Homework-6
Lin
2023-04-11
1 Regular Expression
a) Use the words data set, find all the words that match the following pattern:
• are exactly four letters long
• are either four or five letters long
• the second letter is “s” or “t”
• contains the pattern like “oxx” where “o” is one letter and “x” is another letter
• contains “a”, “e” and “o” at the same time sum(str_detect(words,"^....$"))## [1] 263str_view(words, "^....$")## [2] │ <able>
## [33] │ <also>
## [49] │ <area>
## [64] │ <away>
## [66] │ <baby>
## [67] │ <back>
## [71] │ <ball>
## [72] │ <bank>
## [74] │ <base>
## [77] │ <bear>
## [78] │ <beat>
## [88] │ <best>
## [92] │ <bill>
## [98] │ <blow>
## [99] │ <blue>
## [101] │ <boat>
## [102] │ <body>
## [103] │ <book>
## [104] │ <both>
## [120] │ <busy>
## ... and 243 morestr_view(words, "^.....?$")## [2] │ <able>
## [3] │ <about>
## [14] │ <admit>
## [18] │ <after>
## [20] │ <again>
## [23] │ <agent>
## [25] │ <agree>
## [28] │ <allow>
## [30] │ <along>
## [33] │ <also>
## [42] │ <apart>
## [45] │ <apply>
## [49] │ <area>
## [50] │ <argue>
## [63] │ <aware>
## [64] │ <away>
## [65] │ <awful>
## [66] │ <baby>
## [67] │ <back>
## [71] │ <ball>
## ... and 443 morestr_view(words, "^.[st].*")## [55] │ <as>
## [56] │ <ask>
## [57] │ <associate>
## [58] │ <assume>
## [59] │ <at>
## [60] │ <attend>
## [277] │ <especial>
## [434] │ <issue>
## [435] │ <it>
## [436] │ <item>
## [587] │ <other>
## [588] │ <otherwise>
## [797] │ <staff>
## [798] │ <stage>
## [799] │ <stairs>
## [800] │ <stand>
## [801] │ <standard>
## [802] │ <start>
## [803] │ <state>
## [804] │ <station>
## ... and 18 morestr_view(words, "(.)(.)\\2")## [5] │ <acc>ept
## [6] │ <acc>ount
## [8] │ acr<oss>
## [12] │ <add>
## [13] │ <add>r<ess>
## [16] │ <aff>ect
## [17] │ <aff>ord
## [19] │ after<noo>n
## [25] │ ag<ree>
## [27] │ <all>
## [28] │ <all>ow
## [43] │ <app>arent
## [44] │ <app>ear
## [45] │ <app>ly
## [46] │ <app>oint
## [47] │ <app>roach
## [48] │ <app>ropriate
## [53] │ <arr>ange
## [57] │ <ass>ociate
## [58] │ <ass>ume
## ... and 137 morestr_view(words, "(.*a.*e.*o.*)|(.*a.*o.*e.*)|(.*e.*a.*o.*)|(.*e.*o.*a.*)|(.*o.*a.*e.*)|(.*o.*e.*a.*)")## [4] │ <absolute>
## [19] │ <afternoon>
## [39] │ <another>
## [48] │ <appropriate>
## [57] │ <associate>
## [166] │ <colleague>
## [177] │ <compare>
## [268] │ <encourage>
## [580] │ <operate>
## [585] │ <organize>
## [648] │ <probable>
## [654] │ <programme>
## [683] │ <reason>
## [695] │ <relation>b) Use the sentences data set, make the following plot
• a bar plot counting sentences with and without “the” (or “The”).
• a scatter plot with x being the average length of words in a sentence, and y being the number of words starting with “a” or “e” or “i” or “o” or “u” in the sentence.sentence_1<-as.tibble(sentences)## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.sentence2 <- sentence_1 %>%
mutate(the_group = ifelse(str_detect(value, "(the|The)"), "Yes","No")) %>%
print()## # A tibble: 720 × 2
## value the_group
## <chr> <chr>
## 1 The birch canoe slid on the smooth planks. Yes
## 2 Glue the sheet to the dark blue background. Yes
## 3 It's easy to tell the depth of a well. Yes
## 4 These days a chicken leg is a rare dish. Yes
## 5 Rice is often served in round bowls. No
## 6 The juice of lemons makes fine punch. Yes
## 7 The box was thrown beside the parked truck. Yes
## 8 The hogs were fed chopped corn and garbage. Yes
## 9 Four hours of steady work faced us. No
## 10 A large size in stockings is hard to sell. No
## # … with 710 more rows ggplot(sentence2) + geom_bar(mapping = aes(x = the_group)) +
labs(title = "Comparison sentenes with/without 'the'", x = "Sentences with/without 'the'",y = "Count") +
theme(plot.title = element_text(hjust = 0.5, size = rel(1.5)), axis.title= element_text(size = rel(1.1)))
c) Application
i) Download the Oxford English Dictionary as a “.txt” file from https://course.mt.feitian.edu/files/47745/
download?download_frd=1
ii) Read it into RStudio with read_lines() function (check how to use it by yourself)
iii) Turn the dictionary into a tibble and remove all blank lines
iv) Use regular expression to extract all words for each item in a separate column named “words”mydic_data <- read_lines("Oxford_English_Dictionary.txt")mydic_data1 <- tibble(
word = mydic_data,
i = seq_along(word)
)
mydic_data1## # A tibble: 72,325 × 2
## word i
## <chr> <int>
## 1 "A " 1
## 2 "" 2
## 3 "" 3
## 4 "" 4
## 5 "A- prefix (also an- before a vowel sound) not, without (amoral). [gr… 5
## 6 "" 6
## 7 "Aa abbr. 1 automobile association. 2 alcoholics anonymous. 3 anti-ai… 7
## 8 "" 8
## 9 "Aardvark n. Mammal with a tubular snout and a long tongue, feeding o… 9
## 10 "" 10
## # … with 72,315 more rowsmydic_data2 <- mydic_data1%>%
filter(str_detect(word, "[^.*]")) %>%
print()## # A tibble: 36,740 × 2
## word i
## <chr> <int>
## 1 "A " 1
## 2 "A- prefix (also an- before a vowel sound) not, without (amoral). [gr… 5
## 3 "Aa abbr. 1 automobile association. 2 alcoholics anonymous. 3 anti-ai… 7
## 4 "Aardvark n. Mammal with a tubular snout and a long tongue, feeding o… 9
## 5 "Ab- prefix off, away, from (abduct). [latin]" 11
## 6 "Aback adv. \u007f take aback surprise, disconcert. [old english: rel… 13
## 7 "Abacus n. (pl. -cuses) 1 frame with wires along which beads are slid… 15
## 8 "Abaft naut. —adv. In the stern half of a ship. —prep. Nearer the ste… 17
## 9 "Abandon —v. 1 give up. 2 forsake, desert. 3 (often foll. By to; ofte… 19
## 10 "Abandoned adj. 1 deserted, forsaken. 2 unrestrained, profligate." 21
## # … with 36,730 more rowsmydic_data2 %>%
extract(word,
c("words"),
"(..+ [ ])",
remove = T) %>%
print()## # A tibble: 36,740 × 2
## words i
## <chr> <int>
## 1 <NA> 1
## 2 "A- " 5
## 3 "Aa " 7
## 4 "Aardvark " 9
## 5 "Ab- " 11
## 6 "Aback " 13
## 7 "Abacus " 15
## 8 "Abaft " 17
## 9 "Abandon " 19
## 10 "Abandoned " 21
## # … with 36,730 more rows2. Factors
####a) Use the BankChurners.csv to answer the following questions: • Which features can be regarded as a factor? • Which features can be regarded as an ordered factor (ordinal)? • Read BankChurners.csv into RStudio, then change the columns that you answered above into factors or ordered factors. • Visualize the effect of education level on average utilization ratio
bank_data <- read_csv("BankChurners.csv")Answer: These features can be regarded as a factor : Attrition_Flag, Gender, Education_level,Marital_Status, Income_Category, Card_Category.
Answer:Education_level,Income_Category, Card_Category can be regarded as ordered factor(ordinal).
bank_data$Education_Level <- factor(bank_data$Education_Level)
bank_data$Income_Category <- factor(bank_data$Income_Category)
bank_data$Card_Category <- factor(bank_data$Card_Category)
glimpse(bank_data)## Rows: 10,127
## Columns: 23
## $ CLIENTNUM <dbl> …
## $ Attrition_Flag <chr> …
## $ Customer_Age <dbl> …
## $ Gender <chr> …
## $ Dependent_count <dbl> …
## $ Education_Level <fct> …
## $ Marital_Status <chr> …
## $ Income_Category <fct> …
## $ Card_Category <fct> …
## $ Months_on_book <dbl> …
## $ Total_Relationship_Count <dbl> …
## $ Months_Inactive_12_mon <dbl> …
## $ Contacts_Count_12_mon <dbl> …
## $ Credit_Limit <dbl> …
## $ Total_Revolving_Bal <dbl> …
## $ Avg_Open_To_Buy <dbl> …
## $ Total_Amt_Chng_Q4_Q1 <dbl> …
## $ Total_Trans_Amt <dbl> …
## $ Total_Trans_Ct <dbl> …
## $ Total_Ct_Chng_Q4_Q1 <dbl> …
## $ Avg_Utilization_Ratio <dbl> …
## $ Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 <dbl> …
## $ Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2 <dbl> …ggplot(bank_data) + geom_bar(mapping = aes(x = Education_Level,fill = Attrition_Flag), position = "dodge") +
labs(title = "Education level effect on attrition status", x = "Education level",y = "Count") +
theme(plot.title = element_text(hjust = 0.5, size = rel(1.5)), axis.title= element_text(size = rel(1.1)))
b) Use the gss_cat data set
• What are the levels of marital variable? • Combine “Separated”, “Divorced”, “Widowed” into a new category “Once Married” • Use the new levels, explore whether there is an effect of martial status on tvhours.
Answer: there are 6 levels : “No answer”, “Never married”, “separated”, “Divorced”, “Widowed”, “Married”.
gss_cat1 <- gss_cat %>%
mutate(marital = fct_recode(marital,
"No answer" = "No answer",
"Never married" = "Never married",
"Once_Married" = "Separated",
"Once_Married" = "Divorced",
"Once_Married" = "Widowed",
"Married" = "Married"
)) %>%
print()## # A tibble: 21,483 × 9
## year marital age race rincome partyid relig denom tvhours
## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int>
## 1 2000 Never married 26 White $8000 to 9999 Ind,near … Prot… Sout… 12
## 2 2000 Once_Married 48 White $8000 to 9999 Not str r… Prot… Bapt… NA
## 3 2000 Once_Married 67 White Not applicable Independe… Prot… No d… 2
## 4 2000 Never married 39 White Not applicable Ind,near … Orth… Not … 4
## 5 2000 Once_Married 25 White Not applicable Not str d… None Not … 1
## 6 2000 Married 25 White $20000 - 24999 Strong de… Prot… Sout… NA
## 7 2000 Never married 36 White $25000 or more Not str r… Chri… Not … 3
## 8 2000 Once_Married 44 White $7000 to 7999 Ind,near … Prot… Luth… NA
## 9 2000 Married 44 White $25000 or more Not str d… Prot… Other 0
## 10 2000 Married 47 White $25000 or more Strong re… Prot… Sout… 3
## # … with 21,473 more rowsgss_cat2 <- gss_cat1 %>%
filter(!is.na(tvhours)) %>%
print()## # A tibble: 11,337 × 9
## year marital age race rincome partyid relig denom tvhours
## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int>
## 1 2000 Never married 26 White $8000 to 9999 Ind,near … Prot… Sout… 12
## 2 2000 Once_Married 67 White Not applicable Independe… Prot… No d… 2
## 3 2000 Never married 39 White Not applicable Ind,near … Orth… Not … 4
## 4 2000 Once_Married 25 White Not applicable Not str d… None Not … 1
## 5 2000 Never married 36 White $25000 or more Not str r… Chri… Not … 3
## 6 2000 Married 44 White $25000 or more Not str d… Prot… Other 0
## 7 2000 Married 47 White $25000 or more Strong re… Prot… Sout… 3
## 8 2000 Married 53 White $25000 or more Not str d… Prot… Other 2
## 9 2000 Married 52 White $25000 or more Strong de… Prot… Sout… 1
## 10 2000 Once_Married 52 White $25000 or more Ind,near … None Not … 1
## # … with 11,327 more rowsdata1 <- gss_cat2$tvhours[gss_cat2$marital == "Married"]
data1## [1] 0 3 2 1 7 3 1 2 1 2 1 4 5 3 3 2 2 2 2 1 3 1 4 2
## [25] 2 4 2 1 5 8 1 1 2 1 6 2 2 1 5 15 1 2 2 0 2 2 2 1
## [49] 2 1 5 5 11 2 2 4 5 1 2 2 1 4 11 1 4 3 2 1 3 6 4 1
## [73] 4 10 1 2 1 1 1 3 2 3 1 0 3 1 3 1 2 2 1 8 2 3 4 8
## [97] 2 2 1 2 4 3 1 4 2 5 1 2 4 12 2 3 2 2 0 2 1 2 3 3
## [121] 1 1 2 7 2 7 2 4 4 6 1 15 4 0 2 1 6 1 1 1 1 1 4 4
## [145] 2 2 8 5 2 1 2 2 1 0 1 3 3 2 1 2 2 5 1 20 1 4 2 2
## [169] 2 3 2 2 3 1 2 2 1 1 4 8 2 2 1 2 4 2 2 4 3 2 3 3
## [193] 0 3 6 4 2 3 1 2 4 3 2 1 2 2 1 5 1 2 2 2 1 1 3 2
## [217] 1 4 3 2 1 4 3 1 5 5 4 2 2 2 2 3 2 4 2 1 1 7 2 3
## [241] 1 3 1 2 2 3 1 0 1 1 3 1 1 3 3 6 2 1 4 4 4 4 1 2
## [265] 2 1 3 1 1 2 4 1 2 3 1 0 3 2 2 3 6 6 3 3 12 3 2 5
## [289] 3 2 0 3 2 1 3 3 4 2 2 5 2 2 1 0 2 1 4 13 2 4 3 2
## [313] 1 2 1 2 15 3 1 5 3 1 3 4 2 1 3 1 2 4 1 2 8 2 4 4
## [337] 1 6 2 2 1 2 2 2 2 3 3 8 8 2 1 3 4 1 6 5 4 2 2 6
## [361] 2 2 1 1 2 1 5 3 3 1 3 2 2 1 3 2 3 2 2 3 0 2 2 5
## [385] 2 1 1 2 2 3 2 5 2 2 1 3 1 2 2 2 3 1 2 4 4 3 1 4
## [409] 4 2 2 4 2 2 1 5 8 2 2 2 1 2 1 1 2 1 2 2 2 1 2 4
## [433] 1 4 4 2 2 1 2 4 5 4 4 0 2 2 10 3 4 5 2 1 5 1 4 5
## [457] 2 6 2 0 5 1 1 1 2 5 2 1 3 3 2 1 1 3 1 1 1 4 2 3
## [481] 3 2 1 2 2 3 8 4 1 2 2 2 4 4 2 2 2 3 3 2 3 0 1 1
## [505] 0 4 1 1 0 5 3 1 2 2 4 10 1 12 1 1 2 5 3 3 2 2 5 3
## [529] 0 1 3 1 3 2 3 2 5 1 1 2 1 3 5 1 2 3 2 5 3 2 1 0
## [553] 3 4 3 5 21 3 2 4 4 6 3 6 0 5 4 3 1 2 3 3 3 3 2 2
## [577] 6 5 2 5 2 6 1 4 3 1 0 3 4 3 2 2 1 2 1 1 3 4 4 2
## [601] 1 0 7 2 2 2 4 4 10 4 1 1 3 3 0 3 2 3 1 4 4 0 12 1
## [625] 4 2 4 0 3 1 2 4 4 3 6 6 2 2 0 1 1 4 14 2 3 3 1 6
## [649] 2 2 1 2 4 6 6 15 6 1 3 1 4 3 3 3 6 2 1 2 2 1 2 2
## [673] 1 2 2 4 3 1 6 2 1 4 4 2 2 1 2 2 1 2 3 2 4 3 8 5
## [697] 4 2 1 1 2 1 1 3 3 4 1 1 3 3 10 1 2 3 2 4 3 1 0 2
## [721] 2 2 2 2 4 2 3 2 2 3 5 1 8 2 0 4 2 4 1 0 4 4 1 3
## [745] 1 1 4 2 1 2 2 2 1 2 0 3 3 3 4 11 2 6 1 2 10 2 4 3
## [769] 3 3 2 2 3 3 2 1 2 1 2 5 2 2 12 10 2 1 4 2 4 0 3 1
## [793] 3 3 2 1 2 0 3 3 3 5 0 1 2 0 4 0 2 1 1 1 2 3 7 2
## [817] 2 3 1 3 1 1 1 1 2 2 2 4 2 1 3 1 6 3 1 6 1 3 1 6
## [841] 3 6 6 2 2 4 1 3 6 3 3 1 1 1 3 3 2 3 1 2 2 8 1 1
## [865] 1 1 1 2 2 4 3 1 0 4 5 4 2 2 1 0 5 1 8 3 1 2 1 3
## [889] 1 2 12 6 0 1 1 2 6 4 2 2 6 7 4 4 1 2 3 0 2 3 5 2
## [913] 2 3 4 3 2 4 0 1 1 4 0 2 6 3 1 3 1 0 2 4 4 1 5 0
## [937] 2 1 2 3 1 3 3 4 1 1 2 2 1 3 2 1 3 5 2 2 2 4 5 3
## [961] 2 1 3 2 4 1 2 3 1 2 4 1 3 2 5 2 1 3 1 7 3 2 3 2
## [985] 1 8 3 4 3 11 3 2 1 1 2 3 2 1 3 1 5 3 1 0 3 1 2 1
## [1009] 2 1 2 1 2 2 5 3 2 3 3 5 1 2 1 4 2 8 4 1 2 2 0 3
## [1033] 3 2 1 2 2 3 2 2 3 1 1 2 2 2 3 2 5 2 2 1 1 1 4 3
## [1057] 2 4 2 2 3 3 1 8 2 2 1 3 5 3 1 2 1 3 1 3 2 3 3 4
## [1081] 2 5 2 1 3 1 3 2 1 1 8 2 4 3 2 1 3 1 4 1 1 2 2 3
## [1105] 1 4 1 3 3 4 5 1 1 3 5 1 1 5 2 1 1 4 3 3 1 2 2 1
## [1129] 1 3 2 2 2 3 5 7 2 1 1 4 2 3 4 4 4 5 4 3 8 1 3 3
## [1153] 3 2 2 3 1 1 2 5 1 3 0 2 2 12 3 2 1 1 1 3 3 4 4 1
## [1177] 1 1 4 2 4 2 3 5 2 8 0 4 6 2 2 1 2 1 1 3 4 1 2 0
## [1201] 2 2 4 5 1 2 1 2 2 6 2 5 2 5 12 2 7 0 1 2 2 6 1 2
## [1225] 2 2 2 3 0 1 3 3 1 3 3 8 3 1 2 3 1 1 0 4 5 1 1 1
## [1249] 5 4 3 8 2 1 4 4 2 1 1 3 4 4 1 2 6 0 1 1 1 2 12 1
## [1273] 2 5 3 2 2 1 3 2 2 3 1 8 1 2 3 3 2 0 2 1 1 0 1 2
## [1297] 1 4 0 1 3 3 2 1 2 1 5 2 2 10 1 0 1 2 1 1 4 3 5 4
## [1321] 2 2 1 0 0 0 1 2 1 1 2 1 3 1 2 8 3 4 2 1 1 4 1 2
## [1345] 1 2 2 1 2 2 2 1 2 1 1 7 1 0 3 2 2 4 1 4 2 3 2 1
## [1369] 1 3 0 4 2 1 3 1 3 5 4 3 1 2 3 1 2 2 3 3 1 2 2 1
## [1393] 8 2 3 5 15 3 4 3 2 4 1 2 4 2 3 3 1 6 1 2 2 0 2 1
## [1417] 2 3 5 3 0 4 2 4 1 0 3 2 1 1 4 1 2 1 4 2 2 3 3 2
## [1441] 2 2 5 3 1 16 2 12 1 2 4 1 2 4 4 2 1 2 1 2 1 2 3 3
## [1465] 1 2 3 5 5 0 2 1 4 3 2 1 2 3 1 5 3 3 2 3 3 1 4 0
## [1489] 3 2 1 0 5 1 1 6 0 3 3 2 8 1 2 0 2 2 2 2 5 2 1 2
## [1513] 2 2 1 3 0 2 1 2 2 2 3 1 1 3 1 1 1 5 4 1 2 2 1 2
## [1537] 14 0 1 4 10 5 2 1 3 7 5 1 2 2 8 4 1 4 4 2 2 2 2 2
## [1561] 2 1 2 2 2 0 0 1 2 4 2 4 3 1 1 20 10 1 8 1 2 2 2 2
## [1585] 1 4 1 1 8 1 8 1 1 1 3 2 3 5 0 2 2 20 2 2 1 4 2 2
## [1609] 1 1 4 5 1 3 3 1 2 2 8 1 1 2 4 1 4 2 2 3 2 3 2 1
## [1633] 1 4 4 3 1 1 1 1 0 6 1 1 2 1 2 1 3 1 2 2 2 1 1 2
## [1657] 4 2 1 1 0 3 2 2 3 1 6 1 3 3 1 1 3 1 3 3 2 3 2 0
## [1681] 2 5 2 4 3 2 2 2 0 3 4 1 3 1 2 1 2 3 2 1 4 0 2 5
## [1705] 1 2 6 2 2 2 1 3 2 1 1 2 4 2 2 1 3 2 4 3 0 2 1 4
## [1729] 1 3 3 2 3 1 4 1 4 2 2 2 2 3 0 4 4 3 1 2 1 3 2 4
## [1753] 2 3 2 3 4 1 2 1 1 2 2 2 2 2 1 1 1 5 4 1 2 1 3 1
## [1777] 2 1 1 5 1 2 1 2 6 1 1 2 2 4 1 5 5 2 2 2 1 1 5 3
## [1801] 1 2 5 3 3 1 2 2 6 2 1 4 2 2 2 2 2 1 4 4 4 5 1 5
## [1825] 4 1 1 2 3 2 2 2 1 3 8 6 2 1 1 2 1 5 3 3 2 2 2 1
## [1849] 2 8 2 2 14 4 1 0 4 8 6 4 4 4 4 5 1 0 2 1 2 3 3 2
## [1873] 1 2 2 3 1 1 4 2 3 5 6 3 3 10 3 3 2 1 4 2 1 2 1 2
## [1897] 4 3 3 2 2 3 2 2 3 1 2 3 3 2 0 4 1 1 2 2 1 2 4 2
## [1921] 2 1 2 6 3 2 6 1 2 2 2 2 2 2 2 2 3 1 2 1 1 2 3 2
## [1945] 4 2 1 5 3 0 1 2 6 2 2 1 2 2 1 2 1 2 1 2 2 1 2 5
## [1969] 1 5 6 6 1 4 2 10 4 3 3 1 1 0 2 2 5 2 1 1 5 1 1 2
## [1993] 2 1 4 2 2 2 4 1 2 3 1 1 4 3 2 4 6 3 2 3 1 1 2 1
## [2017] 1 1 1 2 3 4 0 5 6 4 1 3 2 3 3 2 3 1 3 4 1 2 3 8
## [2041] 1 2 6 5 3 2 2 2 1 2 1 5 2 1 3 1 2 2 6 2 3 4 1 0
## [2065] 3 0 1 1 2 4 1 4 2 6 2 1 1 2 3 0 1 5 4 1 2 2 2 5
## [2089] 2 1 1 3 4 2 1 4 6 4 2 4 3 6 4 1 2 3 2 2 2 1 2 6
## [2113] 3 4 2 1 2 1 2 4 1 1 3 2 1 2 2 2 3 2 2 3 1 2 1 1
## [2137] 3 5 4 1 1 1 3 2 3 2 0 2 6 1 2 1 2 2 3 4 12 1 1 1
## [2161] 1 1 1 1 3 3 3 3 3 2 3 1 3 2 2 1 1 6 3 3 3 3 2 5
## [2185] 2 4 3 2 6 7 4 3 4 1 2 3 4 1 7 3 2 1 2 1 1 2 1 1
## [2209] 2 5 0 3 1 4 4 4 2 6 2 4 5 4 2 2 2 1 4 2 3 2 1 3
## [2233] 4 2 1 5 4 2 2 4 2 2 2 4 3 5 3 2 3 1 1 1 0 4 3 5
## [2257] 2 1 1 1 8 7 1 3 2 0 1 2 1 2 2 2 1 1 3 1 2 3 3 2
## [2281] 3 3 1 2 3 1 3 4 3 15 3 1 0 4 5 4 10 2 3 3 2 4 2 1
## [2305] 2 2 2 2 1 1 1 3 2 1 0 2 3 2 1 2 2 4 2 2 4 1 1 3
## [2329] 4 2 1 4 10 2 2 5 5 2 3 1 2 1 2 2 5 1 4 1 5 2 2 5
## [2353] 5 1 8 3 2 1 1 0 2 1 4 1 7 2 5 2 2 0 1 5 2 2 1 2
## [2377] 1 1 1 1 2 0 1 2 3 6 2 2 3 2 2 1 1 4 4 3 2 0 10 1
## [2401] 4 1 2 2 0 2 1 1 4 4 5 3 3 4 0 2 6 2 3 2 2 3 2 4
## [2425] 4 8 2 4 4 6 1 3 3 2 3 4 1 3 1 8 3 1 1 3 1 2 3 2
## [2449] 1 1 3 1 2 3 3 4 8 2 2 1 4 1 3 2 5 1 2 1 2 3 1 2
## [2473] 1 6 2 2 3 2 3 1 1 4 3 1 4 2 7 1 4 2 1 2 3 4 2 2
## [2497] 1 3 2 2 3 2 2 4 3 6 2 1 3 4 3 2 2 12 3 3 10 2 1 4
## [2521] 5 3 2 3 3 8 3 4 3 2 3 1 2 8 1 5 2 2 3 2 2 4 1 2
## [2545] 3 10 2 3 4 12 4 2 3 2 5 1 3 3 4 1 2 0 4 2 3 5 1 3
## [2569] 3 2 3 5 1 4 4 3 6 2 5 2 3 0 14 1 1 2 3 2 2 1 3 0
## [2593] 3 1 2 2 6 2 1 1 3 2 1 2 2 8 2 2 6 2 2 3 4 3 0 2
## [2617] 8 1 2 2 3 5 0 2 4 2 2 2 1 4 1 1 2 2 2 4 1 2 2 4
## [2641] 7 4 2 3 2 1 2 1 3 1 2 0 3 3 1 3 5 2 0 2 4 1 2 1
## [2665] 1 2 1 1 12 1 4 1 3 4 2 1 0 4 2 5 3 3 4 2 2 2 3 1
## [2689] 0 1 4 5 1 2 2 0 2 1 3 1 1 4 2 1 4 5 1 4 2 2 0 2
## [2713] 3 3 2 5 2 1 5 2 2 5 6 2 5 2 2 2 2 3 2 3 5 2 2 1
## [2737] 4 2 0 3 4 2 12 2 1 2 2 0 3 2 2 1 2 1 1 1 4 2 3 0
## [2761] 2 1 3 1 1 1 5 5 2 1 2 3 1 2 4 0 1 2 3 1 10 1 3 2
## [2785] 2 1 1 1 0 2 1 2 5 0 2 4 0 4 3 2 2 1 3 3 4 1 6 3
## [2809] 4 2 3 3 3 6 2 3 1 2 2 2 1 4 1 2 1 1 2 2 3 2 0 1
## [2833] 4 2 1 2 2 2 0 1 1 2 2 1 2 2 1 2 3 2 2 8 4 3 1 4
## [2857] 1 1 2 1 2 1 1 1 3 1 3 2 4 4 12 2 2 2 5 4 1 2 1 6
## [2881] 3 4 3 2 2 6 2 6 2 3 1 4 1 2 2 2 1 3 2 4 1 2 1 3
## [2905] 2 2 2 2 1 2 2 2 1 0 2 2 4 1 1 1 4 0 2 6 4 8 1 2
## [2929] 2 1 0 5 2 3 4 6 4 2 3 3 2 4 2 5 3 4 3 6 1 1 4 1
## [2953] 3 2 4 2 1 6 2 7 4 3 4 5 2 2 3 15 0 2 2 1 1 4 12 0
## [2977] 2 1 8 2 4 1 4 2 3 2 2 3 6 8 4 2 6 1 1 1 1 3 1 1
## [3001] 2 3 3 1 3 2 1 1 1 2 3 2 5 0 0 2 3 3 3 1 2 0 2 2
## [3025] 2 1 1 1 3 3 1 4 1 3 3 2 4 2 1 2 4 2 1 1 1 1 1 4
## [3049] 4 2 4 1 5 2 2 1 1 1 2 2 6 8 0 8 1 5 0 1 2 4 4 4
## [3073] 2 2 2 4 2 1 1 2 4 6 0 2 0 1 2 4 5 3 1 3 3 4 1 2
## [3097] 1 1 1 1 10 2 1 2 8 3 4 5 4 6 2 3 2 2 2 2 3 8 3 3
## [3121] 3 8 2 4 1 3 1 1 3 2 2 5 5 1 4 3 2 3 1 3 3 0 4 3
## [3145] 6 3 1 6 5 1 2 3 4 2 4 2 1 1 3 3 1 3 1 3 4 0 1 1
## [3169] 1 4 3 2 1 1 1 1 5 4 8 5 0 10 6 5 1 2 5 3 6 2 4 4
## [3193] 2 2 3 0 3 3 2 2 2 1 2 6 3 4 4 1 2 2 2 2 3 2 4 1
## [3217] 2 1 4 2 3 3 4 3 3 3 2 8 2 1 2 1 7 2 4 5 3 8 2 2
## [3241] 2 3 3 3 2 2 1 2 4 2 3 1 2 2 1 3 0 4 4 3 2 4 1 2
## [3265] 6 5 0 2 2 2 3 2 4 2 2 3 4 1 3 3 2 3 3 5 2 2 1 2
## [3289] 2 2 1 3 3 0 3 2 2 2 2 1 2 1 1 0 2 0 1 2 2 2 2 4
## [3313] 1 4 1 3 4 2 6 1 2 3 1 3 3 3 2 2 4 3 1 8 2 2 6 3
## [3337] 3 2 2 1 4 5 3 3 3 1 3 4 2 2 4 5 2 3 3 2 2 4 2 1
## [3361] 1 2 2 10 1 2 6 2 2 3 2 0 3 2 3 7 1 2 8 1 1 5 3 1
## [3385] 3 2 1 2 1 2 3 2 2 2 2 3 2 5 2 0 3 1 5 2 0 3 5 2
## [3409] 3 1 3 2 1 3 3 2 1 4 2 3 4 1 3 1 3 3 2 4 8 7 6 3
## [3433] 2 3 3 2 2 0 3 2 1 3 1 1 3 2 2 2 4 3 1 5 1 3 1 3
## [3457] 4 6 1 1 3 7 1 6 8 3 2 3 2 2 2 5 2 2 2 1 1 2 4 14
## [3481] 3 1 1 0 3 2 4 1 0 2 5 3 2 6 1 1 1 2 2 2 2 2 1 4
## [3505] 2 2 2 1 4 2 3 1 0 3 2 0 1 5 0 3 10 2 2 5 2 3 2 1
## [3529] 2 6 2 3 2 2 2 1 4 3 5 1 3 1 1 1 1 3 2 3 4 3 3 2
## [3553] 2 2 2 1 0 3 2 1 2 1 3 1 4 1 2 1 0 4 6 2 3 2 1 2
## [3577] 4 3 2 2 0 4 3 1 1 1 4 2 1 2 1 8 2 5 0 1 1 2 3 3
## [3601] 0 4 6 2 0 2 2 1 3 1 1 5 4 3 5 2 3 1 4 2 2 3 1 3
## [3625] 0 2 1 3 5 2 1 4 3 1 1 1 1 1 2 5 2 2 4 0 2 1 3 1
## [3649] 4 2 3 1 2 1 2 8 3 1 2 2 2 1 2 2 3 3 6 0 4 1 2 3
## [3673] 3 1 1 4 2 1 1 1 2 5 2 1 5 3 3 1 2 1 4 7 6 3 1 1
## [3697] 3 5 2 4 1 0 3 3 3 10 2 4 3 2 5 2 2 1 5 2 5 2 2 1
## [3721] 2 6 2 1 1 0 4 2 0 1 1 0 3 2 1 3 1 1 2 2 2 6 3 4
## [3745] 3 4 2 4 1 0 3 1 5 2 1 5 0 3 1 2 0 2 7 2 2 0 2 3
## [3769] 2 5 3 12 3 5 5 3 2 4 3 12 1 0 1 5 3 2 3 2 2 1 18 1
## [3793] 1 3 4 3 8 2 2 4 0 2 1 2 1 3 3 2 5 2 2 3 4 3 5 5
## [3817] 6 2 8 3 4 4 1 8 6 3 7 4 3 2 4 0 4 2 2 1 3 2 5 2
## [3841] 3 2 6 6 1 1 3 4 4 3 1 2 1 2 2 6 1 0 3 4 1 4 3 2
## [3865] 3 0 2 8 1 3 2 4 1 1 0 0 5 0 1 6 2 2 2 5 1 0 2 3
## [3889] 1 1 1 1 1 1 2 1 2 1 3 1 1 2 2 4 4 3 2 9 2 3 4 2
## [3913] 6 3 3 3 0 2 3 4 3 2 2 1 2 1 0 2 2 2 8 1 1 4 2 3
## [3937] 2 1 2 2 1 2 1 1 2 4 1 2 1 5 0 0 2 2 2 1 0 2 1 3
## [3961] 3 1 3 4 1 2 0 1 2 2 1 3 6 1 2 1 1 4 2 4 3 1 1 2
## [3985] 6 4 2 2 1 3 2 1 0 2 22 5 2 8 1 1 2 3 1 2 0 3 2 3
## [4009] 2 0 2 4 5 2 0 3 4 0 1 1 6 2 2 6 1 1 1 6 3 1 4 1
## [4033] 1 3 1 6 1 1 2 1 4 2 2 5 0 5 1 4 1 1 2 3 2 3 6 2
## [4057] 3 2 1 2 1 2 2 3 2 1 2 4 1 4 1 3 4 8 0 3 1 1 3 1
## [4081] 1 2 1 6 0 2 1 2 0 2 0 1 1 4 1 3 1 1 2 2 3 1 3 4
## [4105] 0 2 3 3 2 2 3 4 3 5 14 2 2 5 2 3 1 1 4 3 2 4 8 2
## [4129] 3 2 2 2 2 2 2 2 3 3 3 1 2 1 1 3 4 0 1 1 4 3 2 5
## [4153] 1 6 4 1 3 4 2 4 0 2 2 3 1 2 1 2 2 5 4 5 2 5 3 6
## [4177] 2 5 0 2 10 6 8 1 2 3 2 1 1 4 2 2 1 7 10 2 4 1 2 12
## [4201] 2 8 4 3 4 5 6 3 2 2 3 2 1 3 2 2 2 2 2 1 2 1 1 1
## [4225] 3 3 3 1 1 3 3 1 1 3 4 1 0 8 2 2 2 3 3 1 2 2 2 7
## [4249] 10 1 6 3 4 3 3 0 15 1 1 1 2 2 4 4 4 5 2 1 0 2 6 0
## [4273] 2 3 3 1 1 1 4 3 8 1 3 0 2 2 13 3 1 2 2 2 6 5 5 1
## [4297] 3 2 2 3 1 1 2 2 4 0 3 12 12 3 1 4 3 0 0 0 2 2 2 2
## [4321] 2 2 1 2 2 2 3 2 1 1 4 1 10 2 3 4 2 6 2 1 1 3 4 3
## [4345] 2 2 4 1 0 2 0 3 2 1 3 1 1 3 2 3 8 1 1 4 4 1 1 3
## [4369] 2 1 3 3 3 4 1 8 4 2 10 2 4 4 1 1 5 4 2 0 3 4 3 1
## [4393] 7 3 2 3 1 1 3 3 4 1 6 10 0 1 4 3 3 2 3 4 10 2 24 4
## [4417] 5 7 6 2 3 4 10 8 2 1 4 3 2 2 4 4 4 2 1 4 0 2 1 1
## [4441] 4 2 1 4 2 2 6 2 1 3 4 2 2 1 2 4 0 1 0 2 1 5 6 2
## [4465] 2 2 2 1 2 0 2 1 1 1 3 2 3 2 1 2 2 1 4 1 14 1 0 3
## [4489] 2 2 2 3 4 2 2 1 3 4 2 6 2 2 3 4 1 2 0 1 3 3 0 1
## [4513] 2 1 4 2 1 2 1 12 2 4 4 2 0 2 3 2 2 2 2 1 2 1 3 3
## [4537] 4 1 1 2 1 0 4 1 1 1 2 4 1 5 1 2 0 2 4 2 1 2 2 1
## [4561] 0 1 2 1 1 3 2 2 3 4 3 2 5 1 3 3 3 12 2 3 5 3 3 2
## [4585] 1 3 2 3 2 1 3 1 1 2 3 2 2 1 2 3 3 3 8 1 2 5 4 1
## [4609] 3 5 1 3 2 3 2 8 3 2 3 1 3 4 1 2 3 5 0 3 2 5 2 1
## [4633] 4 0 5 1 4 3 3 2 3 1 2 3 1 3 12 3 3 1 4 2 4 1 3 2
## [4657] 2 1 3 1 1 1 2 9 4 4 2 1 0 1 3 1 4 2 4 4 1 0 2 2
## [4681] 3 2 2 1 2 1 1 4 1 5 1 3 1 1 4 3 1 2 3 3 1 4 3 1
## [4705] 3 2 3 4 1 2 2 1 1 3 1 0 4 3 2 1 2 3 1 6 5 2 2 0
## [4729] 6 2 2 6 1 4 3 2 3 6 4 4 3 2 4 2 5 2 2 3 2 4 1 2
## [4753] 6 1 5 3 1 2 3 3 4 4 4 2 3 0 3 6 4 6 2 2 2 4 3 2
## [4777] 2 2 2 1 4 2 4 2 5 2 4 6 1 2 3 2 2 1 3 1 4 1 3 1
## [4801] 5 1 1 2 6 1 1 1 6 2 1 1 1 0 1 1 3 2 3 2 4 3 2 1
## [4825] 4 1 2 2 3 0 2 2 1 2 5 3 2 1 6 0 2 2 1 3 10 8 2 3
## [4849] 2 2 6 2 4 0 2 2 2 1 2 1 0 2 2 5 2 3 2 3 4 2 5 1
## [4873] 5 4 2 1 5 3 6 2 1 1 0 2 6 5 1 2 4 4 5 4 4 1 1 5
## [4897] 2 2 3 2 0 6 2 4 1 1 16 4 3 2 4 4 2 4 6 1 4 1 4 2
## [4921] 2 2 3 1 2 2 3 3 3 2 2 5 1 1 3 2 2 3 4 1 2 6 1 3
## [4945] 2 3 3 4 8 2 2 1 1 1 2 3 4 3 4 3 3 6 1 4 0 5 8 1
## [4969] 2 1 1 3 1 1 1 6 1 5 3 1 1 5 2 4 4 0 4 1 1 2 3 3
## [4993] 3 2 3 8 8 6 5 3 1 3 0 10 2 1 3 3 2 3 3 3 1 1 5 4
## [5017] 2 3 2 2 3 5 1 1 1 3 1 8 1 3 4 3 1 3 2 3 0 3 4 2
## [5041] 2 1 3 1 4 2 0 3 6 4 2 5 3 3 6 1 0 1 2 6 1 2 1 3
## [5065] 1 4 1 1 2 4 3 2 6 2 5 3 2 3 4 10 1 1 1 3 4 3 4 3
## [5089] 2 1 0 2 0 0 2 1 2 1 3 4 4 2 4 5 3 5 4 1 0 2 4 3
## [5113] 3 3 0 3 1 3 3 3 2 7 4 4 1 5 2 2 2 3 1 1 5 2 3 0
## [5137] 1 1 2 2 5 1 2 4 4 2 8 5 5 2 4 2 3 24 2 3 2 3 1 3
## [5161] 4 6 1 3 2 0 2 0 0 1 2 0data2 <- gss_cat2$tvhours[gss_cat2$marital == "Once_Married"]
data2## [1] 2 1 1 1 3 4 7 3 2 2 4 3 12 4 8 4 2 7 1 8 2 0 3 1
## [25] 1 3 3 1 3 2 2 4 3 0 5 10 0 3 4 2 1 4 4 2 10 1 2 1
## [49] 4 8 1 1 4 0 3 1 1 3 8 5 4 3 6 6 3 8 3 3 1 1 8 1
## [73] 12 1 2 3 1 12 1 2 6 4 3 11 2 8 2 4 6 1 2 4 12 1 0 5
## [97] 3 3 2 2 8 8 2 0 2 3 1 2 4 1 4 3 4 3 1 5 2 3 2 2
## [121] 3 2 3 6 1 1 2 2 3 5 1 1 4 3 0 0 8 1 5 3 10 3 5 3
## [145] 8 2 1 2 7 4 2 3 3 3 4 2 6 2 7 3 4 4 3 4 2 12 12 2
## [169] 6 1 2 2 1 2 0 1 3 1 0 2 4 4 3 2 2 2 15 0 3 2 6 2
## [193] 0 0 4 2 3 2 2 2 4 8 3 12 2 2 3 8 2 3 1 10 3 4 0 1
## [217] 3 2 1 3 2 4 1 2 2 2 3 3 4 3 7 2 8 1 4 4 3 6 0 2
## [241] 2 1 2 2 2 2 2 3 4 3 2 2 2 4 3 3 10 3 10 1 24 4 2 10
## [265] 1 3 4 4 4 2 0 5 2 1 4 1 2 5 1 0 6 4 10 1 2 3 3 1
## [289] 3 1 1 1 2 1 4 1 1 2 5 8 8 0 2 2 4 2 4 2 2 12 2 6
## [313] 3 5 1 1 4 4 2 3 5 4 2 4 1 1 5 1 1 6 4 4 2 3 4 1
## [337] 2 1 0 2 3 1 2 2 3 0 6 5 1 3 5 2 5 5 1 2 2 2 3 1
## [361] 0 4 7 4 3 2 2 2 2 0 2 2 3 3 3 2 2 8 2 6 3 2 12 5
## [385] 1 3 15 8 3 1 1 1 2 2 3 2 3 3 1 1 4 1 5 2 1 0 5 1
## [409] 3 2 5 1 0 4 0 7 6 0 2 4 4 1 5 0 0 2 2 3 5 4 4 3
## [433] 1 1 2 4 8 2 1 1 4 4 12 1 1 0 2 2 5 5 2 4 4 3 4 10
## [457] 6 5 7 0 2 3 4 2 0 2 2 2 2 1 8 2 3 3 8 3 3 1 2 6
## [481] 4 3 1 2 3 4 3 5 1 2 2 12 2 4 6 8 3 4 3 2 1 5 2 4
## [505] 0 0 2 3 4 5 3 2 8 2 2 3 4 8 3 2 8 2 4 2 1 2 2 5
## [529] 1 0 1 1 0 2 2 2 2 2 1 4 2 1 1 4 6 5 1 2 10 1 5 8
## [553] 8 6 1 2 4 5 4 4 4 4 2 1 2 1 4 6 2 7 1 4 1 1 4 4
## [577] 3 2 4 2 2 2 1 1 1 4 1 3 4 2 2 5 3 2 12 2 5 3 5 6
## [601] 5 2 4 2 5 3 4 3 2 0 5 3 4 3 2 4 2 2 1 5 5 4 4 1
## [625] 2 5 5 3 5 2 1 2 3 2 1 6 8 1 1 2 4 0 5 8 2 5 2 6
## [649] 2 5 5 2 2 5 3 3 2 4 0 3 8 1 4 2 3 2 5 3 5 4 2 3
## [673] 2 4 1 4 2 8 2 2 3 4 6 4 6 5 6 3 16 4 5 3 1 1 1 2
## [697] 4 2 4 8 1 0 2 3 1 1 2 0 1 8 1 3 5 0 2 0 2 8 9 2
## [721] 4 22 2 3 2 2 3 4 5 5 3 3 6 1 6 5 4 5 3 1 2 4 3 4
## [745] 2 2 2 5 10 2 2 3 2 3 1 4 2 12 0 6 1 2 8 1 2 3 2 2
## [769] 2 1 1 2 10 2 3 2 1 1 2 2 3 6 3 2 0 1 3 3 0 4 2 4
## [793] 3 20 3 1 6 2 2 2 1 4 4 1 2 2 3 8 2 5 2 6 1 2 4 8
## [817] 12 4 3 4 3 3 2 6 2 8 1 2 3 1 3 6 2 3 3 2 1 3 4 2
## [841] 12 2 3 1 4 4 5 1 2 7 6 1 1 2 1 1 8 3 0 4 4 2 8 1
## [865] 1 8 2 1 2 1 3 3 5 7 1 3 3 3 2 1 2 1 4 2 3 3 4 1
## [889] 6 1 1 4 10 1 3 3 3 2 1 5 1 0 5 3 1 1 2 4 1 16 1 2
## [913] 2 8 10 1 8 1 7 2 10 14 4 1 3 3 1 2 1 4 4 4 3 1 2 4
## [937] 0 5 4 1 0 3 0 4 4 3 3 4 4 1 1 1 8 2 1 3 2 2 2 2
## [961] 4 4 0 3 4 6 5 4 4 3 2 1 1 4 3 2 6 2 2 2 1 5 6 7
## [985] 3 1 4 6 9 4 1 2 0 2 2 2 4 2 2 4 2 0 2 3 8 4 5 0
## [1009] 0 3 1 4 9 4 0 4 4 12 12 2 6 13 4 1 4 0 1 2 2 2 6 6
## [1033] 4 5 2 1 2 1 4 5 5 9 1 5 1 3 5 1 1 1 4 4 3 1 2 1
## [1057] 5 1 0 5 6 4 4 6 1 4 2 6 5 2 3 1 6 4 2 1 2 5 5 2
## [1081] 2 6 4 1 5 1 1 5 6 2 4 0 3 2 4 6 6 1 2 2 3 9 2 6
## [1105] 3 4 6 3 3 5 2 5 2 4 5 1 1 2 7 4 4 2 3 2 3 2 3 4
## [1129] 3 4 2 2 2 5 0 2 1 4 4 1 2 2 3 10 3 5 6 6 1 2 3 10
## [1153] 12 5 8 2 2 5 2 1 2 5 2 1 2 1 3 2 1 0 6 2 3 1 2 5
## [1177] 2 12 1 3 3 1 4 1 5 1 2 3 2 3 4 1 1 2 2 2 5 2 1 4
## [1201] 5 3 1 2 2 1 2 5 3 2 2 6 2 9 2 6 2 6 5 3 4 2 6 3
## [1225] 6 7 2 2 8 1 5 6 6 2 1 1 2 12 14 1 3 3 4 6 1 10 3 3
## [1249] 5 2 8 0 4 2 3 5 3 6 0 4 2 0 5 2 2 0 3 3 1 5 1 1
## [1273] 2 5 2 3 3 4 2 1 4 2 2 2 1 4 2 2 1 0 3 1 2 2 2 3
## [1297] 2 0 3 3 2 4 4 5 3 7 4 2 4 3 6 10 5 8 3 4 8 5 3 2
## [1321] 6 2 6 3 3 3 2 3 5 5 3 3 2 3 5 3 1 3 1 3 3 2 3 4
## [1345] 6 3 2 5 3 1 3 1 4 2 4 3 3 3 3 3 1 5 2 2 3 2 5 5
## [1369] 5 2 3 3 3 3 7 3 3 7 2 3 0 2 4 6 5 3 2 2 8 0 1 2
## [1393] 5 2 13 2 1 3 1 2 3 2 6 4 5 1 5 2 2 2 1 4 2 3 2 1
## [1417] 0 1 5 2 4 3 2 2 3 2 2 2 4 4 4 5 3 3 2 3 0 2 1 1
## [1441] 4 3 2 4 1 2 4 1 7 4 0 6 8 2 8 2 4 3 0 1 6 1 1 6
## [1465] 3 1 2 4 5 5 2 12 24 18 3 4 6 3 1 1 3 4 5 1 4 3 4 2
## [1489] 0 0 3 4 1 2 2 2 1 3 4 1 14 3 6 4 3 1 12 12 2 2 6 10
## [1513] 3 12 1 4 6 5 2 15 1 2 8 4 14 10 7 6 6 2 2 2 2 2 2 4
## [1537] 3 3 1 3 1 0 8 6 2 4 3 2 1 3 6 8 5 1 4 2 2 1 2 2
## [1561] 3 5 3 0 3 2 7 4 4 5 2 6 3 1 2 2 2 6 3 5 3 4 1 4
## [1585] 6 8 4 2 3 2 2 4 3 4 4 1 3 4 1 1 1 1 2 1 2 5 3 6
## [1609] 1 5 8 6 0 2 6 2 4 3 4 6 2 10 3 1 4 1 10 1 3 3 1 4
## [1633] 2 8 2 5 4 0 4 6 4 6 2 0 4 18 0 4 3 2 3 2 1 4 3 1
## [1657] 2 24 1 2 10 7 1 2 6 1 1 10 3 1 3 4 5 8 3 3 2 0 6 8
## [1681] 2 0 1 3 4 3 4 2 2 2 4 3 1 1 1 4 2 2 3 3 3 3 6 4
## [1705] 2 1 16 6 3 8 5 3 2 0 2 0 4 1 1 5 3 3 1 4 2 8 4 4
## [1729] 4 2 2 3 2 0 8 4 3 2 6 1 2 6 16 8 0 4 9 5 3 14 2 4
## [1753] 4 0 4 6 4 1 2 3 3 2 0 3 2 3 1 2 5 12 1 3 3 2 1 4
## [1777] 3 10 2 2 1 8 2 0 2 2 5 1 0 0 1 1 12 8 6 1 2 2 4 2
## [1801] 1 2 3 2 0 6 3 4 1 2 2 2 3 2 4 8 3 1 0 4 5 3 5 2
## [1825] 1 3 0 2 8 1 4 2 0 3 4 9 6 6 6 1 2 8 6 0 15 4 1 5
## [1849] 5 6 3 24 4 5 3 1 1 1 6 3 3 3 3 3 8 8 5 1 8 10 5 4
## [1873] 6 1 1 4 2 2 3 3 5 2 6 2 5 12 1 2 4 4 2 10 4 2 0 1
## [1897] 2 1 3 0 2 6 1 5 4 4 10 2 2 4 4 7 2 6 2 1 3 5 3 1
## [1921] 2 5 5 0 0 3 3 5 1 4 5 4 2 4 1 6 3 2 0 1 2 4 2 0
## [1945] 3 1 4 4 4 7 1 2 3 2 2 2 2 2 1 3 2 6 20 2 6 5 9 3
## [1969] 3 6 4 6 1 2 4 1 2 0 4 2 2 2 18 24 2 4 7 2 4 1 0 3
## [1993] 3 3 24 2 3 2 5 4 5 8 3 2 1 4 2 6 1 2 4 2 3 4 0 2
## [2017] 3 0 6 1 4 1 3 2 1 6 3 5 0 3 10 3 14 5 4 8 2 1 0 1
## [2041] 3 1 6 12 1 2 1 6 14 5 0 1 4 1 5 2 0 1 8 2 3 2 3 5
## [2065] 3 3 2 7 2 4 3 2 2 2 5 6 1 3 0 5 12 0 5 2 2 2 0 2
## [2089] 3 3 2 8 2 2 5 2 2 2 1 6 5 2 3 3 3 3 7 1 2 2 2 8
## [2113] 2 1 1 3 1 1 1 3 8 5 5 5 5 1 1 1 3 2 4 5 6 4 2 5
## [2137] 4 2 6 8 2 4 1 1 0 5 5 1 3 3 0 5 4 5 5 1 3 4 8 1
## [2161] 2 1 4 1 2 3 2 1 1 7 6 2 2 14 4 1 1 3 3 2 4 2 2 2
## [2185] 0 1 4 1 5 2 2 5 2 3 4 2 1 2 12 6 2 1 5 1 1 3 3 2
## [2209] 1 2 5 2 4 1 3 6 16 2 2 3 5 3 3 1 4 0 4 1 2 5 6 3
## [2233] 1 2 2 7 0 4 4 2 4 6 4 4 6 3 4 1 3 4 4 3 1 4 4 2
## [2257] 1 1 0 12 3 3 3 8 0 3 3 3 5 4 4 0 2 4 1 2 2 5 4 3
## [2281] 0 1 24 1 6 6 4 3 2 3 3 6 2 1 8 4 5 12 0 2 3 5 6 5
## [2305] 3 8 9 2 5 2 4 1 0 2 3 1 2 2 6 4 4 1 0 11 2 3 4 6
## [2329] 2 3 4 2 4 5 2 2 2 24 5 7 7 1 2 1 10 1 2 2 4 3 3 4
## [2353] 4 2 3 3 3 0 2 1 4 2 2 3 5 3 2 1 2 2 4 3 3 0 20 0
## [2377] 0 4 2 4 1 3 1 2 12 2 4 2 15 1 4 4 3 4 4 3 3 2 12 5
## [2401] 1 3 1 2 3 4 7 4 2 3 0 2 3 4 0 4 2 6 3 4 1 2 4 6
## [2425] 8 2 7 2 3 2 2 4 4 8 6 10 4 1 1 2 6 3 1 7 8 8 3 8
## [2449] 3 0 5 6 3 1 4 2 2 3 5 3 0 1 6 6 1 2 2 2 3 3 8 0
## [2473] 8 2 3 4 4 8 4 2 4 2 3 1 3 3 5 2 2 2 3 4 1 4 2 1
## [2497] 1 7 2 6 7 1 0 1 1 0 3 5 2 1 0 12 1 5 2 2 2 1 0 14
## [2521] 3 3 10 2 5 6 6 1 2 4 10 3 4 3 8 1 3 4 0 2 3 8 2 2
## [2545] 4 2 3 2 3 3 3 2 3 10 1 3 0 4 2 2 1 5 1 1 5 0 3 2
## [2569] 2 1 4 0 3 4 2 4 5 4 2 3 3 1 2 1 3 0 0 0 1 3 2 5
## [2593] 24 3 5 1 2 4 2 4 1 2 2 8 4 3 3 6 2 2 7 4 6 3 3 4
## [2617] 8 4 2 6 10 1 0 1 2 5 4 6 12 6 4 2 6 2 1 4 2 1 8 24
## [2641] 8 1 3 5 3 6 6 3 2 2 5 0 5 1 1 5 3 3 2 3 5 2 2 1
## [2665] 4 3 4 7 4 2 4 4 2 2 4 4 6 0 7 3 4 2 6 2 4 4 1 2
## [2689] 3 0 3 1 3 3 4 4 3 2 1 0 2 3 0 3 5 1 2 10 3 2 2 4
## [2713] 3 2 1 0 4 3 2 3 2 1 1 2 10 8 5 5 4 4 1 3 4 0 5 6
## [2737] 4 1 2 6 4 2 0 2 3 5 20 1 1 2 3 1 3 5 4 1 3 5 5 3
## [2761] 4 0 3 0 0 1 3 5 2 3 1 2 1 4 4 6 7 4 6 2 1 5 2 6
## [2785] 4 2 1 6 6 6 12 2 3 2 2 1 2 2 3 2 8 2 2 1 8 5 2 5
## [2809] 1 2 3 1 5 14 4 4 10 2 4 1 5 1 6 0 2 4 3 4 0 6 1 2
## [2833] 1 5 3 3 0 1 4 2 0 8 4 2 1 3 0 4 3 3 0 2 3 5 2 3
## [2857] 6 12 1 4 2 3 0 1 2 2 2 3 8 6 2 2 2 3 6 2 1 1 12 1
## [2881] 5 4 4 1 0 1 5 4 6 1 5 5 0 2 5 4 2 3 8 2 2 3 7 1
## [2905] 3 2 1 0 5 4 0 3 3 2 8 3 4 2 3 7 4 1 2 4 2 5 24 2
## [2929] 1 5 1 6 4 2 6 2 1 4 5 2 0 2 2 8 5 3 5 2 1 4 3 2
## [2953] 4 2 3 2 4 2 1 3 2 4 6 3 1 5 4 2 0 6 2 2 4 3 1 4
## [2977] 10 5 4 6 6 3 3 2 2 4 4 5 12 0 1 2 1 1 4 5 0 4 2 3
## [3001] 3 5 4 2 1 2 3 3 2 2 3 2 2 4 4 2 24 4 4 3 2 4 2 0
## [3025] 3 2 2 6 2 4 3 0 3 3 2 3 2 0 2 4 7 2 2 5 2 2 2 1
## [3049] 3 2 8 8 3 3 2 2 1 3 2 4 5 3 5 2 2 4 3 3 0 1 4 3
## [3073] 1 8 24 4 4 4 5 4 2 6 6 4 3 2 3 2 2 2 0 2 2 3 8 2
## [3097] 4 2 8 0 3 2 3 2 3 2 6 4 4 0 0 2 0 0 6 1 3 2 2 0
## [3121] 1 1 1 6 6 0 1 1 0 4 4 3 2 5 2 4 0 1 2 5 1 5 6 3
## [3145] 2 3 5 6 2 4 3 1 2 0 2 2 3 4 3 4 2t.test(data1, data2)##
## Welch Two Sample t-test
##
## data: data1 and data2
## t = -12.613, df = 5171.8, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.8714187 -0.6369681
## sample estimates:
## mean of x mean of y
## 2.650425 3.404619Answer: Yes, marital status does have effect on tvhours since the p-value is extreme low.