Computer Assignment 2 (again)
Ramin Alibeik
2/23/2020
1. Load Libraries, Set Your Working Directory, & Load Data
library(dplyr) # for manipulating data## Warning: package 'dplyr' was built under R version 3.6.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2) # for making graphs## Warning: package 'ggplot2' was built under R version 3.6.2library(knitr) # for nicer table formatting## Warning: package 'knitr' was built under R version 3.6.2library(summarytools) # for frequency distribution tables## Warning: package 'summarytools' was built under R version 3.6.2## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcppsetwd("C:/Users/ramin/Desktop/2020 winter/Data Analysis/Computer Assignment 2/Dataset")
load(file = "gss98.RData")2. Interpreting Frequency Distributions: RELIG
freq(gss98$RELIG)## Frequencies
## gss98$RELIG
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ---------------- ------ --------- -------------- --------- --------------
## Catholic 250 25.30 25.30 25.00 25.00
## Jewish 20 2.02 27.33 2.00 27.00
## None 144 14.57 41.90 14.40 41.40
## Other 39 3.95 45.85 3.90 45.30
## Protestant 535 54.15 100.00 53.50 98.80
## <NA> 12 1.20 100.00
## Total 1000 100.00 100.00 100.00 100.00gss98 %>% ggplot() + geom_bar( aes(x = RELIG), fill = "darkred" ) + labs(title = "Bar plot for RELIG")
QUESTIONS::
A. How many people in this data set are Protestants? Catholics? Jews?
535 Protestants, 250 Catholics, 20 JewsB. What percentage of all respondents have no religion? What proportion have no religion? How were both numbers calculated?
14.40% of all respondents have no religion, 14.40/1000 people have no religion, both were calculated from the frequency and total respondents.C. What advantage(s) and disadvantage(s) do you see to presenting a bar chart in place of a frequency table?
The bar chart gives a good look at where the data is most and less but the frequency table gives exact data.3. Interpreting Frequency Distribution: FEPRESCH
QUESTIONS::
Generate a frequency distribution for FEPRESCH:
freq(gss98$FEPRESCH, round.digits = 1)Generate a frequency graph for FEPRESCH:
gss98 %>% ggplot() + geom_bar( aes(x = RELIG), fill = "darkblue" ) + labs(title = "Bar plot for RELIG")A. Use the the codebook for the survey to find the exact question wording for the variable FEPRESCH. Copy it into your answer (You can cut and paste.)
PRESCHOOL KIDS SUFFER IF MOTHER WORKSB. How many people in this data set strongly agree with this statement? What percentage of all respondents strongly agree with this statement?
45 people strongly agree with this statement. 4.5% of all respondents strongly agree.C. What percentage of the respondents who gave valid responses strongly agree with this statement? How was this number calculated? Why is this answer different from that in question 3B? Which percentage is most meaningful in this case - the “percent” or the “valid percent”? Why?
7.0% of the people who gave valid responses strongly agree. It was calculated by removing the N/A from the total respondents and dividing the freq bey the new total, the question in 3B included the N/A. The valid percentage is more meaningful because it involves everyone who is valid.D. How many missing cases are there?
There are 361 missing cases.E. What does the 40.7 in the “Cum Percent” column mean? What is the absolute frequency who agreed or strongly agreed? What percentage disagreed or strongly disagreed? What is the absolute frequency who disagreed or strongly disagreed? (Show your work.)
The valid cum is the people who strongly agree in addition with the people who agree. The absolute freuency of the two is 260. 57.7%(50.7+7.0) disagreed or strongly agreed. The absolutre frequency is 324+45 = 369F. Interpret the bar plot for the variable FEPRESCH. Why did I ask you to plot a bar chart and not a histogram for this variable?
The bar chart shows the data better in terms of looks and you can see immediately which is more or less.4. Variable Types
QUESTIONS::
A. Are relig and fepresch nominal level, ordinal level, or interval level variables? How do you know? Write the names of at least two more of each type of variable in the data set.
They are both nominal level variables. Because the bar plot of relig and fepresch are both associated with numbers. They are put into severala categories such as stongly agree, disagree, or Catholic, Jewish and measured by count.5. Comparisons
Frequency distributions for several variables having to do with confidence in U.S. institutions: CONCLERG, CONEDUC, CONFED, CONJUDGE, CONLEGIS, and CONPRESS.
freq(gss98$CONCLERG) ## Frequencies
## gss98$CONCLERG
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ---------------------- ------ --------- -------------- --------- --------------
## great confidence 196 29.74 29.74 19.60 19.60
## some confidence 335 50.83 80.58 33.50 53.10
## Hardly confidnce 128 19.42 100.00 12.80 65.90
## <NA> 341 34.10 100.00
## Total 1000 100.00 100.00 100.00 100.00freq(gss98$CONEDUC)## Frequencies
## gss98$CONEDUC
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ---------------------- ------ --------- -------------- --------- --------------
## great confidence 193 28.59 28.59 19.30 19.30
## some confidence 381 56.44 85.04 38.10 57.40
## Hardly confidnce 101 14.96 100.00 10.10 67.50
## <NA> 325 32.50 100.00
## Total 1000 100.00 100.00 100.00 100.00freq(gss98$CONFED)## Frequencies
## gss98$CONFED
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ---------------------- ------ --------- -------------- --------- --------------
## great confidence 103 15.61 15.61 10.30 10.30
## some confidence 323 48.94 64.55 32.30 42.60
## Hardly confidnce 234 35.45 100.00 23.40 66.00
## <NA> 340 34.00 100.00
## Total 1000 100.00 100.00 100.00 100.00freq(gss98$CONJUDGE)## Frequencies
## gss98$CONJUDGE
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ---------------------- ------ --------- -------------- --------- --------------
## great confidence 216 33.38 33.38 21.60 21.60
## some confidence 347 53.63 87.02 34.70 56.30
## Hardly confidnce 84 12.98 100.00 8.40 64.70
## <NA> 353 35.30 100.00
## Total 1000 100.00 100.00 100.00 100.00freq(gss98$CONLEGIS)## Frequencies
## gss98$CONLEGIS
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ---------------------- ------ --------- -------------- --------- --------------
## great confidence 67 10.18 10.18 6.70 6.70
## some confidence 386 58.66 68.84 38.60 45.30
## Hardly confidnce 205 31.16 100.00 20.50 65.80
## <NA> 342 34.20 100.00
## Total 1000 100.00 100.00 100.00 100.00freq(gss98$CONPRESS)## Frequencies
## gss98$CONPRESS
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ---------------------- ------ --------- -------------- --------- --------------
## great confidence 59 8.85 8.85 5.90 5.90
## some confidence 341 51.12 59.97 34.10 40.00
## Hardly confidnce 267 40.03 100.00 26.70 66.70
## <NA> 333 33.30 100.00
## Total 1000 100.00 100.00 100.00 100.00QUESTIONS::
A. Use the codebook for the survey to find the exact question wording for each variable, Type your answer below:
[VAR: CONCLERG] – CONFIDENCE IN ORGANIZED RELIGION [VAR: CONEDUC] – EDUCATION – VERSION Z [VAR: CONFED] – EXEC BRANCH FED GOVT – VERSION Z [VAR: CONJUDGE] – CONFID. IN UNITED STATES SUPREME COURT [VAR: CONLEGIS] – CONFIDENCE IN CONGRESS [VAR: CONPRESS] – CONPRESS
B. Rank order the six institutions from the one that Americans have the most confidence in to the one they have the least confidence in. Does it make any difference whether you rank order the institutions by the “great confidence” or the “hardly any confidence” percentages?
data.frame( "CONCLERG" = freq(gss98$CONCLERG)[,2], "CONEDUC" = freq(gss98$CONEDUC)[,2], "CONFED" = freq(gss98$CONFED)[,2], "CONJUDGE" = freq(gss98$CONJUDGE)[,2], "CONLEGIS" = freq(gss98$CONLEGIS)[,2], "CONPRESS" = freq(gss98$CONPRESS)[,2] ) %>% kable(digits = 1, caption = "Percent (%) Valid Responses")
CONJUDGE, CONCLERG, CONEDUC, CONFED, CONLEGIS, CONPRESS are ordered from most confidence to least. It doesnt matter wether you find out which is most confident or least by looking at great confidence or hardly confidence, both of them tell you the same thing.)C. Write a short paragraph describing what you learn from the table. How much confidence do Americans seem to have in these institutions? Where do they place the greatest confidence? Use some percentages in your paragraph to make your points more explicit.
From the table we can clearly see that the CONEJUDGE has the most confidence and the CONPRESS has the least confidence. The CONJUDGE has 33.4%confidence while the CONPRESS only has 8.8% confidence. The confidences are very low because they are all below 33.4%.#6. Histograms
read.csv("C:/Users/ramin/Desktop/2020 winter/Data Analysis/Computer Assignment 2/Dataset/census.csv")## census_year state_fips_code total_family_income age sex
## 1 2000 Florida 14550 44 Male
## 2 2000 Florida 22800 20 Female
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## 5 2000 Florida 48000 55 Male
## 6 2000 Florida 74000 43 Female
## 7 2000 Florida 23000 60 Female
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## 9 2000 Florida 60000 54 Female
## 10 2000 Florida 14600 58 Female
## 11 2000 Florida 0 33 Female
## 12 2000 Florida 37000 51 Female
## 13 2000 Florida 32000 62 Female
## 14 2000 Florida 113000 8 Male
## 15 2000 Florida 76900 25 Male
## 16 2000 Florida 100100 44 Female
## 17 2000 Florida 48000 28 Female
## 18 2000 Florida 48000 1 Male
## 19 2000 Florida 57200 31 Male
## 20 2000 Florida 43950 69 Female
## 21 2000 Florida 49000 31 Male
## 22 2000 Florida 31600 80 Female
## 23 2000 Florida 50090 2 Male
## 24 2000 Florida 64800 47 Male
## 25 2000 Florida 90000 12 Male
## 26 2000 Florida 38320 47 Male
## 27 2000 Florida 103700 8 Female
## 28 2000 Florida 0 67 Male
## 29 2000 Florida 70700 17 Female
## 30 2000 Florida 64800 69 Female
## 31 2000 Florida 60000 55 Male
## 32 2000 Florida 118100 18 Female
## 33 2000 Florida 21000 66 Female
## 34 2000 Florida 40000 58 Female
## 35 2000 Florida 17300 21 Male
## 36 2000 Florida 61300 52 Male
## 37 2000 Florida 0 18 Male
## 38 2000 Florida 6000 2 Male
## 39 2000 Florida 30270 26 Male
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## 41 2000 New York 0 46 Female
## 42 2000 New York 165000 63 Male
## 43 2000 New York 15570 83 Male
## 44 2000 New York 48630 35 Female
## 45 2000 New York 13000 1 Male
## 46 2000 New York 68020 2 Female
## 47 2000 New York 130000 54 Female
## 48 2000 New York 0 36 Male
## 49 2000 New York NA 28 Male
## 50 2000 New York 12400 7 Male
## 51 2000 New York 50400 21 Female
## 52 2000 New York 65000 1 Female
## 53 2000 New York 62900 17 Female
## 54 2000 New York 31200 29 Male
## 55 2000 New York 81710 14 Male
## 56 2000 New York 37100 33 Male
## 57 2000 New York 9500 16 Male
## 58 2000 New York 25000 40 Female
## 59 2000 New York 168200 34 Male
## 60 2000 New York 168200 6 Female
## 61 2000 New York 55300 49 Male
## 62 2000 New York NA 17 Male
## 63 2000 New York NA 37 Male
## 64 2000 New York 17000 2 Male
## 65 2000 New York 43000 40 Female
## 66 2000 New York 18800 10 Female
## 67 2000 New York 105000 61 Female
## 68 2000 New York 48400 24 Female
## 69 2000 New York 56000 23 Female
## 70 2000 New York 88000 16 Female
## 71 2000 New York 34900 36 Female
## 72 2000 New York 0 7 Male
## 73 2000 New York 106000 32 Female
## 74 2000 New York 62820 19 Male
## 75 2000 New York 55000 81 Male
## 76 2000 New York 38000 47 Male
## 77 2000 New York 37000 72 Female
## 78 2000 New York 77600 2 Male
## 79 2000 New York 40400 46 Female
## 80 2000 New York 59700 27 Female
## 81 2000 New York 75000 2 Male
## 82 2000 New York 104000 10 Female
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## 84 2000 Rhode Island 43000 39 Male
## 85 2000 Wyoming 146000 12 Female
## 86 2000 Alabama 5500 73 Female
## 87 2000 Alabama 63820 40 Male
## 88 2000 Alabama 11200 60 Female
## 89 2000 Alabama 34500 43 Female
## 90 2000 Alabama 33600 7 Male
## 91 2000 California 52000 40 Female
## 92 2000 California 156000 65 Female
## 93 2000 California 59000 80 Male
## 94 2000 California 37500 46 Female
## 95 2000 California 67500 3 Female
## 96 2000 California 21800 6 Male
## 97 2000 California 79000 26 Female
## 98 2000 California 134000 58 Male
## 99 2000 California 77000 48 Female
## 100 2000 California 130000 62 Male
## 101 2000 California 10000 22 Female
## 102 2000 California 12000 52 Female
## 103 2000 California 101800 60 Female
## 104 2000 California 21000 3 Female
## 105 2000 California 43900 21 Female
## 106 2000 California 113710 30 Female
## 107 2000 California 82300 58 Female
## 108 2000 California 82300 5 Male
## 109 2000 California 80300 20 Male
## 110 2000 California 36100 18 Female
## 111 2000 California 60980 45 Female
## 112 2000 California 58760 20 Male
## 113 2000 California 35000 28 Female
## 114 2000 California 7400 19 Female
## 115 2000 California 141000 60 Male
## 116 2000 California 10200 75 Female
## 117 2000 California NA 22 Male
## 118 2000 California 85400 58 Female
## 119 2000 California 77700 52 Male
## 120 2000 California 13500 58 Male
## 121 2000 California 24800 24 Female
## 122 2000 California 87100 46 Male
## 123 2000 California 47300 43 Female
## 124 2000 California 34850 41 Male
## 125 2000 California 62000 47 Female
## 126 2000 California 90000 40 Female
## 127 2000 California 18000 24 Male
## 128 2000 California 27500 13 Male
## 129 2000 California 57190 41 Male
## 130 2000 California 37600 4 Male
## 131 2000 California 9700 77 Male
## 132 2000 California 353100 60 Male
## 133 2000 California 7500 7 Male
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## 136 2000 California 193000 72 Male
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## 138 2000 California 162600 45 Male
## 139 2000 California 41500 79 Female
## 140 2000 California 14900 16 Male
## 141 2000 California 103000 41 Male
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## 153 2000 Illinois 45000 47 Male
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## 156 2000 Illinois 13000 23 Female
## 157 2000 Illinois 73020 49 Male
## 158 2000 Illinois 20000 21 Female
## 159 2000 Illinois 33300 56 Female
## 160 2000 Illinois 34000 62 Male
## 161 2000 Illinois 84000 40 Female
## 162 2000 Illinois 12800 62 Female
## 163 2000 Illinois 40600 37 Male
## 164 2000 Illinois 154630 50 Female
## 165 2000 Illinois 88000 20 Female
## 166 2000 Illinois 42000 15 Male
## 167 2000 Illinois 48790 39 Male
## 168 2000 Illinois NA 87 Female
## 169 2000 Illinois 20300 78 Female
## 170 2000 Illinois 30000 35 Female
## 171 2000 Illinois 64890 48 Male
## 172 2000 Illinois 38500 41 Female
## 173 2000 Illinois 20600 51 Male
## 174 2000 Kansas 42000 24 Male
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## 176 2000 Kansas 40000 25 Male
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## 178 2000 Maryland NA 25 Male
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## 182 2000 Maryland 9500 54 Male
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## 184 2000 Maryland 95500 2 Female
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## 186 2000 Maryland 360900 6 Male
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## 346 2000 Michigan 51400 13 Male
## 347 2000 Michigan 49000 15 Female
## 348 2000 Michigan 87880 16 Female
## 349 2000 Michigan 57400 1 Female
## 350 2000 Michigan 42000 6 Male
## 351 2000 Michigan 77620 35 Male
## 352 2000 Michigan 24900 71 Male
## 353 2000 Michigan 69220 48 Male
## 354 2000 Michigan 25980 18 Male
## 355 2000 Michigan 92000 46 Male
## 356 2000 Michigan 69000 40 Male
## 357 2000 Michigan 20340 2 Female
## 358 2000 Michigan 113500 44 Male
## 359 2000 Michigan 48000 31 Female
## 360 2000 Minnesota 75000 35 Female
## 361 2000 Minnesota 55600 26 Female
## 362 2000 Minnesota 10000 46 Male
## 363 2000 Minnesota 92900 54 Male
## 364 2000 Minnesota 43010 2 Male
## 365 2000 Minnesota 46000 37 Female
## 366 2000 Minnesota 33200 42 Female
## 367 2000 Minnesota 17000 69 Female
## 368 2000 Minnesota 0 81 Female
## 369 2000 Minnesota 52500 16 Male
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## 371 2000 New Hampshire 58000 10 Male
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## 376 2000 North Carolina 13460 8 Female
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## 384 2000 South Carolina 22000 4 Female
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## 389 2000 South Carolina 29600 7 Male
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## 394 2000 Tennessee 84000 40 Male
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## 399 2000 Washington 36400 49 Male
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## 403 2000 Washington 0 39 Male
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## 425 2000 Indiana 8000 85 Male
## 426 2000 Indiana NA 35 Male
## 427 2000 Indiana 31500 29 Male
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## 429 2000 Indiana 42900 73 Female
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## 431 2000 Indiana 11580 76 Female
## 432 2000 Indiana 78700 37 Male
## 433 2000 Indiana 29340 57 Female
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## 435 2000 Indiana 36000 26 Female
## 436 2000 Indiana 130000 8 Male
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## 456 2000 Mississippi 3300 3 Male
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## 470 2000 Pennsylvania 20000 3 Male
## 471 2000 Pennsylvania 27150 1 Male
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## 475 2000 Pennsylvania 0 25 Female
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## 477 2000 Pennsylvania 24000 9 Female
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## 479 2000 Pennsylvania 32350 13 Male
## 480 2000 Pennsylvania 42300 3 Male
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## 482 2000 Pennsylvania 33000 32 Female
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## 484 2000 Pennsylvania 44000 32 Female
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## 486 2000 Pennsylvania 0 24 Male
## 487 2000 Pennsylvania 69000 18 Male
## 488 2000 Pennsylvania 60300 41 Female
## 489 2000 Pennsylvania 150000 6 Female
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## 492 2000 Pennsylvania 51700 76 Male
## 493 2000 Pennsylvania 119800 24 Male
## 494 2000 Virginia 18000 36 Male
## 495 2000 Virginia 45100 5 Male
## 496 2000 Virginia 8500 68 Female
## 497 2000 Virginia 55400 12 Male
## 498 2000 Virginia 15000 60 Male
## 499 2000 Virginia NA 60 Male
## 500 2000 Virginia 39370 47 Male
## race_general marital_status
## 1 Two major races Married/spouse present
## 2 White Never married/single
## 3 Black Never married/single
## 4 White Never married/single
## 5 White Married/spouse present
## 6 White Married/spouse present
## 7 White Married/spouse present
## 8 White Married/spouse present
## 9 Black Married/spouse present
## 10 White Widowed
## 11 White Never married/single
## 12 White Divorced
## 13 White Divorced
## 14 White Never married/single
## 15 White Never married/single
## 16 White Married/spouse present
## 17 White Married/spouse present
## 18 White Never married/single
## 19 White Married/spouse present
## 20 White Married/spouse present
## 21 White Married/spouse present
## 22 White Widowed
## 23 White Never married/single
## 24 White Never married/single
## 25 White Never married/single
## 26 White Married/spouse present
## 27 White Never married/single
## 28 Black Widowed
## 29 White Never married/single
## 30 White Divorced
## 31 White Married/spouse present
## 32 White Never married/single
## 33 White Married/spouse present
## 34 White Married/spouse present
## 35 Black Never married/single
## 36 White Married/spouse present
## 37 White Never married/single
## 38 White Never married/single
## 39 Other Married/spouse present
## 40 White Widowed
## 41 White Divorced
## 42 White Married/spouse present
## 43 White Widowed
## 44 White Married/spouse present
## 45 White Never married/single
## 46 Chinese Never married/single
## 47 White Married/spouse present
## 48 White Never married/single
## 49 White Separated
## 50 White Never married/single
## 51 White Never married/single
## 52 White Never married/single
## 53 Black Never married/single
## 54 Black Never married/single
## 55 White Never married/single
## 56 White Never married/single
## 57 White Never married/single
## 58 Two major races Divorced
## 59 White Married/spouse present
## 60 White Never married/single
## 61 White Married/spouse present
## 62 Black Never married/single
## 63 Black Never married/single
## 64 Two major races Never married/single
## 65 Black Married/spouse present
## 66 Black Never married/single
## 67 White Divorced
## 68 Chinese Married/spouse present
## 69 Chinese Never married/single
## 70 Other Asian or Pacific Islander Never married/single
## 71 Black Married/spouse present
## 72 White Never married/single
## 73 White Married/spouse present
## 74 Black Never married/single
## 75 White Widowed
## 76 White Married/spouse present
## 77 White Widowed
## 78 White Never married/single
## 79 Black Married/spouse present
## 80 White Married/spouse present
## 81 White Never married/single
## 82 White Never married/single
## 83 White Never married/single
## 84 White Never married/single
## 85 White Never married/single
## 86 Black Divorced
## 87 White Married/spouse present
## 88 Black Divorced
## 89 Other Married/spouse present
## 90 White Never married/single
## 91 White Married/spouse present
## 92 White Married/spouse present
## 93 White Married/spouse present
## 94 White Married/spouse present
## 95 White Never married/single
## 96 Other Asian or Pacific Islander Never married/single
## 97 White Never married/single
## 98 White Married/spouse present
## 99 Two major races Married/spouse present
## 100 White Married/spouse present
## 101 White Never married/single
## 102 White Divorced
## 103 White Married/spouse present
## 104 White Never married/single
## 105 Other Married/spouse absent
## 106 White Married/spouse present
## 107 Chinese Widowed
## 108 Chinese Never married/single
## 109 White Divorced
## 110 Other Married/spouse present
## 111 Other Married/spouse present
## 112 Other Never married/single
## 113 White Married/spouse present
## 114 White Never married/single
## 115 White Married/spouse present
## 116 White Never married/single
## 117 White Never married/single
## 118 White Married/spouse present
## 119 White Married/spouse present
## 120 Other Divorced
## 121 White Married/spouse present
## 122 White Married/spouse present
## 123 White Married/spouse present
## 124 Other Asian or Pacific Islander Married/spouse present
## 125 White Never married/single
## 126 Black Married/spouse present
## 127 Other Never married/single
## 128 Two major races Never married/single
## 129 Black Never married/single
## 130 Two major races Never married/single
## 131 White Married/spouse present
## 132 White Married/spouse present
## 133 Other Never married/single
## 134 Chinese Married/spouse present
## 135 White Married/spouse present
## 136 Chinese Married/spouse present
## 137 Other Married/spouse present
## 138 Other Asian or Pacific Islander Married/spouse present
## 139 White Widowed
## 140 White Never married/single
## 141 White Married/spouse present
## 142 Other Asian or Pacific Islander Separated
## 143 White Married/spouse present
## 144 White Never married/single
## 145 White Never married/single
## 146 White Never married/single
## 147 Other Married/spouse present
## 148 Black Never married/single
## 149 Other Asian or Pacific Islander Never married/single
## 150 Other Married/spouse present
## 151 Two major races Never married/single
## 152 White Married/spouse present
## 153 White Divorced
## 154 White Married/spouse present
## 155 White Married/spouse present
## 156 White Never married/single
## 157 White Married/spouse present
## 158 White Never married/single
## 159 White Married/spouse present
## 160 White Divorced
## 161 White Married/spouse present
## 162 White Married/spouse present
## 163 Other Married/spouse present
## 164 White Married/spouse present
## 165 White Married/spouse present
## 166 White Never married/single
## 167 White Married/spouse absent
## 168 White Married/spouse absent
## 169 White Married/spouse present
## 170 Black Divorced
## 171 White Never married/single
## 172 White Divorced
## 173 White Married/spouse present
## 174 White Never married/single
## 175 White Never married/single
## 176 White Never married/single
## 177 Two major races Never married/single
## 178 Black Married/spouse absent
## 179 White Married/spouse present
## 180 White Never married/single
## 181 White Married/spouse present
## 182 Black Never married/single
## 183 White Married/spouse present
## 184 White Never married/single
## 185 Black Married/spouse present
## 186 White Never married/single
## 187 Two major races Never married/single
## 188 Black Divorced
## 189 Black Married/spouse present
## 190 White Married/spouse present
## 191 White Married/spouse present
## 192 White Divorced
## 193 Black Never married/single
## 194 Other Never married/single
## 195 White Divorced
## 196 Black Married/spouse present
## 197 Two major races Never married/single
## 198 Black Married/spouse present
## 199 Other Asian or Pacific Islander Married/spouse present
## 200 White Never married/single
## 201 White Married/spouse present
## 202 Black Married/spouse present
## 203 White Married/spouse present
## 204 White Never married/single
## 205 White Married/spouse present
## 206 White Married/spouse present
## 207 White Married/spouse present
## 208 White Married/spouse present
## 209 White Never married/single
## 210 White Divorced
## 211 Black Widowed
## 212 Black Never married/single
## 213 White Never married/single
## 214 Black Never married/single
## 215 White Married/spouse present
## 216 Two major races Married/spouse present
## 217 White Widowed
## 218 White Divorced
## 219 White Never married/single
## 220 Other Asian or Pacific Islander Never married/single
## 221 White Married/spouse present
## 222 White Never married/single
## 223 White Married/spouse present
## 224 White Never married/single
## 225 White Never married/single
## 226 White Married/spouse present
## 227 White Never married/single
## 228 Other Never married/single
## 229 White Never married/single
## 230 White Married/spouse present
## 231 White Married/spouse present
## 232 White Never married/single
## 233 White Married/spouse present
## 234 White Married/spouse present
## 235 White Married/spouse present
## 236 White Divorced
## 237 Black Married/spouse present
## 238 White Never married/single
## 239 White Married/spouse present
## 240 White Married/spouse present
## 241 White Never married/single
## 242 White Never married/single
## 243 Black Never married/single
## 244 White Divorced
## 245 White Married/spouse present
## 246 Black Never married/single
## 247 White Never married/single
## 248 Black Never married/single
## 249 White Divorced
## 250 Other Never married/single
## 251 Black Married/spouse absent
## 252 Black Never married/single
## 253 White Never married/single
## 254 White Never married/single
## 255 Black Never married/single
## 256 White Married/spouse present
## 257 White Never married/single
## 258 White Never married/single
## 259 White Married/spouse present
## 260 White Divorced
## 261 Black Married/spouse present
## 262 White Married/spouse present
## 263 Other Never married/single
## 264 White Never married/single
## 265 White Never married/single
## 266 White Married/spouse present
## 267 White Widowed
## 268 White Widowed
## 269 White Never married/single
## 270 White Never married/single
## 271 American Indian or Alaska Native Never married/single
## 272 White Divorced
## 273 White Married/spouse present
## 274 Other Married/spouse absent
## 275 Black Never married/single
## 276 White Divorced
## 277 White Never married/single
## 278 White Married/spouse present
## 279 White Married/spouse present
## 280 White Divorced
## 281 White Never married/single
## 282 White Never married/single
## 283 White Married/spouse present
## 284 White Married/spouse present
## 285 White Married/spouse present
## 286 White Married/spouse present
## 287 Black Married/spouse absent
## 288 Black Widowed
## 289 White Married/spouse present
## 290 White Never married/single
## 291 White Widowed
## 292 Other Never married/single
## 293 White Never married/single
## 294 White Married/spouse present
## 295 White Married/spouse present
## 296 White Never married/single
## 297 White Married/spouse present
## 298 White Married/spouse present
## 299 White Never married/single
## 300 White Married/spouse present
## 301 White Married/spouse present
## 302 Black Never married/single
## 303 White Married/spouse present
## 304 White Never married/single
## 305 Two major races Never married/single
## 306 White Never married/single
## 307 Other Asian or Pacific Islander Never married/single
## 308 Other Married/spouse present
## 309 White Divorced
## 310 White Never married/single
## 311 White Never married/single
## 312 White Never married/single
## 313 White Married/spouse present
## 314 White Never married/single
## 315 Other Never married/single
## 316 White Never married/single
## 317 White Married/spouse present
## 318 White Never married/single
## 319 White Never married/single
## 320 White Married/spouse absent
## 321 White Widowed
## 322 White Widowed
## 323 White Married/spouse present
## 324 White Never married/single
## 325 Japanese Married/spouse present
## 326 White Never married/single
## 327 White Widowed
## 328 White Married/spouse present
## 329 White Never married/single
## 330 White Married/spouse present
## 331 White Married/spouse present
## 332 White Never married/single
## 333 Black Married/spouse absent
## 334 White Never married/single
## 335 White Married/spouse present
## 336 White Divorced
## 337 Black Married/spouse present
## 338 White Never married/single
## 339 Black Never married/single
## 340 Black Married/spouse absent
## 341 Black Never married/single
## 342 Black Divorced
## 343 Black Widowed
## 344 Two major races Married/spouse present
## 345 Black Never married/single
## 346 White Never married/single
## 347 White Never married/single
## 348 White Never married/single
## 349 White Never married/single
## 350 White Never married/single
## 351 White Married/spouse present
## 352 White Married/spouse present
## 353 White Married/spouse present
## 354 White Never married/single
## 355 White Married/spouse present
## 356 Two major races Never married/single
## 357 White Never married/single
## 358 White Married/spouse present
## 359 American Indian or Alaska Native Married/spouse present
## 360 White Married/spouse present
## 361 White Married/spouse present
## 362 White Never married/single
## 363 White Married/spouse present
## 364 White Never married/single
## 365 White Never married/single
## 366 White Never married/single
## 367 White Widowed
## 368 White Widowed
## 369 White Never married/single
## 370 White Never married/single
## 371 White Never married/single
## 372 White Married/spouse present
## 373 White Never married/single
## 374 White Never married/single
## 375 Black Never married/single
## 376 Two major races Never married/single
## 377 Black Married/spouse present
## 378 White Never married/single
## 379 White Married/spouse present
## 380 White Married/spouse present
## 381 White Never married/single
## 382 White Married/spouse present
## 383 White Divorced
## 384 White Never married/single
## 385 White Married/spouse present
## 386 White Married/spouse present
## 387 White Married/spouse present
## 388 Black Married/spouse present
## 389 Two major races Never married/single
## 390 White Married/spouse present
## 391 White Married/spouse present
## 392 White Married/spouse present
## 393 White Never married/single
## 394 White Married/spouse present
## 395 White Married/spouse present
## 396 White Separated
## 397 White Never married/single
## 398 White Never married/single
## 399 White Never married/single
## 400 Other Married/spouse present
## 401 White Never married/single
## 402 White Married/spouse present
## 403 American Indian or Alaska Native Never married/single
## 404 Other Asian or Pacific Islander Widowed
## 405 White Married/spouse present
## 406 Other Asian or Pacific Islander Never married/single
## 407 Other Never married/single
## 408 White Never married/single
## 409 White Widowed
## 410 White Married/spouse present
## 411 White Divorced
## 412 White Never married/single
## 413 White Married/spouse present
## 414 White Never married/single
## 415 White Never married/single
## 416 White Married/spouse present
## 417 White Divorced
## 418 White Never married/single
## 419 White Never married/single
## 420 Black Never married/single
## 421 Other Asian or Pacific Islander Married/spouse present
## 422 Chinese Married/spouse present
## 423 White Married/spouse present
## 424 Other Never married/single
## 425 Black Widowed
## 426 Black Never married/single
## 427 White Married/spouse present
## 428 White Divorced
## 429 White Never married/single
## 430 White Never married/single
## 431 White Widowed
## 432 Other Asian or Pacific Islander Married/spouse present
## 433 White Married/spouse absent
## 434 White Married/spouse present
## 435 White Married/spouse present
## 436 White Never married/single
## 437 White Divorced
## 438 White Never married/single
## 439 White Never married/single
## 440 White Widowed
## 441 White Married/spouse present
## 442 Black Widowed
## 443 White Married/spouse present
## 444 Black Married/spouse absent
## 445 White Married/spouse present
## 446 White Married/spouse present
## 447 Black Never married/single
## 448 White Never married/single
## 449 Black Married/spouse present
## 450 White Never married/single
## 451 Black Divorced
## 452 White Widowed
## 453 Black Never married/single
## 454 White Married/spouse present
## 455 Black Married/spouse present
## 456 Black Never married/single
## 457 White Never married/single
## 458 White Married/spouse present
## 459 Black Never married/single
## 460 White Never married/single
## 461 White Divorced
## 462 White Widowed
## 463 White Never married/single
## 464 White Married/spouse present
## 465 White Never married/single
## 466 White Married/spouse present
## 467 White Never married/single
## 468 White Never married/single
## 469 White Married/spouse present
## 470 White Never married/single
## 471 White Never married/single
## 472 White Married/spouse present
## 473 White Never married/single
## 474 Black Married/spouse absent
## 475 White Never married/single
## 476 White Married/spouse present
## 477 White Never married/single
## 478 Black Never married/single
## 479 White Never married/single
## 480 White Never married/single
## 481 Other Never married/single
## 482 Black Never married/single
## 483 White Widowed
## 484 White Married/spouse present
## 485 White Never married/single
## 486 White Never married/single
## 487 White Never married/single
## 488 White Married/spouse present
## 489 White Never married/single
## 490 White Married/spouse present
## 491 White Married/spouse present
## 492 White Married/spouse present
## 493 White Never married/single
## 494 White Married/spouse present
## 495 White Never married/single
## 496 White Divorced
## 497 White Never married/single
## 498 Black Widowed
## 499 Black Married/spouse absent
## 500 White Married/spouse present
## total_personal_income
## 1 0
## 2 13000
## 3 20000
## 4 NA
## 5 36000
## 6 27000
## 7 11800
## 8 48000
## 9 40000
## 10 14600
## 11 23000
## 12 37000
## 13 32000
## 14 NA
## 15 6000
## 16 16100
## 17 12000
## 18 NA
## 19 20000
## 20 4800
## 21 28000
## 22 31600
## 23 NA
## 24 49300
## 25 NA
## 26 34320
## 27 NA
## 28 8400
## 29 3700
## 30 4800
## 31 53000
## 32 7500
## 33 6000
## 34 0
## 35 4800
## 36 35000
## 37 2000
## 38 NA
## 39 24250
## 40 8800
## 41 17000
## 42 123000
## 43 15570
## 44 15830
## 45 NA
## 46 NA
## 47 64000
## 48 17500
## 49 0
## 50 NA
## 51 4000
## 52 NA
## 53 2000
## 54 10000
## 55 NA
## 56 37100
## 57 1800
## 58 25000
## 59 121600
## 60 NA
## 61 55300
## 62 2000
## 63 0
## 64 NA
## 65 0
## 66 NA
## 67 105000
## 68 0
## 69 0
## 70 0
## 71 19900
## 72 NA
## 73 22000
## 74 720
## 75 55000
## 76 38000
## 77 17300
## 78 NA
## 79 14200
## 80 14500
## 81 NA
## 82 NA
## 83 44004
## 84 43000
## 85 NA
## 86 3000
## 87 47950
## 88 11200
## 89 15300
## 90 NA
## 91 27000
## 92 10000
## 93 52100
## 94 17500
## 95 NA
## 96 NA
## 97 25000
## 98 134000
## 99 0
## 100 115000
## 101 10000
## 102 5100
## 103 4400
## 104 NA
## 105 9500
## 106 38400
## 107 0
## 108 NA
## 109 26300
## 110 0
## 111 0
## 112 4000
## 113 15000
## 114 5600
## 115 111000
## 116 10200
## 117 14100
## 118 32400
## 119 45000
## 120 0
## 121 18800
## 122 36000
## 123 28000
## 124 30000
## 125 34000
## 126 40000
## 127 3600
## 128 NA
## 129 57190
## 130 NA
## 131 9700
## 132 330000
## 133 NA
## 134 0
## 135 58000
## 136 20000
## 137 36000
## 138 160000
## 139 41500
## 140 0
## 141 75000
## 142 10300
## 143 1000
## 144 NA
## 145 NA
## 146 NA
## 147 0
## 148 NA
## 149 32000
## 150 0
## 151 NA
## 152 0
## 153 45000
## 154 50000
## 155 53000
## 156 28000
## 157 43020
## 158 8000
## 159 3000
## 160 34000
## 161 24000
## 162 0
## 163 40600
## 164 0
## 165 5000
## 166 0
## 167 22000
## 168 13000
## 169 1100
## 170 30000
## 171 63000
## 172 38500
## 173 6500
## 174 42000
## 175 484
## 176 40000
## 177 22460
## 178 19200
## 179 59700
## 180 44000
## 181 14000
## 182 1600
## 183 49700
## 184 NA
## 185 9500
## 186 NA
## 187 40000
## 188 4500
## 189 23000
## 190 14000
## 191 0
## 192 36200
## 193 25000
## 194 NA
## 195 8840
## 196 36000
## 197 24500
## 198 36000
## 199 317000
## 200 10
## 201 24000
## 202 4000
## 203 10000
## 204 NA
## 205 22000
## 206 47100
## 207 43030
## 208 4800
## 209 0
## 210 12600
## 211 19604
## 212 NA
## 213 22000
## 214 NA
## 215 23700
## 216 0
## 217 10400
## 218 44200
## 219 NA
## 220 12000
## 221 40000
## 222 NA
## 223 61700
## 224 NA
## 225 16000
## 226 50000
## 227 0
## 228 NA
## 229 72600
## 230 59000
## 231 4200
## 232 NA
## 233 8000
## 234 30030
## 235 16000
## 236 0
## 237 10000
## 238 4600
## 239 38000
## 240 15000
## 241 27000
## 242 NA
## 243 NA
## 244 150500
## 245 155000
## 246 90
## 247 0
## 248 NA
## 249 40050
## 250 0
## 251 0
## 252 NA
## 253 141900
## 254 45800
## 255 NA
## 256 0
## 257 NA
## 258 NA
## 259 20000
## 260 55000
## 261 26300
## 262 36000
## 263 NA
## 264 13000
## 265 NA
## 266 -4400
## 267 0
## 268 5100
## 269 30000
## 270 17190
## 271 26700
## 272 100
## 273 446000
## 274 13000
## 275 3600
## 276 51000
## 277 0
## 278 34300
## 279 36000
## 280 14300
## 281 NA
## 282 NA
## 283 21000
## 284 24000
## 285 3500
## 286 23700
## 287 100000
## 288 3800
## 289 456000
## 290 10700
## 291 22700
## 292 0
## 293 37500
## 294 51500
## 295 60000
## 296 0
## 297 40000
## 298 0
## 299 12500
## 300 60900
## 301 32700
## 302 NA
## 303 36000
## 304 NA
## 305 NA
## 306 NA
## 307 23000
## 308 10600
## 309 44000
## 310 NA
## 311 NA
## 312 NA
## 313 0
## 314 24000
## 315 NA
## 316 NA
## 317 72600
## 318 15000
## 319 NA
## 320 30000
## 321 30000
## 322 0
## 323 14000
## 324 NA
## 325 45000
## 326 NA
## 327 9100
## 328 7600
## 329 10424
## 330 110000
## 331 2900
## 332 NA
## 333 25000
## 334 NA
## 335 46400
## 336 56740
## 337 23300
## 338 NA
## 339 35000
## 340 15000
## 341 16800
## 342 21000
## 343 21200
## 344 19700
## 345 NA
## 346 NA
## 347 0
## 348 480
## 349 NA
## 350 NA
## 351 76020
## 352 18900
## 353 37000
## 354 980
## 355 80900
## 356 69000
## 357 NA
## 358 86000
## 359 20000
## 360 17000
## 361 34100
## 362 0
## 363 43000
## 364 NA
## 365 46000
## 366 33200
## 367 17000
## 368 0
## 369 0
## 370 NA
## 371 NA
## 372 14700
## 373 980
## 374 3600
## 375 0
## 376 NA
## 377 28000
## 378 NA
## 379 60000
## 380 8200
## 381 NA
## 382 70000
## 383 5000
## 384 NA
## 385 26700
## 386 24100
## 387 9000
## 388 15000
## 389 NA
## 390 23000
## 391 12600
## 392 140900
## 393 12800
## 394 68000
## 395 9000
## 396 25000
## 397 15000
## 398 11620
## 399 50000
## 400 16000
## 401 4980
## 402 13500
## 403 0
## 404 15000
## 405 42600
## 406 NA
## 407 NA
## 408 0
## 409 0
## 410 39510
## 411 34500
## 412 38400
## 413 20000
## 414 NA
## 415 33780
## 416 55000
## 417 49100
## 418 6420
## 419 NA
## 420 NA
## 421 22000
## 422 34600
## 423 23000
## 424 NA
## 425 8000
## 426 0
## 427 6500
## 428 36000
## 429 38900
## 430 5000
## 431 11580
## 432 39920
## 433 29340
## 434 45500
## 435 0
## 436 NA
## 437 9000
## 438 NA
## 439 NA
## 440 15800
## 441 24000
## 442 13300
## 443 29800
## 444 0
## 445 6000
## 446 104800
## 447 NA
## 448 21700
## 449 0
## 450 NA
## 451 12100
## 452 8100
## 453 1400
## 454 25000
## 455 0
## 456 NA
## 457 25000
## 458 11800
## 459 54000
## 460 NA
## 461 14000
## 462 9400
## 463 NA
## 464 16300
## 465 0
## 466 15700
## 467 NA
## 468 NA
## 469 68020
## 470 NA
## 471 NA
## 472 2800
## 473 6700
## 474 6500
## 475 16000
## 476 24000
## 477 NA
## 478 NA
## 479 NA
## 480 NA
## 481 0
## 482 27000
## 483 9100
## 484 21000
## 485 9500
## 486 2200
## 487 15000
## 488 9100
## 489 NA
## 490 103000
## 491 68000
## 492 14500
## 493 22000
## 494 18000
## 495 NA
## 496 8500
## 497 NA
## 498 15000
## 499 48600
## 500 19800 census <- read.csv("C:/Users/ramin/Desktop/2020 winter/Data Analysis/Computer Assignment 2/Dataset/census.csv")
names(census)## [1] "census_year" "state_fips_code" "total_family_income"
## [4] "age" "sex" "race_general"
## [7] "marital_status" "total_personal_income" str(census) ## 'data.frame': 500 obs. of 8 variables:
## $ census_year : int 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
## $ state_fips_code : Factor w/ 47 levels "Alabama","Arizona",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ total_family_income : int 14550 22800 0 23000 48000 74000 23000 74000 60000 14600 ...
## $ age : int 44 20 20 6 55 43 60 47 54 58 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 1 2 1 2 1 1 1 1 1 ...
## $ race_general : Factor w/ 8 levels "American Indian or Alaska Native",..: 7 8 2 8 8 8 8 8 2 8 ...
## $ marital_status : Factor w/ 6 levels "Divorced","Married/spouse absent",..: 3 4 4 4 3 3 3 3 3 6 ...
## $ total_personal_income: int 0 13000 20000 NA 36000 27000 11800 48000 40000 14600 ... census %>% ggplot(mapping = aes(x = age)) + geom_histogram( fill = "darkred" )## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
census %>% ggplot(mapping = aes(x = total_personal_income/1000)) + geom_histogram( fill = "darkred" )## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 108 rows containing non-finite values (stat_bin).
QUESTIONS::
Describe the following variables, their types (levels of measurement), and appropriate type of frequency distribution graph:
[VAR: marital_status] -- Shows wether "Divorced","Married/spouse absent", 3 4 4 4 3 3 3 3 3 6, bar chart.
[VAR: sex] -- Has 2 levels "Female","Male", 2 1 2 1 2 1 1 1 1 1 ..., bar chart.
[VAR: age] -- int 44 20 20 6 55 43 60 47 54 58 ..., bar chart.
[VAR: total_personal_income] -- int 0 13000 20000 NA 36000 27000 11800 48000 40000 14600 ..., can be measured from bar chart.Describe the distribution for age:
The distribution is skewed to the right, meaning that most of the people are younger.Describe the distribution for total personal income:
The distribution is skewed to the right, with two ouliers, the mean is 70.