Data Visualization
Sanjay Fuloria
2023-12-01
Data Visualization
This coding session was done as part of the Carpentries workshop at Helmholtz Institute (HIDA)
#Install and Load the tidyverse package
library(tidyverse)## Warning: package 'ggplot2' was built under R version 4.3.1## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#Import the SAFI_clean_one.csv file and name it as interviews
interviews <- read.csv("data/SAFI_clean_one.csv", na = "NULL")
#View the imported file
View(interviews)
head(interviews)## key_ID village interview_date no_membrs years_liv respondent_wall_type
## 1 1 God 2016-11-17T00:00:00Z 3 4 muddaub
## 2 2 God 2016-11-17T00:00:00Z 7 9 muddaub
## 3 3 God 2016-11-17T00:00:00Z 10 15 burntbricks
## 4 4 God 2016-11-17T00:00:00Z 7 6 burntbricks
## 5 5 God 2016-11-17T00:00:00Z 7 40 burntbricks
## 6 6 God 2016-11-17T00:00:00Z 3 3 muddaub
## rooms memb_assoc affect_conflicts liv_count
## 1 1 <NA> <NA> 1
## 2 1 yes once 3
## 3 1 <NA> <NA> 1
## 4 1 <NA> <NA> 2
## 5 1 <NA> <NA> 4
## 6 1 <NA> <NA> 1
## items_owned
## 1 bicycle;television;solar_panel;table
## 2 cow_cart;bicycle;radio;cow_plough;solar_panel;solar_torch;table;mobile_phone
## 3 solar_torch
## 4 bicycle;radio;cow_plough;solar_panel;mobile_phone
## 5 motorcyle;radio;cow_plough;mobile_phone
## 6 <NA>
## no_meals months_lack_food instanceID
## 1 2 Jan uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef
## 2 2 Jan;Sept;Oct;Nov;Dec uuid:099de9c9-3e5e-427b-8452-26250e840d6e
## 3 2 Jan;Feb;Mar;Oct;Nov;Dec uuid:193d7daf-9582-409b-bf09-027dd36f9007
## 4 2 Sept;Oct;Nov;Dec uuid:148d1105-778a-4755-aa71-281eadd4a973
## 5 2 Aug;Sept;Oct;Nov uuid:2c867811-9696-4966-9866-f35c3e97d02d
## 6 2 Aug;Sept;Oct uuid:daa56c91-c8e3-44c3-a663-af6a49a2ca70dim(interviews)## [1] 131 14#Create a vector from the dataframe column "memb_assoc"
memb_assoc <- interviews$memb_assoc
#Convert memb_assoc into a Fcator/Categorical Variable
memb_assoc <- as.factor(memb_assoc)
#Let's see what it looks like
memb_assoc <- interviews$memb_assoc
#Replace the missing data with "undetermined"
memb_assoc[is.na(memb_assoc)] <- "undetermined"
#Convert it into a Factor/Categorical Variable
memb_assoc <- as.factor(memb_assoc)
## Rename levels.
memb_assoc <- fct_recode(memb_assoc, No = "no",
Undetermined = "undetermined", Yes = "yes")
## Reorder levels. Note we need to use the new level names.
memb_assoc <- factor(memb_assoc, levels = c("No", "Yes", "Undetermined"))
#Selecting a few variables from the interviews dataset
interviews %>%
select(key_ID, village, interview_date, instanceID)## key_ID village interview_date
## 1 1 God 2016-11-17T00:00:00Z
## 2 2 God 2016-11-17T00:00:00Z
## 3 3 God 2016-11-17T00:00:00Z
## 4 4 God 2016-11-17T00:00:00Z
## 5 5 God 2016-11-17T00:00:00Z
## 6 6 God 2016-11-17T00:00:00Z
## 7 7 God 2016-11-17T00:00:00Z
## 8 8 Chirodzo 2016-11-16T00:00:00Z
## 9 9 Chirodzo 2016-11-16T00:00:00Z
## 10 10 Chirodzo 2016-12-16T00:00:00Z
## 11 11 God 2016-11-21T00:00:00Z
## 12 12 God 2016-11-21T00:00:00Z
## 13 13 God 2016-11-21T00:00:00Z
## 14 14 God 2016-11-21T00:00:00Z
## 15 15 God 2016-11-21T00:00:00Z
## 16 16 God 2016-11-24T00:00:00Z
## 17 17 God 2016-11-21T00:00:00Z
## 18 18 God 2016-11-21T00:00:00Z
## 19 19 God 2016-11-21T00:00:00Z
## 20 20 God 2016-11-21T00:00:00Z
## 21 21 God 2016-11-21T00:00:00Z
## 22 22 God 2016-11-21T00:00:00Z
## 23 23 Ruaca 2016-11-21T00:00:00Z
## 24 24 Ruaca 2016-11-21T00:00:00Z
## 25 25 Ruaca 2016-11-21T00:00:00Z
## 26 26 Ruaca 2016-11-21T00:00:00Z
## 27 27 Ruaca 2016-11-21T00:00:00Z
## 28 28 Ruaca 2016-11-21T00:00:00Z
## 29 29 Ruaca 2016-11-21T00:00:00Z
## 30 30 Ruaca 2016-11-21T00:00:00Z
## 31 31 Ruaca 2016-11-21T00:00:00Z
## 32 32 Ruaca 2016-11-21T00:00:00Z
## 33 33 Ruaca 2016-11-21T00:00:00Z
## 34 34 Chirodzo 2016-11-17T00:00:00Z
## 35 35 Chirodzo 2016-11-17T00:00:00Z
## 36 36 Chirodzo 2016-11-17T00:00:00Z
## 37 37 Chirodzo 2016-11-17T00:00:00Z
## 38 38 God 2016-11-17T00:00:00Z
## 39 39 God 2016-11-17T00:00:00Z
## 40 40 God 2016-11-17T00:00:00Z
## 41 41 God 2016-11-17T00:00:00Z
## 42 42 God 2016-11-17T00:00:00Z
## 43 43 Chirodzo 2016-11-17T00:00:00Z
## 44 44 Chirodzo 2016-11-17T00:00:00Z
## 45 45 Chirodzo 2016-11-17T00:00:00Z
## 46 46 Chirodzo 2016-11-17T00:00:00Z
## 47 47 Chirodzo 2016-11-17T00:00:00Z
## 48 48 Chirodzo 2016-11-16T00:00:00Z
## 49 49 Chirodzo 2016-11-16T00:00:00Z
## 50 50 Chirodzo 2016-11-16T00:00:00Z
## 51 51 Chirodzo 2016-11-16T00:00:00Z
## 52 52 Chirodzo 2016-11-16T00:00:00Z
## 53 53 Chirodzo 2016-11-16T00:00:00Z
## 54 54 Chirodzo 2016-11-16T00:00:00Z
## 55 55 Chirodzo 2016-11-16T00:00:00Z
## 56 56 Chirodzo 2016-11-16T00:00:00Z
## 57 57 Chirodzo 2016-11-16T00:00:00Z
## 58 58 Chirodzo 2016-11-16T00:00:00Z
## 59 59 Chirodzo 2016-11-16T00:00:00Z
## 60 60 Chirodzo 2016-11-16T00:00:00Z
## 61 61 Chirodzo 2016-11-16T00:00:00Z
## 62 62 Chirodzo 2016-11-16T00:00:00Z
## 63 63 Chirodzo 2016-11-16T00:00:00Z
## 64 64 Chirodzo 2016-11-16T00:00:00Z
## 65 65 Chirodzo 2016-11-16T00:00:00Z
## 66 66 Chirodzo 2016-11-16T00:00:00Z
## 67 67 Chirodzo 2016-11-16T00:00:00Z
## 68 68 Chirodzo 2016-11-16T00:00:00Z
## 69 69 Chirodzo 2016-11-16T00:00:00Z
## 70 70 Chirodzo 2016-11-16T00:00:00Z
## 71 71 Ruaca 2016-11-18T00:00:00Z
## 72 127 Chirodzo 2016-11-16T00:00:00Z
## 73 133 Ruaca 2016-11-23T00:00:00Z
## 74 152 Ruaca 2016-11-24T00:00:00Z
## 75 153 Ruaca 2016-11-24T00:00:00Z
## 76 155 God 2016-11-24T00:00:00Z
## 77 178 Ruaca 2016-11-25T00:00:00Z
## 78 177 God 2016-11-25T00:00:00Z
## 79 180 Ruaca 2016-11-25T00:00:00Z
## 80 181 God 2016-11-25T00:00:00Z
## 81 182 God 2016-11-25T00:00:00Z
## 82 186 God 2016-11-28T00:00:00Z
## 83 187 God 2016-11-28T00:00:00Z
## 84 195 God 2016-11-28T00:00:00Z
## 85 196 God 2016-11-28T00:00:00Z
## 86 197 God 2016-11-28T00:00:00Z
## 87 198 God 2016-11-28T00:00:00Z
## 88 201 God 2016-11-21T00:00:00Z
## 89 202 God 2016-11-17T00:00:00Z
## 90 72 Ruaca 2017-04-26T00:00:00Z
## 91 73 Ruaca 2017-04-26T00:00:00Z
## 92 76 Ruaca 2017-04-26T00:00:00Z
## 93 83 Ruaca 2017-04-27T00:00:00Z
## 94 85 Ruaca 2017-04-27T00:00:00Z
## 95 89 God 2017-04-27T00:00:00Z
## 96 101 God 2017-04-27T00:00:00Z
## 97 103 Ruaca 2017-04-27T00:00:00Z
## 98 102 Ruaca 2017-04-28T00:00:00Z
## 99 78 Ruaca 2017-04-28T00:00:00Z
## 100 80 Ruaca 2017-04-28T00:00:00Z
## 101 104 Ruaca 2017-04-28T00:00:00Z
## 102 105 Ruaca 2017-04-28T00:00:00Z
## 103 106 God 2017-04-30T00:00:00Z
## 104 109 God 2017-05-03T00:00:00Z
## 105 110 Ruaca 2017-05-03T00:00:00Z
## 106 113 Ruaca 2017-05-03T00:00:00Z
## 107 118 Ruaca 2017-05-04T00:00:00Z
## 108 125 Ruaca 2017-05-04T00:00:00Z
## 109 119 Ruaca 2017-05-04T00:00:00Z
## 110 115 Ruaca 2017-05-11T00:00:00Z
## 111 108 God 2017-05-11T00:00:00Z
## 112 116 Ruaca 2017-05-11T00:00:00Z
## 113 117 Ruaca 2017-05-11T00:00:00Z
## 114 144 Ruaca 2017-05-18T00:00:00Z
## 115 143 Ruaca 2017-05-18T00:00:00Z
## 116 150 Ruaca 2017-05-18T00:00:00Z
## 117 159 God 2017-05-18T00:00:00Z
## 118 160 God 2017-06-03T00:00:00Z
## 119 165 Ruaca 2017-06-03T00:00:00Z
## 120 166 Ruaca 2017-06-03T00:00:00Z
## 121 167 Ruaca 2017-06-03T00:00:00Z
## 122 174 Ruaca 2017-06-03T00:00:00Z
## 123 175 Ruaca 2017-06-03T00:00:00Z
## 124 189 Ruaca 2017-06-03T00:00:00Z
## 125 191 Ruaca 2017-06-03T00:00:00Z
## 126 192 Chirodzo 2017-06-03T00:00:00Z
## 127 126 Ruaca 2017-05-18T00:00:00Z
## 128 193 Ruaca 2017-06-04T00:00:00Z
## 129 194 Ruaca 2017-06-04T00:00:00Z
## 130 199 Chirodzo 2017-06-04T00:00:00Z
## 131 200 Chirodzo 2017-06-04T00:00:00Z
## instanceID
## 1 uuid:ec241f2c-0609-46ed-b5e8-fe575f6cefef
## 2 uuid:099de9c9-3e5e-427b-8452-26250e840d6e
## 3 uuid:193d7daf-9582-409b-bf09-027dd36f9007
## 4 uuid:148d1105-778a-4755-aa71-281eadd4a973
## 5 uuid:2c867811-9696-4966-9866-f35c3e97d02d
## 6 uuid:daa56c91-c8e3-44c3-a663-af6a49a2ca70
## 7 uuid:ae20a58d-56f4-43d7-bafa-e7963d850844
## 8 uuid:d6cee930-7be1-4fd9-88c0-82a08f90fb5a
## 9 uuid:846103d2-b1db-4055-b502-9cd510bb7b37
## 10 uuid:8f4e49bc-da81-4356-ae34-e0d794a23721
## 11 uuid:d29b44e3-3348-4afc-aa4d-9eb34c89d483
## 12 uuid:e6ee6269-b467-4e37-91fc-5e9eaf934557
## 13 uuid:6c00c145-ee3b-409c-8c02-2c8d743b6918
## 14 uuid:9b21467f-1116-4340-a3b1-1ab64f13c87d
## 15 uuid:a837e545-ff86-4a1c-a1a5-6186804b985f
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## 70 uuid:1feb0108-4599-4bf9-8a07-1f5e66a50a0a
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## 72 uuid:f6d04b41-b539-4e00-868a-0f62b427587d
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## 74 uuid:59738c17-1cda-49ee-a563-acd76f6bc487
## 75 uuid:7e7961ca-fa1c-4567-9bfa-a02f876e4e03
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## 78 uuid:87998c33-c8d2-49ec-9dae-c123735957ec
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## 82 uuid:268bfd97-991c-473f-bd51-bc80676c65c6
## 83 uuid:0a42c9ee-a840-4dda-8123-15c1bede5dfc
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## 85 uuid:44e427d1-a448-4bf2-b529-7d67b2266c06
## 86 uuid:85c99fd2-775f-40c9-8654-68223f59d091
## 87 uuid:28c64954-739c-444c-a6e0-355878e471c8
## 88 uuid:9e79a31c-3ea5-44f0-80f9-a32db49422e3
## 89 uuid:06d39051-38ef-4757-b68b-3327b1f16b9d
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## 91 uuid:ac3da862-9e6c-4962-94b6-f4c31624f207
## 92 uuid:4178a296-903a-4a8e-9cfa-0cd6143476e8
## 93 uuid:a1e9df00-c8ae-411c-931c-c7df898c68d0
## 94 uuid:4d0f472b-f8ae-4026-87c9-6b5be14b0a70
## 95 uuid:b3b309c6-f234-4830-8b30-87d26a17ee1d
## 96 uuid:3c174acd-e431-4523-9ad6-eb14cddca805
## 97 uuid:e9d79844-ef14-493b-bbd6-d13691cc660e
## 98 uuid:76206b0b-af74-4344-b24f-81e839f0d7b0
## 99 uuid:da3fa7cc-5ce9-44fd-9a78-b8982b607515
## 100 uuid:a85df6df-0336-46fa-a9f4-522bf6f8b438
## 101 uuid:bb2bb365-7d7d-4fe9-9353-b21269676119
## 102 uuid:af0904ee-4fdb-4090-973f-599c81ddf022
## 103 uuid:468797c1-4a65-4f35-9c83-e28ce46972a2
## 104 uuid:602cd3f6-4a97-49c6-80e3-bcfd5c78dfa4
## 105 uuid:e7c51ac4-24e4-475e-88e7-f85e896945e3
## 106 uuid:01210861-aba1-4268-98d0-0260e05f5155
## 107 uuid:77335b2e-8812-4a35-b1e5-ca9ab626dfea
## 108 uuid:02b05c68-302e-4e7a-b229-81cb1377fd29
## 109 uuid:fa201fce-4e94-44b8-b435-c558c2e1ed55
## 110 uuid:628fe23d-188f-43e4-a203-a4bf3257d461
## 111 uuid:e4f4d6ba-e698-45a5-947f-ba6da88cc22b
## 112 uuid:cfee6297-2c0e-4f8a-94cc-9aaee0bd64cb
## 113 uuid:3fe626b3-c794-48e1-a80f-5bfe440c507b
## 114 uuid:0670cef6-d233-4852-89d8-36955261b0a3
## 115 uuid:9a096a12-b335-468c-b3cc-1191180d62de
## 116 uuid:92613d0d-e7b1-4d62-8ea4-451d7cd0a982
## 117 uuid:37577f91-d665-443e-8d70-b914954cef4b
## 118 uuid:f22831ec-6bc3-4b73-9197-4b01e01abb66
## 119 uuid:62f3f7af-f0f3-4f88-b9e0-acf8baa49ae4
## 120 uuid:40aac732-94df-496c-97ba-5b67f59bcc7a
## 121 uuid:a9d1a013-043b-475d-a71b-77ed80abe970
## 122 uuid:43ec6132-478c-4f87-878d-fb3c0c4d0c74
## 123 uuid:64fc743e-8176-40f6-8ae4-36ae97fac1d9
## 124 uuid:c17e374c-280b-4e78-bf21-74a7c1c73492
## 125 uuid:dad53aff-b520-4015-a9e3-f5fdf9168fe1
## 126 uuid:f94409a6-e461-4e4c-a6fb-0072d3d58b00
## 127 uuid:69caea81-a4e5-4e8d-83cd-9c18d8e8d965
## 128 uuid:5ccc2e5a-ea90-48b5-8542-69400d5334df
## 129 uuid:95c11a30-d44f-40c4-8ea8-ec34fca6bbbf
## 130 uuid:ffc83162-ff24-4a87-8709-eff17abc0b3b
## 131 uuid:aa77a0d7-7142-41c8-b494-483a5b68d8a7#Select the village Chirodzo and find its details
interviews %>%
filter(village == "Chirodzo") %>%
select(key_ID, village,interview_date, instanceID) %>%
sample_n(size = 10)## key_ID village interview_date
## 1 48 Chirodzo 2016-11-16T00:00:00Z
## 2 45 Chirodzo 2016-11-17T00:00:00Z
## 3 63 Chirodzo 2016-11-16T00:00:00Z
## 4 51 Chirodzo 2016-11-16T00:00:00Z
## 5 36 Chirodzo 2016-11-17T00:00:00Z
## 6 59 Chirodzo 2016-11-16T00:00:00Z
## 7 67 Chirodzo 2016-11-16T00:00:00Z
## 8 127 Chirodzo 2016-11-16T00:00:00Z
## 9 69 Chirodzo 2016-11-16T00:00:00Z
## 10 66 Chirodzo 2016-11-16T00:00:00Z
## instanceID
## 1 uuid:e180899c-7614-49eb-a97c-40ed013a38a2
## 2 uuid:e3554d22-35b1-4fb9-b386-dd5866ad5792
## 3 uuid:86ed4328-7688-462f-aac7-d6518414526a
## 4 uuid:18ac8e77-bdaf-47ab-85a2-e4c947c9d3ce
## 5 uuid:c90eade0-1148-4a12-8c0e-6387a36f45b1
## 6 uuid:1936db62-5732-45dc-98ff-9b3ac7a22518
## 7 uuid:6c15d667-2860-47e3-a5e7-7f679271e419
## 8 uuid:f6d04b41-b539-4e00-868a-0f62b427587d
## 9 uuid:f86933a5-12b8-4427-b821-43c5b039401d
## 10 uuid:a457eab8-971b-4417-a971-2e55b8702816interviews_plotting <- interviews %>%
#pivot wider by items_owned
separate_longer_delim(items_owned, delim = ";") %>%
#If there were no items listed, changing NA to no_listed_items
replace_na(list(items_owned="no_listed_items")) %>%
mutate(items_owned_logical = TRUE) %>%
pivot_wider(names_from = items_owned,
values_from = items_owned_logical,
values_fill = list(items_owned_logical = FALSE)) %>%
#Pivot wider by months_lack_food
separate_longer_delim(months_lack_food, delim = ";") %>%
mutate(months_lack_food_logical = TRUE) %>%
pivot_wider(names_from = months_lack_food,
values_from = months_lack_food_logical,
values_fill = list(months_lack_food_logical = FALSE)) %>%
#add some summary columns
mutate(number_months_lack_food = rowSums(select(., Jan:May))) %>%
mutate(number_items = rowSums(select(.,bicycle:car)))
write.csv(interviews_plotting, file = "data/interviews_plotting.csv")
interviews_plotting <- read.csv("data/interviews_plotting.csv")##Plots
Here are some of the plots
