Data Summarization Lab Key

Data used

Circulator Lanes Dataset: the data is from https://data.baltimorecity.gov/Transportation/Charm-City-Circulator-Ridership/wwvu-583r

Available on: https://sisbid.github.io/Data-Wrangling/data/Charm_City_Circulator_Ridership.csv

library(tidyverse)

circ <- read_csv("https://sisbid.github.io/Data-Wrangling/data/Charm_City_Circulator_Ridership.csv")

1. How many days are in the data set? You can assume each observation/row is a different day (hint: get the number of rows).

nrow(circ)

[1] 1146

dim(circ)

[1] 1146   15

circ %>% 
  nrow()

[1] 1146

2. What is the total (sum) number of boardings on the green bus (greenBoardings column)?

sum(circ$greenBoardings, na.rm = TRUE)

[1] 935564

circ %>% pull(greenBoardings) %>% sum(na.rm = TRUE)

[1] 935564

count(circ, wt = greenBoardings)

# A tibble: 1 × 1
       n
   <dbl>
1 935564

3. How many days are missing daily ridership (daily column)? Use is.na() and sum().

daily <- circ %>% pull(daily)
sum(is.na(daily))

[1] 124

# Can also
circ %>% 
  count(is.na(daily))

# A tibble: 2 × 2
  `is.na(daily)`     n
  <lgl>          <int>
1 FALSE           1022
2 TRUE             124

4. Group the data by day of the week (day). Next, find the mean daily ridership (daily column) and the sample size. (hint: use group_by and summarize functions)

circ %>% 
  group_by(day) %>% 
  summarise(mean = mean(daily, na.rm = TRUE),
            n = n())

# A tibble: 7 × 3
  day        mean     n
  <chr>     <dbl> <int>
1 Friday    8961.   164
2 Monday    7340.   164
3 Saturday  6743.   163
4 Sunday    4531.   163
5 Thursday  7639.   164
6 Tuesday   7642.   164
7 Wednesday 7779.   164

Extra practice:

5. What is the median of orangeBoardings(use median()).

circ %>% 
  summarise(median = median(orangeBoardings, na.rm = TRUE))

# A tibble: 1 × 1
  median
   <dbl>
1   3074

# OR 
circ %>% pull(orangeBoardings) %>% median(na.rm = TRUE)

[1] 3074

6. Take the median of orangeBoardings(use median()), but this time stratify by day of the week.

circ %>% 
  group_by(day) %>% 
  summarise(median = median(orangeBoardings, na.rm = TRUE))

# A tibble: 7 × 2
  day       median
  <chr>      <dbl>
1 Friday     4014.
2 Monday     3336 
3 Saturday   2963 
4 Sunday     1900 
5 Thursday   3485 
6 Tuesday    3484 
7 Wednesday  3576