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Remove rows with all or some NAs (missing values) in data.frame
(18 answers)
Closed 5 years ago.
I have a dataset.csv in R. I want to remove all NA values from the Rank columns.
The column is like this
Rank State
NA District of Columbiaâ€
1 Connecticut
2 New Jersey
3 Massachusetts
4 Maryland
5 New Hampshire
6 Virginia
7 New York
8 North Dakota
9 Alaska
10 Minnesota
11 Colorado
12 Washington
13 Rhode Island
14 Delaware
15 California
16 Illinois
17 Hawaii
18 Wyoming
19 Pennsylvania
20 Vermont
NA United States
21 Iowa
The dataframe of this CSV is called RacePerState
The code I have tried
subset(RacePerState, State!="United States" && State!="District of Columbiaâ€" && !="Puerto Ricoâ€")
RacePerState <- RacePerState[!(RacePerState$Rank=="NA"),]
But when i write the dataframe to a csv the data is still there.
Any help?
RacePerState <- subset(RacePerState, !is.na(Rank))
or
RacePerState <- RacePerState[!is.na(RacePerState$Rank), ]
or
RacePerState <- RacePerState[complete.cases(RacePerState), ]
or
require(dplyr);
require(magrittr);
RacePerState %>% na.omit();
Related
My data frame has a column called "State" and contains the state name, HB/HF number, and the date the law went into effect. I want the state column to only contain the state name and the second column to contain just the year. How would I do this?
Mintz = read.csv('https://github.com/bandcar/mintz/raw/main/State%20Legislation%20on%20Biosimilars2.csv')
mintz = Mintz
# delete rows if col 2 has a blank value.
mintz = mintz[mintz$Substitution.Requirements != "", ]
# removes entire row if column 1 has the word State
mintz=mintz[mintz$State != "State", ]
#reset row numbers
mintz= mintz %>% data.frame(row.names = 1:nrow(.))
# delete PR
mintz = mintz[-34,]
#reset row numbers
mintz= mintz %>% data.frame(row.names = 1:nrow(.))
I'm almost certain I'll need to use strsplit(gsub()) but I'm not sure how to this since there's no specific pattern
EDIT
I still need help keeping only the state name in column 1.
As for moving the year to a new column, I found the below. It works, but I don't know why it works. From my understanding \d means that \d is the actual character it's searching for. the "." means to search for one character, and I have no idea what the \1 means. Another strange thing is that Minnesota (row 20) did not have a year, so it instead used characters. Isn't \d only supposed to be for digits? Someone care to explain?
mintz2 = mintz
mintz2$Year = sub('.*(\\d{4}).*', '\\1', mintz2$State)
One way could be:
For demonstration purposes select the State column.
Then we use str_extract to extract all numbers with 4 digits with that are at the end of the string \\d{4}-> this gives us the Year column.
Finally we make use of the inbuilt state.name function make a pattern of it an use it again with str_extract and remove NA rows.
library(dplyr)
library(stringr)
mintz %>%
select(State) %>%
mutate(Year = str_extract(State, '\\d{4}$'), .after=State,
State = str_extract(State, paste(state.name, collapse='|'))
) %>%
na.omit()
State Year
2 Arizona 2016
3 California 2016
7 Connecticut 2018
12 Florida 2013
13 Georgia 2015
16 Hawaii 2016
21 Illinois 2016
24 Indiana 2014
28 Iowa 2017
32 Kansas 2017
33 Kentucky 2016
34 Louisiana 2015
39 Maryland 2017
42 Michigan 2018
46 Missouri 2016
47 Montana 2017
50 Nebraska 2018
51 Nevada 2018
54 New Hampshire 2018
55 New Jersey 2016
59 New York 2017
62 North Carolina 2015
63 North Dakota 2013
66 Ohio 2017
67 Oregon 2016
70 Pennsylvania 2016
74 Rhode Island 2016
75 South Carolina 2017
78 South Dakota 2019
79 Tennessee 2015
82 Texas 2015
85 Utah 2015
88 Vermont 2018
89 Virginia 2013
92 Washington 2015
93 West Virginia 2018
96 Wisconsin 2019
97 Wyoming 2018
nubie here with a dataframe/mutate question... I want to update a dataframe (df1) based on data in another dataframe (df2). For one offs I've used MUTATE so I figure this is the way to go. Additionally I would like a check function added (TRUE/FALSE ?) to indicate if the the field in df1 was updated.
For Example..
df1-
State
<chr>
1 N.Y.
2 FL
3 AL
4 MS
5 IL
6 WS
7 WA
8 N.J.
9 N.D.
10 S.D.
11 CALL
df2
State New_State
<chr> <chr>
1 N.Y. New York
2 FL Florida
3 AL Alabama
4 MS Mississippi
5 IL Illinois
6 WS Wisconsin
7 WA Washington
8 N.J. New Jersey
9 N.D. North Dakota
10 S.D. South Dakota
11 CAL California
I want the output to look like this
df3
New_State Test
<chr>
1 New York TRUE
2 Florida TRUE
3 Alabama TRUE
4 Mississippi TRUE
5 Illinois TRUE
6 Wisconsin TRUE
7 Washington TRUE
8 New Jersey TRUE
9 North Dakota TRUE
10 South Dakota TRUE
11 CALL FALSE
In essence I want R to read the data in df1 and change df1 based on the match in df2 chaining out to the full state name and replace. Lastly if the data in df1 was update mark as "TRUE" (N.Y. to NEW YORK) and "FALSE" if not updated (CALL vs CAL)
Thanks in advance for any and all help.
This should give you the result you're looking for:
match_vec <- match(df1$State, table = df2$State)
This vector should match all the abbreviated state names in df1 with those in df2. Where there's no match, you end up with a missing value:
Then the following code using dplyr should produce the df3 you requested.
library(dplyr)
df3 <- df1 %>%
mutate(New_State = df2$New_State[match_vec]) %>%
mutate(Test = !is.na(match_vec)) %>%
mutate(New_State = ifelse(is.na(New_State),
State, New_State)) %>%
select(New_State, Test)
I am not familiar with R , I need your help for this issue ,
I have a data frame composed with 25 variables (25 columns) named df simplified
name experience Club age Position
luc 2 FCB 18 Goalkeeper
jean 9 Real 26 midfielder
ronaldo 14 FCB 32 Goalkeeper
jean 9 Real 26 midfielder
messi 11 Liverpool 35 midfielder
tevez 6 Chelsea 27 Attack
inzaghi 9 Juve 34 Defender
kwfni 17 Bayern 40 Attack
Blabla 9 Real 25 midfielder
wdfood 11 Liverpool 33 midfielder
player2 7 Chelsea 28 Attack
player3 10 Juve 34 Defender
fgh 17 Bayern 40 Attack
I would like to add a column to this data frame named "country".This new column takes into account different conditions .
Juve Italy
FCB Spain
Real Spain
Chelsea England
Liverpool England
Bayern Germany
So let say if the club is FCB or Real the value in country is Spain
the output of df$Country should be as follows
Country
Spain
Spain
Spain
Spain
England
England
Italy
Germany
Spain
England
England
Italy
Germany
The code I started to do is the following
df$country=ifelse(df$Club=="FCB","spain", df$Club=="Real","Spain" ......)
But it seems false .
knowing that my real data set has more than 250 different values in "club" column
and more than 30 in "Country"
doing that manually seems too long .
Could you help me in that point please .
Do you know how to use if-else statements inside for loops? This would be the simplest way out.
Something like this:
df <- data.frame(name = c("a", "b", "c"),
Club = c("FCB", "Real", "Liverpool"),
stringsAsFactors = FALSE)
for(i in 1:nrow(df)){
if(df$Club[i] == "FCB" | df$Club[i] == "Real"){
df$country[i] <- "Spain"
} else if(df$Club[i] == "Liverpool"){
df$country[i] <- "England"
} else{
df$country[i] <- NA
}
}
df
# name Club country
# 1 a FCB Spain
# 2 b Real Spain
# 3 c Liverpool England
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 $lower list. stack() doesn't like it when the list isn't named
Since my other question got closed, here is the required data.
What I'm trying to do is have R calculate the last column 'count' towards the column city so I can map the data. Therefore I would need some kind of code to match this. Since I want to show how many participants (in count) are in the state of e.g Hawaii (HI)
zip city state latitude longitude count
96860 Pearl Harbor HI 24.859832 -168.021815 36
96863 Kaneohe Bay HI 21.439867 -157.74772 39
99501 Anchorage AK 61.216799 -149.87828 12
99502 Anchorage AK 61.153693 -149.95932 17
99506 Elmendorf AFB AK 61.224384 -149.77461 2
what I've tried is
match<- c(match(datazip$state, datazip$number))>$
but I'm really helpless trying to find a solution since I don't even know how to describe this in short. My plan afterwards is to make choropleth map with the data and believe me by now I've seen almost all the pages that try to give advice. so your help is pretty much appreciated. Thanks
# I read your sample data to a data frame
> df
zip city state latitude longitude count
1 96860 Pearl_Harbor HI 24.85983 -168.0218 36
2 96863 Kaneohe_Bay HI 21.43987 -157.7477 39
3 99501 Anchorage AK 61.21680 -149.8783 12
4 99502 Anchorage AK 61.15369 -149.9593 17
5 99506 Elmendorf_AFB AK 61.22438 -149.7746 2
# If you want to sum the number of counts by state
library(plyr)
> ddply(df, .(state), transform, count2 = sum(count))
zip city state latitude longitude count count2
1 99501 Anchorage AK 61.21680 -149.8783 12 31
2 99502 Anchorage AK 61.15369 -149.9593 17 31
3 99506 Elmendorf_AFB AK 61.22438 -149.7746 2 31
4 96860 Pearl_Harbor HI 24.85983 -168.0218 36 75
5 96863 Kaneohe_Bay HI 21.43987 -157.7477 39 75
Maybe aggregate would be a nice and simple solution for you:
df
zip city state latitude longitude count
1 96860 Pearl Harbor HI 24.85983 -168.0218 36
2 96863 Kaneohe Bay HI 21.43987 -157.7477 39
3 99501 Anchorage AK 61.21680 -149.8783 12
4 99502 Anchorage AK 61.15369 -149.9593 17
5 99506 Elmendorf AFB AK 61.22438 -149.7746 2
aggregate(df$count,by=list(df$state),sum)
Group.1 x
1 AK 31
2 HI 75
aggregate(df$count,by=list(df$city),sum)
Group.1 x
1 Anchorage 29
2 Elmendorf AFB 2
3 Kaneohe Bay 39
4 Pearl Harbor 36