Testing
# Programmer: Sarah Akhtar, Date: February 7th, 2025
# 1. Load the tidyverse library
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# 2. Download coasters.csv from dataset and load into R variable
getwd()
## [1] "/Users/sarahak1786/Downloads"
setwd("/Users/sarahak1786/Downloads")
coaster <- read_csv("coasters.csv")
## Rows: 751 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): coaster_name, Location, Status, Type_Main
## dbl (7): Length, Height, Inversions, Duration, Drop, year_introduced, speed
##
## 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.
print(coaster)
## # A tibble: 751 × 11
## coaster_name Length Location Status Height Inversions Duration Drop
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Switchback Railway 600 Coney I… Remov… 50 NA 60 43
## 2 Leap-The-Dips 1452 Lakemon… Opera… 41 NA 60 9
## 3 Racer (1910 roller c… 4500 Kennywo… Opera… 72.5 0 92 50
## 4 Zippin Pippin 2865 Other <NA> 70 NA 118 70
## 5 The Wild One (roller… 4000 Six Fla… <NA> 98 0 112 88
## 6 Jack Rabbit (Kennywo… 2132 Kennywo… Opera… 40 0 96 70
## 7 Jack Rabbit (Seabree… 2130 Seabree… Opera… 75 NA NA NA
## 8 Scenic Railway (roll… 3000 Dreamla… Opera… 40 NA NA 40
## 9 Roller Coaster (Lago… 2500 Lagoon … Opera… 62 0 NA NA
## 10 Big Dipper (Blackpoo… 3300 Blackpo… Opera… 65 0 148 50
## # ℹ 741 more rows
## # ℹ 3 more variables: year_introduced <dbl>, Type_Main <chr>, speed <dbl>
# 3. Use the str() function to get more information about the dataframe
# Answer: There are 751 rows and 11 columns in the dataset.
str(coaster)
## spc_tbl_ [751 × 11] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ coaster_name : chr [1:751] "Switchback Railway" "Leap-The-Dips" "Racer (1910 roller coaster)" "Zippin Pippin" ...
## $ Length : num [1:751] 600 1452 4500 2865 4000 ...
## $ Location : chr [1:751] "Coney Island" "Lakemont Park" "Kennywood" "Other" ...
## $ Status : chr [1:751] "Removed" "Operating" "Operating" NA ...
## $ Height : num [1:751] 50 41 72.5 70 98 40 75 40 62 65 ...
## $ Inversions : num [1:751] NA NA 0 NA 0 0 NA NA 0 0 ...
## $ Duration : num [1:751] 60 60 92 118 112 96 NA NA NA 148 ...
## $ Drop : num [1:751] 43 9 50 70 88 70 NA 40 NA 50 ...
## $ year_introduced: num [1:751] 1884 1902 1910 1912 1917 ...
## $ Type_Main : chr [1:751] "Wood" "Wood" "Wood" "Wood" ...
## $ speed : num [1:751] 6 10 40 40 53 45 42 35 45 40 ...
## - attr(*, "spec")=
## .. cols(
## .. coaster_name = col_character(),
## .. Length = col_double(),
## .. Location = col_character(),
## .. Status = col_character(),
## .. Height = col_double(),
## .. Inversions = col_double(),
## .. Duration = col_double(),
## .. Drop = col_double(),
## .. year_introduced = col_double(),
## .. Type_Main = col_character(),
## .. speed = col_double()
## .. )
## - attr(*, "problems")=<externalptr>
# 4. Classify each variable as categorical or quantitative
# Answer: Categorical Variables: coaster_name, Location, Status, Type_Main
# Quantitative Variables: Length, Height, Inversions, Duration,
# Drop, year_introduced, speed
# 5. Print the 5th through 10th values of the column coaster_name
coaster$coaster_name[5:10]
## [1] "The Wild One (roller coaster)"
## [2] "Jack Rabbit (Kennywood)"
## [3] "Jack Rabbit (Seabreeze)"
## [4] "Scenic Railway (roller coaster)"
## [5] "Roller Coaster (Lagoon)"
## [6] "Big Dipper (Blackpool Pleasure Beach)"
# 6. Use the table() function on the Status column of the dataframe
table(coaster$Status)
##
## Operating Removed
## 471 97
# 7. Create a data frame sub_coasters that contains only Length/Height
sub_coasters <- coaster[, c("Length", "Height")]
print(sub_coasters)
## # A tibble: 751 × 2
## Length Height
## <dbl> <dbl>
## 1 600 50
## 2 1452 41
## 3 4500 72.5
## 4 2865 70
## 5 4000 98
## 6 2132 40
## 7 2130 75
## 8 3000 40
## 9 2500 62
## 10 3300 65
## # ℹ 741 more rows
# 8. Write the first 10 rows of sub_coasters to a csv file and submit the output
first_sub_coasters <- sub_coasters[1:10,]
print(first_sub_coasters)
## # A tibble: 10 × 2
## Length Height
## <dbl> <dbl>
## 1 600 50
## 2 1452 41
## 3 4500 72.5
## 4 2865 70
## 5 4000 98
## 6 2132 40
## 7 2130 75
## 8 3000 40
## 9 2500 62
## 10 3300 65
write_csv(first_sub_coasters, "homework3_coasters.csv")
# 9. Create a bar plot using geom_bar() of the Inversions variable
ggplot(coaster, aes(Inversions)) +
geom_bar()
## Warning: Removed 74 rows containing non-finite outside the scale range
## (`stat_count()`).
ggsave("plot1.png")
## Saving 7 x 5 in image
## Warning: Removed 74 rows containing non-finite outside the scale range
## (`stat_count()`).
# 10. Create a scatterplot using geom_point() showing the relationship
# Answer: It seems that as the length of the ride increases, the duration also
# increases. The relationship seems to show a direction correlation that
# follows a linear path.
ggplot(coaster, aes(Length, Duration)) +
geom_point()
## Warning: Removed 174 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggsave("plot2.png")
## Saving 7 x 5 in image
## Warning: Removed 174 rows containing missing values or values outside the scale range
## (`geom_point()`).
# 11. Create a histogram using geom_histogram() of the year_introduced variable
ggplot(coaster, aes(year_introduced)) +
geom_histogram() +
facet_wrap(~Type_Main)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggsave("plot3.png")
## Saving 7 x 5 in image
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# 12. Create a scatterplot showing the relationship between year_introduced/speed
# Answer: From this graph, we can see that as time passes, the speed of coasters
# increases along with the height and drop length. In addition to this,
# we see that recent coasters are often made with steel rather than wood.
ggplot(coaster, aes(year_introduced, speed,
shape = Type_Main,
color = Drop,
size = Height)) +
geom_point()
ggsave("plot4.png")
## Saving 7 x 5 in image
# 13. Create another visualization that highlights a notable trend or pattern
ggplot(coaster, aes(Height, speed, color = Type_Main)) +
geom_point() +
facet_wrap(~Type_Main) +
geom_smooth(method="lm")
## `geom_smooth()` using formula = 'y ~ x'
# 14. Explain your interpretation of your plot from question 13
# Answer: From the plot, we can see that as the height of the coaster increases,
# the speed also increases. I also separated the coasters by main type
# to demonstrate that steel coasters are not only abundant, but the top
# speeds are much higher between the other and wood types of coasters.
# 15. Turn in your R script file as well as the plots you generated
# [COMPLETED]