Winter 2019 R Bridge Week 2 Assignment

Use the summary function to gain an overview of the data set. Then display the mean and median for at least two attributes

## the dataset used in this work is taken from the 1997-2001 British Election Panel Study (BEPS).
## A data frame with 1525 observations on the following 10 variables:
## vote, age, economic.cond.national, economic.cond.household, Blair, Hague, Kennedy, Europe, political.knowledge, gender

beps <- read.csv(file="beps.csv", header=TRUE, sep=",")
summary(beps) #Summary of the csv data

##        X                      vote          age       
##  Min.   :   1   Conservative    :462   Min.   :24.00  
##  1st Qu.: 382   Labour          :720   1st Qu.:41.00  
##  Median : 763   Liberal Democrat:343   Median :53.00  
##  Mean   : 763                          Mean   :54.18  
##  3rd Qu.:1144                          3rd Qu.:67.00  
##  Max.   :1525                          Max.   :93.00  
##  economic.cond.national economic.cond.household     Blair      
##  Min.   :1.000          Min.   :1.00            Min.   :1.000  
##  1st Qu.:3.000          1st Qu.:3.00            1st Qu.:2.000  
##  Median :3.000          Median :3.00            Median :4.000  
##  Mean   :3.246          Mean   :3.14            Mean   :3.334  
##  3rd Qu.:4.000          3rd Qu.:4.00            3rd Qu.:4.000  
##  Max.   :5.000          Max.   :5.00            Max.   :5.000  
##      Hague          Kennedy          Europe       political.knowledge
##  Min.   :1.000   Min.   :1.000   Min.   : 1.000   Min.   :0.000      
##  1st Qu.:2.000   1st Qu.:2.000   1st Qu.: 4.000   1st Qu.:0.000      
##  Median :2.000   Median :3.000   Median : 6.000   Median :2.000      
##  Mean   :2.747   Mean   :3.135   Mean   : 6.729   Mean   :1.542      
##  3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:10.000   3rd Qu.:2.000      
##  Max.   :5.000   Max.   :5.000   Max.   :11.000   Max.   :3.000      
##     gender   
##  female:812  
##  male  :713  
##              
##              
##              
##

## Mean, Median of age and economic.cond.national attributes

mean_age <- mean(beps$age)
paste0("Mean age: ", mean_age)

## [1] "Mean age: 54.1822950819672"

median_age <- median(beps$age)
paste0("Median age: ", median_age)

## [1] "Median age: 53"

mean_national_condition <- mean(beps$economic.cond.national)
paste0("Mean Nationa Economic condition: ", mean_national_condition)

## [1] "Mean Nationa Economic condition: 3.24590163934426"

median_national_condition <- median(beps$economic.cond.national)
paste0("Median Nationa Economic condition: ", median_national_condition)

## [1] "Median Nationa Economic condition: 3"

Create a new data frame with a subset of the columns and rows. Make sure to rename it. Q3

##creating a new data.frame with subset of the rows (selecting 20 rows) and columns of the BEPS Data
df <- subset(beps, X < 21, select = c("vote","age","economic.cond.national", "economic.cond.household", "Europe"))
df

##                vote age economic.cond.national economic.cond.household
## 1  Liberal Democrat  43                      3                       3
## 2            Labour  36                      4                       4
## 3            Labour  35                      4                       4
## 4            Labour  24                      4                       2
## 5            Labour  41                      2                       2
## 6            Labour  47                      3                       4
## 7  Liberal Democrat  57                      2                       2
## 8            Labour  77                      3                       4
## 9            Labour  39                      3                       3
## 10           Labour  70                      3                       2
## 11           Labour  39                      3                       3
## 12           Labour  66                      4                       3
## 13           Labour  59                      4                       4
## 14           Labour  66                      3                       3
## 15           Labour  77                      2                       3
## 16           Labour  51                      4                       4
## 17           Labour  43                      2                       4
## 18           Labour  41                      4                       4
## 19           Labour  79                      3                       3
## 20           Labour  37                      3                       1
##    Europe
## 1       2
## 2       5
## 3       3
## 4       4
## 5       6
## 6       4
## 7      11
## 8       1
## 9      11
## 10     11
## 11      7
## 12      9
## 13     10
## 14      8
## 15     11
## 16      5
## 17      8
## 18      7
## 19      1
## 20      5

Create new column names for the new data frame.

names(df)<- c("voter", "age group", "national worth", "individual worth", "Euro") ## Renaming the columns

df

##               voter age group national worth individual worth Euro
## 1  Liberal Democrat        43              3                3    2
## 2            Labour        36              4                4    5
## 3            Labour        35              4                4    3
## 4            Labour        24              4                2    4
## 5            Labour        41              2                2    6
## 6            Labour        47              3                4    4
## 7  Liberal Democrat        57              2                2   11
## 8            Labour        77              3                4    1
## 9            Labour        39              3                3   11
## 10           Labour        70              3                2   11
## 11           Labour        39              3                3    7
## 12           Labour        66              4                3    9
## 13           Labour        59              4                4   10
## 14           Labour        66              3                3    8
## 15           Labour        77              2                3   11
## 16           Labour        51              4                4    5
## 17           Labour        43              2                4    8
## 18           Labour        41              4                4    7
## 19           Labour        79              3                3    1
## 20           Labour        37              3                1    5

Use the summary function to create an overview of your new data frame. Then print the mean and median for the same two attributes. Please compare.

summary(df)

##               voter      age group     national worth individual worth
##  Conservative    : 0   Min.   :24.00   Min.   :2.00   Min.   :1.00    
##  Labour          :18   1st Qu.:39.00   1st Qu.:3.00   1st Qu.:2.75    
##  Liberal Democrat: 2   Median :45.00   Median :3.00   Median :3.00    
##                        Mean   :51.35   Mean   :3.15   Mean   :3.10    
##                        3rd Qu.:66.00   3rd Qu.:4.00   3rd Qu.:4.00    
##                        Max.   :79.00   Max.   :4.00   Max.   :4.00    
##       Euro      
##  Min.   : 1.00  
##  1st Qu.: 4.00  
##  Median : 6.50  
##  Mean   : 6.45  
##  3rd Qu.: 9.25  
##  Max.   :11.00

## Mean and Median of age and economic.cond.national attributes in df (New dataframe)

mean_age <- mean(df$`age group`)
paste0("New Mean age: ",mean_age)

## [1] "New Mean age: 51.35"

median_age <- median(df$`age group`)
paste0("New Median age: ", median_age)

## [1] "New Median age: 45"

mean_national_worth <- mean(df$`national worth`)
paste0("New Mean Nationa Economic condition: ",mean_national_worth)

## [1] "New Mean Nationa Economic condition: 3.15"

median_national_condition <- median(df$`individual worth`)
paste0("New Median Nationa Economic condition: ", median_national_condition)

## [1] "New Median Nationa Economic condition: 3"

The Mean and median ages of the original data (at 54 and 53 respectively) are higher than that of the new dataframe (at 51 and 45 respectively). The mean ages were almost unchanged while the median ages remained the same.

For at least 3 values in a column please rename so that every value in that column is renamed. For example, suppose I have 20 values of the letter “e” in one column. Rename those values so that all 20 would show as “excellent”.

df$voter <- gsub("^Labour$", "excellent", df$voter) ## Renaming values in the voter column

df

##               voter age group national worth individual worth Euro
## 1  Liberal Democrat        43              3                3    2
## 2         excellent        36              4                4    5
## 3         excellent        35              4                4    3
## 4         excellent        24              4                2    4
## 5         excellent        41              2                2    6
## 6         excellent        47              3                4    4
## 7  Liberal Democrat        57              2                2   11
## 8         excellent        77              3                4    1
## 9         excellent        39              3                3   11
## 10        excellent        70              3                2   11
## 11        excellent        39              3                3    7
## 12        excellent        66              4                3    9
## 13        excellent        59              4                4   10
## 14        excellent        66              3                3    8
## 15        excellent        77              2                3   11
## 16        excellent        51              4                4    5
## 17        excellent        43              2                4    8
## 18        excellent        41              4                4    7
## 19        excellent        79              3                3    1
## 20        excellent        37              3                1    5

Display enough rows to see examples of all of steps 1-5 above.

head(df,10) ## showing only 10 rows out of the original subsetted 20 rows

##               voter age group national worth individual worth Euro
## 1  Liberal Democrat        43              3                3    2
## 2         excellent        36              4                4    5
## 3         excellent        35              4                4    3
## 4         excellent        24              4                2    4
## 5         excellent        41              2                2    6
## 6         excellent        47              3                4    4
## 7  Liberal Democrat        57              2                2   11
## 8         excellent        77              3                4    1
## 9         excellent        39              3                3   11
## 10        excellent        70              3                2   11

BONUS - place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.

beps_git <- read.csv2("https://raw.githubusercontent.com/henryvalentine/MSDS2019/master/beps.csv", header = TRUE, sep=",")
summary(beps_git) #Summary of the csv data from github.com

##        X                      vote          age       
##  Min.   :   1   Conservative    :462   Min.   :24.00  
##  1st Qu.: 382   Labour          :720   1st Qu.:41.00  
##  Median : 763   Liberal Democrat:343   Median :53.00  
##  Mean   : 763                          Mean   :54.18  
##  3rd Qu.:1144                          3rd Qu.:67.00  
##  Max.   :1525                          Max.   :93.00  
##  economic.cond.national economic.cond.household     Blair      
##  Min.   :1.000          Min.   :1.00            Min.   :1.000  
##  1st Qu.:3.000          1st Qu.:3.00            1st Qu.:2.000  
##  Median :3.000          Median :3.00            Median :4.000  
##  Mean   :3.246          Mean   :3.14            Mean   :3.334  
##  3rd Qu.:4.000          3rd Qu.:4.00            3rd Qu.:4.000  
##  Max.   :5.000          Max.   :5.00            Max.   :5.000  
##      Hague          Kennedy          Europe       political.knowledge
##  Min.   :1.000   Min.   :1.000   Min.   : 1.000   Min.   :0.000      
##  1st Qu.:2.000   1st Qu.:2.000   1st Qu.: 4.000   1st Qu.:0.000      
##  Median :2.000   Median :3.000   Median : 6.000   Median :2.000      
##  Mean   :2.747   Mean   :3.135   Mean   : 6.729   Mean   :1.542      
##  3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:10.000   3rd Qu.:2.000      
##  Max.   :5.000   Max.   :5.000   Max.   :11.000   Max.   :3.000      
##     gender   
##  female:812  
##  male  :713  
##              
##              
##              
##

Winter 2019 R Bridge Week 2 Assignment

Henry Otuadinma

30 December 2018