switching code from active to example (#26)

.Rbuildignore

@@ -23,3 +23,4 @@
 ^cran-comments\.md$
 CONTRIBUTING.md
 ^CRAN-RELEASE$
+vignettes/Introduction-to-work-and-health-nomis-indicators.Rmd

NEWS.md

@@ -6,7 +6,7 @@
 
 * Better error message when API returns empty data in some circumstances.
 
-* Suppressed printing of column types when reading CSV files (#25)
+* Suppressed printing of column types when reading CSV files (#25, thanks jackobailey)
 
 
 # nomisr 0.4.4

cran-comments.md

@@ -1,13 +1,13 @@
 
 ## Release summary
 
-This is an resubmission of the `nomisr` package, version number 0.4.4. It 
-removes the use of the deprecated `tibble::as.tibble` function. 
-It also includes a second vignette showing a detailed package use case.
+This is an resubmission of the `nomisr` package, version number 0.4.5. It 
+features improved error messages and makes output less verbose when reading 
+CSV files.
 
 
 ## Test environments
-* local macOS install, R 4.0.3
+* local macOS install, R 4.1.1
 * win-builder (devel and release)
 * Windows Server 2019, release (on GitHub Actions)
 * macOS Catalina 10.15, release (on GitHub Actions)

vignettes/Introduction-to-work-and-health-nomis-indicators.Rmd

@@ -26,18 +26,14 @@ The nomisr package is an easy way to obtain data from Nomis. In this introductio
 The first step is to understanding what datasets are available in `nomisr` and this can be done using the `nomis_get_info()` function. If no particular id is inputted inside the brackets, the command will return all available datasets.
 
 ```{r packages, echo=FALSE}
-
 library(dplyr)
 library(nomisr)
-
 ```
 
 ```{r all_datasets, echo=TRUE}
-
 nomis_data <- nomis_data_info() %>%
   select(id, description.value, name.value)
 head(nomis_data)
-
 ```
 
 This gives you a list of all available datasets in nomis. The list of ids/datasets used in this script are:
@@ -49,14 +45,11 @@ This gives you a list of all available datasets in nomis. The list of ids/datase
 If you wanted to look at more information on a specific dataset, you can add this inside the bracket of the `nomis_get_info()` function or use the `nomis_get_metadata()` function. Both of these will provide you with the different options (concepts) available for your chosen dataset.
 
 ```{r APS_Dataset, echo=TRUE}
-
 NM_17_5 <- nomis_data_info("NM_17_5") 
 tibble::glimpse(NM_17_5)
 
-
 NM_17_5 <- nomis_get_metadata("NM_17_5")
 head(NM_17_5)
-
 ```
 
 # Querying data variables
@@ -68,10 +61,8 @@ As mentioned previously, the `nomis_get_info()` and `nomis_get_metadata()` funct
 To explore the concepts we need to specify this in the code. For example to explore the dates available in the APS dataset, we need to specify the concept as "time". 
 
 ```{r Exploring_Dates, echo=TRUE}
-
 NM_17_5_DATES <- nomis_get_metadata("NM_17_5", concept = "time") 
 tail(NM_17_5_DATES)
-
 ```
 
 Instead of referring to the ids for the specific dates you can also use the following options:
@@ -85,34 +76,27 @@ Instead of referring to the ids for the specific dates you can also use the foll
 To explore the variables available in the APS dataset, we need to specify the concept as "variable". 
 
 ```{r Exploring_Variables, echo=TRUE}
-
 NM_17_5_VARIABLE <- nomis_get_metadata("NM_17_5", concept = "variable") 
 head(NM_17_5_VARIABLE)
-
 ```
 
 To have a look at what geographies are available in the APS dataset, we need to specify the concept as "geography". 
 
 ```{r Exploring_Geography, echo=TRUE}
-
 NM_17_5_GEOG <- nomis_get_metadata("NM_17_5", concept = "geography") 
 head(NM_17_5_GEOG)
-
 ```
 
 This gives the top level geographies available but if we wanted to identify what type of geographies are available, we would need to use 'type' in the code.
 
 ```{r Exploring_Geography_Type, echo=TRUE}
-
 NM_17_5_GEOG <- nomis_get_metadata("NM_17_5", concept = "geography", "type") 
 head(NM_17_5_GEOG)
-
 ```
 
 If you wanted all local authorities, combined authorities and regions in England, it is easier to create a vector which you can then feed into the `nomis_get_data()` function.
 
 ```{r Creating_Geography_Vector, echo=TRUE}
-
 EN_LAs <- nomis_get_metadata(id = "NM_17_5", concept = "geography",
                              type = "TYPE464")[1:326, ] %>%
   mutate(type = 464)
@@ -134,7 +118,6 @@ print(EN_geos)
 # Save it as a vector so can plug it into nomis_get_data function
 EN_geographies <- c(1946157057:1946157382, 1853882369:1853882372,
                     1853882374:1853882379, 2013265921:2013265929, 2092957699)
-
 ```
 
 # Downloading data
@@ -148,13 +131,11 @@ Now you have enough information to use the `nomis_get_data()` function.
 + Step 5: select the measures of interest - in this example all measures available are pulled
 
 ```{r working_age_pop, echo=TRUE}
-
 working_age_pop <- nomis_get_data(id = "NM_17_5", date = "latest",
                                   geography = EN_geographies,
                                   variable = 18,
                                   measures = c(20599, 21001, 21002, 21003))
 print(working_age_pop)
-
 ```
 
 ## Stacked bar chart showing labour market summary for Bolton
@@ -191,7 +172,6 @@ Stack 4: economically active, long term sick, temp sick, student, looking after
 The data extracted is for all of the geographies outlined in the 'EN_geographies' vector, but the chart is filtered for Bolton only.
 
 ```{r Stacked_Bar, echo=TRUE}
-
 library(ggplot2)
 
 # FIRST STACK: Working age population
@@ -361,7 +341,6 @@ p <- ggplot(data, aes(fill=Key, y=OBS_VALUE, x=grouping)) +
   theme(legend.position = "right") 
 
 p
-
 ```
 
 ## Pie charts showing economic activity in Bolton 
@@ -504,7 +483,9 @@ pie2 = pie2 + coord_polar("y", start=0) + geom_text(aes(label = paste0(OBS_VALUE
 #using phe colours for the slices
 pie2 = pie2 + scale_fill_manual(values=c("#862633","#00AB8E","#01216A"))
 
-pie2 = pie2 + labs(x = NULL, y = NULL, fill = NULL, title = (paste0("Participation in the labour market, ", area_of_interest_name, ", January 2019 to December 2019")), subtitle = "Working age population with conditions or illnesses lasting more than 12 months")
+pie2 = pie2 + labs(x = NULL, y = NULL, fill = NULL,
+                   title = (paste0("Participation in the labour market, ", area_of_interest_name, ", January 2019 to December 2019")),
+                   subtitle = "Working age population with conditions or illnesses lasting more than 12 months")
 
 
 # Tidy up the theme
@@ -547,7 +528,8 @@ ESA_data_condition[is.na(ESA_data_condition)] <- 0
 ESA_data_condition <- ESA_data_condition %>%
   group_by(GEOGRAPHY_CODE, ICDGP_CONDITION) %>%
   mutate(total = sum(OBS_VALUE)) %>%
-  select(DATE_NAME, GEOGRAPHY: GEOGRAPHY_CODE, ICDGP_CONDITION: ICDGP_CONDITION_NAME, SEX_NAME, total) %>%
+  select(DATE_NAME, GEOGRAPHY: GEOGRAPHY_CODE,
+         ICDGP_CONDITION: ICDGP_CONDITION_NAME, SEX_NAME, total) %>%
   distinct(total, .keep_all = TRUE)
 
 # Total for each area
@@ -558,7 +540,8 @@ ESA_total <- ESA_data_condition %>%
   distinct(tot, .keep_all = TRUE)
 
 # Link the two tables together to calculate the percentage contributed
-ESA_total_final <- left_join(ESA_data_condition, ESA_total, by = c("GEOGRAPHY_CODE", "GEOGRAPHY_NAME")) %>%
+ESA_total_final <- left_join(ESA_data_condition, ESA_total,
+                             by = c("GEOGRAPHY_CODE", "GEOGRAPHY_NAME")) %>%
   select(DATE_NAME: ICDGP_CONDITION_NAME, SEX_NAME, total, tot) %>%
   mutate(PERC = total / tot *100,
          PERC = round(PERC, 1)) %>%
@@ -572,17 +555,21 @@ ESA_condition_rank <- ESA_total_final %>%
   arrange(GEOGRAPHY_CODE, desc(PERC)) %>%
   group_by(GEOGRAPHY_CODE) %>%
   mutate(rank = 1:n()) %>%
-  select(GEOGRAPHY_CODE, GEOGRAPHY_NAME, ICDGP_CONDITION: ICDGP_CONDITION_NAME, rank)
+  select(GEOGRAPHY_CODE, GEOGRAPHY_NAME,
+         ICDGP_CONDITION: ICDGP_CONDITION_NAME, rank)
 print(ESA_condition_rank)
 
 # Step 2: Extract conditions to map on to treemap and create appropriate age-groups
 
 # get ESA data by condition for latest data 
 # Don't need the total (ID=0), factors (ID=21), Codes for special purposes (ID=22), Claimants without any diagnosis (ID=23)
-treemap_data <- nomis_get_data(id = "NM_134_1", date = "latest", geography = EN_geographies, icdgp_condition = c(1:19),
-                               sex = 7, esa_phase = 0, payment_type = 0, ethnic_group = 0, age = c(1:8),
-                               duration = 0, measures = 20100) %>%
-  select(DATE_NAME, GEOGRAPHY: GEOGRAPHY_CODE, ICDGP_CONDITION: ICDGP_CONDITION_NAME, SEX_NAME, AGE:AGE_NAME, OBS_VALUE)
+treemap_data <- nomis_get_data(
+  id = "NM_134_1", date = "latest", geography = EN_geographies, 
+  icdgp_condition = c(1:19), sex = 7, esa_phase = 0, payment_type = 0,
+  ethnic_group = 0, age = c(1:8), duration = 0, measures = 20100) %>%
+  select(
+    DATE_NAME, GEOGRAPHY: GEOGRAPHY_CODE, ICDGP_CONDITION: ICDGP_CONDITION_NAME,
+    SEX_NAME, AGE:AGE_NAME, OBS_VALUE)
 
 
 # replace na with 0
@@ -607,7 +594,8 @@ treemap_data_final <- agegrp1 %>%
   arrange(GEOGRAPHY, ICDGP_CONDITION_NAME, NEW_AGE) %>%
   group_by(GEOGRAPHY, ICDGP_CONDITION_NAME, NEW_AGE) %>%
   mutate(OBS = sum(OBS_VALUE)) %>%
-  select(DATE_NAME, GEOGRAPHY: GEOGRAPHY_CODE, ICDGP_CONDITION: ICDGP_CONDITION_NAME, SEX_NAME, NEW_AGE, OBS) %>%
+  select(DATE_NAME, GEOGRAPHY: GEOGRAPHY_CODE,
+         ICDGP_CONDITION: ICDGP_CONDITION_NAME, SEX_NAME, NEW_AGE, OBS) %>%
   distinct(GEOGRAPHY, ICDGP_CONDITION_NAME, NEW_AGE, .keep_all = TRUE)
 print(treemap_data_final)
 

vignettes/introduction.Rmd

@@ -128,28 +128,23 @@ a
 
 If provided with a concept name it returns the available values for that concept. However, in some cases, espescially with the geography concept, there are multiple options available, which Nomis labels types. In that case `nomis_get_metadata()` returns the values of the lowest indexed type available.
 
-```{r concepts, echo=TRUE}  
+```
 b <- nomis_get_metadata(id = "NM_893_1", concept = "GEOGRAPHY")
-
-b
 ```
 
 We can now pass a generic "type" string to the `type` parameter in `nomis_get_metadata()`, which returns all available geography types for dataset "NM_893_1".
 
 
-```{r concept-types, echo=TRUE}  
+```
 c <- nomis_get_metadata(id = "NM_893_1", concept = "geography", type = "type")
-
-c
 ```
 
 
 Passing a specific type to the `type` parameter, in this case "TYPE460" for all post-2010 parliamentary constituencies, returns a tibble with geographic codes for those specific constituencies, which can be used to filter queries.
 
-```{r concept-type460, echo=TRUE}  
+```
 d <- nomis_get_metadata(id = "NM_893_1", concept = "geography", type = "TYPE460")
 
-d
 ```
 
 
@@ -159,20 +154,15 @@ The vast majority (98% as of February 2018) of Nomis datasets include a geograph
 
 Using the information above, we can now query the latest data on bedroom occupancy per household type in different NHS clinical commissioning groups.
 
-```{r download-data, echo=TRUE}
+```
 z <- nomis_get_data(id = "NM_893_1", time = "latest", geography = "TYPE266")
-
-z
 ```
 
-
 We can also query bedroom occupancy per household type in the Manchester, Gorton and Manchester, Withington parliamentary constituencies.
 
-```{r download-specific-area, echo=TRUE}
+```
 x <- nomis_get_data(id = "NM_893_1", time = "latest",
                     geography = c("1929380119", "1929380120"))
-
-x
 ```
 
 
@@ -182,7 +172,7 @@ x
 ```{r jsa-claimaints}
 library(ggplot2)
 
-jsa <- nomis_get_data(id = "NM_1_1", time = "2015-01-2020-01",
+jsa <- nomis_get_data(id = "NM_1_1", time = "2018-01-2021-10",
                       geography = "TYPE480", measures=20201,
                       sex=c(5,6), item = 1, tidy = TRUE)