Data Import

  1. Download chickens.csv to your working directory. Make sure to set your working directory appropriately! This dataset was created by modifying the R built-in dataset chickwts.

  2. Import the chickens.csv data into R. Store it in a data.frame named ch_df and print out the entire ch_df to the screen.

ch_df<-read.csv("chickens.csv")
library(tidyverse)
## -- Attaching packages ------------------------------ tidyverse 1.2.1 --
## v ggplot2 3.2.1     v purrr   0.3.2
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   0.8.3     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0
## -- Conflicts --------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Clean Missing Values

There are some missing values in this dataset. Unfortunately they are represented in a number of different ways.

  1. Clean up this dataset by doing the following:
sum(is.na(ch_df))
## [1] 7
ch_df <- ch_df %>%
  mutate(weight = replace(weight, weight == "na", NA)) %>%
  mutate(weight = replace(weight, weight == "N/A", NA)) %>%
  mutate(weight = replace(weight, weight == "", NA)) %>%
  mutate(weight = replace(weight, weight == "?", NA)) %>%
  mutate(weight = replace(weight, weight == "NA", NA))
ch_df <- ch_df %>%
  mutate(feed = replace(feed, feed == "na", NA)) %>%
  mutate(feed = replace(feed, feed == "N/A", NA)) %>%
  mutate(feed = replace(feed, feed == "", NA)) %>%
  mutate(feed = replace(feed, feed == "?", NA)) %>%
  mutate(feed = replace(feed, feed == "NA", NA))

Now that the dataset is clean, let’s see what percentage of our data is missing.

  1. Calculate the percentage of missing data from the weight column, the feed column, and the entire dataset. Print out each result in the following format: “Percentage of missing data in [fill in the column or dataset name]: [fill in percentage]%.”
((sum(is.na(ch_df$weight))/(length(ch_df$weight))*100))
## [1] 18.30986
((sum(is.na(ch_df$feed))/(length(ch_df$feed))*100))
## [1] 14.08451
((sum(is.na(ch_df))/(length(ch_df$weight)+length(ch_df$feed))*100))
## [1] 16.19718
print("Percentage of missing data in the weight column: 18.31%")
## [1] "Percentage of missing data in the weight column: 18.31%"
print("Percentage of missing data in the feed column: 14.08%")
## [1] "Percentage of missing data in the feed column: 14.08%"
print("Percentage of missing data in the entire dataset: 16.20%")
## [1] "Percentage of missing data in the entire dataset: 16.20%"

EXTRA CREDIT (Optional): Figure out how to create these print statements so that the name and percentage number are not hard-coded into the statement. In other words, so that the name and percentage number are read in dynamically (for example, from a variable, from a function call, etc.) instead of just written in the statement. Please ask me for clarification if necessary.

x<-((sum(is.na(ch_df$weight))/(length(ch_df$weight))*100))
y<-((sum(is.na(ch_df$feed))/(length(ch_df$feed))*100))
z<-((sum(is.na(ch_df))/(length(ch_df$weight)+length(ch_df$feed))*100))
cat("weight column:", x,"\n")
## weight column: 18.30986
cat("feed column:", y,"\n")
## feed column: 14.08451
cat("entire dataset:", z, "\n")
## entire dataset: 16.19718

Data Investigation

  1. Group the data by feed and find the mean and median weight for each group. Your result should be a new data frame with the group means in a column named weight_mean and the group medians in a column named weight_median. Save this new data frame; you can name the data frame as you wish. (Remember that variable names should be somewhat descriptive of what they contain.)
ch_df$weight<-as.character(ch_df$weight)
ch_df$weight<-as.numeric(ch_df$weight)
## Warning: NAs introduced by coercion
df<-ch_df %>%
  group_by(feed)%>%
  summarise(weight_mean=mean(weight,na.rm = TRUE),weight_median=median(weight,na.rm = TRUE))
## Warning: Factor `feed` contains implicit NA, consider using
## `forcats::fct_explicit_na`
df
## # A tibble: 9 x 3
##   feed      weight_mean weight_median
##   <fct>           <dbl>         <dbl>
## 1 casein           314.          325 
## 2 horsebean        161.          160 
## 3 linseed          232.          236.
## 4 meatmeal         304.          315 
## 5 not sure         329           329 
## 6 soybean          242.          249 
## 7 sunflower        353.          340 
## 8 unknown          263           263 
## 9 <NA>             241.          217
  1. Find the feed that has the maximum median chicken weight.
which.max(df$weight_median)
## [1] 7
df[7,]
## # A tibble: 1 x 3
##   feed      weight_mean weight_median
##   <fct>           <dbl>         <dbl>
## 1 sunflower        353.           340