Complete ALL of the essentials below correctly to earn an ‘S’ on the lab.
Complete the Depth portion successful to earn credit toward a depth boost (every 2 lab depth assignments completed earns a 1/3 letter grade boost to your final grade)
Render your document as a .pdf or .html and submit it to the google folder on Moodle for grading.
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0 ✔ purrr 1.0.1
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.2.1 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(patchwork)
library(ggsci)
library(ggplot2)We can use this fun ferris wheels data I found online to make some practice graphs!
wheels <- read.csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-08-09/wheels.csv')
head(wheels) X name height diameter opened closed country
1 1 360 Pensacola Beach 200.00 NA 2012-07-03 2013-01-01 USA
2 2 Amuran 303.00 199.8 2004-01-01 <NA> Japan
3 3 Asiatique Sky 200.00 200.0 2012-12-15 <NA> Tailand
4 4 Aurora Wheel 295.00 272.0 <NA> <NA> Japan
5 5 Baghdad Eye 180.00 NA 2011-01-01 <NA> Iraq
6 6 Beijing Great Wheel 692.64 642.7 <NA> <NA> China
location number_of_cabins passengers_per_cabin
1 Pensacola Beach; Florida 42 6
2 Kagoshima; Kyushu 36 NA
3 Asiatique the Riverfront 42 NA
4 Nagashima Spa Land; Mie; Honshu NA NA
5 Al-Zawraa Park; Baghdad 40 6
6 Chaoyang Park; Beijing 48 40
seating_capacity hourly_capacity ride_duration_minutes climate_controlled
1 252 1260 12.0 Yes
2 NA NA 14.5 Yes
3 NA NA NA Yes
4 NA NA NA <NA>
5 240 960 15.0 <NA>
6 1920 5760 20.0 yes
construction_cost status design_manufacturer type
1 Unknown Moved Realty Masters of FL Transportable
2 Unknown Operating <NA> <NA>
3 Unknown Operating Dutch Wheels (Vekoma) <NA>
4 Unknown Operating <NA> Fixed
5 $6 million USD Operating <NA> <NA>
6 $290 million USD Delayed The Great Wheel Corporation Fixed
vip_area ticket_cost_to_ride official_website turns
1 Yes <NA> <NA> 4
2 <NA> <NA> <NA> 1
3 <NA> <NA> http://www.asiatiquesky.com/ NA
4 <NA> <NA> http://www.nagashima-onsen.co.jp/ NA
5 <NA> 3.5 <NA> NA
6 <NA> <NA> <NA> 11.) What’s in a graph?
Write a paragraph explaining some tenants of good vs. bad graphics. Be specific!
very simple, clearly labelled axis, indicators of significance (p values), error indicators (confidence interval,
does not show the data or the patterns in the plot are difficult to see/follow pie charts, too much information, pie charts, no clear axis labels and legends and , poor formatting (text is not esy to read) etc.
2.) Make the following plots with the ferris wheel data: histogram, boxplot, bar graph, line graph, scatterplot
wheels_1 <- wheels
summary(wheels_1) X name height diameter
Min. : 1 Length:73 Min. :130.0 Min. : 70.0
1st Qu.:19 Class :character 1st Qu.:197.0 1st Qu.:165.4
Median :37 Mode :character Median :271.5 Median :271.0
Mean :37 Mean :304.2 Mean :286.8
3rd Qu.:55 3rd Qu.:378.8 3rd Qu.:361.0
Max. :73 Max. :728.0 Max. :700.0
NA's :1 NA's :35
opened closed country location
Length:73 Length:73 Length:73 Length:73
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
number_of_cabins passengers_per_cabin seating_capacity hourly_capacity
Min. :15.00 Min. : 2.00 Min. : 64.0 Min. : 349
1st Qu.:36.00 1st Qu.: 6.00 1st Qu.: 258.0 1st Qu.: 960
Median :42.00 Median : 8.00 Median : 336.0 Median :1440
Mean :41.94 Mean :11.51 Mean : 456.4 Mean :1571
3rd Qu.:48.00 3rd Qu.: 8.00 3rd Qu.: 426.0 3rd Qu.:1680
Max. :68.00 Max. :40.00 Max. :1920.0 Max. :5760
NA's :11 NA's :18 NA's :18 NA's :24
ride_duration_minutes climate_controlled construction_cost status
Min. : 6.00 Length:73 Length:73 Length:73
1st Qu.:12.00 Class :character Class :character Class :character
Median :15.00 Mode :character Mode :character Mode :character
Mean :17.73
3rd Qu.:20.00
Max. :40.00
NA's :12
design_manufacturer type vip_area ticket_cost_to_ride
Length:73 Length:73 Length:73 Length:73
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
official_website turns
Length:73 Min. :1
Class :character 1st Qu.:1
Mode :character Median :1
Mean :2
3rd Qu.:3
Max. :4
NA's :53 wheels_4 <- wheels %>%
drop_na(seating_capacity)ggplot(data = wheels_1, mapping = aes(x = height)) +
geom_histogram(binwidth = 45)+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
xlab("height") +
ylab("frequency") +
theme_classic()+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))Warning: Removed 1 rows containing non-finite values (`stat_bin()`).
ggplot(data = wheels_1,aes(x = country, y = seating_capacity, color = as.factor(status))) +
geom_bar(stat = "identity")+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
xlab(" fun ferries wheels") +
ylab("height")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))Warning: Removed 18 rows containing missing values (`position_stack()`).
ggplot(data = wheels_1, mapping = aes(x = country, y = height)) +
geom_boxplot()+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
xlab("height") +
ylab("frequency") +
theme_classic()+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
ggplot(data = wheels_1, mapping = aes(x = height, y = seating_capacity, color = country)) +
geom_line()+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
xlab("height") +
ylab("seating capacity") +
theme_classic()+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))Warning: Removed 13 rows containing missing values (`geom_line()`).
ggplot(data = wheels_4, mapping = aes(x = height, y = seating_capacity, color = country)) +
geom_point()+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
xlab("height") +
ylab("seating capacity") +
theme_classic()+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))
this could be better - we could choose one country and just focus on the how the sitting capacity changes with height of the ferry or even make a new column with (Asia, Europe etc categories) and try to view the same patterns. only one sample - no distribution in the data and thus standardard deviation is zero.
3.) Using your scatterplot from #2 and remove the grey background from the plot. Continue using this same plot for 3-6
ggplot(data = wheels_4, mapping = aes(x = height, y = seating_capacity, color = country)) +
geom_point()+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
xlab("height") +
ylab("seating capacity") +
theme_classic()+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))
4.) Change the colors away from default colors. Show me an example of manually changing the colors and an example of you using ggsci to change the colors.
ggplot(data = wheels_4, mapping = aes(x = height, y = seating_capacity, color = country)) +
geom_point()+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
labs(legend = "country", x="height", y="seating capacity") +
scale_fill_manual()
theme_classic()+
theme_bw()+
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- attr(*, "complete")= logi TRUE
- attr(*, "validate")= logi TRUE5.) Change the shape and size of your points!
ggplot(data = wheels_4, mapping = aes(x = height, y = seating_capacity, color = country)) +
geom_point()+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
labs(legend = "country", x="height", y="seating capacity") +
scale_fill_manual()
theme_classic()+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))List of 94
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- attr(*, "validate")= logi TRUE6.) Add lines to connect your points to one another.
ggplot(data = wheels_4, mapping = aes(x = height, y = seating_capacity, color = country)) +
geom_point()+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
labs(legend = "country", x="height", y="seating capacity") +
scale_fill_manual()
theme_classic()+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))List of 94
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.. ..- attr(*, "unit")= int 8
..$ debug : NULL
..$ inherit.blank: logi TRUE
..- attr(*, "class")= chr [1:2] "element_text" "element"
$ plot.caption.position : chr "panel"
$ plot.tag :List of 11
..$ family : NULL
..$ face : NULL
..$ colour : NULL
..$ size : 'rel' num 1.2
..$ hjust : num 0.5
..$ vjust : num 0.5
..$ angle : NULL
..$ lineheight : NULL
..$ margin : NULL
..$ debug : NULL
..$ inherit.blank: logi TRUE
..- attr(*, "class")= chr [1:2] "element_text" "element"
$ plot.tag.position : chr "topleft"
$ plot.margin : 'margin' num [1:4] 5.5points 5.5points 5.5points 5.5points
..- attr(*, "unit")= int 8
$ strip.background :List of 5
..$ fill : chr "grey85"
..$ colour : chr "grey20"
..$ linewidth : NULL
..$ linetype : NULL
..$ inherit.blank: logi TRUE
..- attr(*, "class")= chr [1:2] "element_rect" "element"
$ strip.background.x : NULL
$ strip.background.y : NULL
$ strip.clip : chr "inherit"
$ strip.placement : chr "inside"
$ strip.text :List of 11
..$ family : NULL
..$ face : NULL
..$ colour : chr "grey10"
..$ size : 'rel' num 0.8
..$ hjust : NULL
..$ vjust : NULL
..$ angle : NULL
..$ lineheight : NULL
..$ margin : 'margin' num [1:4] 4.4points 4.4points 4.4points 4.4points
.. ..- attr(*, "unit")= int 8
..$ debug : NULL
..$ inherit.blank: logi TRUE
..- attr(*, "class")= chr [1:2] "element_text" "element"
$ strip.text.x : NULL
$ strip.text.y :List of 11
..$ family : NULL
..$ face : NULL
..$ colour : NULL
..$ size : NULL
..$ hjust : NULL
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..$ angle : num -90
..$ lineheight : NULL
..$ margin : NULL
..$ debug : NULL
..$ inherit.blank: logi TRUE
..- attr(*, "class")= chr [1:2] "element_text" "element"
$ strip.switch.pad.grid : 'simpleUnit' num 2.75points
..- attr(*, "unit")= int 8
$ strip.switch.pad.wrap : 'simpleUnit' num 2.75points
..- attr(*, "unit")= int 8
$ strip.text.y.left :List of 11
..$ family : NULL
..$ face : NULL
..$ colour : NULL
..$ size : NULL
..$ hjust : NULL
..$ vjust : NULL
..$ angle : num 90
..$ lineheight : NULL
..$ margin : NULL
..$ debug : NULL
..$ inherit.blank: logi TRUE
..- attr(*, "class")= chr [1:2] "element_text" "element"
- attr(*, "class")= chr [1:2] "theme" "gg"
- attr(*, "complete")= logi TRUE
- attr(*, "validate")= logi TRUE7.) Using your bar graph, change both the color and fill and see how those are different.
ggplot(data = wheels_1,aes(x = country, y = seating_capacity, color = as.factor(status))) +
geom_bar(stat = "identity", fill = "orange", color ="black")+
ggtitle("seating capacity of fun ferries per country") +
xlab("Countries") +
ylab("seating capacity")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 10))Warning: Removed 18 rows containing missing values (`position_stack()`).
8.) Use facet_wrap to alter one of your graphs!
ggplot(data = wheels_1,aes(x = country, y = seating_capacity, color = as.factor(status))) +
geom_bar(stat = "identity")+
ggtitle("Boxplots showing distribution of height within fun ferris wheels") +
xlab(" fun ferries wheels") +
ylab("height")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, size = 6))+
facet_wrap(~turns, ncol = 3)Warning: Removed 18 rows containing missing values (`position_stack()`).
1.) Using patchwork, put all of your graphs from Essentials #2 on the same output page!
2.) Using the wheels data, group data, calculate an average, and plot data with error bars! Ask for help if you need it. We may not have learned all of this in class just yet.
wheels_3 <- wheels %>%
group_by(country) %>%
summarize(mean_height = mean(height), sd_height = sd(height), n=n(), se = sd_height/sqrt(n)) %>%
filter(sd_height!= "NA")
p <- ggplot(data = wheels_3,aes(y = mean_height, x = country)) +
geom_bar(stat = "identity", fill="blue")+
labs( title = "Fun ferry wheels distribution per country", x= "Country", y ="height")+
theme_bw()+
theme(axis.text.x=element_text(angle = 60, size = 10))
p+geom_errorbar(aes(ymin=mean_height-se, ymax=mean_height+se), width=.2,
position=position_dodge(.9))+
theme_classic()
3.) Using theme() and labs() add custom labels to your X and Y axis. Add a title. Change the size of the text on both axes. This may be beyond our tutorial in class, so ask Justin and/or use google or resources linked above. Theme() is extremely powerful and will always be useful for us!