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I am trying to run the following:
library(ggplot2)
library(RColorBrewer)
state_df <- map_data('state')
county_df <- map_data('county')
transform_mapdata <- function(x){
names(x)[5:6] <- c('state','county')
for(u in c('state','county'){
x[,u] <- sapply(x[,u],simpleCap)
}
return(x)
}
state_df <- transform_mapdata(state_df)
county_df <- transform_mapdata(county_df)
I keep getting this message:
Error in x[, u] : incorrect number of dimensions
> }
Error: unexpected '}' in " }"
The data seems ok, so. I guess the problem has something to do with the transformation.
> head(state_df)
long lat group order region subregion
1 -87.46201 30.38968 1 1 alabama <NA>
2 -87.48493 30.37249 1 2 alabama <NA>
3 -87.52503 30.37249 1 3 alabama <NA>
4 -87.53076 30.33239 1 4 alabama <NA>
5 -87.57087 30.32665 1 5 alabama <NA>
6 -87.58806 30.32665 1 6 alabama <NA>
> head(county_df)
long lat group order region subregion
1 -86.50517 32.34920 1 1 alabama autauga
2 -86.53382 32.35493 1 2 alabama autauga
3 -86.54527 32.36639 1 3 alabama autauga
4 -86.55673 32.37785 1 4 alabama autauga
5 -86.57966 32.38357 1 5 alabama autauga
6 -86.59111 32.37785 1 6 alabama autauga
First issue seems to be a missing parenthesis in:
"for(u in c('state','county'){"
Should be:
for(u in c('state','county')){
Although when that is fixed this error comes up:
Error in sapply(x[, u], simpleCap) : object 'simpleCap' not found
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I have a data.frame and would like to average a column where there is an NA present.
when performing the calculation I noticed that R cannot calculate the average, returning NA as a result.
OBS: I cannot remove the line with NA as it would remove other columns with values that interest me.
df1<-read.table(text="st date ph
1 01/02/2004 5
16 01/02/2004 6
2 01/02/2004 8
2 01/02/2004 8
2 01/02/2004 8
16 01/02/2004 6
1 01/02/2004 NA
1 01/02/2004 5
16 01/02/2004 NA
", sep="", header=TRUE)
df2<-df1%>%
group_by(st, date)%>%
summarise(ph=mean(ph))
View(df2)
out
my expectation was this result:
You need to use na.rm = TRUE:
df2<-df1%>%
group_by(st, date)%>%
summarise(ph=mean(ph, na.rm = TRUE))
df2
# A tibble: 3 x 3
# Groups: st [3]
st date ph
<int> <chr> <dbl>
1 1 01/02/2004 5
2 2 01/02/2004 8
3 16 01/02/2004 6
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?
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I have a tibble, Agencies, with two columns as follows:
> head(Agencies, 10)
# A tibble: 10 x 2
AgencyNumber State
<int> <chr>
1 1 AR
2 2 Arkansas
3 3 Texas
4 4 Texas
5 5 TX
6 6 IL
7 7 Illinois
8 8 Illinois
9 9 IL
10 10 IL
I'm trying to add a column (Agencies$STATE) with the full state name. If Agencies$State is an abbreviation, it should use the abbr2state function to save the full name to the new column. If Agencies$State already has the full name, it should store the value of Agencies$State to the new column.
I'm using the following code:
Agencies$STATE <- "NA"
for(i in 1:nrow(Agencies)) {
if(nchar(Agencies$State[i] == 2)) {
Agencies$STATE[i] <- abbr2state(Agencies$State[i])
}
else {
Agencies$STATE[i] <- Agencies$State[i]
}
}
The output is unexpected. It appears to evaluate the first if statement as expected, but ignores the else statement.
> head(Agencies, 10)
# A tibble: 10 x 3
AgencyNumber State STATE
<int> <chr> <chr>
1 1 AR Arkansas
2 2 Arkansas <NA>
3 3 Texas <NA>
4 4 Texas <NA>
5 5 TX Texas
6 6 IL Illinois
7 7 Illinois <NA>
8 8 Illinois <NA>
9 9 IL Illinois
10 10 IL Illinois
I'm a bit new to R so this may be an obvious error, but I'm missing it.
Any suggestions on why this isn't doing what I expect?
Thanks,
Jeff
Your statement nchar(Agencies$State[i] == 2)
should be (nchar(Agencies$State[i]) == 2)
You misplace the parenthesis
You can also use dplyr to avoid the loops
library(dplyr)
Agencies %>%
mutate(state = ifelse( stringi::stri_length(State) == 2,abbr2state(State),State))
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$Primary_County)
countyPerCap <- full_join(countyLicense, countyPops,
by=c("Primary_State", "Primary_County"), copy=TRUE)
Those three lines did the trick. Thanks everyone!
I am working with maps and ggplot2 to visualize the number of certain crimes in each state for different years. The data set that I am working with was produced by the FBI and can be downloaded from their site or from here (if you don't want to download the dataset I don't blame you, but it is way too big to copy and paste into this question, and including a fraction of the data set wouldn't help, as there wouldn't be enough information to recreate the graph).
The problem is easier seen than described.
As you can see California is missing a large chunk as well as a few other states. Here is the code that produced this plot:
# load libraries
library(maps)
library(ggplot2)
# load data
fbi <- read.csv("http://www.hofroe.net/stat579/crimes-2012.csv")
fbi <- subset(fbi, state != "United States")
states <- map_data("state")
# merge data sets by region
fbi$region <- tolower(fbi$state)
fbimap <- merge(fbi, states, by="region")
# plot robbery numbers by state for year 2012
fbimap12 <- subset(fbimap, Year == 2012)
qplot(long, lat, geom="polygon", data=fbimap12,
facets=~Year, fill=Robbery, group=group)
This is what the states data looks like:
long lat group order region subregion
1 -87.46201 30.38968 1 1 alabama <NA>
2 -87.48493 30.37249 1 2 alabama <NA>
3 -87.52503 30.37249 1 3 alabama <NA>
4 -87.53076 30.33239 1 4 alabama <NA>
5 -87.57087 30.32665 1 5 alabama <NA>
6 -87.58806 30.32665 1 6 alabama <NA>
And this is what the fbi data looks like:
Year Population Violent Property Murder Forcible.Rape Robbery
1 1960 3266740 6097 33823 406 281 898
2 1961 3302000 5564 32541 427 252 630
3 1962 3358000 5283 35829 316 218 754
4 1963 3347000 6115 38521 340 192 828
5 1964 3407000 7260 46290 316 397 992
6 1965 3462000 6916 48215 395 367 992
Aggravated.Assault Burglary Larceny.Theft Vehicle.Theft abbr state region
1 4512 11626 19344 2853 AL Alabama alabama
2 4255 11205 18801 2535 AL Alabama alabama
3 3995 11722 21306 2801 AL Alabama alabama
4 4755 12614 22874 3033 AL Alabama alabama
5 5555 15898 26713 3679 AL Alabama alabama
6 5162 16398 28115 3702 AL Alabama alabama
I then merged the two sets along region. The subset I am trying to plot is
region Year Robbery long lat group
8283 alabama 2012 5020 -87.46201 30.38968 1
8284 alabama 2012 5020 -87.48493 30.37249 1
8285 alabama 2012 5020 -87.95475 30.24644 1
8286 alabama 2012 5020 -88.00632 30.24071 1
8287 alabama 2012 5020 -88.01778 30.25217 1
8288 alabama 2012 5020 -87.52503 30.37249 1
... ... ... ...
Any ideas on how I can create this plot without those ugly missing spots?
I played with your code. One thing I can tell is that when you used merge something happened. I drew states map using geom_path and confirmed that there were a couple of weird lines which do not exist in the original map data. I, then, further investigated this case by playing with merge and inner_join. merge and inner_join are doing the same job here. However, I found a difference. When I used merge, order changed; the numbers were not in the right sequence. This was not the case with inner_join. You will see a bit of data with California below. Your approach was right. But merge somehow did not work in your favour. I am not sure why the function changed order, though.
library(dplyr)
### Call US map polygon
states <- map_data("state")
### Get crime data
fbi <- read.csv("http://www.hofroe.net/stat579/crimes-2012.csv")
fbi <- subset(fbi, state != "United States")
fbi$state <- tolower(fbi$state)
### Check if both files have identical state names: The answer is NO
### states$region does not have Alaska, Hawaii, and Washington D.C.
### fbi$state does not have District of Columbia.
setdiff(fbi$state, states$region)
#[1] "alaska" "hawaii" "washington d. c."
setdiff(states$region, fbi$state)
#[1] "district of columbia"
### Select data for 2012 and choose two columns (i.e., state and Robbery)
fbi2 <- fbi %>%
filter(Year == 2012) %>%
select(state, Robbery)
Now I created two data frames with merge and inner_join.
### Create two data frames with merge and inner_join
ana <- merge(fbi2, states, by.x = "state", by.y = "region")
bob <- inner_join(fbi2, states, by = c("state" ="region"))
ana %>%
filter(state == "california") %>%
slice(1:5)
# state Robbery long lat group order subregion
#1 california 56521 -119.8685 38.90956 4 676 <NA>
#2 california 56521 -119.5706 38.69757 4 677 <NA>
#3 california 56521 -119.3299 38.53141 4 678 <NA>
#4 california 56521 -120.0060 42.00927 4 667 <NA>
#5 california 56521 -120.0060 41.20139 4 668 <NA>
bob %>%
filter(state == "california") %>%
slice(1:5)
# state Robbery long lat group order subregion
#1 california 56521 -120.0060 42.00927 4 667 <NA>
#2 california 56521 -120.0060 41.20139 4 668 <NA>
#3 california 56521 -120.0060 39.70024 4 669 <NA>
#4 california 56521 -119.9946 39.44241 4 670 <NA>
#5 california 56521 -120.0060 39.31636 4 671 <NA>
ggplot(data = bob, aes(x = long, y = lat, fill = Robbery, group = group)) +
geom_polygon()
The problem is in the order of arguments to merge
fbimap <- merge(fbi, states, by="region")
has the thematic data first and the geo data second. Switching the order with
fbimap <- merge(states, fbi, by="region")
the polygons should all close up.