Skip to content

Latest commit

 

History

History
169 lines (143 loc) · 6.21 KB

p8105_hw1_pw2551.md

File metadata and controls

169 lines (143 loc) · 6.21 KB

p8105_hw1_pw2551

Paula Wu 9/23/2021

# I set message = FALSE for concision
library(tidyverse)

Problem 1:

Create a data frame with variables:

set.seed(1)
p1_df = tibble(
  vec_numeric = rnorm(10),
  vec_logical = vec_numeric > 0,
  vec_char = c("will", "you", "get", "a", 
                 "headache", "if", "you", "think", "too", "hard"),
  vec_factor = factor(c("high", "low", "medium", "high", "medium", 
                        "medium", "low", "high", "high", "low"))
)
p1_df
## # A tibble: 10 × 4
##    vec_numeric vec_logical vec_char vec_factor
##          <dbl> <lgl>       <chr>    <fct>     
##  1      -0.626 FALSE       will     high      
##  2       0.184 TRUE        you      low       
##  3      -0.836 FALSE       get      medium    
##  4       1.60  TRUE        a        high      
##  5       0.330 TRUE        headache medium    
##  6      -0.820 FALSE       if       medium    
##  7       0.487 TRUE        you      low       
##  8       0.738 TRUE        think    high      
##  9       0.576 TRUE        too      high      
## 10      -0.305 FALSE       hard     low

Take the mean of each variable:

# pulled each columns from the data frame for future uses
numeric_col = pull(p1_df, vec_numeric)
logical_col = pull(p1_df, vec_logical)
char_col = pull(p1_df, vec_char)
factor_col = pull(p1_df, vec_factor)

# calculate means
mean(numeric_col)
## [1] 0.1322028
mean(logical_col)
## [1] 0.6
mean(char_col)
## Warning in mean.default(char_col): argument is not numeric or logical: returning
## NA
## [1] NA
mean(factor_col)
## Warning in mean.default(factor_col): argument is not numeric or logical:
## returning NA
## [1] NA
  • Taking the means for numeric and logical variables works. It’s because numeric and logical variables have arithmetic values: numeric variables have their literal values, while logical variables have 1 for TRUE and 0 for FALSE.
  • Taking means for character and factor variables doesn’t work, because neither of them have arithmetic values that can be added or divided.

To explicitly convert types:

as.numeric(logical_col)
as.numeric(char_col)
as.numeric(factor_col)
  • The conversion works for logical and factor variables but not for character variables.
  • For logical variables, the method as.numeric() will convert TRUE to 1 and FALSE to 0.
  • For factor variables, according to rdocumentation.com, as.numeric() will return the underlying numeric representation. Since there are three “levels” for factor variables, each of the element is converted to its arbitrarily corresponding number.
  • For character variables, as.numeric() gives us a “NAs introduced by coercion” warning. Texts just can’t be converted into numbers.
  • as.numeric() helps explain why logic variables work and character variables don’t work when I tried to take the mean. However, it doesn’t help explain why factor variables don’t work: factor variables can be converted to numeric variables based on their levels, but applying the mean() function on factor variables yields an NA result.

Problem 2:

Download and read dataset:

# packages installed in the terminal
data("penguins", package = "palmerpenguins")
penguins
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # … with 334 more rows, and 2 more variables: sex <fct>, year <int>
  • The variable names are: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex, year. Among these 8 attributes, “bill_length_mm”, “bill_depth_mm”, “flipper_length_mm”, and “body_mass_g” look important and meaningful to me. The values, specifically the mean values, for these important variables will be calculated in r code chunks below. 
# I get rid of the Null values
bill_l = mean(pull(penguins, bill_length_mm), na.rm = TRUE)
bill_d = mean(pull(penguins, bill_depth_mm), na.rm = TRUE)
flip_l = mean(pull(penguins, flipper_length_mm), na.rm = TRUE)
body_m = mean(pull(penguins, body_mass_g), na.rm = TRUE)
c(bill_l, bill_d, flip_l, body_m)
## [1]   43.92193   17.15117  200.91520 4201.75439
  • Thus, the means, rounded to 2 decimal places, for “bill_length_mm” is 43.92, for “bill_depth_mm” is 17.15, for “flipper_length_mm” is 200.92, and for “body_mass_g” is 4201.75.
  • For this penguins data set, there are 344 rows and 8 columns.
  • The mean of flipper length (mm) is mentioned above, which is 200.92.

To plot the scatterplot using ggplot:

ggplot(penguins, aes(x = bill_length_mm, y = flipper_length_mm, col = species)) + 
  geom_point() + 
  ggtitle("Flipper and Bill Length for Penguins") + 
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(y = "Flipper Length (mm)", x = "Bill Length (mm)")
## Warning: Removed 2 rows containing missing values (geom_point).

To save the graph using ggsave:

ggsave("./scatterplot.pdf", height = 4, width = 6)
## Warning: Removed 2 rows containing missing values (geom_point).