Assignment 3

Source file ⇒ Assignment_3.Rmd

5.4

ggplot(CPS85, aes(exper,wage)) +
  geom_point(aes(alpha = married)) +
  facet_wrap(~sector, ncol = 4) +
  scale_x_log10() +
  scale_y_log10()

6.3

Star glyph

* x-position
* y-poition
* number of stars (0, 1, or 2)

Range/Error bar

* x-position - protein
* y-poition of center dot/value
* color - polarity
* label - protein name
* length of bars 

2. The x-axis is the categorical variable “protein”. The y-axis is labelled “cell density”.

3. Color is not an attribute of **.

4. Guides: There are tick marks on the y-axis. There is no guide directly on the x-axis but the protein names act as a guide for he x-axis

6.6

4. Glyph: rectangle. Graphical attributes: color and text

6.8

Small <-
  NCHS %>%
  sample_n(size=5000)

ggplot(Small, aes(bmi,weight)) +
  geom_point(aes(alpha = smoker)) 

6.2

1. List all of the guides in the graph. For each one, say which variable is being mapped to which graphical attribute.

• x-axis tick marks (bottom) - genes
• x-axis ranges (top) - target
• y-axis tick marks - enzyme concentration
• fill key - flux
• color key - molecule

2. The basic glyph is a dot. Say what are the graphical attributes of the dot (e.g. color, size, . . . ). For each graphical attribute found in the graph, say which variable is mapped to that attribute.

• x-position - target, gene
• y-position - enzyme concentration
• fill - Flux
• color - molecule

3. Which two variables set the frame?

• enzyme concentration
• target

4. The scaling of the horizontal variable (e.g. the translation of position to variable levels) is set by a combination of two variables. Which two?

• target
• gene

7.2

1. Which color diamonds seem to be largest on average (in terms of carats)?

diamonds %>%
  group_by(color) %>%
  summarise(avg = mean(carat)) %>%
  arrange(desc(avg)) %>%
  head(1)

## Source: local data frame [1 x 2]
## 
##    color      avg
##   (fctr)    (dbl)
## 1      J 1.162137

• Color J seems to be largest on average.

2. Which clarity of diamonds has the largest average “table” per carat?

diamonds %>%
  group_by(clarity) %>%
  summarise(tablesPerCarat = mean(table/carat)) %>%
  arrange(desc(tablesPerCarat)) %>%
  head(1)

## Source: local data frame [1 x 2]
## 
##   clarity tablesPerCarat
##    (fctr)          (dbl)
## 1    VVS1       141.4822

• Clarity VVS1 seems to have the largest “table” per carat.

7.4

1. The column “first” does not exist in BabyNames.

2. Should be written as:

Tmp <- group_by( BabyNames, year, sex ) %>%
  summarise(totalBirths=mean(count))

c)Should be written as:

Tmp <- group_by(BabyNames, year, sex)
summarise(Tmp, totalBirths=sum(count))

## Source: local data frame [268 x 3]
## Groups: year [?]
## 
##     year   sex totalBirths
##    (int) (chr)       (int)
## 1   1880     F       90993
## 2   1880     M      110491
## 3   1881     F       91954
## 4   1881     M      100746
## 5   1882     F      107850
## 6   1882     M      113687
## 7   1883     F      112322
## 8   1883     M      104630
## 9   1884     F      129022
## 10  1884     M      114446
## ..   ...   ...         ...

7.5

data("BabyNames")

BabyNames %>%
  arrange(sex,count) %>%
  head()

##        name sex count year
## 1    Adelle   F     5 1880
## 2     Adina   F     5 1880
## 3  Adrienne   F     5 1880
## 4 Albertine   F     5 1880
## 5      Alys   F     5 1880
## 6       Ana   F     5 1880

BabyNames %>%
  filter(sex =="F") %>%
  head()

##        name sex count year
## 1      Mary   F  7065 1880
## 2      Anna   F  2604 1880
## 3      Emma   F  2003 1880
## 4 Elizabeth   F  1939 1880
## 5    Minnie   F  1746 1880
## 6  Margaret   F  1578 1880

BabyNames %>%
  filter(sex == "M", count > 10) %>%
  head()

##      name sex count year
## 1    John   M  9655 1880
## 2 William   M  9532 1880
## 3   James   M  5927 1880
## 4 Charles   M  5348 1880
## 5  George   M  5126 1880
## 6   Frank   M  3242 1880

BabyNames %>%
  summarise(total = sum(count)) %>%
  head()

##       total
## 1 333417770

BabyNames %>%
  select(name, count) %>%
  head()

##        name count
## 1      Mary  7065
## 2      Anna  2604
## 3      Emma  2003
## 4 Elizabeth  1939
## 5    Minnie  1746
## 6  Margaret  1578

7.6

data("Minneapolis2013")

1. There are 80101 cases.

Minneapolis2013 %>%
  group_by(Second) %>%
  summarise(count = n()) %>%
  rename(candidate = Second) %>%
  arrange(desc(count)) %>%
  head(5)

## Source: local data frame [5 x 2]
## 
##            candidate count
##                (chr) (int)
## 1       BETSY HODGES 14399
## 2        DON SAMUELS 14170
## 3        MARK ANDREW 12757
## 4          undervote 10598
## 5 JACKIE CHERRYHOMES  6470

Minneapolis2013 %>%
  group_by(First) %>%
  filter(First == "undervote") %>%
  summarise(count=n()) 

## Source: local data frame [1 x 2]
## 
##       First count
##       (chr) (int)
## 1 undervote   834

Minneapolis2013 %>%
  group_by(Second) %>%
  filter(Second == "undervote") %>%
  summarise(count=n()) 

## Source: local data frame [1 x 2]
## 
##      Second count
##       (chr) (int)
## 1 undervote 10598

Minneapolis2013 %>%
  group_by(Third) %>%
  filter(Third == "undervote") %>%
  summarise(count=n()) 

## Source: local data frame [1 x 2]
## 
##       Third count
##       (chr) (int)
## 1 undervote 19210

8.1

1. ggplot()

2. geom_point(), geom_segment, geom_histogram

3. ylab()

4. facet_grid() , facet_grid()

5. xlim(), scale_y_log10()

8.2

frame <- CPS85 %>%
  ggplot(aes(x=age,y=wage)) 
frame +
  geom_point(aes(shape=married)) +
  facet_wrap(~sector) +
  ylim(0,30) +
  theme(legend.position = "top")

## Warning: Removed 1 rows containing missing values (geom_point).

frame <- CPS85 %>% 
  ggplot(aes(x=age, y=wage))
frame +
  geom_point() +
  facet_grid(sex~married) +
  ylim(0,40)

## Warning: Removed 1 rows containing missing values (geom_point).