```
hsg.ppl2$XYZ <- for(i in 1:100)
if (hsg.ppl2$HISP[i] == 1 & hsg.ppl2$RAC1P[i] == 1) { "NHWht" } else if (hsg.ppl2$HISP[i] == 1 & hsg.ppl2$RAC1P[i] == 2) { "NHBlack" } else
if (hsg.ppl2$HISP[i] == 1 & (hsg.ppl2$RAC1P[i] == 6 | hsg.ppl2$RAC1P[i] == 7)) {"NHAsian" } else
if (hsg.ppl2$HISP[i] == 1 & (hsg.ppl2$RAC1P[i] == 3 | hsg.ppl2$RAC1P[i] == 4 |hsg.ppl2$RAC1P[i] == 5 | hsg.ppl2$RAC1P[i] == 8 | hsg.ppl2$RAC1P[i] == 9)) {"NHOther" } else
if (hsg.ppl2$HISP[i] >= 1) {"HISP"}
else {NA}
```

Hi priya53, I'd ask this kind of question with a minimal reprex. (FAQ: What's a reproducible example (`reprex`) and how do I do one?)

For the "find out how many people" question, check out dplyr's `group_by`

+ `tally`

function. https://dplyr.tidyverse.org/reference/tally.html

For example,

```
# tally() is short-hand for summarise()
mtcars %>% tally()
#> n
#> 1 32
mtcars %>% group_by(cyl) %>% tally()
#> # A tibble: 3 x 2
#> cyl n
#> <dbl> <int>
#> 1 4 11
#> 2 6 7
#> 3 8 14
# count() is a short-hand for group_by() + tally()
```

`mtcars %>% group_by(cyl) %>% tally()`

gives you how many observations `mtcars`

has for each unique value of `cyl`

.

There are two packages, `tidycensus`

and `acs`

that provide access to the US Census decennials and the annual estimates. `acs`

will tell you how to get an Census API key and some guidance in forming queries.

With tidycensus, it's going to look like

```
# obtain 50-state ACS 2017 population estimates with geographic data
states_pop <- get_estimates("state", product = "population", geometry = TRUE, shift_geo = TRUE) %>% filter(variable == "POP") %>% filter(GEOID != 11) %>% mutate(geoid = as.integer(GEOID)) %>% select(GEOID, NAME, geoid, value) %>% mutate(POP_TOT = value) %>% select(-value)
# obtain 50_state ACS 2017 population estimates for white, non-Hispanic
# requires manual selection on ASC site to limit estimate to that ethnicity
states_ethnic <- get_acs(geography = "state", variables = "B01001H_001") %>% filter(GEOID != 11 & GEOID != 72) %>% select(GEOID, NAME, estimate) %>% mutate(WHITE = estimate) %>% select(-estimate)
```

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