I cannot get my data frame to add an additional column. I have reviewed so many stack overflows, but here is a subset (Adding a new column in a matrix in R, adding new column to data frame in R, new column not added to dataframe in R,R: complete a dataset with a new column added, R: add a new column to dataframes from a function)
I need a single column that tells us if there is a positive or "1" in any of the viral rows I have.
I am trying to determine probability and from what I see, I will need this column to do further calculations, so please help if able!
Sample data
Filovirus (MOD) PCR : Phlebo (Sanchez-Seco) PCR
0 0
0 1
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
species code forest site
<fctr> <dbl> <fctr>
SM 1 UMNP-mangabey
SM 1 UMNP-mangabey
RC 9 UMNP-hondohondoc
BWC 9 UMNP-hondohondod
BWC 9 UMNP-hondohondod
BWC 9 UMNP-hondohondod
BWC 9 UMNP-hondohondod
BWC 9 UMNP-hondohondod
BWC 9 UMNP-hondohondod
BWC 9 UMNP-hondohondod
The closest I have gotten is getting base R to call which rows have the positive value
I followed the solution here but have yet to get it to work for me.
tmp=which(data==1,arr.ind=T)
tmp=tmp[order(tmp[,"row"]),]
c("positive","negative")[tmp[,"col"]] -> data$new
Any advice is greatly appreciated.
Dput
structure(list(`Filovirus (MOD) PCR` = c("0", "0", "0", "0",
"0", "0", "0", "0", "0", "0"), `Filovirus (A) PCR` = c("0", "0",
"0", "0", "0", "0", "0", "0", "0", "0"), `Filovirus (B) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Filo C PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Filovirus (D) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Coronavirus (Quan) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Coronavirus (Watanabe) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Paramyxo (Tong) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Flavivirus Moureau PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Flavivirus Sanchez-seco PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Arena Lozano 1 PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Retrovirus Courgnard PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Simian Foamy Goldberg (Pol) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Simian Foamy Goldberg (LTR Region) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Influenza (Anthony) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Influenza (Liang) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Rhabdo (CII) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Enterovirus CII I PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Enterovirus CII-II PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Alphav (Sanchez-Seco) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Lyssavirus (Vasquez-Moron) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Seadornavirus (CII) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Hantavirus (Raboni) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Hantavirus (Klempa) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Nipah (Wacharapleusadee) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Henipa (Feldman) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Bunya S (Briese) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Bunya L (Briese) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), `Phlebo (Sanchez-Seco) PCR` = c("0",
"0", "0", "0", "0", "0", "0", "0", "0", "0"), species = structure(c(3L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("SM", "SY", "BWC",
"YB", "RC"), class = "factor"), code = c(2, 5, 5, 5, 5, 5, 5,
5, 5, 5), forestsite = structure(c(3L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L), .Label = c("Magombera1", "Magombera2", "NDUFR",
"Ndundulu1", "Ndundulu2", "Ndundulu3", "Nyumbanitu", "UMNP-campsite3",
"UMNP-hondohondoa", "UMNP-hondohondob", "UMNP-hondohondoc", "UMNP-hondohondod",
"UMNP-hondohondoe", "UMNP-HQ", "MamaGoti", "UMNP-mangabey", "UMNP-njokamoni",
"UMNP-Sanje1", "UMNP-Sanje2", "UMNP-Sanje3", "Sonjo", "SonjoRoad"
), class = "factor")), row.names = c(NA, -10L), class = c("tbl_df",
"tbl", "data.frame"))
Update:
Your 0 and 1 are character type. Transforming to number with type.convert(as.is = TRUE) will make the code work:
library(dplyr)
df %>%
type.convert(as.is=TRUE) %>%
mutate(new_column = if_else(rowSums(select(., contains("PCR"))) > 0, "positive", "negative"))
Filovirus (…¹ Filov…² Filov…³ Filo …⁴ Filov…⁵ Coron…⁶ Coron…⁷ Param…⁸ Flavi…⁹ Flavi…˟ Arena…˟ Retro…˟ Simia…˟ Simia…˟ Influ…˟
<int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
# … with 18 more variables: `Influenza (Liang) PCR` <int>, `Rhabdo (CII) PCR` <int>, `Enterovirus CII I PCR` <int>,
# `Enterovirus CII-II PCR` <int>, `Alphav (Sanchez-Seco) PCR` <int>, `Lyssavirus (Vasquez-Moron) PCR` <int>,
# `Seadornavirus (CII) PCR` <int>, `Hantavirus (Raboni) PCR` <int>, `Hantavirus (Klempa) PCR` <int>,
# `Nipah (Wacharapleusadee) PCR` <int>, `Henipa (Feldman) PCR` <int>, `Bunya S (Briese) PCR` <int>,
# `Bunya L (Briese) PCR` <int>, `Phlebo (Sanchez-Seco) PCR` <int>, species <chr>, code <int>, forestsite <chr>,
# new_column <chr>, and abbreviated variable names ¹`Filovirus (MOD) PCR`, ²`Filovirus (A) PCR`, ³`Filovirus (B) PCR`,
# ⁴`Filo C PCR`, ⁵`Filovirus (D) PCR`, ⁶`Coronavirus (Quan) PCR`, ⁷`Coronavirus (Watanabe) PCR`, …
# ℹ Use `colnames()` to see all variable names
First answer:
The dplyr pendant would be:
Data taken from @langtang(many thanks):
library(dplyr)
df %>%
mutate(new_column = if_else(rowSums(select(., contains("PCR"))) > 0, "positive", "negative"))
species code forest_site Filovirus (MOD) PCR Phlebo (Sanchez-Seco) PCR
1 SM 1 UMNP-mangabey negative negative
2 SM 1 UMNP-mangabey negative positive
3 RC 9 UMNP-hondohondoc negative negative
4 BWC 9 UMNP-hondohondod negative negative
5 BWC 9 UMNP-hondohondod negative negative
6 BWC 9 UMNP-hondohondod negative negative
7 BWC 9 UMNP-hondohondod negative negative
8 BWC 9 UMNP-hondohondod negative negative
9 BWC 9 UMNP-hondohondod negative negative
10 BWC 9 UMNP-hondohondod negative negative