How To Remove Data From Dataframe In R

Drop rows in R with weather can exist washed with the assistance of subset () part. Permit'due south run across how to delete or drop rows with multiple conditions in R with an example. Drop rows with missing and null values is accomplished using omit(), complete.cases() and slice() function. Drop rows by row index (row number) and row name in R

drop rows with status in R using subset function
driblet rows with null values or missing values using omit(), consummate.cases() in R
drop rows with slice() role in R dplyr package
drib duplicate rows in R using dplyr using unique() and singled-out() role
drib rows based on row number i.eastward. row index in R
drib rows based on row proper name in R

Drop rows in R with conditions in R 35

Let's first create the dataframe.

# create dataframe df1 = information.frame(Name = c('George','Andrea', 'Micheal','Maggie','Ravi','Xien','Jalpa'),                   Grade_score=c(4,6,two,9,v,7,8),                  Mathematics1_score=c(45,78,44,89,66,49,72),                  Science_score=c(56,52,45,88,33,90,47)) df1

So the resultant dataframe volition be

Delete or Drop rows in R with conditions R 1

Delete or Drop rows in R with conditions:

Method i:

Delete rows with proper name equally George or Andrea

df2<-df1[!(df1$Name=="George" | df1$Proper noun=="Andrea"),] df2

Resultant dataframe will be

Delete or Drop rows in R with conditions R 2

Method ii: drop rows using subset() role

Drib rows with conditions in R using subset role.

df2<-subset(df1, Name!="George" & Name!="Andrea") df2

Resultant dataframe will be

Delete or Drop rows in R with conditions R 3

Method iii: using slice() function in dplyr packet of R

Drop rows with conditions in R using slice() function.

### Drop rows using slice() function in R  library(dplyr)  df2 <- df1 %>% piece(-c(two, 4, 6)) df2

Resultant dataframe with 2d, 4th and 6th rows removed as shown below

drop rows with multiple conditions in R 1

Drop Rows by row proper name and Row number (Row index) in R:

Drop rows in R with conditions in R 33

Drop Row by row number or row index:

Drop Rows past row number or Row index in R tin can be accomplished either past slice() office and also past the '-' operator.

### Drib rows using slice() role in R  library(dplyr)  df2 <- df1 %>% slice(-c(2, iv, 6)) df2

### Drib rows using "-" operator in R  df2 <- df1[-c(2, iv, 6), ] df2

Resultant dataframe with second, 4th and 6th rows removed as shown below

drop rows with multiple conditions in R 1

Drib Row by row name :

Drop Rows past row name or Row index in R can be accomplished either by slice() function and besides past the '-' operator.

### Drop rows using slice() function in R  library(dplyr)  df1[!(row.names(df1) %in% c('1','2')), ]

Row names are nil just row index numbers in this case

Drop rows in R with conditions in R 31

Driblet rows with missing values in R (Drop NA, Drop NaN) :

Drop rows in R with conditions in R 34

Let's showtime create the dataframe with NA values as shown below

df1 = data.frame(Proper name = c('George','Andrea', 'Micheal','Maggie','Ravi','Xien','Jalpa',''),                   Mathematics_score=c(45,78,44,89,66,NaN,72,87),                  Science_score=c(56,52,NA,88,33,xc,47,76)) df1

dataframe will be

Drop rows with missing values in R 1

Method i: Remove or Drop rows with NA using omit() function:

Using na.omit() to remove (missing) NA and NaN values

df1_complete = na.omit(df1) # Method one - Remove NA df1_complete

so subsequently removing NA and NaN the resultant dataframe will exist

Drop rows with missing values in R 2

Method 2: Remove or Drop rows with NA using consummate.cases() role

Using complete.cases() to remove (missing) NA and NaN values

df1[complete.cases(df1),]

and then after removing NA and NaN the resultant dataframe will be

Drop rows with missing values in R 3

Removing Both Null and missing:

By subsetting each column with non NAs and not null is round nearly manner to remove both Null and missing values as shown below

# Remove null  &amp; NA values df1[!(is.na(df1$Name) | df1$Name=="" | is.na(df1$Science_score) | df1$Science_score==""|is.na(df1$Mathematics_score) | df1$Mathematics_score==""),]

so later removing Null, NA and NaN the resultant dataframe will exist

Drop rows with missing values in R 4

Drib Indistinguishable row in R :

Drop rows in R with conditions in R 32

Nosotros will be using the following dataframe to depict the drib duplicates in R. Lets outset create the dataframe.

# elementary Information frame creation  mydata = information.frame (NAME =c ('Alisa','Bobby','jodha','jack','raghu','Cathrine',                       'Alisa','Bobby','kumar','Alisa','jack','Cathrine'),                       Age = c (26,24,26,22,23,24,26,24,22,26,22,25),                       Score =c(85,63,55,74,31,77,85,63,42,85,74,78))  mydata

so the resultant information frame will exist

remove duplicates in R dplyr 1

distinct() Function in Dplyr – Remove indistinguishable rows of a dataframe in R:

library(dplyr)  # Remove duplicate rows of the dataframe singled-out(mydata)

In this dataset, all the indistinguishable rows are eliminated so information technology returns the unique rows in mydata.

remove duplicates in R dplyr 2

Driblet Duplicates in R using unique() part in R

When nosotros apply unique function to the above data frame

## Utilize unique function for data frame in R unique(mydata)

Indistinguishable entries in the data frame are eliminated and the concluding output volition exist
unique function in R 5

Remove Duplicates based on a cavalcade using duplicated() function

duplicated() function along with [!] takes upward the column name equally argument and results in identifying unique value of the particular column equally shown beneath

              ## unique value of the column in R dataframe  mydata[!duplicated(mydata$NAME), ]

so the dataframe with unique values of the NAME cavalcade will be

remove duplicates in R dplyr 3