Delete Rows Containing Specific Strings in R

Delete rows containing specific strings in R

This should do the trick:

df[- grep("REVERSE", df$Name),]

Or a safer version would be:

df[!grepl("REVERSE", df$Name),]

Remove row if any column contains a specific string

We can use rowSums to create a logical vector and subset based on it

df1[rowSums(df1 == "no_data")==0, , drop = FALSE]
#   time speed wheels
#4 3:00    50     18

data

df1 <- structure(list(time = c("1:00", "2:00", "no_data", "3:00"), speed = c("30", 
"no_data", "no_data", "50"), wheels = c("no_data", "18", "no_data", 
"18")), .Names = c("time", "speed", "wheels"), class = "data.frame", 
row.names = c(NA, -4L))

Remove Rows occurring after a String R Data frame

We can subset with row_numberand which

library(dplyr)

df %>% filter(row_number() < which(A=='total'))

             A     B
1    Bob Smith 01005
2   Carl Jones 01008
3 Syndey Lewis 01185

Remove rows which have less than string into a specific column

If you want to use tidyverse packages you could use:

library(dplyr)
library(stringr)

dd %>% filter(str_count(text, " ") >= 3)

Here we assume that "less than 4 strings" means less than 3 spaces. By counting characters, you can have a much more efficient solution than actually going though the work of splitting the string up and allocating the memory for the separate pieces when you don't really need them.

Remove Rows From Data Frame where a Row matches a String

Just use the == with the negation symbol (!). If dtfm is the name of your data.frame:

dtfm[!dtfm$C == "Foo", ]

Or, to move the negation in the comparison:

dtfm[dtfm$C != "Foo", ]

Or, even shorter using subset():

subset(dtfm, C!="Foo")

Deleting rows corresponding to particular string, but keep NA values

We can create a condition with is.na to return those elements that are NA as well with |

 subset(df, Col1 != 'string' | is.na(Col1))

Delete rows based on two columns (& containing specific strings)

Does this work:

library(dplyr)
library(stringr)

report %>% group_by(Trial) %>% filter(all(str_detect(Code, 'test', negate = TRUE)))
# A tibble: 10 x 5
# Groups:   Trial [2]
   Subject Trial `Event type` Code                    Time   
   <chr>   <dbl> <chr>        <chr>                   <chr>  
 1 VP02_RP    22 Picture      face01_n                1700595
 2 VP02_RP    22 Sound        mnegsound1              1700886
 3 VP02_RP    22 Picture      pospic1                 1716953
 4 VP02_RP    22 Nothing      ev_mnegposneg_adj_onset 1748603
 5 VP02_RP    22 Response     14                      1779212
 6 VP02_RP    25 Picture      face02_n                1801100
 7 VP02_RP    25 Sound        fpossound1              1801392
 8 VP02_RP    25 Picture      negpic1                 1816560
 9 VP02_RP    25 Nothing      ev_fnegnegpos_adj_onset 1846190
10 VP02_RP    25 Response     15                      1877413

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