library(tidyverse)
## ── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
grad <- read_csv("data/graduate-programs.csv")
## Parsed with column specification:
## cols(
## subject = col_character(),
## Inst = col_character(),
## AvNumPubs = col_double(),
## AvNumCits = col_double(),
## PctFacGrants = col_double(),
## PctCompletion = col_double(),
## MedianTimetoDegree = col_double(),
## PctMinorityFac = col_double(),
## PctFemaleFac = col_double(),
## PctFemaleStud = col_double(),
## PctIntlStud = col_double(),
## AvNumPhDs = col_double(),
## AvGREs = col_double(),
## TotFac = col_double(),
## PctAsstProf = col_double(),
## NumStud = col_double()
## )
# grad <- read_csv("exercises/2a/data/graduate-programs.csv")
grad
## # A tibble: 412 x 16
## subject Inst AvNumPubs AvNumCits PctFacGrants PctCompletion MedianTimetoDeg…
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 econom… ARIZ… 0.9 1.57 31.3 31.7 5.6
## 2 econom… AUBU… 0.79 0.64 77.6 44.4 3.84
## 3 econom… BOST… 0.51 1.03 43.5 46.8 5
## 4 econom… BOST… 0.49 2.66 36.9 34.2 5.5
## 5 econom… BRAN… 0.3 3.03 36.8 48.7 5.29
## 6 econom… BROW… 0.84 2.31 27.1 54.6 6
## 7 econom… CALI… 0.99 2.31 56.4 83.3 4
## 8 econom… CARN… 0.43 1.67 35.2 45.6 5.05
## 9 econom… CITY… 0.35 1.06 38.1 27.9 5.2
## 10 econom… CLAR… 0.47 0.7 24.7 37.7 5.17
## # … with 402 more rows, and 9 more variables: PctMinorityFac <dbl>,
## # PctFemaleFac <dbl>, PctFemaleStud <dbl>, PctIntlStud <dbl>,
## # AvNumPhDs <dbl>, AvGREs <dbl>, TotFac <dbl>, PctAsstProf <dbl>,
## # NumStud <dbl>
What is the average number of graduate students per economics program?
grad %>%
# first we filter to only look at "economics
## filter(subject == ___) %>%
filter(subject == "economics") %>%
summarise(mean = mean(NumStud))
## # A tibble: 1 x 1
## mean
## <dbl>
## 1 60.7