I have a for loop printing values out of this small test dataframe.
USA Finland China Sweden
1 1 3 5.505962 8.310596
2 2 4 11.033347 5.425747
3 3 5 14.932882 3.272544
4 4 6 10.155517 5.980190
5 5 7 11.020148 3.692313
Total 0 0 0.000000 0.000000
This line prints out a line from the dataframe:
print(countries[2,])
and results in this:
USA Finland China Sweden
2 2 4 11.03335 5.425747
So based on that, I imagine I could do the same in a for loop and print out all the lines. Code for the loop:
for (i in countries[1,])
{
print(countries[i,])
}
However this results in only every second line printed out which doesn't make sense. The result I get is this:
USA Finland China Sweden
1 1 3 5.505962 8.310596
USA Finland China Sweden
3 3 5 14.93288 3.272544
USA Finland China Sweden
5 5 7 11.02015 3.692313
USA Finland China Sweden
NA NA NA NA NA
What could possibly lead to this happening? I'm using R studio so could it be the console logging not keeping up with the values?
#lmo comment suggest solution. I think that you want to know why this happend, so I'll try to answer that.
You are using this code:
1: for (i in countries[1,])
2: {
3: print(countries[i,])
4: }
In line 1 you are selecting a vector of values that i will be using. This vector happens to be the first row of your data: 1 3 5.505962 8.310596. It translates to a vector c(1,3,5,8) - as indexes.
So in line 3 you are printing lines 1, 3, 5, 8 (because you choose that indexes). It was quite random that it were even rows, but I hope you understand it better.
Of course you should use df[1:5,] or print(df) instead of for.
Related
I have (left)joined two data frames by country-year.
df<- left_join(df, df2, by="country-year")
leading to the following example output:
country country-year a b
1 France France2000 NA NA
2 France France2001 1000 1000
3 France France2002 NA NA
4 France France2003 1600 2200
5 France France2004 NA NA
6 UK UK2000 1000 1000
7 UK UK2001 NA NA
8 UK UK2002 1000 1000
9 UK UK2003 NA NA
10 UK UK2004 NA NA
I initially wanted to remove all values for which both of the added columns (a,b) were NA.
df<-df[!is.na( df$a | df$b ),]
However, in second instance, I decided I wanted to interpolate the data I had (but not extrapolate). So instead I would like to remove all the columns for which I cannot interpolate; in the example:
1 France France2000 NA NA
5 France France2004 NA NA
9 UK UK2003 NA NA
10 UK UK2004 NA NA
I believe there are 2 options. First I somehow adapt this function:
library(tidyerse)
TRcomplete<-TRcomplete%>%
group_by(country) %>%
mutate_at(a:b,~na.fill(.x,"extend"))
to interpolate only, and then remove then apply df<-df[!is.na( df$a | df$b ),]
or I write a code to remove the "outer"columns first and then use extend like normal. Desired output:
country country-year a b
2 France France2001 1000 1000
3 France France2002 1300 1600
4 France France2003 1600 2200
6 UK UK2000 1000 1000
7 UK UK2001 0 0
8 UK UK2002 1000 1000
Any suggestions?
There are options in na.fill to specify what is done. If you look at ?na.fill, you see that fill can specify the left, interior and right, so if you specify the left and right are NA and the interior is "extend", then it will only fill the interior data. You can then filter the rows with NA.
library(tidyverse)
library(zoo)
df %>%
group_by(country) %>%
mutate_at(vars(a:b),~na.fill(.x,c(NA, "extend", NA))) %>%
filter(!is.na(a) | !is.na(b))
By the way, you have a typo in your library(tidyverse) statement; you are missing the v.
I realize there have already been many asked and answered questions about merging datasets here, but I've been unable to find one that addresses my issue.
What I'm trying to do is merge to datasets using two variables and keeping all data from each. I've tried merge and all of the join operations from dplyr, as well as cbind and have not gotten the result I want. Usually what happens is that one column from one of the datasets gets overwritten with NAs. Another thing that will happen, as when I do full_join in dplyr or all = TRUE in merge is that I get double the number of rows.
Here's my data:
Primary_State Primary_County n
<fctr> <fctr> <int>
1 AK 12
2 AK Aleutians West 1
3 AK Anchorage 961
4 AK Bethel 1
5 AK Fairbanks North Star 124
6 AK Haines 1
Primary_County Primary_State Population
1 Autauga AL 55416
2 Baldwin AL 208563
3 Barbour AL 25965
4 Bibb AL 22643
5 Blount AL 57704
6 Bullock AL 10362
So I want to merge or join based on Primary_State and Primary_County, which is necessary because there are a lot of duplicate county names in the U.S. and retain the data from both n and Population. From there I can then divide the Population by n and get a per capita figure for each county. I just can't figure out how to do it and keep all of the data, so any help would be appreciated. Thanks in advance!
EDIT: Adding code examples of what I've already described above.
This code (as well as left_join):
countyPerCap <- merge(countyLicense, countyPops, all.x = TRUE)
Produces this:
Primary_State Primary_County n Population
1 AK 12 NA
2 AK Aleutians West 1 NA
3 AK Anchorage 961 NA
4 AK Bethel 1 NA
5 AK Fairbanks North Star 124 NA
6 AK Haines 1 NA
This code:
countyPerCap <- right_join(countyLicense, countyPops)
Produces this:
Primary_State Primary_County n Population
<chr> <chr> <int> <int>
1 AL Autauga NA 55416
2 AL Baldwin NA 208563
3 AL Barbour NA 25965
4 AL Bibb NA 22643
5 AL Blount NA 57704
6 AL Bullock NA 10362
Hope that's helpful.
EDIT: This is what happens with the following code:
countyPerCap <- merge(countyLicense, countyPops, all = TRUE)
Primary_State Primary_County n Population
1 AK 12 NA
2 AK Aleutians East NA 3296
3 AK Aleutians West 1 NA
4 AK Aleutians West NA 5647
5 AK Anchorage 961 NA
6 AK Anchorage NA 298192
It duplicates state and county and then adds n to one record and Population in another. Is there a way to deduplicate the dataset and remove the NAs?
We can give column names in merge by mentioning "by" in merge statement
merge(x,y, by=c(col1, col2 names))
in merge statement
I figured it out. There were trailing whitespaces in the Census data's county names, so they weren't matching with the other dataset's county names. (Note to self: Always check that factors match when trying to merge datasets!)
trim.trailing <- function (x) sub("\\s+$", "", x)
countyPops$Primary_County <- trim.trailing(countyPops$Primary_County)
countyPerCap <- full_join(countyLicense, countyPops,
by=c("Primary_State", "Primary_County"), copy=TRUE)
Those three lines did the trick. Thanks everyone!
I have a very large and complex data set with many observations of companies. Some of the observations of the companies are redundant and I need to make a key to map the redundant observations to a single one. However the only way to tell if they are actually representing the same company is through the similarity of a variety of variables. I think the appropriate approach is a kind of clustering based on a variety of conditions or perhaps even some kind of propensity score matching. Perhaps I just need flexible tools for making a complex kind of similarity matrix.
Unfortunately, I am not quite sure how to go about that in R. Most of the tools I've seen for clustering and categorizing seem to do so with either numerical distance or categorical data, but don't seem to allow multiple conditions or user specified conditions.
Below I've tried to create a smaller, public example of the kind of data I am working with and the result I am trying to produce. There are some conditions that must apply, for example, the location must be the same. There are some features that may associate one with another, for example var1 and var2. Then there are some features that may associate one with another, but they must not conflict, such as var3.
An additional layer of complexity is that the kind of association I am trying to use to map the redundant observation varies. For example, id1 and id2 are the same company redundantly entered into the data twice. In one place its name is "apples" and another "red apples". They share the same location, var1 value and var3 (after adjusting for formatting). Similarly ids 3, 5 and 6, are also really just one company, though much of the input for each is different. Some clusters would identify multiple observations, others would only have one. Ideally I would like to find a way to categorize or associate the observations based on several conditions, for example:
1. Test that the location is the same
2. Test whether var3 is different
3. Test whether the names is a substring of others
4. Test the edit distance of names
5. Test the similarity of var1 and var2 between observations
Anyways, hopefully there are better, more flexible tools for this than what I am finding or someone has experience with this kind of data work in R. Any and all suggestions and advice are much appreciated!
Data
id name location var1 var2 var3
1 apples US 1 abc 12345
2 red apples US 1 NA 12-345
3 green apples Mexico 2 def 235-92
4 bananas Brazil 2 abc NA
5 oranges Mexico 2 NA 23592
6 green apple Mexico NA def NA
7 tangerines Honduras NA abc 3498
8 mango Honduras 1 NA NA
9 strawberries Honduras NA abcd 3498
10 strawberry Honduras NA abc 3498
11 blueberry Brazil 1 abcd 2348
12 blueberry Brazil 3 abc NA
13 blueberry Mexico NA def 1859
14 bananas Brazil 1 def 2348
15 blackberries Honduras NA abc NA
16 grapes Mexico 6 qrs NA
17 grapefruits Brazil 1 NA 1379
18 grapefruit Brazil 2 bcd 1379
19 mango Brazil 3 efaq NA
20 fuji apples US 4 NA 189-35
Result
id name location var1 var2 var3 Result
1 apples US 1 abc 12345 1
2 red apples US 1 NA 12-345 1
3 green apples Mexico 2 def 235-92 3
4 bananas Brazil 2 abc NA 4
5 oranges Mexico 2 NA 23592 3
6 green apple Mexico NA def NA 3
7 tangerines Honduras NA abc 3498 7
8 mango Honduras 1 NA NA 8
9 strawberries Honduras NA abcd 3498 7
10 strawberry Honduras NA abc 3498 7
11 blueberry Brazil 1 abcd 2348 11
12 blueberry Brazil 3 abc NA 11
13 blueberry Mexico NA def 1859 13
14 bananas Brazil 1 def 2348 11
15 blackberries Honduras NA abc NA 15
16 grapes Mexico 6 qrs NA 16
17 grapefruits Brazil 1 NA 1379 17
18 grapefruit Brazil 2 bcd 1379 17
19 mango Brazil 3 efaq NA 19
20 fuji apples US 4 NA 189-35 20
Thanks in advance for your time and help!
library(stringdist)
getMatches <- function(df, tolerance=6){
out <- integer(nrow(df))
for(row in 1:nrow(df)){
dists <- numeric(nrow(df))
for(col in 1:ncol(df)){
tempDist <- stringdist(df[row, col], df[ , col], method="lv")
# WARNING: Matches NA perfectly.
tempDist[is.na(tempDist)] <- 0
dists <- dists + tempDist
}
dists[row] <- Inf
min_dist <- min(dists)
if(min_dist < tolerance){
out[row] <- which.min(dists)
}
else{
out[row] <- row
}
}
return(out)
}
test$Result <- getMatches(test[, -1])
Where test is your data. This probably definitely needs some refining and certainly needs some postprocessing. This creates a column with the index of the closest match. If it can't find a match within the given tolerance, it returns the index of itself.
EDIT: I will attempt some more later.
I am new to R and started learning two weeks ago. I want to take a list of tropical cyclone counts for various years (where some years are absent, because there were no tropical cyclones) and create a list with a column of every year from 1907-2013 and a column of the number of tropical cyclones.
In the example I include the list of occurrences to 1973 (before 1912 there were none).
Year Count
1 1912 1
2 1913 1
3 1921 1
4 1940 1
5 1953 1
6 1958 1
7 1959 1
8 1960 1
9 1966 1
10 1969 1
11 1971 1
12 1973 2
I tried using a for loop and if/else statement, but it does not work. I get the message "longer object length is not a multiple of shorter object length" and "the condition has length > 1 and only the first element will be used."
tc.SP=matrix(0,len.tc.yr,2)
tc.SP[,1]=tc.year.list
for (i in 1:len.tc.yr) #107 yrs (1907-2013)
{
if (tc.SP5.count[,1] == tc.SP[,1]) #tc.SP5.count is various years of TC occ.
{tc.SP[,2]= tc.SP5.count[,2]}
else
{tc.SP[,2]= 0}
}
Thank you for any help in advance.
When you say list, i'm going to assume you want to create a data.frame. Let's say the data above is in a data.frame called cyclone. The easiest way to create a data.frame for every year is just to merge it with a complete list. For example
cyclone.full <- merge(cyclone, data.frame(Year=1907:2013), all=T)
Here the data.frames will automatically merge on the Year column because both sets have that column. This will put NA values in all the missing years. If you want the default to be 0, you can do
cyclone.full$Count[is.na(cyclone.full$Count)] <- 0
Then yo uget
head(cyclone.full)
# Year Count
# 1 1907 0
# 2 1908 0
# 3 1909 0
# 4 1910 0
# 5 1911 0
# 6 1912 1
So what I have is data of cod weights at different ages. This data is taken at several locations over time.
What I would like to create is "weight at age", basically a mean value of weights at a certain age. I want do this for each location at each year.
However, the ages are not sampled the same way (all old fish caught are measured, while younger fish are sub sampled), so I can't just create a normal average, I would like to bootstrap samples.
The bootstrap should take out 5 random values of weight at an age, create a mean value and repeat this a 1000 times, and then create an average of the means. The values should be able to be used again (replace). This should be done for each age at every AreaCode for every year. Dependent factors: Year-location-Age.
So here's an example of what my data could look like.
df <- data.frame( Year= rep(c(2000:2008),2), AreaCode = c("39G4", "38G5","40G5"), Age = c(0:8), IndWgt = c(rnorm(18, mean=5, sd=3)))
> df
Year AreaCode Age IndWgt
1 2000 39G4 0 7.317489899
2 2001 38G5 1 7.846606144
3 2002 40G5 2 0.009212455
4 2003 39G4 3 6.498688035
5 2004 38G5 4 3.121134937
6 2005 40G5 5 11.283096043
7 2006 39G4 6 0.258404136
8 2007 38G5 7 6.689780137
9 2008 40G5 8 10.180511929
10 2000 39G4 0 5.972879108
11 2001 38G5 1 1.872273650
12 2002 40G5 2 5.552962065
13 2003 39G4 3 4.897882549
14 2004 38G5 4 5.649438631
15 2005 40G5 5 4.525012587
16 2006 39G4 6 2.985615831
17 2007 38G5 7 8.042884181
18 2008 40G5 8 5.847629941
AreaCode contains the different locations, in reality I have 85 different levels. The time series stretches 1991-2013, the ages 0-15. IndWgt contain the weight. My whole data frame has a row length of 185726.
Also, every age does not exist for every location and every year. Don't know if this would be a problem, just so the scripts isn't based on references to certain row number. There are some NA values in the weight column, but I could just remove them before hand.
I was thinking that I maybe should use replicate, and apply or another plyr function. I've tried to understand the boot function but I don't really know if I would write my arguments under statistics, and in that case how. So yeah, basically I have no idea.
I would be thankful for any help I can get!
How about this with plyr. I think from the question you wanted to bootstrap only the "young" fish weights and use actual means for the older ones. If not, just replace the ifelse() statement with its last argument.
require(plyr)
#cod<-read.csv("cod.csv",header=T) #I loaded your data from csv
bootstrap<-function(Age,IndWgt){
ifelse(Age>2, # treat differently for old/young fish
res<-mean(IndWgt), # old fish mean
res<-mean(replicate(1000,sample(IndWgt,5,replace = TRUE))) # young fish bootstrap
)
return(res)
}
ddply(cod,.(Year,AreaCode,Age),summarize,boot_mean=bootstrap(Age,IndWgt))
Year AreaCode Age boot_mean
1 2000 39G4 0 6.650294
2 2001 38G5 1 4.863024
3 2002 40G5 2 2.724541
4 2003 39G4 3 5.698285
5 2004 38G5 4 4.385287
6 2005 40G5 5 7.904054
7 2006 39G4 6 1.622010
8 2007 38G5 7 7.366332
9 2008 40G5 8 8.014071
PS: If you want to sample all ages in the same way, no need for the function, just:
ddply(cod,.(Year,AreaCode,Age),
summarize,
boot_mean=mean(replicate(1000,mean(sample(IndWgt,5,replace = TRUE)))))
Since you don't provide enough code, it's too hard (lazy) for me to test it properly. You should get your first step using the following code. If you wrap this into replicate, you should get your end result that you can average.
part.result <- aggregate(IndWgt ~ Year + AreaCode + Age, data = data, FUN = function(x) {
rws <- length(x)
get.em <- sample(x, size = 5, replace = TRUE)
out <- mean(get.em)
out
})
To handle any missing combination of year/age/location, you could probably add an if statement checking for NULL/NA and producing a warning and/or skipping the iteration.