Problem:
I have two different size data sets that I would like to merge together. Without abandoning rows or inserting NA's. To compare this to a excel document situation you would have five columns and you would drago down 3 of them to populate the blank space left by the rows inserted by adding your data to the 4th and 5th column.
Example Data Set
zipcode = a, step3 = b in my later brainstorming code to solve my problem
> head(zipcode_joincsv)
zip city abv latitude longitude median mean pop
226 01749 Hudson AL 42.38981 -71.55791 76500 85689 18081
227 01752 Marlborough AL 42.35091 -71.54753 71835 89002 36273
228 01754 Maynard AL 42.43078 -71.45594 76228 82167 10414
229 01756 Mendon AL 42.09201 -71.54474 102625 117692 5257
230 01757 Milford AL 42.14918 -71.52149 68565 82206 26877
231 01760 Natick AL 42.29076 -71.35368 90673 113933 31763
> head(step3_df)
tolower.state.name. state.abb
1 alabama AL
2 alaska AK
3 arizona AZ
4 arkansas AR
5 california CA
6 colorado CO
Desired Result:
One DF where each zipcode city combo is combined with their states pop and
income. A column in common they have is the abbreviation column.
tolower.state.name. zip city abv latitude longitude median mean pop
1 alabama 01749 Hudson AL 42.38981 -71.55791 76500 85689 18081
2 alabama 01752 Marlborough AL 42.35091 -71.54753 71835 89002 36273
3 alabama 01754 Maynard AL 42.43078 -71.45594 76228 82167 10414
4 alabama 01756 Mendon AL 42.09201 -71.54474 102625 117692 5257
5 alabama 01757 Milford AL 42.14918 -71.52149 68565 82206 26877
6 alabama 01760 Natick AL 42.29076 -71.35368 90673 113933 31763
7 alaska data from these rows
8 arizona data from these rows
9 arkansas data from these rows
10 california data from these rows
11 colorado data from these rows
I've contemplated using something like
sqldf ("SELECT a.Zip, a.City, a.State Abv, a.Lat, a.Long, a.median, a.mean, a.pop, b.state.name, b.states.abb, b.pop, b.income
FROM a a
LEFT JOIN b b using (abv)")
I know that is probably not going to work if only that if it worked all the rows that there was not a matching set from A would input a NA where what I would like is that for every abv of NY the states average income and total population gets copied down the line. Than for every AR and every AL etc until the two data sets are one that a ggplot using all of the data can be created.
dplyr::left_join(a, b, by="abv") should work.
Related
I have a dataframe like the one below:
State<-c("Alabama","Alabama","Alaska","Alaska")
StateCode<-c("AL","AL","AK","AK")
County<-c("AUTAUGA","BALDWIN","ANCHORAGE","BETHEL")
CountyCode<-c("AL001","AL003","AK020","AK050")
Murder<-c(5,6,7,8)
d<-data.frame(State,StateCode,County,CountyCode, Num)
State StateCode County CountyCode Num
1 Alabama AL AUTAUGA AL001 5
2 Alabama AL BALDWIN AL003 6
3 Alaska AK ANCHORAGE AK020 7
4 Alaska AK BETHEL AK050 8
I have been searching for an option between R packages to create a drill-down map from State to County level out of this but I can't find a working example with code anywhere. Here is an example Any feedback on this?
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!
This question already has answers here:
Collapsing a data frame over one variable
(3 answers)
Closed 7 years ago.
I have a data set that looks like this:
geoid zip dealers Year County
1001 36703 1 2001 Autauga County, AL
1001 36704 3 2001 Autauga County, AL
1003 36535 7 2000 Baldwin County, AL
1003 36536 3 2000 Baldwin County, AL
And I want to take all the rows that are the same except for 'dealers' and 'zip' and combine them into one row with the dealer variable summed from all the similar rows. (I'm not sure what the easiest thing is to do with zip, either list them all or leave it out? Doesn't really matter.) So this would become:
geoid dealers Year County
1001 4 2001 Autauga County, AL
1003 10 2000 Baldwin County, AL
Is there any way to create a new dataset like this? (Incidentally, I got here by merging three datasets, so if there's a better way to merge without creating these duplicates, that would be helpful as well.)
This gives you the desired result:
df <- read.table(header=TRUE, text=
'geoid zip dealers Year County
1001 36703 1 2001 "Autauga County, AL"
1001 36704 3 2001 "Autauga County, AL"
1003 36535 7 2000 "Baldwin County, AL"
1003 36536 3 2000 "Baldwin County, AL"')
aggregate(dealers ~ geoid+Year+County, data=df[-2], FUN=sum) # or
aggregate(dealers ~ geoid+Year+County, data=df, FUN=sum)
I have a data frame that I'm working with in R, and am trying to check how many times a value occurs within its larger, associated group. Specifically, I'm trying to count the number of cities that are listed for each particular country.
My data look something like this:
City Country
=========================
New York US
San Francisco US
Los Angeles US
Paris France
Nantes France
Berlin Germany
It seems that table() is the way to go, but I can't quite figure it out — how can I find out how many cities are listed for each country? That is to say, how can I find out how many fields in one column are associated with a particular value in another column?
EDIT:
I'm hoping for something along the lines of
3 US
2 France
1 Germany
I guess you can try table.
table(df$Country)
# France Germany US
# 2 1 3
Or using data.table
library(data.table)
setDT(df)[, .N, by=Country]
# Country N
#1: US 3
#2: France 2
#3: Germany 1
Or
library(plyr)
count(df$Country)
# x freq
#1 France 2
#2 Germany 1
#3 US 3
Hopefully title is not too badly worded. I have a tree that I used cutree to obtain groups from, but it is clear that the groups are not numbered left-to-right or right-to-left (I know the orientation within a branch doesn't matter so much, was hoping the grouping would be the same as the ordering in the hclust object). Is it possible to extract groups from a tree (using the height option of cutree) and know which of those groups are more related to one another? I walk through an example using USArrests below.
hc <- hclust(dist(USArrests), "ave")
plot(hc)
cutree(hc,h=60)
Alabama Alaska Arizona Arkansas California
1 1 1 2 1
Colorado Connecticut Delaware Florida Georgia
2 3 1 4 2
Hawaii Idaho Illinois Indiana Iowa
3 3 1 3 3
Kansas Kentucky Louisiana Maine Maryland
3 3 1 3 1
Massachusetts Michigan Minnesota Mississippi Missouri
2 1 3 1 2
Montana Nebraska Nevada New Hampshire New Jersey
3 3 1 3 2
New Mexico New York North Carolina North Dakota Ohio
1 1 4 3 3
Oklahoma Oregon Pennsylvania Rhode Island South Carolina
2 2 3 2 1
South Dakota Tennessee Texas Utah Vermont
3 2 2 3 3
Virginia Washington West Virginia Wisconsin Wyoming
2 2 3 3 2
If you plot the tree it is clear that groups 1 and 4 are more related then groups 2 and 3 are more related. However when I just print the contents of each group there is no way to know what that relationship is. Is there a function or standard process I am missing? The real data I'm working with I split 36k values into 10 groups, so it would be tough to visually validate the relationships as I do with the example data, and want to code it as a script for future analyses. Thanks ahead of time.
I think you want to use
hc <- hclust(dist(USArrests), "ave")
cuthc <- cut(as.dendrogram(hc), h=60)
This will return a list with an $upper showing the tree above the cut, and a $lower element which is a list of each of the subtrees made from the cut. We can plot them with
layout(matrix(1:4, ncol=2))
sapply(1:4, function(i) plot(cuthc$lower[[i]]))
Then, if you want to extract the names and groups in the order they appear in the dendrograms, you can do
stack(setNames(Map(labels, cuthc$lower),seq_along(cuthc$lower)))
Here I use stack() and setNames() just to assign a unique ID to each element in the $lower list. stack() doesn't like it when the list isn't named