I'm new to Lasso regression and am trying to get glmnet to work in preparation for lasso regression. Unfortunately, I hit a problem pretty early on. Could someone let me know what I've done wrong? Thank you!

```
df0<- read_csv("data_cleaned.csv")
#Categorical variables set to factors
df0$PL_Binary_Score<-as.factor(df0$PL_Binary_Score)
df0$Gender<-as.factor(df0$Gender)
df0$Ethnicity<-as.factor(df0$Ethnicity)
df0$Age<-as.factor(df0$Age)
df0$Location<-as.factor(df0$Location)
df0$Income<-as.factor(df0$Income)
df0$Education<-as.factor(df0$Education)
df0$Working_From_Home<-as.factor(df0$Working_From_Home)
#Omit all NAs
PL<-na.omit(df0)
fit <- PL[c("Social_Media_Time",
"Ethnicity",
"Age",
"Location",
"Income",
"Education",
"Working_From_Home",
"Traditional_Time_Hrs",
"Private_Use",
"Anxiety_Diagnosed",
"Gender",
"Public_Use")]
PL.2<- as.matrix(fit)
fit = glmnet(PL.2, "PL_Binary_Score")
```

I get the following error:

```
number of observations in y (1) not equal to the number of rows of x (287).
```

I'm a bit confused because there are no missing values that could explain a difference in row length.