.980834016 |
| A | XLK | 6/23/2003 | 0.08585 | 1.898560068 |
| A | XLK | 6/24/2003 | 0.085500002 | |
| AAPL | XLK | 6/20/2003 | 0.070080002 | |
| AAPL | XLK | 6/23/2003 | 0.06868 | |
| AAPL | XLK | 6/24/2003 | 0.068400002 | |
| AA | XLK | 6/20/2003 | 1.910754014 | |
| AA | XLK | 6/23/2003 | 1.829880067 | |
| AA | XLK | 6/24/2003 | 1.819775 | |
| | | | | |
| | | | | |
+---------+---------+-----------+-------------+-------------+
There are several fTicker groups with many sTicker in them (10 to 70), some sTicker may belong to several fTicker. The end result should be an excl_val for each sTicker on each DATE_f and for each fTicker.
I did it by transposing in SAS with resulting file about 6 gb but the same approach in R, blew memory up to 40 gb and it's basically unworkable.
In R, I got as far as this
weights$excl_val <- with(weights, aggregate(wght, list(fTicker, DATE_f), sum, na.rm=T))
but it's just a simple sum (without excluding the necessary observation) and there is mismatch between rows length. If i could condition the sum to exclude the sTicker obs for wght from the summation, i think it might work.
About the excl_val length: i computed it in excel, for just 2 cells, that's why it's short
Thank you!
Arsenio
When you have data in a data.frame, it is better if the rows are meaningful
(in particular, the columns should have the same length):
in this case, excl_val looks like a separate vector.
After putting the information it contains in the data.frame,
things become easier.
# Sample data
k <- 5
d <- data.frame(
sTicker = rep(LETTERS[1:k], k),
fTicker = rep(LETTERS[1:k], each=k),
DATE_f = sample( seq(Sys.Date(), length=2, by=1), k*k, replace=TRUE ),
wght = runif(k*k)
)
excl_val <- sample(d$wght, k)
# Add a "valid" column to the data.frame
d$valid <- ! d$wght %in% excl_val
# Compute the sum
library(plyr)
ddply(d, c("fTicker","DATE_f"), summarize, sum=sum(wght[valid]))