Homework 10
Key
2025-02-26
Instructions
Answer the following questions and complete the exercises in
RMarkdown. Please embed all of your code and push your final work to
your repository. Your final lab report should be organized, clean, and
run free from errors. Remember, you must remove the # for
the included code chunks to run. Be sure to add your name to the author
header above.
Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!
Load the libraries
library(tidyverse)
library(janitor)
library(naniar)For this homework, we will take a departure from biological data and
use data about California colleges. These data are a subset of the
national college scorecard (https://collegescorecard.ed.gov/data/). Load the
ca_college_data.csv as a new object called
colleges.
colleges <- readr::read_csv("data/ca_college_data.csv") %>% janitor::clean_names()## Rows: 341 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): INSTNM, CITY, STABBR, ZIP
## dbl (6): ADM_RATE, SAT_AVG, PCIP26, COSTT4_A, C150_4_POOLED, PFTFTUG1_EF
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.The variables are a bit hard to decipher, here is a key:
INSTNM: Institution name
CITY: California city
STABBR: Location state
ZIP: Zip code
ADM_RATE: Admission rate
SAT_AVG: SAT average score
PCIP26: Percentage of degrees awarded in Biological And Biomedical
Sciences
COSTT4_A: Annual cost of attendance
C150_4_POOLED: 4-year completion rate
PFTFTUG1_EF: Percentage of undergraduate students who are first-time,
full-time degree/certificate-seeking undergraduate students
- Use your preferred function(s) to have a look at the data and get an idea of its structure. Make sure you summarize NA’s and determine whether or not the data are tidy. You may also consider dealing with any naming issues.
glimpse(colleges)## Rows: 341
## Columns: 10
## $ instnm <chr> "Grossmont College", "College of the Sequoias", "College…
## $ city <chr> "El Cajon", "Visalia", "San Mateo", "Ventura", "Oxnard",…
## $ stabbr <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "C…
## $ zip <chr> "92020-1799", "93277-2214", "94402-3784", "93003-3872", …
## $ adm_rate <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ sat_avg <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pcip26 <dbl> 0.0016, 0.0066, 0.0038, 0.0035, 0.0085, 0.0151, 0.0000, …
## $ costt4_a <dbl> 7956, 8109, 8278, 8407, 8516, 8577, 8580, 9181, 9281, 93…
## $ c150_4_pooled <dbl> NA, NA, NA, NA, NA, NA, 0.2334, NA, NA, NA, NA, 0.1704, …
## $ pftftug1_ef <dbl> 0.3546, 0.5413, 0.3567, 0.3824, 0.2753, 0.4286, 0.2307, …names(colleges)## [1] "instnm" "city" "stabbr" "zip"
## [5] "adm_rate" "sat_avg" "pcip26" "costt4_a"
## [9] "c150_4_pooled" "pftftug1_ef"naniar::miss_var_summary(colleges)## # A tibble: 10 × 3
## variable n_miss pct_miss
## <chr> <int> <num>
## 1 sat_avg 276 80.9
## 2 adm_rate 240 70.4
## 3 c150_4_pooled 221 64.8
## 4 costt4_a 124 36.4
## 5 pftftug1_ef 53 15.5
## 6 pcip26 35 10.3
## 7 instnm 0 0
## 8 city 0 0
## 9 stabbr 0 0
## 10 zip 0 0- Which cities in California have the highest number of colleges?
colleges %>%
count(city) %>%
arrange(desc(n))## # A tibble: 161 × 2
## city n
## <chr> <int>
## 1 Los Angeles 24
## 2 San Diego 18
## 3 San Francisco 15
## 4 Sacramento 10
## 5 Berkeley 9
## 6 Oakland 9
## 7 Claremont 7
## 8 Pasadena 6
## 9 Fresno 5
## 10 Irvine 5
## # ℹ 151 more rows- Based on your answer to #2, make a plot that shows the number of colleges in the top 10 cities.
colleges %>%
count(city) %>%
top_n(10, n) %>%
ggplot(aes(x=reorder(city,n), y=n))+
geom_col()+
coord_flip()
- The column
COSTT4_Ais the annual cost of each institution. Which city has the highest average cost? Where is it located?
colleges %>%
group_by(city) %>%
summarize(mean_cost_yr=mean(costt4_a, na.rm=TRUE),
total=n()) %>%
arrange(desc(mean_cost_yr))## # A tibble: 161 × 3
## city mean_cost_yr total
## <chr> <dbl> <int>
## 1 Claremont 66498 7
## 2 Malibu 66152 1
## 3 Valencia 64686 1
## 4 Orange 64501 3
## 5 Redlands 61542 1
## 6 Moraga 61095 1
## 7 Atherton 56035 1
## 8 Thousand Oaks 54373 1
## 9 Rancho Palos Verdes 50758 1
## 10 La Verne 50603 1
## # ℹ 151 more rows- Based on your answer to #4, make a plot that compares the cost of the individual colleges in the most expensive city. Bonus! Add UC Davis here to see how it compares :>).
colleges %>%
filter(instnm== "University of California-Davis" | city=="Claremont" & costt4_a!="NA") %>%
ggplot(aes(x=instnm, y=costt4_a)) +
geom_col()+
coord_flip()
- The column
ADM_RATEis the admissions rate by college andC150_4_POOLEDis the four-year completion rate. Use a scatterplot to show the relationship between these two variables. What do you think this means?
colleges %>%
ggplot(aes(x=adm_rate, y=c150_4_pooled))+
geom_point(na.rm=T)+
geom_smooth(method=lm, se=F, na.rm=T)## `geom_smooth()` using formula = 'y ~ x'
- Is there a relationship between cost and four-year completion rate? (You don’t need to do the stats, just produce a plot). What do you think this means?
colleges %>%
ggplot(aes(x=costt4_a, y=c150_4_pooled))+
geom_point(na.rm=T)+
geom_smooth(method=lm, se=F, na.rm=T)## `geom_smooth()` using formula = 'y ~ x'
- The column titled
INSTNMis the institution name. We are only interested in the University of California colleges. Make a new data frame that is restricted to UC institutions. You can removeHastings College of LawandUC San Franciscoas we are only interested in undergraduate institutions.
colleges %>%
filter(if_any(everything(), ~ str_detect(.x, "University of California")))## # A tibble: 10 × 10
## instnm city stabbr zip adm_rate sat_avg pcip26 costt4_a c150_4_pooled
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 University… La J… CA 92093 0.357 1324 0.216 31043 0.872
## 2 University… Irvi… CA 92697 0.406 1206 0.107 31198 0.876
## 3 University… Rive… CA 92521 0.663 1078 0.149 31494 0.73
## 4 University… Los … CA 9009… 0.180 1334 0.155 33078 0.911
## 5 University… Davis CA 9561… 0.423 1218 0.198 33904 0.850
## 6 University… Sant… CA 9506… 0.578 1201 0.193 34608 0.776
## 7 University… Berk… CA 94720 0.169 1422 0.105 34924 0.916
## 8 University… Sant… CA 93106 0.358 1281 0.108 34998 0.816
## 9 University… San … CA 9410… NA NA NA NA NA
## 10 University… San … CA 9414… NA NA NA NA NA
## # ℹ 1 more variable: pftftug1_ef <dbl>univ_calif <- colleges %>%
filter(str_detect(instnm, "University of California"))
univ_calif## # A tibble: 10 × 10
## instnm city stabbr zip adm_rate sat_avg pcip26 costt4_a c150_4_pooled
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 University… La J… CA 92093 0.357 1324 0.216 31043 0.872
## 2 University… Irvi… CA 92697 0.406 1206 0.107 31198 0.876
## 3 University… Rive… CA 92521 0.663 1078 0.149 31494 0.73
## 4 University… Los … CA 9009… 0.180 1334 0.155 33078 0.911
## 5 University… Davis CA 9561… 0.423 1218 0.198 33904 0.850
## 6 University… Sant… CA 9506… 0.578 1201 0.193 34608 0.776
## 7 University… Berk… CA 94720 0.169 1422 0.105 34924 0.916
## 8 University… Sant… CA 93106 0.358 1281 0.108 34998 0.816
## 9 University… San … CA 9410… NA NA NA NA NA
## 10 University… San … CA 9414… NA NA NA NA NA
## # ℹ 1 more variable: pftftug1_ef <dbl>Remove Hastings College of Law and
UC San Francisco and store the final data frame as a new
object univ_calif_final.
univ_calif_final <- univ_calif %>%
filter(city!="San Francisco")
univ_calif_final## # A tibble: 8 × 10
## instnm city stabbr zip adm_rate sat_avg pcip26 costt4_a c150_4_pooled
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 University … La J… CA 92093 0.357 1324 0.216 31043 0.872
## 2 University … Irvi… CA 92697 0.406 1206 0.107 31198 0.876
## 3 University … Rive… CA 92521 0.663 1078 0.149 31494 0.73
## 4 University … Los … CA 9009… 0.180 1334 0.155 33078 0.911
## 5 University … Davis CA 9561… 0.423 1218 0.198 33904 0.850
## 6 University … Sant… CA 9506… 0.578 1201 0.193 34608 0.776
## 7 University … Berk… CA 94720 0.169 1422 0.105 34924 0.916
## 8 University … Sant… CA 93106 0.358 1281 0.108 34998 0.816
## # ℹ 1 more variable: pftftug1_ef <dbl>Use separate() to separate institution name into two new
columns “UNIV” and “CAMPUS”.
univ_calif_final <- univ_calif_final %>%
separate(instnm, c("univ", "campus"), sep="-")
univ_calif_final## # A tibble: 8 × 11
## univ campus city stabbr zip adm_rate sat_avg pcip26 costt4_a c150_4_pooled
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Univ… San D… La J… CA 92093 0.357 1324 0.216 31043 0.872
## 2 Univ… Irvine Irvi… CA 92697 0.406 1206 0.107 31198 0.876
## 3 Univ… River… Rive… CA 92521 0.663 1078 0.149 31494 0.73
## 4 Univ… Los A… Los … CA 9009… 0.180 1334 0.155 33078 0.911
## 5 Univ… Davis Davis CA 9561… 0.423 1218 0.198 33904 0.850
## 6 Univ… Santa… Sant… CA 9506… 0.578 1201 0.193 34608 0.776
## 7 Univ… Berke… Berk… CA 94720 0.169 1422 0.105 34924 0.916
## 8 Univ… Santa… Sant… CA 93106 0.358 1281 0.108 34998 0.816
## # ℹ 1 more variable: pftftug1_ef <dbl>- The column
ADM_RATEis the admissions rate by campus. Which UC has the lowest and highest admissions rates? Produce a numerical summary and an appropriate plot.
univ_calif_final %>%
select(campus, adm_rate) %>%
arrange(adm_rate)## # A tibble: 8 × 2
## campus adm_rate
## <chr> <dbl>
## 1 Berkeley 0.169
## 2 Los Angeles 0.180
## 3 San Diego 0.357
## 4 Santa Barbara 0.358
## 5 Irvine 0.406
## 6 Davis 0.423
## 7 Santa Cruz 0.578
## 8 Riverside 0.663univ_calif_final %>%
select(campus, adm_rate) %>%
top_n(-1, adm_rate)## # A tibble: 1 × 2
## campus adm_rate
## <chr> <dbl>
## 1 Berkeley 0.169univ_calif_final %>%
ggplot(aes(x=reorder(campus, adm_rate), y=adm_rate))+
geom_col()+
coord_flip()
- If you wanted to get a degree in biological or biomedical sciences, which campus confers the majority of these degrees? Produce a numerical summary and an appropriate plot.
univ_calif_final %>%
select(campus, pcip26) %>%
arrange(pcip26)## # A tibble: 8 × 2
## campus pcip26
## <chr> <dbl>
## 1 Berkeley 0.105
## 2 Irvine 0.107
## 3 Santa Barbara 0.108
## 4 Riverside 0.149
## 5 Los Angeles 0.155
## 6 Santa Cruz 0.193
## 7 Davis 0.198
## 8 San Diego 0.216univ_calif_final %>%
ggplot(aes(x=reorder(campus, pcip26), y=pcip26))+
geom_col()