the number of participants who thought they didn’t and did have COVID

amount_covid <- COVID %>% 
  group_by(Ever_covid) %>% 
  count(Ever_covid)
print(amount_covid)

## # A tibble: 2 x 2
## # Groups:   Ever_covid [2]
##                           Ever_covid     n
##                            <dbl+lbl> <int>
## 1 0 [Think have not had coronavirus]  4656
## 2 1 [Think have had coronavirus]      1493

how many had an antigen test for COVID

amount_covid1_antigen <- COVID %>% 
  group_by(q7beentested) %>%
  count(q7beentested)
print(amount_covid1_antigen)

## # A tibble: 3 x 2
## # Groups:   q7beentested [3]
##                                   q7beentested     n
##                                      <dbl+lbl> <int>
## 1 0 [Not been tested]                           5574
## 2 1 [Tested and result showed no coronavirus]    330
## 3 2 [Tested and result showed yes coronavirus]   245

how many tested negative but thought they had COVID

negativethought <- COVID %>% 
  group_by(q7beentested, Ever_covid) %>% 
  filter(Ever_covid == 1, q7beentested == 1) %>% 
  count(Ever_covid)
print(negativethought)

## # A tibble: 1 x 3
## # Groups:   q7beentested, Ever_covid [1]
##                                 q7beentested                    Ever_covid     n
##                                    <dbl+lbl>                     <dbl+lbl> <int>
## 1 1 [Tested and result showed no coronaviru… 1 [Think have had coronaviru…   187

how many tested positive but thought they didnt have COVID

positivethought <- COVID %>% 
  group_by(q7beentested, Ever_covid) %>% 
  filter(Ever_covid == 0, q7beentested == 2) %>% 
  count(Ever_covid)
print(positivethought)

## # A tibble: 1 x 3
## # Groups:   q7beentested, Ever_covid [1]
##                               q7beentested                      Ever_covid     n
##                                  <dbl+lbl>                       <dbl+lbl> <int>
## 1 2 [Tested and result showed yes coronav… 0 [Think have not had coronavi…    56

how many males/females

COVID <- read_sav(file ="coviddata.sav")
covidgender <- COVID %>% 
  group_by(gender, Ever_covid) %>%
  mutate(gender = case_when(gender == 1 ~ "Male", gender == 2 ~ "female")) %>% 
  mutate(Ever_covid = case_when(Ever_covid == 0 ~ "Think have not had COVID-19", Ever_covid == 1 ~ "Think have had COVID-19")) %>%
  count(gender)
print(covidgender)

## # A tibble: 4 x 3
## # Groups:   gender, Ever_covid [4]
##   gender Ever_covid                      n
##   <chr>  <chr>                       <int>
## 1 female Think have had COVID-19       796
## 2 female Think have not had COVID-19  2459
## 3 Male   Think have had COVID-19       697
## 4 Male   Think have not had COVID-19  2197

how many in each age category

age <- COVID %>% 
  group_by(age_categories) %>% 
  mutate(age_categories = case_when(age_categories == 1 ~ "18 to 24 years", age_categories == 2 ~ "25 to 34 years", age_categories == 3 ~ "35 to 44 years", age_categories == 4 ~ "45 to 54 years", age_categories == 5 ~ "55 years and over")) %>%
  count(age_categories)
print(age)

## # A tibble: 5 x 2
## # Groups:   age_categories [5]
##   age_categories        n
##   <chr>             <int>
## 1 18 to 24 years     1422
## 2 25 to 34 years     1223
## 3 35 to 44 years     1045
## 4 45 to 54 years      718
## 5 55 years and over  1741

how many have children

They excluded 361 people here for some reason. Our guess is that they didn’t answer the question

child <- COVID %>% 
  group_by(Has_child) %>% 
  count(Has_child)
print(child)

## # A tibble: 3 x 2
## # Groups:   Has_child [3]
##                    Has_child     n
##                    <dbl+lbl> <int>
## 1  0 [Does not have a child]  2626
## 2  1 [Has a child]            3162
## 3 NA                           361

employment status

They excluded 83 people here

employment <- COVID %>% 
  group_by(Working) %>% 
  count(Working)
print(employment)

## # A tibble: 3 x 2
## # Groups:   Working [3]
##                                             Working     n
##                                           <dbl+lbl> <int>
## 1  0 [Not working]                                   2071
## 2  1 [Working (full or part time or self-employed)]  3995
## 3 NA                                                   83

how many are working in key sector

worker <- COVID %>% 
  group_by(Key_worker) %>% 
  count(Key_worker)
print(worker)

## # A tibble: 2 x 2
## # Groups:   Key_worker [2]
##           Key_worker     n
##            <dbl+lbl> <int>
## 1 0 [Not key worker]  3858
## 2 1 [Key worker]      2291

level of education

They excluded 92 people, maybe those who didn’t fit into either category?

education <- COVID %>% 
  group_by(degree) %>% 
  count(degree)
print(education)

## # A tibble: 3 x 2
## # Groups:   degree [3]
##                                                  degree     n
##                                               <dbl+lbl> <int>
## 1  0 [GCSE/vocational/A-level/no formal qualifications]  4442
## 2  1 [Degree or higher (Bachelors, Masters, PhD)]        1615
## 3 NA                                                       92

how many from each region

region1 <- COVID %>% 
  group_by(region) %>% 
  count(region)
print(region1)

## # A tibble: 5 x 2
## # Groups:   region [5]
##              region     n
##           <dbl+lbl> <int>
## 1 1 [Midlands]       1032
## 2 2 [South & East]   1785
## 3 3 [North]          1455
## 4 4 [London]         1000
## 5 5 [Wales/Scot/NI]   877

how many agreed/strongly agreed that they had some immunity to COVID & this also compared to if they thought they had COVID

agreecovid <- COVID %>% 
  group_by(q8haveimmunity) %>% 
  filter(q8haveimmunity > 3) %>% 
  count(q8haveimmunity)
print(agreecovid)

## # A tibble: 2 x 2
## # Groups:   q8haveimmunity [2]
##       q8haveimmunity     n
##            <dbl+lbl> <int>
## 1 4 [Agree]            841
## 2 5 [Strongly agree]   299

agreecovid1 <- COVID %>% 
  group_by(q8haveimmunity, Ever_covid) %>% 
  filter(q8haveimmunity > 3) %>% 
  count(Ever_covid)
print(agreecovid1)

## # A tibble: 4 x 3
## # Groups:   q8haveimmunity, Ever_covid [4]
##       q8haveimmunity                         Ever_covid     n
##            <dbl+lbl>                          <dbl+lbl> <int>
## 1 4 [Agree]          0 [Think have not had coronavirus]   382
## 2 4 [Agree]          1 [Think have had coronavirus]       459
## 3 5 [Strongly agree] 0 [Think have not had coronavirus]   118
## 4 5 [Strongly agree] 1 [Think have had coronavirus]       181

Table 1

This code finds the numbers and percentages for variables in table 1. First, we used zap labels to remove the pre-existing labels from each vector. We used pivot longer in order to find the total number for each variable to create percentages. We filtered out variables not needed for table 1 and then omitted NA values. We found percentages using mutate, rounding to 1 decimal place.

covid_1 <- COVID %>%
  zap_labels() %>% 
  pivot_longer(gender:region, names_to = "vars", values_to = "values") %>% 
  group_by(Ever_covid, vars,values) %>% 
  count(name = "number") %>% 
  na.omit %>% 
  group_by(vars,values) %>% 
  mutate(percentage = round(number/sum(number) * 100, 1))
print(covid_1)

## # A tibble: 40 x 5
## # Groups:   vars, values [20]
##    Ever_covid vars           values number percentage
##         <dbl> <chr>           <dbl>  <int>      <dbl>
##  1          0 age_categories      1   1003       70.5
##  2          0 age_categories      2    823       67.3
##  3          0 age_categories      3    751       71.9
##  4          0 age_categories      4    554       77.2
##  5          0 age_categories      5   1525       87.6
##  6          0 degree              0   3382       76.1
##  7          0 degree              1   1200       74.3
##  8          0 gender              1   2197       75.9
##  9          0 gender              2   2459       75.5
## 10          0 Has_child           0   2005       76.4
## # … with 30 more rows

We used this code to find how many thought they have not had COVID and how many thought they did. These numbers are at the top row of table 1 and table 3

amount_covid <- COVID %>% 
  group_by(Ever_covid) %>% 
  count(Ever_covid)
  print(amount_covid)

## # A tibble: 2 x 2
## # Groups:   Ever_covid [2]
##                           Ever_covid     n
##                            <dbl+lbl> <int>
## 1 0 [Think have not had coronavirus]  4656
## 2 1 [Think have had coronavirus]      1493

We now changed the data back to wide data to begin reproducing the table. We again used zap labels to remove any leftover labels since we previously grouped before omitting NA values, so now there should not be any more labels. We used mutate to turn everything into a factor.

table1 <- covid_1 %>% 
  pivot_wider(names_from = vars, values_from = values) %>% 
zap_labels() %>% mutate_if(is.numeric, as.factor)
print(table1)

## # A tibble: 40 x 10
##    Ever_covid number percentage age_categories degree gender Has_child
##    <fct>      <fct>  <fct>      <fct>          <fct>  <fct>  <fct>    
##  1 0          1003   70.5       1              <NA>   <NA>   <NA>     
##  2 0          823    67.3       2              <NA>   <NA>   <NA>     
##  3 0          751    71.9       3              <NA>   <NA>   <NA>     
##  4 0          554    77.2       4              <NA>   <NA>   <NA>     
##  5 0          1525   87.6       5              <NA>   <NA>   <NA>     
##  6 0          3382   76.1       <NA>           0      <NA>   <NA>     
##  7 0          1200   74.3       <NA>           1      <NA>   <NA>     
##  8 0          2197   75.9       <NA>           <NA>   1      <NA>     
##  9 0          2459   75.5       <NA>           <NA>   2      <NA>     
## 10 0          2005   76.4       <NA>           <NA>   <NA>   0        
## # … with 30 more rows, and 3 more variables: Key_worker <fct>, region <fct>,
## #   Working <fct>

Now using factors, we can create the labels from the original table.

  table1$Ever_covid <- factor(table1$Ever_covid, labels = c("Think have not had COVID-19 n = 4656", "Think have had COVID-19 n = 1493"))
table1$gender <-factor(table1$gender, labels = c("Male", "Female"))
table1$age_categories <- factor(table1$age_categories, labels = c("18 to 24 years", "25 to 34 years", "35 to 44 years", "45 to 54 years", "55 years and over"))
table1$Has_child <- factor(table1$Has_child, labels = c("No", "Yes"))
table1$Working <- factor(table1$Working, labels = c("Not working", "Working"))
table1$Key_worker <- factor(table1$Key_worker, labels = c("No", "Yes"))
table1$degree <-factor(table1$degree, labels = c("GCSE/vocational/A-level/No formal qualifications", "Degree or higher (Bachelors, Masters, PhD"))
table1$region <- factor(table1$region, labels = c("Midlands", "South and East", "North", "London", "Wales, Scotland and Northern Ireland"))

Now we pivot again to long data to find our percentages using mutate, and add the rest of the labels.

table1 <- table1 %>% 
  pivot_longer(age_categories:Working, names_to = "Participant Characteristics", values_to = "Levels") %>% 
  na.omit %>% 
  mutate(Real = paste0(number,"(",percentage,"%)")) %>%
  select(-percentage, -number) %>% 
  pivot_wider(id_cols = -Real, names_from = Ever_covid, values_from = Real) 
print(table1)

## # A tibble: 20 x 4
##    `Participant Chara… Levels          `Think have not had … `Think have had CO…
##    <chr>               <fct>           <chr>                 <chr>              
##  1 age_categories      18 to 24 years  1003(70.5%)           419(29.5%)         
##  2 age_categories      25 to 34 years  823(67.3%)            400(32.7%)         
##  3 age_categories      35 to 44 years  751(71.9%)            294(28.1%)         
##  4 age_categories      45 to 54 years  554(77.2%)            164(22.8%)         
##  5 age_categories      55 years and o… 1525(87.6%)           216(12.4%)         
##  6 degree              GCSE/vocationa… 3382(76.1%)           1060(23.9%)        
##  7 degree              Degree or high… 1200(74.3%)           415(25.7%)         
##  8 gender              Male            2197(75.9%)           697(24.1%)         
##  9 gender              Female          2459(75.5%)           796(24.5%)         
## 10 Has_child           No              2005(76.4%)           621(23.6%)         
## 11 Has_child           Yes             2386(75.5%)           776(24.5%)         
## 12 Key_worker          No              3105(80.5%)           753(19.5%)         
## 13 Key_worker          Yes             1551(67.7%)           740(32.3%)         
## 14 region              Midlands        781(75.7%)            251(24.3%)         
## 15 region              South and East  1369(76.7%)           416(23.3%)         
## 16 region              North           1120(77%)             335(23%)           
## 17 region              London          701(70.1%)            299(29.9%)         
## 18 region              Wales, Scotlan… 685(78.1%)            192(21.9%)         
## 19 Working             Not working     1714(82.8%)           357(17.2%)         
## 20 Working             Working         2871(71.9%)           1124(28.1%)

table1$`Participant Characteristics` <- as.factor(table1$`Participant Characteristics`)
table1$`Participant Characteristics` <- factor(table1$`Participant Characteristics`, levels = c ( "gender",  "age_categories", "Has_child", "Working", "Key_worker","degree", "region"), labels = c ( "Gender", "Age", "Have a child","Employment Status", "Working in Key Sector","Highest Education or Professional Qualification","Region"))

Using gt to create the table. Using rowname_col and groupname_col to create the headings and values. We pretty much used the gt package from here onwards for this table, reordering the groups and adding labels and fixing text to match the original table

  table1 <- table1 %>% 
  gt(rowname_col = "Levels",
     groupname_col = "Participant Characteristics") %>% 
  tab_spanner(label = md("**Had COVID-19**"), 
              columns = vars("Think have not had COVID-19 n = 4656","Think have had COVID-19 n = 1493")) %>% 
    row_group_order(c("Gender", "Age", "Have a child","Employment Status", "Working in Key Sector","Highest Education or Professional Qualification","Region")) %>% 
    cols_label(
      'Think have not had COVID-19 n = 4656' = md("**Think have not had COVID-19 n = 4656**"),
      'Think have had COVID-19 n = 1493' = md( "**Think have had COVID-19 n = 1493**")
    ) %>% 
    tab_style(
      style = list(
        cell_text(weight = "bold"),
        cell_fill()
      ),
  locations = cells_row_groups())

Table 2

Started off by finding the mean and SD for variables in table 2, as this is what is needed to reproduce the original.

 MSD <- COVID %>% 
   group_by(Ever_covid) %>% 
   summarise(across(c(q8haveimmunity,Going_out_total,q9worry,q10arisk,q10brisk),
                    list(mean = mean, standard_deviation = sd)))  %>% 
   mutate(across(2:11, round,2))
print(MSD)

## # A tibble: 2 x 11
##   Ever_covid q8haveimmunity_… q8haveimmunity_… Going_out_total… Going_out_total…
##    <dbl+lbl>            <dbl>            <dbl>            <dbl>            <dbl>
## 1 0 [Think …             2.38             1.01             6.69             5.63
## 2 1 [Think …             3.33             1                9.35             7.69
## # … with 6 more variables: q9worry_mean <dbl>,
## #   q9worry_standard_deviation <dbl>, q10arisk_mean <dbl>,
## #   q10arisk_standard_deviation <dbl>, q10brisk_mean <dbl>,
## #   q10brisk_standard_deviation <dbl>

Once we have the means/SD, we pivot to long data, selecting only the variables we want and arranging by Ever_covid

table2mean <- MSD %>% 
  select(ends_with("mean"), Ever_covid) %>% 
  pivot_longer(ends_with("mean") ,  names_to = "Mean_vars", values_to = "Mean_values")

table2SD <- MSD %>% 
  select(ends_with("deviation"), Ever_covid) %>% 
  pivot_longer(ends_with("deviation") ,  names_to = "SD_vars", values_to = "SD_values")

And now we bind these together using bind_cols, and mutate to change into the labels we want for our table, as well as renaming them.

  table2 <- bind_cols(table2SD,table2mean) %>% 
  mutate(real = paste0("M = ", Mean_values,",", " SD = ", SD_values)) %>% 
  select(Ever_covid...1, Mean_vars, real) %>% 
  mutate(Level = case_when(startsWith(Mean_vars,"q8") ~ "1 = Strongly disagree to  5 = Strongly agree", startsWith(Mean_vars,"q9") ~"1 = not worried at all to  5 = extremely worried", startsWith(Mean_vars, "Going") ~"Range = 0 to 42", TRUE ~"1 = not risk at all to  4 = major risk ")) %>% 
  rename("Participant characteristics" = "Mean_vars") 

## New names:
## * Ever_covid -> Ever_covid...1
## * Ever_covid -> Ever_covid...4

print(table2)

## # A tibble: 10 x 4
##            Ever_covid...1 `Participant charact… real       Level                
##                 <dbl+lbl> <chr>                 <chr>      <chr>                
##  1 0 [Think have not had… q8haveimmunity_mean   M = 2.38,… "1 = Strongly disagr…
##  2 0 [Think have not had… Going_out_total_mean  M = 6.69,… "Range = 0 to 42"    
##  3 0 [Think have not had… q9worry_mean          M = 3.59,… "1 = not worried at …
##  4 0 [Think have not had… q10arisk_mean         M = 2.81,… "1 = not risk at all…
##  5 0 [Think have not had… q10brisk_mean         M = 3.39,… "1 = not risk at all…
##  6 1 [Think have had cor… q8haveimmunity_mean   M = 3.33,… "1 = Strongly disagr…
##  7 1 [Think have had cor… Going_out_total_mean  M = 9.35,… "Range = 0 to 42"    
##  8 1 [Think have had cor… q9worry_mean          M = 3.38,… "1 = not worried at …
##  9 1 [Think have had cor… q10arisk_mean         M = 2.81,… "1 = not risk at all…
## 10 1 [Think have had cor… q10brisk_mean         M = 3.3, … "1 = not risk at all…

Now we change them into factors..

  table2$Ever_covid...1 <- as.factor(table2$Ever_covid...1)
  table2$`Participant characteristics` <- as.factor(table2$`Participant characteristics`)
  table2$Ever_covid...1 <- factor(table2$Ever_covid...1, labels = c("Think have not had COVID-19", "Think have had COVID-19"))
  table2$`Participant characteristics` <- factor(table2$`Participant characteristics`, labels = c("I think I have some immunity to COVID-19", "Total out-of-home activity in the last seven days","Worry about COVID-19","Perceived risk of COVID-19 to onself","Perceived risk of COVID-19 to people in the UK"))

Back to wide data to use gt. We add our subheadings and heading/title and it’s complete.

  table2 <- table2 %>% 
    pivot_wider(id_cols = -real, names_from = Ever_covid...1, values_from = real) %>% 
    gt(rowname_col = "Levels",
       groupname_col = "Participant Characteristics") %>% 
    tab_spanner(label = "Had COVID-19", 
                columns = vars("Think have not had COVID-19", "Think have had COVID-19"))

Table 3

We pivot to longer data to find numbers for each variable in table 3, sorted according to if they have had COVID-19 or not.

  t3_top <- COVID %>% 
   pivot_longer(Adhere_shop_groceries:Sx_covid_nomissing, names_to = "vars" , values_to = "values") %>%
   group_by(vars, values) %>% 
   count(name = "number")
  print(t3_top)

## # A tibble: 8 x 3
## # Groups:   vars, values [8]
##   vars                  values number
##   <chr>                  <dbl>  <int>
## 1 Adhere_meet_friends        0    878
## 2 Adhere_meet_friends        1   5271
## 3 Adhere_shop_groceries      0   2389
## 4 Adhere_shop_groceries      1   3760
## 5 Adhere_shop_other          0   1833
## 6 Adhere_shop_other          1   4316
## 7 Sx_covid_nomissing         0   2517
## 8 Sx_covid_nomissing         1   3632

We now include Ever_covid while pivoting to long data as this is needed for the left column in our table. Omitting any NA values and mutating creates a new column with our percentages.

  covid_3 <- COVID %>% 
   pivot_longer(Adhere_shop_groceries:Sx_covid_nomissing, names_to = "vars", values_to = "values") %>% 
   group_by(Ever_covid,vars,values) %>% 
   count(name = "number") %>% 
   na.omit %>% 
   group_by(Ever_covid, vars) %>% 
   mutate(percentage = round(number/sum(number) * 100,1))
  print(covid_3)

## # A tibble: 16 x 5
## # Groups:   Ever_covid, vars [8]
##                          Ever_covid vars                values number percentage
##                           <dbl+lbl> <chr>                <dbl>  <int>      <dbl>
##  1 0 [Think have not had coronavir… Adhere_meet_friends      0    456        9.8
##  2 0 [Think have not had coronavir… Adhere_meet_friends      1   4200       90.2
##  3 0 [Think have not had coronavir… Adhere_shop_grocer…      0   1701       36.5
##  4 0 [Think have not had coronavir… Adhere_shop_grocer…      1   2955       63.5
##  5 0 [Think have not had coronavir… Adhere_shop_other        0   1156       24.8
##  6 0 [Think have not had coronavir… Adhere_shop_other        1   3500       75.2
##  7 0 [Think have not had coronavir… Sx_covid_nomissing       0   1729       37.1
##  8 0 [Think have not had coronavir… Sx_covid_nomissing       1   2927       62.9
##  9 1 [Think have had coronavirus]   Adhere_meet_friends      0    422       28.3
## 10 1 [Think have had coronavirus]   Adhere_meet_friends      1   1071       71.7
## 11 1 [Think have had coronavirus]   Adhere_shop_grocer…      0    688       46.1
## 12 1 [Think have had coronavirus]   Adhere_shop_grocer…      1    805       53.9
## 13 1 [Think have had coronavirus]   Adhere_shop_other        0    677       45.3
## 14 1 [Think have had coronavirus]   Adhere_shop_other        1    816       54.7
## 15 1 [Think have had coronavirus]   Sx_covid_nomissing       0    788       52.8
## 16 1 [Think have had coronavirus]   Sx_covid_nomissing       1    705       47.2

Back to wide data to prepare our tibble for the table by removing pre-existing labels using zap labels like before, and changing into factors.

  table3 <- covid_3 %>% 
    pivot_wider(names_from = vars, values_from = values) %>% 
    zap_labels() %>% mutate_if(is.numeric, as.factor) 

## `mutate_if()` ignored the following grouping variables:
## Column `Ever_covid`

print(table3)

## # A tibble: 16 x 7
## # Groups:   Ever_covid [2]
##    Ever_covid number percentage Adhere_meet_friends Adhere_shop_groceries
##         <dbl> <fct>  <fct>      <fct>               <fct>                
##  1          0 456    9.8        0                   <NA>                 
##  2          0 4200   90.2       1                   <NA>                 
##  3          0 1701   36.5       <NA>                0                    
##  4          0 2955   63.5       <NA>                1                    
##  5          0 1156   24.8       <NA>                <NA>                 
##  6          0 3500   75.2       <NA>                <NA>                 
##  7          0 1729   37.1       <NA>                <NA>                 
##  8          0 2927   62.9       <NA>                <NA>                 
##  9          1 422    28.3       0                   <NA>                 
## 10          1 1071   71.7       1                   <NA>                 
## 11          1 688    46.1       <NA>                0                    
## 12          1 805    53.9       <NA>                1                    
## 13          1 677    45.3       <NA>                <NA>                 
## 14          1 816    54.7       <NA>                <NA>                 
## 15          1 788    52.8       <NA>                <NA>                 
## 16          1 705    47.2       <NA>                <NA>                 
## # … with 2 more variables: Adhere_shop_other <fct>, Sx_covid_nomissing <fct>

  table3$Ever_covid <- as.factor(table3$Ever_covid)
  table3$Ever_covid <- factor(table3$Ever_covid, labels = c("Think have not had COVID-19", "Think have had COVID-19"))
  table3$Adhere_shop_groceries <- factor(table3$Adhere_shop_groceries, labels = c("On one or fewer days in the last week, n = 2389", "On two or more days in the last week, n = 3760"))
  table3$Adhere_meet_friends <- factor(table3$Adhere_meet_friends, labels = c("Not at all in the last week, n = 5271", "On one or more days in the last week, n = 878"))
  table3$Adhere_shop_other <- factor(table3$Adhere_shop_other, labels = c("Not at all in the last week, n = 1833", "On one or more days in the last week, n = 4316"))
  table3$Sx_covid_nomissing <- factor(table3$Sx_covid_nomissing, labels = c("Did not correctly identify symptoms, n = 2390", "Correctly identified common symptoms, n = 3632"))

Pivoting to long data once again to fix up our column headings

  table3 <- table3 %>% 
    pivot_longer(Adhere_meet_friends:Sx_covid_nomissing, names_to = "Participant Characteristics", values_to = "Levels") %>% 
    na.omit %>% 
    mutate(Real = paste0(number,"(",percentage,")")) %>% 
    select(-number,-percentage) %>%
    pivot_wider(id_cols = -Real, names_from = Ever_covid, values_from = Real) 
    print(table3)

## # A tibble: 8 x 4
##   `Participant Charac… Levels             `Think have not ha… `Think have had C…
##   <chr>                <fct>              <chr>               <chr>             
## 1 Adhere_meet_friends  Not at all in the… 456(9.8)            422(28.3)         
## 2 Adhere_meet_friends  On one or more da… 4200(90.2)          1071(71.7)        
## 3 Adhere_shop_groceri… On one or fewer d… 1701(36.5)          688(46.1)         
## 4 Adhere_shop_groceri… On two or more da… 2955(63.5)          805(53.9)         
## 5 Adhere_shop_other    Not at all in the… 1156(24.8)          677(45.3)         
## 6 Adhere_shop_other    On one or more da… 3500(75.2)          816(54.7)         
## 7 Sx_covid_nomissing   Did not correctly… 1729(37.1)          788(52.8)         
## 8 Sx_covid_nomissing   Correctly identif… 2927(62.9)          705(47.2)

Changing to factors

  table3$`Participant Characteristics` <- as.factor(table3$`Participant Characteristics`)
  table3$`Participant Characteristics` <- factor(table3$`Participant Characteristics`, labels = c(
    "Meet up with family and friends","Shopping for groceries/pharmacy", "Shopping for items other than groceries or pharmacy",
    "Correct identification of main symptoms"
  ))

Using gt to create the table. Complete

  table3 <- table3 %>% 
    gt(rowname_col = "Levels",
       groupname_col = "Participant Characteristics") %>% 
    tab_spanner(label = "Had COVID-19", 
                columns = vars("Think have not had COVID-19",
                               "Think have had COVID-19")) %>% 
    row_group_order(c("Meet up with family and friends", "Shopping for groceries/pharmacy",
                      "Shopping for items other than groceries or pharmacy", "Correct identification of main symptoms" ))

Figure 1 graph

This reproduces the original graph

We first removed the pre-existing labels with zap_labels and pivoted to long data with all our variables as columns. Once we had a column with our number of participants for each variable and also a column stating whether or not they thought they have had COVID, we were able to create a percentage column and filter for the values we needed from each variable. Finally, we add our labels back for the Ever_Covid variable.

  graph_values_real <- COVID %>% 
   zap_labels() %>% 
   mutate(Going_out_total = case_when(Going_out_total > 7 ~ 1, TRUE ~0)) %>% 
   pivot_longer(q8haveimmunity:Sx_covid_nomissing, names_to = "vars", values_to = "values") %>%
   group_by(Ever_covid,vars,values) %>% 
   count(name = "number") %>% 
   na.omit %>% 
   group_by(Ever_covid, vars) %>% 
   mutate(Percentage = round(number/sum(number) * 100,1)) %>% 
   filter(case_when(vars == "q8haveimmunity" ~ values == 5,
                    vars == "Sx_covid_nomissing" ~ values == 0 , startsWith(vars,'Adhere') ~ values ==0,
                    TRUE ~ values == 1)) %>% 
   select(vars, Ever_covid, Percentage) %>% 
  mutate(qcovid = case_when(Ever_covid==1 ~ "Think have had COVID-19", TRUE ~ "Think have not had COVID-19"))
 print(graph_values_real)

## # A tibble: 18 x 4
## # Groups:   Ever_covid, vars [18]
##    vars                  Ever_covid Percentage qcovid                     
##    <chr>                      <dbl>      <dbl> <chr>                      
##  1 Adhere_meet_friends            0        9.8 Think have not had COVID-19
##  2 Adhere_shop_groceries          0       36.5 Think have not had COVID-19
##  3 Adhere_shop_other              0       24.8 Think have not had COVID-19
##  4 Going_out_total                0       37.6 Think have not had COVID-19
##  5 q10arisk                       0        3.2 Think have not had COVID-19
##  6 q10brisk                       0        1   Think have not had COVID-19
##  7 q8haveimmunity                 0        2.5 Think have not had COVID-19
##  8 q9worry                        0        2.5 Think have not had COVID-19
##  9 Sx_covid_nomissing             0       37.1 Think have not had COVID-19
## 10 Adhere_meet_friends            1       28.3 Think have had COVID-19    
## 11 Adhere_shop_groceries          1       46.1 Think have had COVID-19    
## 12 Adhere_shop_other              1       45.3 Think have had COVID-19    
## 13 Going_out_total                1       51.2 Think have had COVID-19    
## 14 q10arisk                       1        3.9 Think have had COVID-19    
## 15 q10brisk                       1        1.3 Think have had COVID-19    
## 16 q8haveimmunity                 1       12.1 Think have had COVID-19    
## 17 q9worry                        1        6   Think have had COVID-19    
## 18 Sx_covid_nomissing             1       52.8 Think have had COVID-19

 covid_1 <- COVID %>%
  zap_labels() %>% 
  pivot_longer(gender:region, names_to = "vars", values_to = "values") %>% 
  group_by(Ever_covid, vars,values) %>% 
  count(name = "number") %>% 
  na.omit %>% 
  group_by(vars,values) %>% 
  mutate(percentage = round(number/sum(number) * 100, 1))
   
  alldata <- covid_1 %>% 
  pivot_wider(names_from = vars, values_from = values) %>% 
zap_labels() %>% mutate_if(is.numeric, as.factor)

changing each variable into a factor and adding our labels from the original table

  alldata$Ever_covid <- factor(alldata$Ever_covid, labels = c("no_covid", "yes_covid"))
alldata$gender <-factor(alldata$gender, labels = c("Male", "Female"))
alldata$age_categories <- factor(alldata$age_categories, labels = c("18 to 24 years", "25 to 34 years", "35 to 44 years", "45 to 54 years", "55 years and over"))
alldata$Has_child <- factor(alldata$Has_child, labels = c("No", "Yes"))
alldata$Working <- factor(alldata$Working, labels = c("Not working", "Working"))
alldata$Key_worker <- factor(alldata$Key_worker, labels = c("No", "Yes"))
alldata$degree <-factor(alldata$degree, labels = c("GCSE/vocational/A-level/No formal qualifications", "Degree or higher (Bachelors, Masters, PhD"))
alldata$region <- factor(alldata$region, labels = c("Midlands", "South and East", "North", "London", "Wales, Scotland and Northern Ireland"))
    
alldata <- alldata %>% 
  pivot_longer(age_categories:Working, names_to = "vars", values_to = "values") %>% 
  na.omit %>% 
  mutate(Real = paste0(number,"(",percentage,")")) %>%
  select(-percentage, -number) %>% 
  pivot_wider(id_cols = -Real, names_from = Ever_covid, values_from = Real) %>% 
  rename("Think have not had COVID-19" = "no_covid") %>% 
  rename("Think have had COVID-19" = "yes_covid") %>% 
  rename("Level" = "values") %>% 
  rename("Participant Characteristics" = "vars") 
print(alldata)  

## # A tibble: 20 x 4
##    `Participant Charac… Level             `Think have not ha… `Think have had C…
##    <chr>                <fct>             <chr>               <chr>             
##  1 age_categories       18 to 24 years    1003(70.5)          419(29.5)         
##  2 age_categories       25 to 34 years    823(67.3)           400(32.7)         
##  3 age_categories       35 to 44 years    751(71.9)           294(28.1)         
##  4 age_categories       45 to 54 years    554(77.2)           164(22.8)         
##  5 age_categories       55 years and over 1525(87.6)          216(12.4)         
##  6 degree               GCSE/vocational/… 3382(76.1)          1060(23.9)        
##  7 degree               Degree or higher… 1200(74.3)          415(25.7)         
##  8 gender               Male              2197(75.9)          697(24.1)         
##  9 gender               Female            2459(75.5)          796(24.5)         
## 10 Has_child            No                2005(76.4)          621(23.6)         
## 11 Has_child            Yes               2386(75.5)          776(24.5)         
## 12 Key_worker           No                3105(80.5)          753(19.5)         
## 13 Key_worker           Yes               1551(67.7)          740(32.3)         
## 14 region               Midlands          781(75.7)           251(24.3)         
## 15 region               South and East    1369(76.7)          416(23.3)         
## 16 region               North             1120(77)            335(23)           
## 17 region               London            701(70.1)           299(29.9)         
## 18 region               Wales, Scotland … 685(78.1)           192(21.9)         
## 19 Working              Not working       1714(82.8)          357(17.2)         
## 20 Working              Working           2871(71.9)          1124(28.1)

alldata$`Participant Characteristics` <- as.factor(alldata$`Participant Characteristics`)
alldata$`Participant Characteristics` <- factor(alldata$`Participant Characteristics`, levels = c ( "gender",  "age_categories","Has_child", "Working", "Key_worker","degree", "region"),labels = c ( "Gender", "Age", "Have a child","Employment Status", "Working in Key Sector","Highest Education or Professional Qualification","Region"))

Using gt to give a rough outline of our graph in the form of a table, making sure our percentages are rounded to 2 decimal places

  alldata <- alldata %>% 
  gt() %>% 
  tab_spanner(label = "Had COVID-19", 
              columns = vars("Think have not had COVID-19","Think have had COVID-19"))
print(alldata)

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## <div id="fsdeledpoe" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;"><table class="gt_table">
##   
##   <thead class="gt_col_headings">
##     <tr>
##       <th class="gt_col_heading gt_center gt_columns_bottom_border" rowspan="2" colspan="1">Participant Characteristics</th>
##       <th class="gt_col_heading gt_center gt_columns_bottom_border" rowspan="2" colspan="1">Level</th>
##       <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2">
##         <span class="gt_column_spanner">Had COVID-19</span>
##       </th>
##     </tr>
##     <tr>
##       <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">Think have not had COVID-19</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">Think have had COVID-19</th>
##     </tr>
##   </thead>
##   <tbody class="gt_table_body">
##     <tr>
##       <td class="gt_row gt_center">Age</td>
##       <td class="gt_row gt_center">18 to 24 years</td>
##       <td class="gt_row gt_left">1003(70.5)</td>
##       <td class="gt_row gt_left">419(29.5)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Age</td>
##       <td class="gt_row gt_center">25 to 34 years</td>
##       <td class="gt_row gt_left">823(67.3)</td>
##       <td class="gt_row gt_left">400(32.7)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Age</td>
##       <td class="gt_row gt_center">35 to 44 years</td>
##       <td class="gt_row gt_left">751(71.9)</td>
##       <td class="gt_row gt_left">294(28.1)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Age</td>
##       <td class="gt_row gt_center">45 to 54 years</td>
##       <td class="gt_row gt_left">554(77.2)</td>
##       <td class="gt_row gt_left">164(22.8)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Age</td>
##       <td class="gt_row gt_center">55 years and over</td>
##       <td class="gt_row gt_left">1525(87.6)</td>
##       <td class="gt_row gt_left">216(12.4)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Highest Education or Professional Qualification</td>
##       <td class="gt_row gt_center">GCSE/vocational/A-level/No formal qualifications</td>
##       <td class="gt_row gt_left">3382(76.1)</td>
##       <td class="gt_row gt_left">1060(23.9)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Highest Education or Professional Qualification</td>
##       <td class="gt_row gt_center">Degree or higher (Bachelors, Masters, PhD</td>
##       <td class="gt_row gt_left">1200(74.3)</td>
##       <td class="gt_row gt_left">415(25.7)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Gender</td>
##       <td class="gt_row gt_center">Male</td>
##       <td class="gt_row gt_left">2197(75.9)</td>
##       <td class="gt_row gt_left">697(24.1)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Gender</td>
##       <td class="gt_row gt_center">Female</td>
##       <td class="gt_row gt_left">2459(75.5)</td>
##       <td class="gt_row gt_left">796(24.5)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Have a child</td>
##       <td class="gt_row gt_center">No</td>
##       <td class="gt_row gt_left">2005(76.4)</td>
##       <td class="gt_row gt_left">621(23.6)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Have a child</td>
##       <td class="gt_row gt_center">Yes</td>
##       <td class="gt_row gt_left">2386(75.5)</td>
##       <td class="gt_row gt_left">776(24.5)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Working in Key Sector</td>
##       <td class="gt_row gt_center">No</td>
##       <td class="gt_row gt_left">3105(80.5)</td>
##       <td class="gt_row gt_left">753(19.5)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Working in Key Sector</td>
##       <td class="gt_row gt_center">Yes</td>
##       <td class="gt_row gt_left">1551(67.7)</td>
##       <td class="gt_row gt_left">740(32.3)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Region</td>
##       <td class="gt_row gt_center">Midlands</td>
##       <td class="gt_row gt_left">781(75.7)</td>
##       <td class="gt_row gt_left">251(24.3)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Region</td>
##       <td class="gt_row gt_center">South and East</td>
##       <td class="gt_row gt_left">1369(76.7)</td>
##       <td class="gt_row gt_left">416(23.3)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Region</td>
##       <td class="gt_row gt_center">North</td>
##       <td class="gt_row gt_left">1120(77)</td>
##       <td class="gt_row gt_left">335(23)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Region</td>
##       <td class="gt_row gt_center">London</td>
##       <td class="gt_row gt_left">701(70.1)</td>
##       <td class="gt_row gt_left">299(29.9)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Region</td>
##       <td class="gt_row gt_center">Wales, Scotland and Northern Ireland</td>
##       <td class="gt_row gt_left">685(78.1)</td>
##       <td class="gt_row gt_left">192(21.9)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Employment Status</td>
##       <td class="gt_row gt_center">Not working</td>
##       <td class="gt_row gt_left">1714(82.8)</td>
##       <td class="gt_row gt_left">357(17.2)</td>
##     </tr>
##     <tr>
##       <td class="gt_row gt_center">Employment Status</td>
##       <td class="gt_row gt_center">Working</td>
##       <td class="gt_row gt_left">2871(71.9)</td>
##       <td class="gt_row gt_left">1124(28.1)</td>
##     </tr>
##   </tbody>
##   
##   
## </table></div>

Percentage <- c("Percentage")

graph_values_real$Percentage <- as.numeric(graph_values_real$Percentage)
graph_values_real$Ever_covid <- as.factor(graph_values_real$Ever_covid)

 graphh <- graph_values_real %>% 
ggplot(aes(fill=Ever_covid, x= vars, y=Percentage)) +
   geom_bar(position = position_dodge(width = 0.8),stat = "identity", width = 0.6 ) +
   scale_y_continuous(limits = c(0,60), breaks = c(0,10,20,30,40,50,60)) +
   scale_fill_grey(start = 0.8, end = 0) +
   theme_bw() +
   theme(axis.text.x = element_text(angle = 55, vjust = 1, hjust = 1), 
         legend.position = "bottom",
         legend.title = element_blank(),
         axis.title.x =  element_blank(),
         axis.title.y = element_text(vjust = 4))

This is our improved version of the graph

summarising all the adhere values since they are needed to reproduce the graph. We now also round to 2 decimal places.

  graph2 <- COVID %>% 
   group_by(Ever_covid) %>% 
   summarise(across(c(starts_with("adhere")), list(mean = mean, standard_deviation = sd)))
 
 graph2 <- graph2 %>% 
   mutate(across(everything(), round,2))
 
print(graph2)

## # A tibble: 2 x 7
##   Ever_covid Adhere_shop_gro… Adhere_shop_gro… Adhere_shop_oth… Adhere_shop_oth…
##        <dbl>            <dbl>            <dbl>            <dbl>            <dbl>
## 1          0             0.63             0.48             0.75             0.43
## 2          1             0.54             0.5              0.55             0.5 
## # … with 2 more variables: Adhere_meet_friends_mean <dbl>,
## #   Adhere_meet_friends_standard_deviation <dbl>

pivoting to long data where we have our means, and repeating for SD

 graph2mean <- graph2 %>% 
   select(ends_with("mean"), Ever_covid) %>% 
   pivot_longer(ends_with("mean") ,  names_to = "Mean_vars", values_to = "Mean_values")

 graph2SD <- graph2 %>% 
   select(ends_with("deviation"), Ever_covid) %>% 
   pivot_longer(ends_with("deviation") ,  names_to = "SD_vars", values_to = "SD_values")

using bind_cols to bind these two together, and changing to a factor. Also labelling the Ever_covid variable like it will appear in the graph

 graph2 <- bind_cols(graph2SD,graph2mean)

## New names:
## * Ever_covid -> Ever_covid...1
## * Ever_covid -> Ever_covid...4

 graph2$Ever_covid...1 <- as.factor(graph2$Ever_covid...1)
 graph2$Ever_covid...1 <- factor(graph2$Ever_covid...1,levels = c(0,1), labels = c("Think have not had COVID-19","Think have had COVID-19"))