1$params['game'] = 'fallout';
2
3$gameQuery = Gaming::query();
4
5$gameSelect = isset($params['game']) ? $params['game'] : null;
6if ($gameSelect) {
7    $gameQuery = $gameQuery->where('game' $gameSelect);
8}
9$params['game'] = 'fallout';
10
11$gameQuery = Gaming::query();
12
13$gameQuery->when($params['game'], function ($query) use ($params) {
14    $query->where('game', $params['game']);
15});
16$gameQuery->when(isset($params['game']) 
17, function ($query) use ($params) {
18     $query->where('game', $params['game']);
19 });
20

1const contacts = [
2  {
3    firstName: "Akira",
4    lastName: "Laine",
5    number: "0543236543",
6    likes: ["Pizza", "Coding", "Brownie Points"],
7  },
8  {
9    firstName: "Harry",
10    lastName: "Potter",
11    number: "0994372684",
12    likes: ["Hogwarts", "Magic", "Hagrid"],
13  },
14  {
15    firstName: "Sherlock",
16    lastName: "Holmes",
17    number: "0487345643",
18    likes: ["Intriguing Cases", "Violin"],
19  },
20  {
21    firstName: "Kristian",
22    lastName: "Vos",
23    number: "unknown",
24    likes: ["JavaScript", "Gaming", "Foxes"],
25  },
26];
27
28
29function lookUpProfile(name, prop) {
30
31  for(let i = 0; i<contacts.length;i++){
32    if(contacts[i].firstName == name && contacts[i].hasOwnProperty(prop)){
33        return contacts[i][prop];
34      }
35      else {
36        return "No such property"
37      } 
38    }
39  return "No such contact";
40}
41
42console.log(lookUpProfile("Kristian", "lastName")); //Output: No such property
43function lookUpProfile(name, prop) {
44
45  for(let i = 0; i<contacts.length;i++){
46    if(contacts[i].firstName == name){
47      if(contacts[i].hasOwnProperty(prop)){
48        return contacts[i][prop];
49      }
50      else {
51        return "No such property"
52      } 
53    }
54  }
55  return "No such contact";
56}
57
58console.log(lookUpProfile("Kristian", "lastName")); //Output: Vos
59for (/*...*/) {
60    if (a) {
61        if (b) {
62            return X;
63        } else {
64            return Y;
65        }
66    }
67}
68

1const contacts = [
2  {
3    firstName: "Akira",
4    lastName: "Laine",
5    number: "0543236543",
6    likes: ["Pizza", "Coding", "Brownie Points"],
7  },
8  {
9    firstName: "Harry",
10    lastName: "Potter",
11    number: "0994372684",
12    likes: ["Hogwarts", "Magic", "Hagrid"],
13  },
14  {
15    firstName: "Sherlock",
16    lastName: "Holmes",
17    number: "0487345643",
18    likes: ["Intriguing Cases", "Violin"],
19  },
20  {
21    firstName: "Kristian",
22    lastName: "Vos",
23    number: "unknown",
24    likes: ["JavaScript", "Gaming", "Foxes"],
25  },
26];
27
28
29function lookUpProfile(name, prop) {
30
31  for(let i = 0; i<contacts.length;i++){
32    if(contacts[i].firstName == name && contacts[i].hasOwnProperty(prop)){
33        return contacts[i][prop];
34      }
35      else {
36        return "No such property"
37      } 
38    }
39  return "No such contact";
40}
41
42console.log(lookUpProfile("Kristian", "lastName")); //Output: No such property
43function lookUpProfile(name, prop) {
44
45  for(let i = 0; i<contacts.length;i++){
46    if(contacts[i].firstName == name){
47      if(contacts[i].hasOwnProperty(prop)){
48        return contacts[i][prop];
49      }
50      else {
51        return "No such property"
52      } 
53    }
54  }
55  return "No such contact";
56}
57
58console.log(lookUpProfile("Kristian", "lastName")); //Output: Vos
59for (/*...*/) {
60    if (a) {
61        if (b) {
62            return X;
63        } else {
64            return Y;
65        }
66    }
67}
68for (/*...*/) {
69    if (a && b) {
70        return X;
71    } else {
72        return Y;
73    }
74}
75

1const contacts = [
2  {
3    firstName: "Akira",
4    lastName: "Laine",
5    number: "0543236543",
6    likes: ["Pizza", "Coding", "Brownie Points"],
7  },
8  {
9    firstName: "Harry",
10    lastName: "Potter",
11    number: "0994372684",
12    likes: ["Hogwarts", "Magic", "Hagrid"],
13  },
14  {
15    firstName: "Sherlock",
16    lastName: "Holmes",
17    number: "0487345643",
18    likes: ["Intriguing Cases", "Violin"],
19  },
20  {
21    firstName: "Kristian",
22    lastName: "Vos",
23    number: "unknown",
24    likes: ["JavaScript", "Gaming", "Foxes"],
25  },
26];
27
28
29function lookUpProfile(name, prop) {
30
31  for(let i = 0; i<contacts.length;i++){
32    if(contacts[i].firstName == name && contacts[i].hasOwnProperty(prop)){
33        return contacts[i][prop];
34      }
35      else {
36        return "No such property"
37      } 
38    }
39  return "No such contact";
40}
41
42console.log(lookUpProfile("Kristian", "lastName")); //Output: No such property
43function lookUpProfile(name, prop) {
44
45  for(let i = 0; i<contacts.length;i++){
46    if(contacts[i].firstName == name){
47      if(contacts[i].hasOwnProperty(prop)){
48        return contacts[i][prop];
49      }
50      else {
51        return "No such property"
52      } 
53    }
54  }
55  return "No such contact";
56}
57
58console.log(lookUpProfile("Kristian", "lastName")); //Output: Vos
59for (/*...*/) {
60    if (a) {
61        if (b) {
62            return X;
63        } else {
64            return Y;
65        }
66    }
67}
68for (/*...*/) {
69    if (a && b) {
70        return X;
71    } else {
72        return Y;
73    }
74}
75for (/*...*/) {
76    if (a && b) {
77        return X;
78    } else if (a) { // <===
79        return Y;
80    }
81}
82

1Descs<-structure(list(Mean = c(NaN, 943330388, NaN, NaN, NaN, 543234645, 
245831420, NaN, 27301292, 160818771), Median = c(NaN, 943330388, 
3NaN, NaN, NaN, 543234645, 45831420, NaN, 27301292, 160818771), 
4    SD = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
5    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), MAD = c(NA, 
6    0, NA, NA, NA, 0, 0, NA, 0, 0), MIN = c(NA, 943330388, NA, 
7    NA, NA, 543234645, 45831420, NA, 27301292, 160818771), MAX = c(NA, 
8    943330388, NA, NA, NA, 543234645, 45831420, NA, 27301292, 
9    160818771), VAR = c(NA_real_, NA_real_, NA_real_, NA_real_, 
10    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_
11    ), RANGE = structure(c(NA, 943330388, NA, NA, NA, 543234645, 
12    45831420, NA, 27301292, 160818771, NA, 943330388, NA, NA, 
13    NA, 543234645, 45831420, NA, 27301292, 160818771), .Dim = c(10L, 
14    2L)), QUANTILES = structure(c(NA, 943330388, NA, NA, NA, 
15    543234645, 45831420, NA, 27301292, 160818771, NA, 943330388, 
16    NA, NA, NA, 543234645, 45831420, NA, 27301292, 160818771), .Dim = c(10L, 
17    2L), .Dimnames = list(NULL, c("25%", "75%")))), row.names = c("Comedy", 
18"Education", "Entertainment", "Film & Animation", "Gaming", "Howto & Style", 
19"Music", "People & Blogs", "Science & Technology", "Sports"), class = "data.frame")
20
21library(kableExtra)
22
23kable(Descs) %>%
24    kable_styling(
25      font_size = 15,
26      bootstrap_options = c("striped", "hover", "condensed")
27    ) 
28library(kableExtra)
29
30kable(Descs,
31      format.args = list(big.mark = ",")) %>%
32  kable_styling(
33    font_size = 15,
34    bootstrap_options = c("striped", "hover", "condensed")
35  ) 
36
37

1df_long<-structure(list(WORDS = c("Comedy", "Education", "Entertainment", 
2"Film & Animation", "Gaming", "Howto & Style", "Music", "People & Blogs", 
3"Science & Technology", "Sports"), TOTALS = c(0, 943330388, 0, 
40, 0, 543234645, 45831420, 0, 27301292, 160818771)), row.names = c(NA, 
5-10L), class = c("tbl_df", "tbl", "data.frame"))
6
7p <- ggplot(df_long, aes(x = WORDS, y = TOTALS, fill = WORDS)) + 
8  geom_boxplot()
9
10p  +  
11  theme_minimal()+ 
12  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, 
13                                   hjust=1))+ 
14  xlab("Industries") +
15  ylab("Value")+ guides(fill=guide_legend(title="Industries"))
16+
17  scale_y_continuous(formatter = comma)+scale_fill_continuous(formatter = comma)
18library(ggplot2)
19library(scales)
20
21ggplot(df_long, aes(x = WORDS, y = TOTALS, fill = WORDS)) + 
22  geom_boxplot()  +  
23  theme_minimal()+ 
24  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, 
25                                   hjust=1))+ 
26  xlab("Industries") +
27  ylab("Value")+ 
28  guides(fill = guide_legend(title="Industries")) +
29  scale_y_continuous(labels = comma)
30

1Did you misspecify an argument?
2  54: <Anonymous>
3Error : `...` is not empty.
4
5
6
7
8
9library(tidyverse)
10library(shiny)
11library(matrixStats)
12library(tibble)
13library(knitr)
14library(kableExtra)
15    Abstract_50<-structure(list(ID = c("UC_Ba9vCsY19LF7-gc46lWQQ", "UC_k6e2PPDUnxEdMJC9Q044g"
16), AbstractName = c("ABC 1", "ABC 2"), Country = c("US", "US"
17), Category = c("Gaming", "Comedy"), MadeK = c("No", "No"), SChan = c("Yes", 
18"Yes"), Language = c("en", "EN"), URL1 = c("NA", "NA"), URL2 = c("NA", 
19"NA"), `2018-03` = c(NA_real_, NA_real_), `2018-04` = c(NA_real_, 
20NA_real_), `2018-05` = c(NA_real_, NA_real_), `2018-06` = c(NA_real_, 
21NA_real_), `2018-07` = c(NA_real_, NA_real_), `2018-08` = c(NA_real_, 
22NA_real_), `2018-09` = c(NA_real_, NA_real_), `2018-10` = c(NA_real_, 
23NA_real_), `2018-11` = c(NA_real_, NA_real_), `2018-12` = c(NA_real_, 
24NA_real_), `2019-01` = c(NA_real_, NA_real_), `2019-02` = c(NA_real_, 
25NA_real_), `2019-03` = c(NA_real_, NA_real_), `2019-04` = c(NA_real_, 
26NA_real_), `2019-05` = c(NA_real_, NA_real_), `2019-06` = c(NA_real_, 
27NA_real_), `2019-07` = c(NA_real_, NA_real_), `2019-08` = c(NA_real_, 
28NA_real_), `2019-09` = c(NA_real_, NA_real_), `2019-10` = c(NA_real_, 
29NA_real_), `2019-11` = c(NA_real_, NA_real_), `2019-12` = c(NA_real_, 
30NA_real_), `2020-01` = c(NA_real_, NA_real_), `2020-02` = c(NA_real_, 
31NA_real_), `2020-03` = c(NA_real_, NA_real_), `2020-04` = c(NA_real_, 
32NA_real_), `2020-05` = c(NA_real_, NA_real_), `2020-06` = c(NA, 
3387710), `2020-07` = c(NA, 162416), `2020-08` = c(NA, 187892), 
34    `2020-09` = c(NA, 374661), `2020-10` = c(NA, 449634), `2020-12` = c(NA, 
35    1328523), `2021-01` = c(NA, 1525466), `2021-02` = c(NA, 1836399
36    ), `2021-03` = c(NA, 2752575), `2021-04` = c(86334, 1520546
37    ), `2021-05` = c(165124612, 3205193), `2021-06` = c(392264433,1534225), `2021-07` = c(282087438, 704348), `2021-08` = c(142305290, 
38    49416984), `2021-09` = c(103659781, 49469088), `2021-10` = c(124671803, 
39    20090134), `2021-11` = c(195965776, 363679038), `2021-12` = c(216612017, 
40    774113580), `2022-01` = c(376009215, 796724246), `2022-02` = c(193946191, 
41    599590598)), row.names = c(NA, -2L), class = c("tbl_df", 
42"tbl", "data.frame"))
43    
44shinyApp(
45  
46  ui = fluidPage(
47    selectInput("month", "Month:",
48                choices = colnames(Abstract_50)[10:56],multiple=T,selected=colnames(Abstract_50)[10:14]),
49    htmlOutput("tableset")
50  ),
51  
52  server = function(input, output) {
53    Ind<-Abstract_50[,c(4,10:56)] %>% 
54      group_by(Category) %>%
55      summarise(across(everything(), sum))
56    
57    Ind2<-reactive({
58      Ind <- Ind[,c("Category",input$month), drop = FALSE]
59      
60    })
61    Ind3<-reactive(Ind2() %>% remove_rownames %>% column_to_rownames(var="Category"))
62
63    Descs<-reactive(
64      Descs2<-tibble(Mean = rowMeans(Ind3(), na.rm = TRUE), 
65                     Median = rowMedians(as.matrix(Ind3()), na.rm = TRUE),
66                     SD=rowSds(as.matrix(Ind3()),na.rm = T),
67                     MAD=rowMads(as.matrix(Ind3(),na.rm=T)),
68                     MIN=rowMins(as.matrix(Ind3(),na.rm=T)),
69                     MAX=rowMaxs(as.matrix(Ind3(),na.rm=T)),
70                     VAR=rowVars(as.matrix(Ind3(),na.rm=T)),
71                     RANGE=rowRanges(as.matrix(Ind3(),na.rm=T)),
72                     QUANTILES=rowQuantiles(as.matrix(Ind3()), na.rm = TRUE)[, c(2, 4)]),
73      
74      Descs2<-as.data.frame(Descs2),
75      rownames(Descs2)<-rownames(Ind3()),
76      Descs2
77    )
78    
79    
80    
81    
82    output$tableset <- renderText({
83      kable(Descs()) %>%
84        kable_styling(
85          font_size = 15,
86          bootstrap_options = c("striped", "hover", "condensed")
87        ) 
88    })
89  },
90  
91  options = list(height = 500)
92  
93)
94 Descs2<-as.data.frame(Descs2),
95 rownames(Descs2)<-rownames(Ind3()),
96

1Did you misspecify an argument?
2  54: <Anonymous>
3Error : `...` is not empty.
4
5
6
7
8
9library(tidyverse)
10library(shiny)
11library(matrixStats)
12library(tibble)
13library(knitr)
14library(kableExtra)
15    Abstract_50<-structure(list(ID = c("UC_Ba9vCsY19LF7-gc46lWQQ", "UC_k6e2PPDUnxEdMJC9Q044g"
16), AbstractName = c("ABC 1", "ABC 2"), Country = c("US", "US"
17), Category = c("Gaming", "Comedy"), MadeK = c("No", "No"), SChan = c("Yes", 
18"Yes"), Language = c("en", "EN"), URL1 = c("NA", "NA"), URL2 = c("NA", 
19"NA"), `2018-03` = c(NA_real_, NA_real_), `2018-04` = c(NA_real_, 
20NA_real_), `2018-05` = c(NA_real_, NA_real_), `2018-06` = c(NA_real_, 
21NA_real_), `2018-07` = c(NA_real_, NA_real_), `2018-08` = c(NA_real_, 
22NA_real_), `2018-09` = c(NA_real_, NA_real_), `2018-10` = c(NA_real_, 
23NA_real_), `2018-11` = c(NA_real_, NA_real_), `2018-12` = c(NA_real_, 
24NA_real_), `2019-01` = c(NA_real_, NA_real_), `2019-02` = c(NA_real_, 
25NA_real_), `2019-03` = c(NA_real_, NA_real_), `2019-04` = c(NA_real_, 
26NA_real_), `2019-05` = c(NA_real_, NA_real_), `2019-06` = c(NA_real_, 
27NA_real_), `2019-07` = c(NA_real_, NA_real_), `2019-08` = c(NA_real_, 
28NA_real_), `2019-09` = c(NA_real_, NA_real_), `2019-10` = c(NA_real_, 
29NA_real_), `2019-11` = c(NA_real_, NA_real_), `2019-12` = c(NA_real_, 
30NA_real_), `2020-01` = c(NA_real_, NA_real_), `2020-02` = c(NA_real_, 
31NA_real_), `2020-03` = c(NA_real_, NA_real_), `2020-04` = c(NA_real_, 
32NA_real_), `2020-05` = c(NA_real_, NA_real_), `2020-06` = c(NA, 
3387710), `2020-07` = c(NA, 162416), `2020-08` = c(NA, 187892), 
34    `2020-09` = c(NA, 374661), `2020-10` = c(NA, 449634), `2020-12` = c(NA, 
35    1328523), `2021-01` = c(NA, 1525466), `2021-02` = c(NA, 1836399
36    ), `2021-03` = c(NA, 2752575), `2021-04` = c(86334, 1520546
37    ), `2021-05` = c(165124612, 3205193), `2021-06` = c(392264433,1534225), `2021-07` = c(282087438, 704348), `2021-08` = c(142305290, 
38    49416984), `2021-09` = c(103659781, 49469088), `2021-10` = c(124671803, 
39    20090134), `2021-11` = c(195965776, 363679038), `2021-12` = c(216612017, 
40    774113580), `2022-01` = c(376009215, 796724246), `2022-02` = c(193946191, 
41    599590598)), row.names = c(NA, -2L), class = c("tbl_df", 
42"tbl", "data.frame"))
43    
44shinyApp(
45  
46  ui = fluidPage(
47    selectInput("month", "Month:",
48                choices = colnames(Abstract_50)[10:56],multiple=T,selected=colnames(Abstract_50)[10:14]),
49    htmlOutput("tableset")
50  ),
51  
52  server = function(input, output) {
53    Ind<-Abstract_50[,c(4,10:56)] %>% 
54      group_by(Category) %>%
55      summarise(across(everything(), sum))
56    
57    Ind2<-reactive({
58      Ind <- Ind[,c("Category",input$month), drop = FALSE]
59      
60    })
61    Ind3<-reactive(Ind2() %>% remove_rownames %>% column_to_rownames(var="Category"))
62
63    Descs<-reactive(
64      Descs2<-tibble(Mean = rowMeans(Ind3(), na.rm = TRUE), 
65                     Median = rowMedians(as.matrix(Ind3()), na.rm = TRUE),
66                     SD=rowSds(as.matrix(Ind3()),na.rm = T),
67                     MAD=rowMads(as.matrix(Ind3(),na.rm=T)),
68                     MIN=rowMins(as.matrix(Ind3(),na.rm=T)),
69                     MAX=rowMaxs(as.matrix(Ind3(),na.rm=T)),
70                     VAR=rowVars(as.matrix(Ind3(),na.rm=T)),
71                     RANGE=rowRanges(as.matrix(Ind3(),na.rm=T)),
72                     QUANTILES=rowQuantiles(as.matrix(Ind3()), na.rm = TRUE)[, c(2, 4)]),
73      
74      Descs2<-as.data.frame(Descs2),
75      rownames(Descs2)<-rownames(Ind3()),
76      Descs2
77    )
78    
79    
80    
81    
82    output$tableset <- renderText({
83      kable(Descs()) %>%
84        kable_styling(
85          font_size = 15,
86          bootstrap_options = c("striped", "hover", "condensed")
87        ) 
88    })
89  },
90  
91  options = list(height = 500)
92  
93)
94 Descs2<-as.data.frame(Descs2),
95 rownames(Descs2)<-rownames(Ind3()),
96 ui = fluidPage(
97    selectInput("month", "Month:",
98                choices = colnames(Abstract_50)[10:56],multiple=TRUE,selected=colnames(Abstract_50)[10:14]),
99    htmlOutput("tableset")
100  )
101  
102  server = function(input, output) {
103    Ind<-Abstract_50[,c(4,10:56)] %>% 
104      group_by(Category) %>%
105      summarise(across(everything(), sum))
106    
107    Ind2<-reactive({
108      Ind <- Ind[,c("Category",input$month), drop = FALSE]
109      
110    })
111    Ind3<-reactive({
112         req(Ind2())
113         Ind2() %>% remove_rownames %>% column_to_rownames(var="Category")
114    }
115         
116         )
117    
118    
119    Descs<-reactive({
120         req(Ind3())
121         tmp <- Ind3()
122      Descs2<-tibble(Mean = rowMeans(tmp, na.rm = TRUE), 
123                     Median = rowMedians(as.matrix(tmp), na.rm = TRUE),
124                     SD=rowSds(as.matrix(tmp),na.rm = TRUE),
125                     MAD=rowMads(as.matrix(tmp,na.rm=TRUE)),
126                     MIN=rowMins(as.matrix(tmp,na.rm=TRUE)),
127                     MAX=rowMaxs(as.matrix(tmp,na.rm=TRUE)),
128                     VAR=rowVars(as.matrix(tmp,na.rm=TRUE)),
129                     RANGE=rowRanges(as.matrix(tmp,na.rm=TRUE)),
130                     QUANTILES=rowQuantiles(as.matrix(tmp), na.rm = TRUE)[, c(2, 4)])
131      
132      Descs2<-as.data.frame(Descs2)
133      rownames(Descs2)<-rownames(tmp)
134      Descs2
135    }
136    )
137    
138    
139    
140    
141    output$tableset <- renderText({
142      kable(Descs()) %>%
143        kable_styling(
144          font_size = 15,
145          bootstrap_options = c("striped", "hover", "condensed")
146        ) 
147    })
148  }
149  
150shinyApp(ui = ui, server = server, options = list(height = 500))
151

1    df<-structure(list(Comedy = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
30, 0, 0, 0, 0, 0, 192993657, 2027127534, 1826083072, 1128287155, 
41757374990, 2694138537, 3190204219, 2038778526), Education = c(943330388, 
51155823837, 1360355713, 1826278073, 2188996970, 2021212414, 2289941566, 
62127165749, 1783936602, 2872498991, 2853872150, 3064968119, 3215161644, 
73049311469, 2820083900, 2044822024, 3779135880, 2844133676, 3376034265, 
83041618231, 3105665413, 3550804553, 3539721850, 4456458622, 4991538788, 
95828853483, 4966268171, 4512424020, 4502737563, 4375577460, 4552548135, 
103738145324, 3995222561, 3953186608, 3594443364, 3585009005, 2700428379, 
112902711237, 2359179701, 2740895126, 1867652957, 2048359210, 2314370309, 
122998250015, 3491065357, 3903773976, 2935444472), Entertainment = c(0, 
130, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
140, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7788928842, 7204965173, 
156992191235, 5457111773, 6860197195, 8855099815, 10013159307, 
1610474839762, 9524669175), `Film & Animation` = c(0, 0, 0, 0, 
170, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 759717307, 
18615753280, 318711634, 333904916, -693533921, 2091024234, 950022270, 
191001308730, 1026354524, 1081811412, 1199434291, 1433216491, 1442529761, 
201532974436, 1395606514, 1363861251, 1389014794, 1593050956, 1685049970, 
211547131073, 1518807877, 1233700577, 1218615927, 1454858377, 1352274781
22), Gaming = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
230, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2510727459, 2443299082, 3488510114, 5039516826, 3532430927, 3672818367, 3326539144, 
243130946776, 3280046870, 3691388930, 2605046632), `Howto & Style` = c(543234645, 
25637460061, 715598918, 853921148, 863342506, 795767480, 746997762, 
26653842221, 788862290, 591282170, 568671091, 548087579, 485300284, 
27436535293, 568286241, 478040015, 521541708, 405161168, 319837479, 
28280725401, 360157763, 165904320, 212343880, 327743010, 395400144, 
29426654776, 366884505, 357665076, 364828829, 345995485, 297049174, 
30240780776, 274207911, 273912428, 319470376, 392600212, 360147433, 
31264181288, 251605545, 236415550, 204441940, 421922863, 256820429, 
32424107264, 431256372, 390931344, 274420963), Music = c(45831420, 
3338904148, 73012836, 84645886, 111060240, 57262179, 26950500, 
3439134840, 60187872, 47610185, 23140910, 25985719, 28712442, 25722654, 
3547876708, 25865517, 32238860, 40636947, 44902937, 55838200, 36025492, 
3628671767, 82421655, 31274307, 42413334, 7507664, 35619244, -11045119, 
3737486877, 20923182, 27529830, 30924536, 28565411, 39739293, 23233821, 
38115184742, 25449369, 23944670, 41185546, -16964201, 47813282, 
3921983523, 17880023, 13962494, 12342364, 13356074, 13021208), 
40    `People & Blogs` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
41    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
42    0, 0, 0, 0, 398375824, 454784769, 370688348, 369779164, 500101683,833749803, 1063138342, 1479795070, 1619160390, 1834784208, 
43    2111705491, 1350498139), `Science & Technology` = c(27301292, 
44    39573248, 48043856, 39855934, 52061005, 49053872, 49842103, 
45    44976795, 124632168, 50023068, 62941217, 47587876, 77099065, 
46    84765100, 58321781, 91284611, 96894838, 94260454, 72295712, 
47    102476421, 127194669, 67833402, 83318605, 114770114, 65328120, 
48    142092816, 123237585, 88539117, 176004414, 125341790, 142739116, 
49    86111812, 160017617, 131590671, 112033237, 129916112, 122655167, 
50    135571501, 63475989, 116497124, 94155011, 100342778, 90332692, 
51    88069070, 138735396, 76689085, 48123575), Sports = c(160818771, 
52    208830991, 189706047, 254772059, 310324223, 175281803, 245742536, 
53    213430460, 221505212, 231476966, 175724338, 258531007, 190968565, 
54    223061017, 210376637, 260369412, 289167706, 200040944, 260041965, 
55    200611576, 169604151, 276707678, 215539732, 214705917, 175674357, 
56    278796775, 285834056, 181252193, 270411741, 188726163, 189031053, 
57    155748840, 195702049, 160777215, 163685423, 191998118, 150304968, 
58    133674014, 162583897, 142010262, 123440234, 99242284, 100782041, 
59    81297079, 94100459, 123463360, 104218370)), class = "data.frame", row.names = c("2018-03", 
60"2018-04", "2018-05", "2018-06", "2018-07", "2018-08", "2018-09", 
61"2018-10", "2018-11", "2018-12", "2019-01", "2019-02", "2019-03","2019-04", "2019-05", "2019-06", "2019-07", "2019-08", "2019-09", 
62"2019-10", "2019-11", "2019-12", "2020-01", "2020-02", "2020-03", 
63"2020-04", "2020-05", "2020-06", "2020-07", "2020-08", "2020-09", 
64"2020-10", "2020-12", "2021-01", "2021-02", "2021-03", "2021-04", 
65"2021-05", "2021-06", "2021-07", "2021-08", "2021-09", "2021-10", 
66"2021-11", "2021-12", "2022-01", "2022-02"))
67    
68
69p<-ggplot(df, aes(x=WORDS, y=TOTALS, fill=WORDS)) +
70      geom_boxplot()
71    p+theme_minimal()+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+ 
72      xlab("Industries") + ylab("Value")
73library(dplyr)
74library(tidyr)
75library(ggplot2)
76df_long <- pivot_longer(df, cols = everything(), names_to = 'WORDS', 
77       values_to = 'TOTALS') 
78
79p <- ggplot(df_long, aes(x = WORDS, y = TOTALS, fill = WORDS)) + 
80      geom_boxplot()
81
82p  +  
83   theme_minimal()+ 
84    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, 
85        hjust=1))+ 
86     xlab("Industries") +
87     ylab("Value")
88

1structure(list(`2018-03` = c(NA, 943330388, NA, NA, NA, 543234645, 
245831420, NA, 27301292, 160818771), `2018-04` = c(NA, 1155823837, 
3NA, NA, NA, 637460061, 38904148, NA, 39573248, 208830991), `2018-05` = c(NA, 
41360355713, NA, NA, NA, 715598918, 73012836, NA, 48043856, 189706047
5)), class = "data.frame", row.names = c("Comedy", "Education", 
6"Entertainment", "Film & Animation", "Gaming", "Howto & Style", 
7"Music", "People & Blogs", "Science & Technology", "Sports"))
8library(matrixStats)
9library(tibble)
10tibble(Mean = rowMeans(df1, na.rm = TRUE), 
11      Median = rowMedians(as.matrix(df1), na.rm = TRUE))
12

1    Channel_ID  Video_Category_Name score_pct
20   UC--bUZc5c9WseZNqGR6KLxA    Autos & Vehicles    0.213702
31   UC--bUZc5c9WseZNqGR6KLxA    Entertainment   0.786298
42   UC-B1L3oT81XgeeGh6S12qgQ    People & Blogs  1.000000
53   UC-N_7HFKrSsYxCSA_kfdRSA    People & Blogs  0.137261
64   UC-N_7HFKrSsYxCSA_kfdRSA    Pets & Animals  0.862739
7... ... ... ...
8819 UCzsNLZ9GrGXRjt0QmvWFm2Q    Entertainment   0.945243
9820 UCzsNLZ9GrGXRjt0QmvWFm2Q    Film & Animation    0.002046
10821 UCzsNLZ9GrGXRjt0QmvWFm2Q    Music   0.002797
11822 UCzsNLZ9GrGXRjt0QmvWFm2Q    News & Politics 0.000433
12823 UCzsNLZ9GrGXRjt0QmvWFm2Q    People & Blogs  0.000358
13df.Video_Category_Name.unique()
14array(['Autos & Vehicles', 'Entertainment', 'People & Blogs',
15       'Pets & Animals', 'Howto & Style', 'Education', 'Gaming', 'Music',
16       'Comedy', 'Travel & Events', 'Science & Technology',
17       'Nonprofits & Activism', 'Sports', 'Film & Animation',
18       'News & Politics'], dtype=object)
19 In [3]: iwantthis
20  Out[3]:
21     Channel_ID  Autos & Vehicles Entertainment People & Blogs ...
22  0  UC--bUZc5c9WseZNqGR6KLxA  0.213702 0.786298 0 ...
23  1  UC-B1L3oT81XgeeGh6S12qgQ  0        0        1.0000 ...
24df = df.pivot(index='Channel_ID', columns='Video_Category_Name', values='score_pct').fillna(0).reset_index()
25

1    Channel_ID  Video_Category_Name score_pct
20   UC--bUZc5c9WseZNqGR6KLxA    Autos & Vehicles    0.213702
31   UC--bUZc5c9WseZNqGR6KLxA    Entertainment   0.786298
42   UC-B1L3oT81XgeeGh6S12qgQ    People & Blogs  1.000000
53   UC-N_7HFKrSsYxCSA_kfdRSA    People & Blogs  0.137261
64   UC-N_7HFKrSsYxCSA_kfdRSA    Pets & Animals  0.862739
7... ... ... ...
8819 UCzsNLZ9GrGXRjt0QmvWFm2Q    Entertainment   0.945243
9820 UCzsNLZ9GrGXRjt0QmvWFm2Q    Film & Animation    0.002046
10821 UCzsNLZ9GrGXRjt0QmvWFm2Q    Music   0.002797
11822 UCzsNLZ9GrGXRjt0QmvWFm2Q    News & Politics 0.000433
12823 UCzsNLZ9GrGXRjt0QmvWFm2Q    People & Blogs  0.000358
13df.Video_Category_Name.unique()
14array(['Autos & Vehicles', 'Entertainment', 'People & Blogs',
15       'Pets & Animals', 'Howto & Style', 'Education', 'Gaming', 'Music',
16       'Comedy', 'Travel & Events', 'Science & Technology',
17       'Nonprofits & Activism', 'Sports', 'Film & Animation',
18       'News & Politics'], dtype=object)
19 In [3]: iwantthis
20  Out[3]:
21     Channel_ID  Autos & Vehicles Entertainment People & Blogs ...
22  0  UC--bUZc5c9WseZNqGR6KLxA  0.213702 0.786298 0 ...
23  1  UC-B1L3oT81XgeeGh6S12qgQ  0        0        1.0000 ...
24df = df.pivot(index='Channel_ID', columns='Video_Category_Name', values='score_pct').fillna(0).reset_index()
25Video_Category_Name Channel_ID  Autos & Vehicles    Comedy  Education   Entertainment   Film & Animation    Gaming  Howto & Style   Music   News & Politics Nonprofits & Activism   People & Blogs  Pets & Animals  Science & Technology    Sports  Travel & Events
260   UC--bUZc5c9WseZNqGR6KLxA    0.213702    0.0 0.0 0.786298    0.0 0.0 0.0 0.0 0.0 0.0 0.000000    0.000000    0.0 0.0 0.0
271   UC-B1L3oT81XgeeGh6S12qgQ    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 1.000000    0.000000    0.0 0.0 0.0
282   UC-N_7HFKrSsYxCSA_kfdRSA    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 0.137261    0.862739    0.0 0.0 0.0
293   UC-T4JheeuNl2DVg-B-v7McA    0.000000    0.0 0.0 0.000000    0.0 0.0 1.0 0.0 0.0 0.0 0.000000    0.000000    0.0 0.0 0.0
304   UC-WG1VP4am6NaUtANEJxRQw    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 1.000000    0.000000    0.0 0.0 0.0
31

1    Channel_ID  Video_Category_Name score_pct
20   UC--bUZc5c9WseZNqGR6KLxA    Autos & Vehicles    0.213702
31   UC--bUZc5c9WseZNqGR6KLxA    Entertainment   0.786298
42   UC-B1L3oT81XgeeGh6S12qgQ    People & Blogs  1.000000
53   UC-N_7HFKrSsYxCSA_kfdRSA    People & Blogs  0.137261
64   UC-N_7HFKrSsYxCSA_kfdRSA    Pets & Animals  0.862739
7... ... ... ...
8819 UCzsNLZ9GrGXRjt0QmvWFm2Q    Entertainment   0.945243
9820 UCzsNLZ9GrGXRjt0QmvWFm2Q    Film & Animation    0.002046
10821 UCzsNLZ9GrGXRjt0QmvWFm2Q    Music   0.002797
11822 UCzsNLZ9GrGXRjt0QmvWFm2Q    News & Politics 0.000433
12823 UCzsNLZ9GrGXRjt0QmvWFm2Q    People & Blogs  0.000358
13df.Video_Category_Name.unique()
14array(['Autos & Vehicles', 'Entertainment', 'People & Blogs',
15       'Pets & Animals', 'Howto & Style', 'Education', 'Gaming', 'Music',
16       'Comedy', 'Travel & Events', 'Science & Technology',
17       'Nonprofits & Activism', 'Sports', 'Film & Animation',
18       'News & Politics'], dtype=object)
19 In [3]: iwantthis
20  Out[3]:
21     Channel_ID  Autos & Vehicles Entertainment People & Blogs ...
22  0  UC--bUZc5c9WseZNqGR6KLxA  0.213702 0.786298 0 ...
23  1  UC-B1L3oT81XgeeGh6S12qgQ  0        0        1.0000 ...
24df = df.pivot(index='Channel_ID', columns='Video_Category_Name', values='score_pct').fillna(0).reset_index()
25Video_Category_Name Channel_ID  Autos & Vehicles    Comedy  Education   Entertainment   Film & Animation    Gaming  Howto & Style   Music   News & Politics Nonprofits & Activism   People & Blogs  Pets & Animals  Science & Technology    Sports  Travel & Events
260   UC--bUZc5c9WseZNqGR6KLxA    0.213702    0.0 0.0 0.786298    0.0 0.0 0.0 0.0 0.0 0.0 0.000000    0.000000    0.0 0.0 0.0
271   UC-B1L3oT81XgeeGh6S12qgQ    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 1.000000    0.000000    0.0 0.0 0.0
282   UC-N_7HFKrSsYxCSA_kfdRSA    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 0.137261    0.862739    0.0 0.0 0.0
293   UC-T4JheeuNl2DVg-B-v7McA    0.000000    0.0 0.0 0.000000    0.0 0.0 1.0 0.0 0.0 0.0 0.000000    0.000000    0.0 0.0 0.0
304   UC-WG1VP4am6NaUtANEJxRQw    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 1.000000    0.000000    0.0 0.0 0.0
31df = df.reset_index().pivot(index=['index', 'Channel_ID'], columns='Video_Category_Name', values='score_pct').fillna(0).reset_index(level=1)
32

1    Channel_ID  Video_Category_Name score_pct
20   UC--bUZc5c9WseZNqGR6KLxA    Autos & Vehicles    0.213702
31   UC--bUZc5c9WseZNqGR6KLxA    Entertainment   0.786298
42   UC-B1L3oT81XgeeGh6S12qgQ    People & Blogs  1.000000
53   UC-N_7HFKrSsYxCSA_kfdRSA    People & Blogs  0.137261
64   UC-N_7HFKrSsYxCSA_kfdRSA    Pets & Animals  0.862739
7... ... ... ...
8819 UCzsNLZ9GrGXRjt0QmvWFm2Q    Entertainment   0.945243
9820 UCzsNLZ9GrGXRjt0QmvWFm2Q    Film & Animation    0.002046
10821 UCzsNLZ9GrGXRjt0QmvWFm2Q    Music   0.002797
11822 UCzsNLZ9GrGXRjt0QmvWFm2Q    News & Politics 0.000433
12823 UCzsNLZ9GrGXRjt0QmvWFm2Q    People & Blogs  0.000358
13df.Video_Category_Name.unique()
14array(['Autos & Vehicles', 'Entertainment', 'People & Blogs',
15       'Pets & Animals', 'Howto & Style', 'Education', 'Gaming', 'Music',
16       'Comedy', 'Travel & Events', 'Science & Technology',
17       'Nonprofits & Activism', 'Sports', 'Film & Animation',
18       'News & Politics'], dtype=object)
19 In [3]: iwantthis
20  Out[3]:
21     Channel_ID  Autos & Vehicles Entertainment People & Blogs ...
22  0  UC--bUZc5c9WseZNqGR6KLxA  0.213702 0.786298 0 ...
23  1  UC-B1L3oT81XgeeGh6S12qgQ  0        0        1.0000 ...
24df = df.pivot(index='Channel_ID', columns='Video_Category_Name', values='score_pct').fillna(0).reset_index()
25Video_Category_Name Channel_ID  Autos & Vehicles    Comedy  Education   Entertainment   Film & Animation    Gaming  Howto & Style   Music   News & Politics Nonprofits & Activism   People & Blogs  Pets & Animals  Science & Technology    Sports  Travel & Events
260   UC--bUZc5c9WseZNqGR6KLxA    0.213702    0.0 0.0 0.786298    0.0 0.0 0.0 0.0 0.0 0.0 0.000000    0.000000    0.0 0.0 0.0
271   UC-B1L3oT81XgeeGh6S12qgQ    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 1.000000    0.000000    0.0 0.0 0.0
282   UC-N_7HFKrSsYxCSA_kfdRSA    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 0.137261    0.862739    0.0 0.0 0.0
293   UC-T4JheeuNl2DVg-B-v7McA    0.000000    0.0 0.0 0.000000    0.0 0.0 1.0 0.0 0.0 0.0 0.000000    0.000000    0.0 0.0 0.0
304   UC-WG1VP4am6NaUtANEJxRQw    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 1.000000    0.000000    0.0 0.0 0.0
31df = df.reset_index().pivot(index=['index', 'Channel_ID'], columns='Video_Category_Name', values='score_pct').fillna(0).reset_index(level=1)
32df = df.rename_axis(None, axis=1) 
33

1    Channel_ID  Video_Category_Name score_pct
20   UC--bUZc5c9WseZNqGR6KLxA    Autos & Vehicles    0.213702
31   UC--bUZc5c9WseZNqGR6KLxA    Entertainment   0.786298
42   UC-B1L3oT81XgeeGh6S12qgQ    People & Blogs  1.000000
53   UC-N_7HFKrSsYxCSA_kfdRSA    People & Blogs  0.137261
64   UC-N_7HFKrSsYxCSA_kfdRSA    Pets & Animals  0.862739
7... ... ... ...
8819 UCzsNLZ9GrGXRjt0QmvWFm2Q    Entertainment   0.945243
9820 UCzsNLZ9GrGXRjt0QmvWFm2Q    Film & Animation    0.002046
10821 UCzsNLZ9GrGXRjt0QmvWFm2Q    Music   0.002797
11822 UCzsNLZ9GrGXRjt0QmvWFm2Q    News & Politics 0.000433
12823 UCzsNLZ9GrGXRjt0QmvWFm2Q    People & Blogs  0.000358
13df.Video_Category_Name.unique()
14array(['Autos & Vehicles', 'Entertainment', 'People & Blogs',
15       'Pets & Animals', 'Howto & Style', 'Education', 'Gaming', 'Music',
16       'Comedy', 'Travel & Events', 'Science & Technology',
17       'Nonprofits & Activism', 'Sports', 'Film & Animation',
18       'News & Politics'], dtype=object)
19 In [3]: iwantthis
20  Out[3]:
21     Channel_ID  Autos & Vehicles Entertainment People & Blogs ...
22  0  UC--bUZc5c9WseZNqGR6KLxA  0.213702 0.786298 0 ...
23  1  UC-B1L3oT81XgeeGh6S12qgQ  0        0        1.0000 ...
24df = df.pivot(index='Channel_ID', columns='Video_Category_Name', values='score_pct').fillna(0).reset_index()
25Video_Category_Name Channel_ID  Autos & Vehicles    Comedy  Education   Entertainment   Film & Animation    Gaming  Howto & Style   Music   News & Politics Nonprofits & Activism   People & Blogs  Pets & Animals  Science & Technology    Sports  Travel & Events
260   UC--bUZc5c9WseZNqGR6KLxA    0.213702    0.0 0.0 0.786298    0.0 0.0 0.0 0.0 0.0 0.0 0.000000    0.000000    0.0 0.0 0.0
271   UC-B1L3oT81XgeeGh6S12qgQ    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 1.000000    0.000000    0.0 0.0 0.0
282   UC-N_7HFKrSsYxCSA_kfdRSA    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 0.137261    0.862739    0.0 0.0 0.0
293   UC-T4JheeuNl2DVg-B-v7McA    0.000000    0.0 0.0 0.000000    0.0 0.0 1.0 0.0 0.0 0.0 0.000000    0.000000    0.0 0.0 0.0
304   UC-WG1VP4am6NaUtANEJxRQw    0.000000    0.0 0.0 0.000000    0.0 0.0 0.0 0.0 0.0 0.0 1.000000    0.000000    0.0 0.0 0.0
31df = df.reset_index().pivot(index=['index', 'Channel_ID'], columns='Video_Category_Name', values='score_pct').fillna(0).reset_index(level=1)
32df = df.rename_axis(None, axis=1) 
33df = df.rename_axis(None, axis=0)
34