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Aggregating all unique values of each column of data frame
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I would like to create a column in my R data frame based on the order in which multiple values occur in one column.
For example, my data frame has an id column and an item type column, and the values of the order column is what I would like to add. Is there a way to tell R to look at the order of values in the item column so that it can spit out "ABCD" or "ADCB" (any other order) as the cell value under the 3rd column?
| id | item | order |
| 11 | A | ABCD |
| 11 | A | ABCD |
| 11 | B | ABCD |
| 11 | B | ABCD |
| 11 | C | ABCD |
| 11 | C | ABCD |
| 11 | D | ABCD |
| 11 | D | ABCD |
| 12 | A | ADCB |
| 12 | A | ADCB |
| 12 | D | ADCB |
| 12 | D | ADCB |
| 12 | C | ADCB |
| 12 | C | ADCB |
| 12 | B | ADCB |
| 12 | B | ADCB |
...
Let's consider two dataframes df1 and df2. I would like to join dataframes based on the date difference only. For Example;
Dataframe 1: (df1)
| version_id | date_invoiced | product_id |
-------------------------------------------
| 1 | 03-07-2020 | 201 |
| 1 | 02-07-2020 | 2013 |
| 3 | 02-07-2020 | 2011 |
| 6 | 01-07-2020 | 2018 |
| 7 | 01-07-2020 | 201 |
Dataframe 2: (df2)
| validfrom | pricelist| pricelist_id |
------------------------------------------
|02-07-2020 | 10 | 101 |
|01-07-2020 | 20 | 102 |
|29-06-2020 | 30 | 103 |
|28-07-2020 | 10 | 104 |
|25-07-2020 | 5 | 105 |
I need to map the pricelist_id and the pricelist based on the the validfrom column present in df2. Say that, based on the least difference between the date_invoiced (df1) and validfrom (df2), the row should be mapped.
Expected Outcome:
| version_id | date_invoiced | product_id | date_diff | pricelist_id | pricelist |
----------------------------------------------------------------------------------
| 1 | 03-07-2020 | 201 | 1 | 101 | 10 |
| 1 | 02-07-2020 | 2013 | 1 | 102 | 20 |
| 3 | 02-07-2020 | 2011 | 1 | 102 | 20 |
| 6 | 01-07-2020 | 2018 | 1 | 103 | 30 |
| 7 | 01-07-2020 | 201 | 1 | 103 | 30 |
I need to map purely based on the difference and the difference should be the least. Always, the date_invoiced (df1), should have closest difference comparing to validfrom (df2). Thanks
Perhaps you might want to try using date.table and nearest roll. Here, the join is made on DATE which would be DATEINVOICED from df1 and VALIDFROM in df2.
library(data.table)
setDT(df1)
setDT(df2)
df1$DATEINVOICED <- as.Date(df1$DATEINVOICED, format = "%d-%m-%y")
df2$VALIDFROM <- as.Date(df2$VALIDFROM, format = "%d-%m-%y")
setkey(df1, DATEINVOICED)[, DATE := DATEINVOICED]
setkey(df2, VALIDFROM)[, DATE := VALIDFROM]
df2[df1, on = "DATE", roll='nearest']
I'm trying to build a function in R in which I can subset my raw dataframe according to some specifications, and thereafter convert this subsetted dataframe into a proportion table.
Unfortunately, some of these subsettings yields to an empty dataframe as for some particular specifications I do not have data; hence no proportion table can be calculated. So, what I would like to do is to take the closest time step from which I have a non-empty subsetted dataframe and use it as an input for the empty subsetted dataframe.
Here some insights to my dataframe and function:
My raw dataframe looks +/- as follows:
| year | quarter | area | time_comb | no_individuals | lenCls | age |
|------|---------|------|-----------|----------------|--------|-----|
| 2005 | 1 | 24 | 2005.1.24 | 8 | 380 | 3 |
| 2005 | 2 | 24 | 2005.2.24 | 4 | 490 | 2 |
| 2005 | 1 | 24 | 2005.1.24 | 3 | 460 | 6 |
| 2005 | 1 | 21 | 2005.1.21 | 25 | 400 | 2 |
| 2005 | 2 | 24 | 2005.2.24 | 1 | 680 | 6 |
| 2005 | 2 | 21 | 2005.2.21 | 2 | 620 | 5 |
| 2005 | 3 | 21 | 2005.3.21 | NA | NA | NA |
| 2005 | 1 | 21 | 2005.1.21 | 1 | 510 | 5 |
| 2005 | 1 | 24 | 2005.1.24 | 1 | 670 | 4 |
| 2006 | 1 | 22 | 2006.1.22 | 2 | 750 | 4 |
| 2006 | 4 | 24 | 2006.4.24 | 1 | 660 | 8 |
| 2006 | 2 | 24 | 2006.2.24 | 8 | 540 | 3 |
| 2006 | 2 | 24 | 2006.2.24 | 4 | 560 | 3 |
| 2006 | 1 | 22 | 2006.1.22 | 2 | 250 | 2 |
| 2006 | 3 | 22 | 2006.3.22 | 1 | 520 | 2 |
| 2006 | 2 | 24 | 2006.2.24 | 1 | 500 | 2 |
| 2006 | 2 | 22 | 2006.2.22 | NA | NA | NA |
| 2006 | 2 | 21 | 2006.2.21 | 3 | 480 | 2 |
| 2006 | 1 | 24 | 2006.1.24 | 1 | 640 | 5 |
| 2007 | 4 | 21 | 2007.4.21 | 2 | 620 | 3 |
| 2007 | 2 | 21 | 2007.2.21 | 1 | 430 | 3 |
| 2007 | 4 | 22 | 2007.4.22 | 14 | 410 | 2 |
| 2007 | 1 | 24 | 2007.1.24 | NA | NA | NA |
| 2007 | 2 | 24 | 2007.2.24 | NA | NA | NA |
| 2007 | 3 | 24 | 2007.3.22 | NA | NA | NA |
| 2007 | 4 | 24 | 2007.4.24 | NA | NA | NA |
| 2007 | 3 | 21 | 2007.3.21 | 1 | 560 | 4 |
| 2007 | 1 | 21 | 2007.1.21 | 7 | 300 | 3 |
| 2007 | 3 | 23 | 2007.3.23 | 1 | 640 | 5 |
Here year, quarter and area refers to a particular time (Year & Quarter) and area for which X no. of individuals were measured (no_individuals). For example, from the first row we get that in the first quarter of the year 2005 in area 24 I had 8 individuals belonging to a length class (lenCLs) of 380 mm and age=3. It is worth to mention that for a particular year, quarter and area combination I can have different length classes and ages (thus, multiple rows)!
So what I want to do is basically to subset the raw dataframe for a particular year, quarter and area combination, and from that combination calculate a proportion table based on the number of individuals in each length class.
So far my basic function looks as follows:
LAK <- function(df, Year="2005", Quarter="1", Area="22", alkplot=T){
require(FSA)
# subset alk by year, quarter and area
sALK <- subset(df, year==Year & quarter==Quarter & area==Area)
dfexp <- sALK[rep(seq(nrow(sALK)), sALK$no_individuals), 1:ncol(sALK)]
raw <- t(table(dfexp$lenCls, dfexp$age))
key <- round(prop.table(raw, margin=1), 3)
return(key)
if(alkplot==TRUE){
alkPlot(key,"area",xlab="Age")
}
}
From the dataset example above, one can notice that for year=2005 & quarter=3 & area=21, I do not have any measured individuals. Yet, for the same area AND year I have data for either quarter 1 or 2. The most reasonable assumption would be to take the subsetted dataframe from the closest time step (herby quarter 2 with the same area and year), and replace the NA from the columns "no_individuals", "lenCls" and "age" accordingly.
Note also that for some cases I do not have data for a particular year! In the example above, one can see this by looking into area 24 from year 2007. In this case I can not borrow the information from the nearest quarter, and would need to borrow from the previous year instead. This would mean that for year=2007 & area=24 & quarter=1 I would borrow the information from year=2006 & area=24 & quarter 1, and so on and so forth.
I have tried to include this in my function by specifying some extra rules, but due to my poor programming skills I didn't make any progress.
So, any help here will be very much appreciated.
Here my LAK function which I'm trying to update:
LAK <- function(df, Year="2005", Quarter="1", Area="22", alkplot=T){
require(FSA)
# subset alk by year, quarter and area
sALK <- subset(df, year==Year & quarter==Quarter & area==Area)
# In case of empty dataset
#if(is.data.frame(sALK) && nrow(sALK)==0){
if(sALK[rowSums(is.na(sALK)) > 0,]){
warning("Empty subset combination; data will be subsetted based on the
nearest timestep combination")
FIXME: INCLDUE IMPUTATION RULES HERE
}
dfexp <- sALK[rep(seq(nrow(sALK)), sALK$no_individuals), 1:ncol(sALK)]
raw <- t(table(dfexp$lenCls, dfexp$age))
key <- round(prop.table(raw, margin=1), 3)
return(key)
if(alkplot==TRUE){
alkPlot(key,"area",xlab="Age")
}
}
So, I finally came up with a partial solution to my problem and will include my function here in case it might be of someone's interest:
LAK <- function(df, Year="2005", Quarter="1", Area="22",alkplot=T){
require(FSA)
# subset alk by year, quarter, area and species
sALK <- subset(df, year==Year & quarter==Quarter & area==Area)
print(sALK)
if(nrow(sALK)==1){
warning("Empty subset combination; data has been subsetted to the nearest input combination")
syear <- unique(as.numeric(as.character(sALK$year)))
sarea <- unique(as.numeric(as.character(sALK$area)))
sALK2 <- subset(df, year==syear & area==sarea)
vals <- as.data.frame(table(sALK2$comb_index))
colnames(vals)[1] <- "comb_index"
idx <- which(vals$Freq>1)
quarterId <- as.numeric(as.character(vals[idx,"comb_index"]))
imput <- subset(df,year==syear & area==sarea & comb_index==quarterId)
dfexp2 <- imput[rep(seq(nrow(imput)), imput$no_at_length_age), 1:ncol(imput)]
raw2 <- t(table(dfexp2$lenCls, dfexp2$age))
key2 <- round(prop.table(raw2, margin=1), 3)
print(key2)
if(alkplot==TRUE){
alkPlot(key2,"area",xlab="Age")
}
} else {
dfexp <- sALK[rep(seq(nrow(sALK)), sALK$no_at_length_age), 1:ncol(sALK)]
raw <- t(table(dfexp$lenCls, dfexp$age))
key <- round(prop.table(raw, margin=1), 3)
print(key)
if(alkplot==TRUE){
alkPlot(key,"area",xlab="Age")
}
}
}
This solves my problem when I have data for at least one quarter of a particular Year & Area combination. Yet, I'm still struggling to figure out how to deal when I do not have data for a particular Year & Area combination. In this case I need to borrow data from the closest Year that contains data for all the quarters for the same area.
For the example exposed above, this would mean that for year=2007 & area=24 & quarter=1 I would borrow the information from year=2006 & area=24 & quarter 1, and so on and so forth.
I don't know if you have ever encountered MICE, but it is a pretty cool and comprehensive tool for variable imputation. It also allows you to see how the imputed data is distributed so that you can choose the method most suited for your problem. Check this brief explanation and the original package description
I have the following dataset with 21 columns - 19 variables and Month and Date as date type columns.
The aim is to analyze how correlation change over time calculating a daily correlation between variables summarized in one month. For example, see this "monthly correlation" over time. (X-axis as month type)
+------------+---------+-----+-----+--------+---------+-------------+
| Date | Month | AOV | ASP | Clicks | Traffic | Impressions |
+------------+---------+-----+-----+--------+---------+-------------+
| 2017-01-01 | 2017-01 | 50 | 6 | 700 | 10000 | 4500 |
+------------+---------+-----+-----+--------+---------+-------------+
| 2017-01-02 | 2017-01 | 55 | 7 | 800 | 20000 | 4600 |
+------------+---------+-----+-----+--------+---------+-------------+
| 2017-02 | 2017-02 | 58 | 8 | 700 | 4599 | 2300 |
+------------+---------+-----+-----+--------+---------+-------------+
At the moment I have the following code but I only can compare two variables at the same time
ddply(corr,"Month",summarise,corr=cor(AOV,ASP))
I get the table below
+---------+------------+
| Month | corr |
+---------+------------+
| 2017-1 | 0.4958738 |
+---------+------------+
| 2017-10 | 0.8527522 |
+---------+------------+
| 2017-11 | -0.2751771 |
+---------+------------+
| 2017-12 | NA |
+---------+------------+
| 2017-2 | 0.6596346 |
+---------+------------+
| 2017-3 | 0.6399969 |
+---------+------------+
| 2017-4 | 0.7926245 |
+---------+------------+
| 2017-5 | 0.6429613 |
+---------+------------+
| 2017-6 | 0.3824414 |
+---------+------------+
| 2017-7 | 0.9154873 |
+---------+------------+
| 2017-8 | 0.7235767 |
+---------+------------+
| 2017-9 | 0.8264006 |
+---------+------------+
I have been using combn to create the combinations set but I'm not quite sure how to use it with ddply. I get 171 combinations in pairs.
combn(corr,2,simplify = F)
You can just do:
cor(your_data_frame)
Just starting out with R and trying to figure out what works for my needs when it comes to creating "summary tables." I am used to Custom Tables in SPSS, and the CrossTable function in the package gmodels gets me almost where I need to be; not to mention it is easy to navigate for someone just starting out in R.
That said, it seems like the Hmisc table is very good at creating various summaries and exporting to LaTex (ultimately what I need to do).
My questions are:1)can you create the table below easily in the Hmsic page? 2) if so, can I interact variables (2 in the the column)? and finally 3) can I access p-values of significance tests (chi square).
Thanks in advance,
Brock
Cell Contents
|-------------------------|
| Count |
| Row Percent |
| Column Percent |
|-------------------------|
Total Observations in Table: 524
| asq[, 23]
asq[, 4] | 1 | 2 | 3 | 4 | 5 | Row Total |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
0 | 76 | 54 | 93 | 46 | 54 | 323 |
| 23.529% | 16.718% | 28.793% | 14.241% | 16.718% | 61.641% |
| 54.286% | 56.250% | 63.265% | 63.889% | 78.261% | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
1 | 64 | 42 | 54 | 26 | 15 | 201 |
| 31.841% | 20.896% | 26.866% | 12.935% | 7.463% | 38.359% |
| 45.714% | 43.750% | 36.735% | 36.111% | 21.739% | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
Column Total | 140 | 96 | 147 | 72 | 69 | 524 |
| 26.718% | 18.321% | 28.053% | 13.740% | 13.168% | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
The gmodels package has a function called CrossTable, which is very nice for those used to SPSS and SAS output. Try this example:
library(gmodels) # run install.packages("gmodels") if you haven't installed the package yet
x <- sample(c("up", "down"), 100, replace = TRUE)
y <- sample(c("left", "right"), 100, replace = TRUE)
CrossTable(x, y, format = "SPSS")
This should provide you with an output just like the one you displayed on your question, very SPSS-y. :)
If you are coming from SPSS, you may be interested in the package Deducer ( http://www.deducer.org ). It has a contingency table function:
> library(Deducer)
> data(tips)
> tables<-contingency.tables(
+ row.vars=d(smoker),
+ col.vars=d(day),data=tips)
> tables<-add.chi.squared(tables)
> print(tables,prop.r=T,prop.c=T,prop.t=F)
================================================================================================================
==================================================================================
========== Table: smoker by day ==========
| day
smoker | Fri | Sat | Sun | Thur | Row Total |
-----------------------|-----------|-----------|-----------|-----------|-----------|
No Count | 4 | 45 | 57 | 45 | 151 |
Row % | 2.649% | 29.801% | 37.748% | 29.801% | 61.885% |
Column % | 21.053% | 51.724% | 75.000% | 72.581% | |
-----------------------|-----------|-----------|-----------|-----------|-----------|
Yes Count | 15 | 42 | 19 | 17 | 93 |
Row % | 16.129% | 45.161% | 20.430% | 18.280% | 38.115% |
Column % | 78.947% | 48.276% | 25.000% | 27.419% | |
-----------------------|-----------|-----------|-----------|-----------|-----------|
Column Total | 19 | 87 | 76 | 62 | 244 |
Column % | 7.787% | 35.656% | 31.148% | 25.410% | |
Large Sample
Test Statistic DF p-value | Effect Size est. Lower (%) Upper (%)
Chi Squared 25.787 3 <0.001 | Cramer's V 0.325 0.183 (2.5) 0.44 (97.5)
-----------
================================================================================================================
You can get the counts and test to latex or html using the xtable package:
> library(xtable)
> xtable(drop(extract.counts(tables)[[1]]))
> test <- contin.tests.to.table((tables[[1]]$tests))
> xtable(test)