7. 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.

cdata<-read.csv(url("https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/Stat2Data/Election08.csv"))

1.Use the summary function to gain an overview of the data set.

summary(cdata)

##        X                State         Abr         Income     
##  Min.   : 1.0   Alabama    : 1   AK     : 1   Min.   :28845  
##  1st Qu.:13.5   Alaska     : 1   AL     : 1   1st Qu.:33537  
##  Median :26.0   Arizona    : 1   AR     : 1   Median :36047  
##  Mean   :26.0   Arkansas   : 1   AZ     : 1   Mean   :37642  
##  3rd Qu.:38.5   California : 1   CA     : 1   3rd Qu.:40544  
##  Max.   :51.0   Colorado   : 1   CO     : 1   Max.   :61092  
##                 (Other)    :45   (Other):45                  
##        HS              BA           Dem.Rep          ObamaWin     
##  Min.   :78.50   Min.   :17.30   Min.   :-23.00   Min.   :0.0000  
##  1st Qu.:83.00   1st Qu.:24.20   1st Qu.:  3.00   1st Qu.:0.0000  
##  Median :87.00   Median :25.80   Median : 12.00   Median :1.0000  
##  Mean   :86.00   Mean   :27.15   Mean   : 12.31   Mean   :0.5686  
##  3rd Qu.:89.05   3rd Qu.:29.65   3rd Qu.: 19.00   3rd Qu.:1.0000  
##  Max.   :91.20   Max.   :47.50   Max.   : 75.00   Max.   :1.0000  
##

Then display the mean and median for at least two attributes.

mean(cdata$Income)

## [1] 37641.57

mean(cdata$HS)

## [1] 86

median(cdata$Income)

## [1] 36047

median(cdata$HS)

## [1] 87

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

newData=subset(cdata, cdata$Income>40000 & cdata$ObamaWin==1)

summary(newData)

##        X                          State        Abr        Income     
##  Min.   : 5.00   California          :1   CA     :1   Min.   :40322  
##  1st Qu.: 8.50   Colorado            :1   CO     :1   1st Qu.:40821  
##  Median :22.00   Connecticut         :1   CT     :1   Median :41512  
##  Mean   :22.27   Delaware            :1   DC     :1   Mean   :45015  
##  3rd Qu.:30.50   District of Columbia:1   DE     :1   3rd Qu.:48234  
##  Max.   :48.00   Illinois            :1   IL     :1   Max.   :61092  
##                  (Other)             :9   (Other):9                  
##        HS              BA           Dem.Rep         ObamaWin
##  Min.   :80.20   Min.   :21.80   Min.   : 9.00   Min.   :1  
##  1st Qu.:85.70   1st Qu.:29.90   1st Qu.:14.00   1st Qu.:1  
##  Median :87.40   Median :32.50   Median :19.00   Median :1  
##  Mean   :86.88   Mean   :32.68   Mean   :23.27   Mean   :1  
##  3rd Qu.:88.65   3rd Qu.:34.85   3rd Qu.:26.00   3rd Qu.:1  
##  Max.   :91.00   Max.   :47.50   Max.   :75.00   Max.   :1  
##

print(newData)

##     X                State Abr Income   HS   BA Dem.Rep ObamaWin
## 5   5          California   CA  41571 80.2 29.5      19        1
## 6   6            Colorado   CO  41042 88.9 35.0      11        1
## 7   7         Connecticut   CT  54117 88.0 34.7      26        1
## 8   8            Delaware   DE  40608 87.4 26.1      23        1
## 9   9 District of Columbia  DC  61092 85.7 47.5      75        1
## 14 14            Illinois   IL  40322 85.7 29.5      24        1
## 21 21            Maryland   MD  46021 87.4 35.2      26        1
## 22 22       Massachusetts   MA  49082 88.4 37.9      34        1
## 24 24           Minnesota   MN  41034 91.0 31.0      15        1
## 29 29              Nevada   NV  40480 83.7 21.8      11        1
## 30 30       New Hampshire   NH  41512 90.5 32.5      13        1
## 31 31          New Jersey   NJ  49194 87.0 33.9      19        1
## 33 33            New York   NY  47385 84.1 31.7      27        1
## 47 47            Virginia   VA  41347 85.9 33.6       9        1
## 48 48          Washington   WA  40414 89.3 30.3      17        1

3. Create new column names for the new data frame.

names(newData)

## [1] "X"        "State"    "Abr"      "Income"   "HS"       "BA"      
## [7] "Dem.Rep"  "ObamaWin"

names(newData)[4]<-"StateIncome"
names(newData)[8]<-"BarakWin"
newData

##     X                State Abr StateIncome   HS   BA Dem.Rep BarakWin
## 5   5          California   CA       41571 80.2 29.5      19        1
## 6   6            Colorado   CO       41042 88.9 35.0      11        1
## 7   7         Connecticut   CT       54117 88.0 34.7      26        1
## 8   8            Delaware   DE       40608 87.4 26.1      23        1
## 9   9 District of Columbia  DC       61092 85.7 47.5      75        1
## 14 14            Illinois   IL       40322 85.7 29.5      24        1
## 21 21            Maryland   MD       46021 87.4 35.2      26        1
## 22 22       Massachusetts   MA       49082 88.4 37.9      34        1
## 24 24           Minnesota   MN       41034 91.0 31.0      15        1
## 29 29              Nevada   NV       40480 83.7 21.8      11        1
## 30 30       New Hampshire   NH       41512 90.5 32.5      13        1
## 31 31          New Jersey   NJ       49194 87.0 33.9      19        1
## 33 33            New York   NY       47385 84.1 31.7      27        1
## 47 47            Virginia   VA       41347 85.9 33.6       9        1
## 48 48          Washington   WA       40414 89.3 30.3      17        1

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

summary(newData)

##        X                          State        Abr     StateIncome   
##  Min.   : 5.00   California          :1   CA     :1   Min.   :40322  
##  1st Qu.: 8.50   Colorado            :1   CO     :1   1st Qu.:40821  
##  Median :22.00   Connecticut         :1   CT     :1   Median :41512  
##  Mean   :22.27   Delaware            :1   DC     :1   Mean   :45015  
##  3rd Qu.:30.50   District of Columbia:1   DE     :1   3rd Qu.:48234  
##  Max.   :48.00   Illinois            :1   IL     :1   Max.   :61092  
##                  (Other)             :9   (Other):9                  
##        HS              BA           Dem.Rep         BarakWin
##  Min.   :80.20   Min.   :21.80   Min.   : 9.00   Min.   :1  
##  1st Qu.:85.70   1st Qu.:29.90   1st Qu.:14.00   1st Qu.:1  
##  Median :87.40   Median :32.50   Median :19.00   Median :1  
##  Mean   :86.88   Mean   :32.68   Mean   :23.27   Mean   :1  
##  3rd Qu.:88.65   3rd Qu.:34.85   3rd Qu.:26.00   3rd Qu.:1  
##  Max.   :91.00   Max.   :47.50   Max.   :75.00   Max.   :1  
##

mean(newData$StateIncome)

## [1] 45014.73

mean(newData$HS)

## [1] 86.88

median(newData$StateIncome)

## [1] 41512

median(newData$HS)

## [1] 87.4

5. 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”.

rData<-as.data.frame(sapply(newData,gsub,pattern="e",replacement="Excellent",ignore.case = TRUE))
print(rData)

##     X                               State        Abr StateIncome   HS   BA
## 1   5                         California          CA       41571 80.2 29.5
## 2   6                           Colorado          CO       41042 88.9   35
## 3   7                ConnExcellentcticut          CT       54117   88 34.7
## 4   8           DExcellentlawarExcellent  DExcellent       40608 87.4 26.1
## 5   9                District of Columbia         DC       61092 85.7 47.5
## 6  14                           Illinois          IL       40322 85.7 29.5
## 7  21                           Maryland          MD       46021 87.4 35.2
## 8  22              MassachusExcellenttts          MA       49082 88.4 37.9
## 9  24                  MinnExcellentsota          MN       41034   91   31
## 10 29                     NExcellentvada          NV       40480 83.7 21.8
## 11 30      NExcellentw HampshirExcellent          NH       41512 90.5 32.5
## 12 31 NExcellentw JExcellentrsExcellenty          NJ       49194   87 33.9
## 13 33                   NExcellentw York          NY       47385 84.1 31.7
## 14 47                           Virginia          VA       41347 85.9 33.6
## 15 48                         Washington          WA       40414 89.3 30.3
##    Dem.Rep BarakWin
## 1       19        1
## 2       11        1
## 3       26        1
## 4       23        1
## 5       75        1
## 6       24        1
## 7       26        1
## 8       34        1
## 9       15        1
## 10      11        1
## 11      13        1
## 12      19        1
## 13      27        1
## 14       9        1
## 15      17        1

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

library(knitr)
kable(cdata)

X	State	Abr	Income	HS	BA	Dem.Rep	ObamaWin
1	Alabama	AL	32404	80.4	21.4	-1	0
2	Alaska	AK	40352	90.5	26.0	-11	0
3	Arizona	AZ	33029	83.5	25.3	0	0
4	Arkansas	AR	30060	81.1	19.3	12	0
5	California	CA	41571	80.2	29.5	19	1
6	Colorado	CO	41042	88.9	35.0	11	1
7	Connecticut	CT	54117	88.0	34.7	26	1
8	Delaware	DE	40608	87.4	26.1	23	1
9	District of Columbia	DC	61092	85.7	47.5	75	1
10	Florida	FL	38444	84.9	25.8	9	1
11	Georgia	GA	33457	82.9	27.1	4	0
12	Hawaii	HI	39239	89.4	29.2	34	1
13	Idaho	ID	31197	88.4	24.5	-15	0
14	Illinois	IL	40322	85.7	29.5	24	1
15	Indiana	IN	33616	85.8	22.1	9	1
16	Iowa	IA	35023	89.6	24.3	18	1
17	Kansas	KS	36768	89.1	28.8	-2	0
18	Kentucky	KY	31111	80.1	20.0	13	0
19	Louisiana	LA	34756	79.9	20.4	9	0
20	Maine	ME	33722	89.4	26.7	19	1
21	Maryland	MD	46021	87.4	35.2	26	1
22	Massachusetts	MA	49082	88.4	37.9	34	1
23	Michigan	MI	35086	87.4	24.7	17	1
24	Minnesota	MN	41034	91.0	31.0	15	1
25	Mississippi	MS	28845	78.5	18.9	1	0
26	Missouri	MO	34389	85.6	24.5	11	0
27	Montana	MT	32458	90.0	27.0	4	0
28	Nebraska	NE	36471	89.6	27.5	-7	0
29	Nevada	NV	40480	83.7	21.8	11	1
30	New Hampshire	NH	41512	90.5	32.5	13	1
31	New Jersey	NJ	49194	87.0	33.9	19	1
32	New Mexico	NM	31474	82.3	24.8	14	1
33	New York	NY	47385	84.1	31.7	27	1
34	North Carolina	NC	33636	83.0	25.6	11	1
35	North Dakota	ND	34846	89.0	25.7	1	0
36	Ohio	OH	34874	87.1	24.1	18	1
37	Oklahoma	OK	34153	84.8	22.8	6	0
38	Oregon	OR	34784	88.0	28.3	17	1
39	Pennsylvania	PA	38788	86.8	25.8	16	1
40	Rhode Island	RI	39463	83.0	29.8	37	1
41	South Carolina	SC	31013	82.1	23.5	0	0
42	South Dakota	SD	33905	88.2	25.0	1	0
43	Tennessee	TN	33280	81.4	21.8	5	0
44	Texas	TX	37187	79.1	25.2	2	0
45	Utah	UT	31189	90.2	28.7	-23	0
46	Vermont	VT	36670	90.3	33.6	33	1
47	Virginia	VA	41347	85.9	33.6	9	1
48	Washington	WA	40414	89.3	30.3	17	1
49	West Virginia	WV	29537	81.2	17.3	19	0
50	Wisconsin	WI	36047	89.0	25.4	18	1
51	Wyoming	WY	43226	91.2	23.4	-20	0

library(knitr)
kable(newData)

	X	State	Abr	StateIncome	HS	BA	Dem.Rep	BarakWin
5	5	California	CA	41571	80.2	29.5	19	1
6	6	Colorado	CO	41042	88.9	35.0	11	1
7	7	Connecticut	CT	54117	88.0	34.7	26	1
8	8	Delaware	DE	40608	87.4	26.1	23	1
9	9	District of Columbia	DC	61092	85.7	47.5	75	1
14	14	Illinois	IL	40322	85.7	29.5	24	1
21	21	Maryland	MD	46021	87.4	35.2	26	1
22	22	Massachusetts	MA	49082	88.4	37.9	34	1
24	24	Minnesota	MN	41034	91.0	31.0	15	1
29	29	Nevada	NV	40480	83.7	21.8	11	1
30	30	New Hampshire	NH	41512	90.5	32.5	13	1
31	31	New Jersey	NJ	49194	87.0	33.9	19	1
33	33	New York	NY	47385	84.1	31.7	27	1
47	47	Virginia	VA	41347	85.9	33.6	9	1
48	48	Washington	WA	40414	89.3	30.3	17	1

Week #2 -Assigment

Joseph E. Garcia

July 17, 2018

7. 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.

1.Use the summary function to gain an overview of the data set.

Then display the mean and median for at least two attributes.

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

3. Create new column names for the new data frame.

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

5. 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”.

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