Selecting rows with certain values

I have a data frame from which I would like to keep only certain rows.

The data frame (in total around 1000 similar rows and more possibilities in var1) looks like this:

I would like to keep only the rows of people which both have 'car' and 'house' (so keep the rows with Jay and Ashley, and discard rows with Kate , Miranda and Mike). I know how to select rows based on certain conditions, but I can think of only methods that will also keep Kate and Miranda or Mike in the data frame. Im not sure how to approach this problem? Thanks for the advice.

With base R, we can use subset + ave like below

> subset(df, as.logical(ave(var1, name, FUN = \(x) all(c("car", "house") %in% x))))
    name  var1
1    Jay   car
2    Jay house
5 Ashley   car
6 Ashley house

Try this:

library(tidyverse)
df %>%
  # for each `name`...
  group_by(name) %>%
  # ...paste all `var1` values together:
  mutate(temp = str_c(var1, collapse = ",")) %>%
  # filter rows of value combinations of interest:
  filter(temp == "car,house") %>%
  # remove `temp`:
  select(-temp)

EDIT:

Picking up on a comment by @zx8754, you can first arrange the values in var1 in alphabetic order:

df %>%
  group_by(name) %>%
  arrange(var1) %>%
  mutate(temp = str_c(var1, collapse = ",")) %>%
  filter(temp == "car,house") %>%
  select(-temp)

Data

df=structure(list(var1 = c("car", "car", "car", "car", "house", 
"house", "house"), name = c("Jay", "Kate", "Ashley", "Mike", 
"Jay", "Miranda", "Ashley"), w = c(1, 1, 1, 1, 1, 1, 1)), row.names = c(NA, 
-7L), class = "data.frame")

first option

do.call(
  rbind,
  by(df,list(df$name),function(x){
    if (all(c("house","car") %in% unlist(x["var1"]))) x else NULL
  })
)

           name  var1
Ashley.5 Ashley   car
Ashley.6 Ashley house
Jay.1       Jay   car
Jay.2       Jay house

second option

x=data.frame(
  "var1"=c("house","car"),
  "w"=1
)
df=merge(df,x,by="var1")
df[ave(df$w,df$name,FUN=sum)==nrow(x),]

   var1   name w
1   car    Jay 1
3   car Ashley 1
5 house    Jay 1
7 house Ashley 1

Get counts of assets per name where assets match our set, then subset:

assets <- c("car", "house")
x <- table(df[ df$var1 %in% assets, "name"])

df[ df$name %in% names(x)[ x >= length(assets) ], ]
#     name  var1
# 1    Jay   car
# 2    Jay house
# 5 Ashley   car
# 6 Ashley house

We can do a simple filter for groups in which all desired values are found in var1, .by name, then filter again with ungrouped data to remove instances of "yatch" and others from the selected groups

library(dplyr) #version >= 1.1.0
df %>% 
    filter(all(c("car", "house") %in% var1), .by = name) %>% 
    filter(var1 %in% c("car", "house"))

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